Variable plant silicon defences suppresse herbivore performance globally but feeding strategy is key
Electronic Supplementary Material
Scott N. Johnson, Jamie M. Waterman, Susan E. Hartley, Julia Cooke, James M.W. Ryalls, Adam Frew, Malgorzata Lagisz and Shinichi Nakagawa
12 November 2023
Setup
Loading packages and custom functions
To run the script, some of packages need to be installed from
Github.
# loading packages
# devtools::install_github("thomasp85/patchwork")
# if (!requireNamespace("BiocManager", quietly = TRUE))
# install.packages("BiocManager")
#
# BiocManager::install("ggtree")
# install.packages("devtools")
# install.packages("tidyverse")
# install.packages("metafor")
# install.packages("patchwork")
# install.packages("R.rsp")
#install.packages("devtools")
#library(devtools)
#install_github("daniel1noble/metaAidR")
#devtools::install_github("daniel1noble/orchaRd", force = TRUE)
#install.packages("pacman")
#> Installing package into '/Users/danielnoble/Library/R/3.6/library'
#> (as 'lib' is unspecified)
#pacman::p_load(devtools, tidyverse, metafor, patchwork, R.rsp)
# Install orchaRd
#> Downloading GitHub repo itchyshin/orchard_plot@master
#> Installing package into '/Users/danielnoble/Library/R/3.6/library'
#> (as 'lib' is unspecified)
#library(orchaRd)
pacman::p_load(
tidyverse, # tidy family and related packages below
kableExtra,
gridExtra, # may not use this
purrr,
magrittr, # extending piping
pander, # nice tables
metafor, # package for meta-analysis
#ggbeeswarm, # making bee-swarm plots possible
ape, # pfor hylogenetic comparative methods
MuMIn, # multi-model inference
performance, # getting R2 from lmer + glmer objects
#png, # reading png files
grid, # graphic layout manipulation
patchwork, # putting ggplots together - you need to install via devtool
here, # gives a path
#brms, # Bayesian mixed model
orchaRd, # drawing orchard and caterpillars plot
MCMCglmm, # Bayesin mixed model
DIZtools
#metaAidR # some helper functions for meta-analysis
)
#options(mc.cores = parallel::detectCores())
#rstan_options(auto_write = TRUE)Custom functions
We have 5 custom functions named : cont_gen(),
get_pred1, get_pred2, mr_results,
and make_VCV_matrix all of which are used later (see below
for their functionality) and the code are included here.
# custom functions
#' Title: Contrast name generator
#'
#' @param name: a vector of character strings
cont_gen <- function(name) {
combination <- combn(name, 2)
name_dat <- t(combination)
names <- paste(name_dat[, 1], name_dat[, 2], sep = "-")
return(names)
}
#' @title get_pred1: intercept-less model
#' @description Function to get CIs (confidence intervals) and PIs (prediction intervals) from rma objects (metafor)
#' @param model: rma.mv object
#' @param mod: the name of a moderator
get_pred1 <- function (model, mod = " ") {
name <- name <- firstup(as.character(stringr::str_replace(row.names(model$beta), mod, "")))
len <- length(name)
if (len != 1) {
newdata <- matrix(NA, ncol = len, nrow = len)
for (i in 1:len) {
pos <- which(model$X[, i] == 1)[[1]]
newdata[, i] <- model$X[pos, ]
}
pred <- metafor::predict.rma(model, newmods = newdata)
}
else {
pred <- metafor::predict.rma(model)
}
estimate <- pred$pred
lowerCL <- pred$ci.lb
upperCL <- pred$ci.ub
lowerPR <- pred$cr.lb
upperPR <- pred$cr.ub
table <- tibble(name = factor(name, levels = name, labels = name), estimate = estimate,
lowerCL = lowerCL, upperCL = upperCL,
pval = model$pval,
lowerPR = lowerPR, upperPR = upperPR)
}
#' @title get_pred2: normal model
#' @description Function to get CIs (confidence intervals) and PIs (prediction intervals) from rma objects (metafor)
#' @param model: rma.mv object
#' @param mod: the name of a moderator
get_pred2 <- function (model, mod = " ") {
name <- as.factor(str_replace(row.names(model$beta),
paste0("relevel", "\\(", mod,", ref = name", "\\)"),
""))
len <- length(name)
if(len != 1){
newdata <- diag(len)
pred <- predict.rma(model, intercept = FALSE, newmods = newdata[,-1])
}
else {
pred <- predict.rma(model)
}
estimate <- pred$pred
lowerCL <- pred$ci.lb
upperCL <- pred$ci.ub
lowerPR <- pred$cr.lb
upperPR <- pred$cr.ub
table <- tibble(name = factor(name, levels = name, labels = name), estimate = estimate,
lowerCL = lowerCL, upperCL = upperCL,
pval = model$pval,
lowerPR = lowerPR, upperPR = upperPR)
}
#' @title mr_results
#' @description Function to put results of meta-regression and its contrasts
#' @param res1: data frame 1
#' @param res1: data frame 2
mr_results <- function(res1, res2) {
restuls <-tibble(
`Fixed effect` = c(as.character(res1$name), cont_gen(res1$name)),
Estimate = c(res1$estimate, res2$estimate),
`Lower CI [0.025]` = c(res1$lowerCL, res2$lowerCL),
`Upper CI [0.975]` = c(res1$upperCL, res2$upperCL),
`P value` = c(res1$pval, res2$pval),
`Lower PI [0.025]` = c(res1$lowerPR, res2$lowerPR),
`Upper PI [0.975]` = c(res1$upperPR, res2$upperPR),
)
}
# from metaAidR - https://protect-au.mimecast.com/s/8aMMC71RQMcG993psQ4RGW?domain=github.com
#' @title Covariance and correlation matrix function basing on shared level ID
#' @description Function for generating simple covariance and correlation matrices
#' @param data Dataframe object containing effect sizes, their variance, unique IDs and clustering variable
#' @param V Name of the variable (as a string – e.g, "V1") containing effect size variances variances
#' @param cluster Name of the variable (as a string – e.g, "V1") indicating which effects belong to the same cluster. Same value of 'cluster' are assumed to be nonindependent (correlated).
#' @param obs Name of the variable (as a string – e.g, "V1") containing individual IDs for each value in the V (Vector of variances). If this parameter is missing, label will be labelled with consecutive integers starting from 1.
#' @param rho Known or assumed correlation value among effect sizes sharing same 'cluster' value. Default value is 0.5.
#' @param type Optional logical parameter indicating whether a full variance-covariance matrix (default or "vcv") is needed or a correlation matrix ("cor") for the non-independent blocks of variance values.
#' @export
make_VCV_matrix <- function(data, V, cluster, obs, type=c("vcv", "cor"), rho=0.5){
type <- match.arg(type)
if (missing(data)) {
stop("Must specify dataframe via 'data' argument.")
}
if (missing(V)) {
stop("Must specify name of the variance variable via 'V' argument.")
}
if (missing(cluster)) {
stop("Must specify name of the clustering variable via 'cluster' argument.")
}
if (missing(obs)) {
obs <- 1:length(V)
}
if (missing(type)) {
type <- "vcv"
}
new_matrix <- matrix(0,nrow = dim(data)[1],ncol = dim(data)[1]) #make empty matrix of the same size as data length
rownames(new_matrix) <- data[ ,obs]
colnames(new_matrix) <- data[ ,obs]
# find start and end coordinates for the subsets
shared_coord <- which(data[ ,cluster] %in% data[duplicated(data[ ,cluster]), cluster]==TRUE)
# matrix of combinations of coordinates for each experiment with shared control
combinations <- do.call("rbind", tapply(shared_coord, data[shared_coord,cluster], function(x) t(utils::combn(x,2))))
if(type == "vcv"){
# calculate covariance values between values at the positions in shared_list and place them on the matrix
for (i in 1:dim(combinations)[1]){
p1 <- combinations[i,1]
p2 <- combinations[i,2]
p1_p2_cov <- rho * sqrt(data[p1,V]) * sqrt(data[p2,V])
new_matrix[p1,p2] <- p1_p2_cov
new_matrix[p2,p1] <- p1_p2_cov
}
diag(new_matrix) <- data[ ,V] #add the diagonal
}
if(type == "cor"){
# calculate covariance values between values at the positions in shared_list and place them on the matrix
for (i in 1:dim(combinations)[1]){
p1 <- combinations[i,1]
p2 <- combinations[i,2]
p1_p2_cov <- rho
new_matrix[p1,p2] <- p1_p2_cov
new_matrix[p2,p1] <- p1_p2_cov
}
diag(new_matrix) <- 1 #add the diagonal of 1
}
return(new_matrix)
} The Silicon Herbivore Dataset
Table of the dataset
The dataset used for our meta-analysis is below, followed by explanations of 10 variables extracted from the papers included (not all variables were used for our analyses; variables not directly used in our analyses are indicated by *).
Extended Data Table 1: The meta-analytic dataset of this study.
# getting the data and formatting some variables (turning character vectors to factors)
full_data <- read_csv(here("data", "data_01_Oct_2023.csv")) %>%
mutate_if(is.character, as.factor)
full_data<-subset(full_data, Xc > 0 & full_data$Xe > 0)
#full_data[full_data$Xc < 0, ][,'Xc']<-0.1
#full_data[full_data$Xe < 0, ][,'Xe']<-0.1
#determine whether value is negative
#full_data[full_data$Xe > full_data$Xc, ][,'Negative']<--1
#full_data[full_data$Xe < full_data$Xc, ][,'Negative']<-1
# making a scrollable table
kable(full_data, "html") %>%
kable_styling("striped", position = "left") %>%
scroll_box(width = "100%", height = "500px")| Effect | Study | Author | Year | Journal | Plant_Species | Plant_Phylogeny | Plant_lifespan | Poaceae_or_Non | Herbivore_common_name | Herbivore_Latin_name | Herbivore_Phylogeny | Herbivore_diet_breadth | Feeding_guild | Performance_parameter | Nc | Ne | Xc | Xe | Dev_c | Dev_e | Negative | Si_Nc | Si_Ne | Si_Xc | Si_Xe | Si_Dev_c | Si_Dev_e |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 1 | 1 | Abbasi | 2020 | INTERNATIONAL JOURNAL OF PEST MANAGEMENT | Gossypium hirsutum | Gossypium_hirsutum | Perennial | Non-Poaceae | Whitefly | Bemisia tabaci | Bemisia_tabaci | Generalist | Fluid-feeding arthropods | Reproduction | 3.00 | 3.00 | 103.2500000 | 52.1300000 | 1.0220000 | 1.5430000 | 1 | NA | NA | NA | NA | NA | NA |
| 2 | 1 | Abbasi | 2020 | INTERNATIONAL JOURNAL OF PEST MANAGEMENT | Gossypium hirsutum | Gossypium_hirsutum | Perennial | Non-Poaceae | Whitefly | Bemisia tabaci | Bemisia_tabaci | Generalist | Fluid-feeding arthropods | Growth / Development | 3.00 | 3.00 | 1.8540000 | 2.9600000 | 1.0220000 | 1.5430000 | -1 | NA | NA | NA | NA | NA | NA |
| 3 | 1 | Abbasi | 2020 | INTERNATIONAL JOURNAL OF PEST MANAGEMENT | Gossypium hirsutum | Gossypium_hirsutum | Perennial | Non-Poaceae | Whitefly | Bemisia tabaci | Bemisia_tabaci | Generalist | Fluid-feeding arthropods | Growth / Development | 3.00 | 3.00 | 1.8170000 | 3.3990000 | 1.0220000 | 1.5430000 | -1 | NA | NA | NA | NA | NA | NA |
| 4 | 1 | Abbasi | 2020 | INTERNATIONAL JOURNAL OF PEST MANAGEMENT | Gossypium hirsutum | Gossypium_hirsutum | Perennial | Non-Poaceae | Whitefly | Bemisia tabaci | Bemisia_tabaci | Generalist | Fluid-feeding arthropods | Growth / Development | 3.00 | 3.00 | 2.1230000 | 3.5190000 | 1.0220000 | 1.5430000 | -1 | NA | NA | NA | NA | NA | NA |
| 5 | 1 | Abbasi | 2020 | INTERNATIONAL JOURNAL OF PEST MANAGEMENT | Gossypium hirsutum | Gossypium_hirsutum | Perennial | Non-Poaceae | Whitefly | Bemisia tabaci | Bemisia_tabaci | Generalist | Fluid-feeding arthropods | Growth / Development | 3.00 | 3.00 | 2.5820000 | 3.6120000 | 1.0220000 | 1.5430000 | -1 | NA | NA | NA | NA | NA | NA |
| 6 | 2 | Faraone | 2020 | Scientific Reports | Solanum lycopersicum | Solanum_lycopersicum | Perennial | Non-Poaceae | Two spotted spider mite | Tetranychus urticae | Tetranychus_urticae | Generalist | Cell-feeding arthropods | Mortality / Survivability | 20.00 | 20.00 | 7.5000000 | 33.0000000 | 7.6026311 | 15.6524758 | -1 | 5 | 5.000 | 1350.0000000 | 913.300000 | 489.6988871 | 5.366563e+01 |
| 7 | 3 | Assis | 2013 | International Journal of Pest Management | Helianthus annuus | Helianthus_annuus | Annual | Non-Poaceae | Sunflower caterpillar | Chlosyne lacinia saundersii | Chlosyne_lacinia_saundersii | Specialist | Chewing arthropods | Feeding efficiency | 10.00 | 10.00 | 31.9000000 | 20.7000000 | 11.0047263 | 15.7481427 | 1 | 10 | 10.000 | 0.1000000 | 0.510000 | 0.1581139 | 2.213594e-01 |
| 8 | 3 | Assis | 2013 | International Journal of Pest Management | Helianthus annuus | Helianthus_annuus | Annual | Non-Poaceae | Sunflower caterpillar | Chlosyne lacinia saundersii | Chlosyne_lacinia_saundersii | Specialist | Chewing arthropods | Feeding efficiency | 10.00 | 10.00 | 15.3000000 | 7.5000000 | 6.8621425 | 7.2732386 | 1 | 10 | 10.000 | 0.1000000 | 1.400000 | 0.1581139 | 1.581139e-01 |
| 9 | 3 | Assis | 2013 | International Journal of Pest Management | Helianthus annuus | Helianthus_annuus | Annual | Non-Poaceae | Sunflower caterpillar | Chlosyne lacinia saundersii | Chlosyne_lacinia_saundersii | Specialist | Chewing arthropods | Growth / Development | 10.00 | 10.00 | 5.6000000 | 2.4000000 | 2.4033310 | 1.3914022 | 1 | 10 | 10.000 | 0.1000000 | 0.510000 | 0.1581139 | 2.213594e-01 |
| 10 | 3 | Assis | 2013 | International Journal of Pest Management | Helianthus annuus | Helianthus_annuus | Annual | Non-Poaceae | Sunflower caterpillar | Chlosyne lacinia saundersii | Chlosyne_lacinia_saundersii | Specialist | Chewing arthropods | Mortality / Survivability | 10.00 | 10.00 | 16.0000000 | 27.0000000 | 13.5029256 | 17.6771321 | -1 | 10 | 10.000 | 0.1000000 | 0.510000 | 0.1581139 | 2.213594e-01 |
| 11 | 4 | Assis | 2015 | Journal of Agricultural Science & Technology | Helianthus annuus | Helianthus_annuus | Annual | Non-Poaceae | Sunflower caterpillar | Chlosyne lacinia saundersii | Chlosyne_lacinia_saundersii | Specialist | Chewing arthropods | Mortality / Survivability | 10.00 | 10.00 | 72.9000000 | 40.8000000 | 31.4962855 | 44.1770189 | 1 | 10 | 10.000 | 0.6000000 | 1.000000 | 0.1264911 | 9.486830e-02 |
| 12 | 4 | Assis | 2015 | Journal of Agricultural Science & Technology | Helianthus annuus | Helianthus_annuus | Annual | Non-Poaceae | Sunflower caterpillar | Chlosyne lacinia saundersii | Chlosyne_lacinia_saundersii | Specialist | Chewing arthropods | Feeding efficiency | 10.00 | 10.00 | 50.0000000 | 62.0000000 | 9.9295519 | 12.0166551 | 1 | 10 | 10.000 | 0.6000000 | 1.000000 | 0.1264911 | 9.486830e-02 |
| 13 | 4 | Assis | 2015 | Journal of Agricultural Science & Technology | Helianthus annuus | Helianthus_annuus | Annual | Non-Poaceae | Sunflower caterpillar | Chlosyne lacinia saundersii | Chlosyne_lacinia_saundersii | Specialist | Chewing arthropods | Growth / Development | 10.00 | 10.00 | 132.0000000 | 152.7000000 | 25.7409402 | 16.0011250 | 1 | 10 | 10.000 | 0.6000000 | 1.000000 | 0.1264911 | 9.486830e-02 |
| 14 | 4 | Assis | 2015 | Journal of Agricultural Science & Technology | Helianthus annuus | Helianthus_annuus | Annual | Non-Poaceae | Sunflower caterpillar | Chlosyne lacinia saundersii | Chlosyne_lacinia_saundersii | Specialist | Chewing arthropods | Growth / Development | 10.00 | 10.00 | 21.7000000 | 22.8000000 | 3.1622777 | 2.3400855 | -1 | 10 | 10.000 | 0.6000000 | 1.000000 | 0.1264911 | 9.486830e-02 |
| 15 | 4 | Assis | 2015 | Journal of Agricultural Science & Technology | Helianthus annuus | Helianthus_annuus | Annual | Non-Poaceae | Sunflower caterpillar | Chlosyne lacinia saundersii | Chlosyne_lacinia_saundersii | Specialist | Chewing arthropods | Growth / Development | 10.00 | 10.00 | 4.8000000 | 3.6000000 | 1.2965338 | 1.7708755 | -1 | 10 | 10.000 | 0.6000000 | 1.000000 | 0.1264911 | 9.486830e-02 |
| 16 | 4 | Assis | 2015 | Journal of Agricultural Science & Technology | Helianthus annuus | Helianthus_annuus | Annual | Non-Poaceae | Sunflower caterpillar | Chlosyne lacinia saundersii | Chlosyne_lacinia_saundersii | Specialist | Chewing arthropods | Growth / Development | 10.00 | 10.00 | 8.7000000 | 9.0000000 | 4.6485482 | 5.3442492 | 1 | 10 | 10.000 | 0.6000000 | 1.000000 | 0.1264911 | 9.486830e-02 |
| 17 | 4 | Assis | 2015 | Journal of Agricultural Science & Technology | Helianthus annuus | Helianthus_annuus | Annual | Non-Poaceae | Sunflower caterpillar | Chlosyne lacinia saundersii | Chlosyne_lacinia_saundersii | Specialist | Chewing arthropods | Growth / Development | 10.00 | 10.00 | 49.8000000 | 21.5000000 | 12.3012601 | 4.9015304 | 1 | 10 | 10.000 | 0.6000000 | 1.000000 | 0.1264911 | 9.486830e-02 |
| 18 | 5 | Barker | 1989 | Journal of Economic Entomology | Lolium multiflorum | Lolium_multiflorum | Perennial | Poaceae | Weevil | Listronotus bonariensis | Listronotus_bonariensis | Specialist | Chewing arthropods | Feeding efficiency | 10.00 | 10.00 | 8.4000000 | 4.9000000 | 2.8460499 | 2.8460499 | 1 | 10 | 10.000 | 32.0000000 | 186.000000 | 34.7850543 | 7.273239e+01 |
| 19 | 5 | Barker | 1989 | Journal of Economic Entomology | Lolium multiflorum | Lolium_multiflorum | Perennial | Poaceae | Weevil | Listronotus bonariensis | Listronotus_bonariensis | Specialist | Chewing arthropods | Feeding efficiency | 5.00 | 5.00 | 12.3000000 | 7.8000000 | 3.3541020 | 3.3541020 | 1 | 10 | 10.000 | 32.0000000 | 186.000000 | 34.7850543 | 7.273239e+01 |
| 20 | 5 | Barker | 1989 | Journal of Economic Entomology | Lolium multiflorum | Lolium_multiflorum | Perennial | Poaceae | Weevil | Listronotus bonariensis | Listronotus_bonariensis | Specialist | Chewing arthropods | Reproduction | 5.00 | 5.00 | 3.7000000 | 0.9000000 | 1.3416408 | 1.3416408 | 1 | 10 | 10.000 | 32.0000000 | 186.000000 | 34.7850543 | 7.273239e+01 |
| 21 | 5 | Barker | 1989 | Journal of Economic Entomology | Lolium multiflorum | Lolium_multiflorum | Perennial | Poaceae | Weevil | Listronotus bonariensis | Listronotus_bonariensis | Specialist | Chewing arthropods | Reproduction | 10.00 | 10.00 | 4.4000000 | 2.0000000 | 2.2135944 | 2.2135944 | 1 | 10 | 10.000 | 32.0000000 | 186.000000 | 34.7850543 | 7.273239e+01 |
| 22 | 5 | Barker | 1989 | Journal of Economic Entomology | Lolium perenne | Lolium_perenne | Perennial | Poaceae | Weevil | Listronotus bonariensis | Listronotus_bonariensis | Specialist | Chewing arthropods | Feeding efficiency | 10.00 | 10.00 | 5.1000000 | 3.1000000 | 2.5298221 | 2.5298221 | 1 | 10 | 10.000 | 51.0000000 | 293.000000 | 82.2192192 | 1.170043e+02 |
| 23 | 5 | Barker | 1989 | Journal of Economic Entomology | Lolium perenne | Lolium_perenne | Perennial | Poaceae | Weevil | Listronotus bonariensis | Listronotus_bonariensis | Specialist | Chewing arthropods | Feeding efficiency | 5.00 | 5.00 | 7.5000000 | 3.4000000 | 3.8013156 | 3.8013156 | 1 | 10 | 10.000 | 51.0000000 | 293.000000 | 82.2192192 | 1.170043e+02 |
| 24 | 5 | Barker | 1989 | Journal of Economic Entomology | Lolium perenne | Lolium_perenne | Perennial | Poaceae | Weevil | Listronotus bonariensis | Listronotus_bonariensis | Specialist | Chewing arthropods | Reproduction | 5.00 | 5.00 | 2.6000000 | 0.4000000 | 1.1180340 | 1.1180340 | 1 | 10 | 10.000 | 51.0000000 | 293.000000 | 82.2192192 | 1.170043e+02 |
| 25 | 5 | Barker | 1989 | Journal of Economic Entomology | Lolium perenne | Lolium_perenne | Perennial | Poaceae | Weevil | Listronotus bonariensis | Listronotus_bonariensis | Specialist | Chewing arthropods | Reproduction | 10.00 | 10.00 | 3.1000000 | 0.5000000 | 2.5298221 | 2.5298221 | 1 | 10 | 10.000 | 51.0000000 | 293.000000 | 82.2192192 | 1.170043e+02 |
| 26 | 6 | Basagli | 2003 | Neotropical Entomology | Triticum aestivum | Triticum_aestivum | Annual | Poaceae | Greenbug (aphid) | Schizaphis graminum | Schizaphis_graminum | Specialist | Fluid-feeding arthropods | Growth / Development | 10.00 | 10.00 | 5.2000000 | 5.4000000 | 0.6324555 | 0.9803061 | -1 | NA | NA | NA | NA | NA | NA |
| 27 | 6 | Basagli | 2003 | Neotropical Entomology | Triticum aestivum | Triticum_aestivum | Annual | Poaceae | Greenbug (aphid) | Schizaphis graminum | Schizaphis_graminum | Specialist | Fluid-feeding arthropods | Reproduction | 10.00 | 10.00 | 21.1000000 | 17.0000000 | 2.3084627 | 4.7750393 | 1 | NA | NA | NA | NA | NA | NA |
| 28 | 6 | Basagli | 2003 | Neotropical Entomology | Triticum aestivum | Triticum_aestivum | Annual | Poaceae | Greenbug (aphid) | Schizaphis graminum | Schizaphis_graminum | Specialist | Fluid-feeding arthropods | Mortality / Survivability | 10.00 | 10.00 | 71.0000000 | 68.0000000 | 12.1431462 | 29.4091822 | 1 | NA | NA | NA | NA | NA | NA |
| 29 | 6 | Basagli | 2003 | Neotropical Entomology | Triticum aestivum | Triticum_aestivum | Annual | Poaceae | Greenbug (aphid) | Schizaphis graminum | Schizaphis_graminum | Specialist | Fluid-feeding arthropods | Growth / Development | 10.00 | 10.00 | 24.1000000 | 19.0000000 | 2.8776727 | 5.7553453 | 1 | NA | NA | NA | NA | NA | NA |
| 30 | 6 | Basagli | 2003 | Neotropical Entomology | Triticum aestivum | Triticum_aestivum | Annual | Poaceae | Greenbug (aphid) | Schizaphis graminum | Schizaphis_graminum | Specialist | Fluid-feeding arthropods | Growth / Development | 10.00 | 10.00 | 5.2000000 | 5.4000000 | 0.6324555 | 0.9803061 | -1 | NA | NA | NA | NA | NA | NA |
| 31 | 6 | Basagli | 2003 | Neotropical Entomology | Triticum aestivum | Triticum_aestivum | Annual | Poaceae | Greenbug (aphid) | Schizaphis graminum | Schizaphis_graminum | Specialist | Fluid-feeding arthropods | Reproduction | 10.00 | 10.00 | 21.1000000 | 17.0000000 | 2.3084627 | 4.7750393 | 1 | NA | NA | NA | NA | NA | NA |
| 32 | 6 | Basagli | 2003 | Neotropical Entomology | Triticum aestivum | Triticum_aestivum | Annual | Poaceae | Greenbug (aphid) | Schizaphis graminum | Schizaphis_graminum | Specialist | Fluid-feeding arthropods | Reproduction | 10.00 | 10.00 | 15.8000000 | 8.5000000 | 2.4981994 | 1.4546477 | 1 | NA | NA | NA | NA | NA | NA |
| 33 | 6 | Basgali | 2003 | Neotropical Entomology | Triticum aestivum | Triticum_aestivum | Annual | Poaceae | Greenbug (aphid) | Schizaphis graminum | Schizaphis_graminum | Specialist | Fluid-feeding arthropods | Reproduction | 10.00 | 10.00 | 13.1000000 | 6.5000000 | 1.5178933 | 1.8341210 | 1 | NA | NA | NA | NA | NA | NA |
| 34 | 7 | Carvalho | 1999 | Anais da Sociedade Entomologica do Brasil | Sorghum bicolor | Sorghum_bicolor | Annual | Poaceae | Greenbug (aphid) | Schizaphis graminum | Schizaphis_graminum | Specialist | Fluid-feeding arthropods | Abundance / Preference | 10.00 | 10.00 | 5.8000000 | 3.7666667 | 57.6588627 | 7.6948756 | 1 | 10 | 10.000 | 0.4000000 | 0.600000 | 0.3162278 | 3.162278e-01 |
| 35 | 7 | Carvalho | 1999 | Anais da Sociedade Entomologica do Brasil | Sorghum bicolor | Sorghum_bicolor | Annual | Poaceae | Greenbug (aphid) | Schizaphis graminum | Schizaphis_graminum | Specialist | Fluid-feeding arthropods | Growth / Development | 10.00 | 10.00 | 5.9000000 | 5.4000000 | 3.1622777 | 7.5894664 | -1 | 10 | 10.000 | 0.4000000 | 0.600000 | 0.3162278 | 3.162278e-01 |
| 36 | 7 | Carvalho | 1999 | Anais da Sociedade Entomologica do Brasil | Sorghum bicolor | Sorghum_bicolor | Annual | Poaceae | Greenbug (aphid) | Schizaphis graminum | Schizaphis_graminum | Specialist | Fluid-feeding arthropods | Reproduction | 10.00 | 10.00 | 26.0000000 | 15.8000000 | 38.8960152 | 33.8363710 | 1 | 10 | 10.000 | 0.4000000 | 0.600000 | 0.3162278 | 3.162278e-01 |
| 37 | 7 | Carvalho | 1999 | Anais da Sociedade Entomologica do Brasil | Sorghum bicolor | Sorghum_bicolor | Annual | Poaceae | Greenbug (aphid) | Schizaphis graminum | Schizaphis_graminum | Specialist | Fluid-feeding arthropods | Growth / Development | 10.00 | 10.00 | 31.9000000 | 21.2000000 | 38.8960152 | 38.2635597 | 1 | 10 | 10.000 | 0.4000000 | 0.600000 | 0.3162278 | 3.162278e-01 |
| 38 | 7 | Carvalho | 1999 | Anais da Sociedade Entomologica do Brasil | Sorghum bicolor | Sorghum_bicolor | Annual | Poaceae | Greenbug (aphid) | Schizaphis graminum | Schizaphis_graminum | Specialist | Fluid-feeding arthropods | Reproduction | 10.00 | 10.00 | 219.3000000 | 121.5000000 | 418.3693344 | 332.3553821 | 1 | 10 | 10.000 | 0.4000000 | 0.600000 | 0.3162278 | 3.162278e-01 |
| 39 | 8 | Cherry | 2012 | Journal of Entomological Sciences | Stenotaphrum secundatum | Stenotaphrum_secundatum | Perennial | Poaceae | Southern chinch bug | Blissus insularis | Blissus_insularis | Specialist | Fluid-feeding arthropods | Mortality / Survivability | 5.00 | 5.00 | 3.8666667 | 3.8000000 | 1.9000000 | 2.6700000 | 1 | NA | NA | NA | NA | NA | NA |
| 40 | 9 | Correa | 2005 | Neotropical Entomology | Cucumis sativus | Cucumis_sativus | Annual | Non-Poaceae | Whitefly | Bemisia tabaci | Bemisia_tabaci | Generalist | Fluid-feeding arthropods | Reproduction | 10.00 | 10.00 | 487.0000000 | 317.2000000 | 88.8916250 | 107.2644582 | 1 | NA | NA | NA | NA | NA | NA |
| 41 | 9 | Correa | 2005 | Neotropical Entomology | Cucumis sativus | Cucumis_sativus | Annual | Non-Poaceae | Whitefly | Bemisia tabaci | Bemisia_tabaci | Generalist | Fluid-feeding arthropods | Mortality / Survivability | 10.00 | 10.00 | 87.5000000 | 70.5000000 | 4.1109610 | 11.0047263 | 1 | NA | NA | NA | NA | NA | NA |
| 42 | 9 | Correa | 2005 | Neotropical Entomology | Cucumis sativus | Cucumis_sativus | Annual | Non-Poaceae | Whitefly | Bemisia tabaci | Bemisia_tabaci | Generalist | Fluid-feeding arthropods | Reproduction | 10.00 | 10.00 | 476.2000000 | 393.0000000 | 102.2996823 | 131.8669784 | 1 | NA | NA | NA | NA | NA | NA |
| 43 | 9 | Correa | 2005 | Neotropical Entomology | Cucumis sativus | Cucumis_sativus | Annual | Non-Poaceae | Whitefly | Bemisia tabaci | Bemisia_tabaci | Generalist | Fluid-feeding arthropods | Mortality / Survivability | 10.00 | 10.00 | 86.8000000 | 81.6000000 | 4.0793382 | 7.4629753 | 1 | NA | NA | NA | NA | NA | NA |
| 44 | 9 | Correa | 2005 | Neotropical Entomology | Cucumis sativus | Cucumis_sativus | Annual | Non-Poaceae | Whitefly | Bemisia tabaci | Bemisia_tabaci | Generalist | Fluid-feeding arthropods | Growth / Development | 4.00 | 4.00 | 19.2000000 | 24.3000000 | 0.6200000 | 0.5800000 | -1 | NA | NA | NA | NA | NA | NA |
| 45 | 9 | Correa | 2005 | Neotropical Entomology | Cucumis sativus | Cucumis_sativus | Annual | Non-Poaceae | Whitefly | Bemisia tabaci | Bemisia_tabaci | Generalist | Fluid-feeding arthropods | Mortality / Survivability | 4.00 | 4.00 | 91.7000000 | 90.3000000 | 3.2000000 | 2.7800000 | 1 | NA | NA | NA | NA | NA | NA |
| 46 | 9 | Correa | 2005 | Neotropical Entomology | Cucumis sativus | Cucumis_sativus | Annual | Non-Poaceae | Whitefly | Bemisia tabaci | Bemisia_tabaci | Generalist | Fluid-feeding arthropods | Mortality / Survivability | 4.00 | 4.00 | 13.6000000 | 33.8000000 | 2.7200000 | 7.1200000 | -1 | NA | NA | NA | NA | NA | NA |
| 47 | 10 | Costa | 2006 | Neotropical Entomology | Triticum aestivum | Triticum_aestivum | Annual | Poaceae | Greenbug (aphid) | Schizaphis graminum | Schizaphis_graminum | Specialist | Fluid-feeding arthropods | Growth / Development | 10.00 | 10.00 | 0.3400000 | 0.2430000 | 0.0379473 | 0.0537587 | 1 | NA | NA | NA | NA | NA | NA |
| 48 | 10 | Costa | 2006 | Neotropical Entomology | Triticum aestivum | Triticum_aestivum | Annual | Poaceae | Greenbug (aphid) | Schizaphis graminum | Schizaphis_graminum | Specialist | Fluid-feeding arthropods | Reproduction | 10.00 | 10.00 | 109.7000000 | 26.9000000 | 25.3298441 | 16.6335805 | 1 | NA | NA | NA | NA | NA | NA |
| 49 | 10 | Costa | 2006 | Neotropical Entomology | Triticum aestivum | Triticum_aestivum | Annual | Poaceae | Greenbug (aphid) | Schizaphis graminum | Schizaphis_graminum | Specialist | Fluid-feeding arthropods | Growth / Development | 10.00 | 10.00 | 8.5000000 | 2.8000000 | 2.1187260 | 3.9528471 | 1 | NA | NA | NA | NA | NA | NA |
| 50 | 10 | Costa | 2006 | Neotropical Entomology | Triticum aestivum | Triticum_aestivum | Annual | Poaceae | Greenbug (aphid) | Schizaphis graminum | Schizaphis_graminum | Specialist | Fluid-feeding arthropods | Growth / Development | 10.00 | 10.00 | 36.8000000 | 23.0000000 | 8.6013952 | 8.6962636 | 1 | NA | NA | NA | NA | NA | NA |
| 51 | 10 | Costa | 2006 | Neotropical Entomology | Triticum aestivum | Triticum_aestivum | Annual | Poaceae | Greenbug (aphid) | Schizaphis graminum | Schizaphis_graminum | Specialist | Fluid-feeding arthropods | Abundance / Preference | 10.00 | 10.00 | 197.1900000 | 189.4100000 | 79.4996604 | 79.4996604 | 1 | NA | NA | NA | NA | NA | NA |
| 52 | 10 | Costa | 2006 | Neotropical Entomology | Triticum aestivum | Triticum_aestivum | Annual | Poaceae | Greenbug (aphid) | Schizaphis graminum | Schizaphis_graminum | Specialist | Fluid-feeding arthropods | Abundance / Preference | 10.00 | 10.00 | 486.4500000 | 289.6200000 | 223.6362761 | 223.6362761 | 1 | NA | NA | NA | NA | NA | NA |
| 53 | 11 | Pereira | 2010 | Sci. Agric. | Triticum aestivum | Triticum_aestivum | Annual | Poaceae | Greenbug (aphid) | Schizaphis graminum | Schizaphis_graminum | Specialist | Fluid-feeding arthropods | Growth / Development | 10.00 | 10.00 | 7.4000000 | 8.1000000 | 0.4743416 | 0.9803061 | -1 | NA | NA | NA | NA | NA | NA |
| 54 | 11 | Pereira | 2010 | Sci. Agric. | Triticum aestivum | Triticum_aestivum | Annual | Poaceae | Greenbug (aphid) | Schizaphis graminum | Schizaphis_graminum | Specialist | Fluid-feeding arthropods | Reproduction | 10.00 | 10.00 | 14.1000000 | 11.0000000 | 9.6765696 | 8.6330180 | 1 | NA | NA | NA | NA | NA | NA |
| 55 | 11 | Pereira | 2010 | Sci. Agric. | Triticum aestivum | Triticum_aestivum | Annual | Poaceae | Greenbug (aphid) | Schizaphis graminum | Schizaphis_graminum | Specialist | Fluid-feeding arthropods | Growth / Development | 10.00 | 10.00 | 4.1000000 | 3.4000000 | 3.7947332 | 5.3126265 | 1 | NA | NA | NA | NA | NA | NA |
| 56 | 11 | Pereira | 2010 | Sci. Agric. | Triticum aestivum | Triticum_aestivum | Annual | Poaceae | Greenbug (aphid) | Schizaphis graminum | Schizaphis_graminum | Specialist | Fluid-feeding arthropods | Abundance / Preference | 10.00 | 10.00 | 27.9000000 | 14.7000000 | 27.5118156 | 13.8507762 | 1 | NA | NA | NA | NA | NA | NA |
| 57 | 11 | Pereira | 2010 | Sci. Agric. | Triticum aestivum | Triticum_aestivum | Annual | Poaceae | Greenbug (aphid) | Schizaphis graminum | Schizaphis_graminum | Specialist | Fluid-feeding arthropods | Growth / Development | 10.00 | 10.00 | 25.5000000 | 22.5000000 | 11.2893312 | 11.9534096 | 1 | NA | NA | NA | NA | NA | NA |
| 58 | 11 | Pereira | 2010 | Sci. Agric. | Triticum aestivum | Triticum_aestivum | Annual | Poaceae | Greenbug (aphid) | Schizaphis graminum | Schizaphis_graminum | Specialist | Fluid-feeding arthropods | Reproduction | 10.00 | 10.00 | 0.3000000 | 0.2000000 | 0.0948683 | 0.0948683 | 1 | NA | NA | NA | NA | NA | NA |
| 59 | 11 | Pereira | 2010 | Sci. Agric. | Triticum aestivum | Triticum_aestivum | Annual | Poaceae | Greenbug (aphid) | Schizaphis graminum | Schizaphis_graminum | Specialist | Fluid-feeding arthropods | Feeding efficiency | 10.00 | 10.00 | 13.4000000 | 5.0000000 | 4.7434165 | 4.4271887 | 1 | NA | NA | NA | NA | NA | NA |
| 60 | 11 | Pereira | 2010 | Sci. Agric. | Triticum aestivum | Triticum_aestivum | Annual | Poaceae | Greenbug (aphid) | Schizaphis graminum | Schizaphis_graminum | Specialist | Fluid-feeding arthropods | Feeding efficiency | 25.00 | 25.00 | 90.5000000 | 67.2000000 | 59.0000000 | 52.5000000 | 1 | NA | NA | NA | NA | NA | NA |
| 61 | 11 | Pereira | 2010 | Sci. Agric. | Triticum aestivum | Triticum_aestivum | Annual | Poaceae | Greenbug (aphid) | Schizaphis graminum | Schizaphis_graminum | Specialist | Fluid-feeding arthropods | Feeding efficiency | 24.00 | 17.00 | 222.2000000 | 92.2000000 | 116.5957118 | 136.8871068 | 1 | NA | NA | NA | NA | NA | NA |
| 62 | 12 | Costa | 2011 | Journal of Applied Entomology | Triticum aestivum | Triticum_aestivum | Annual | Poaceae | Greenbug (aphid) | Schizaphis graminum | Schizaphis_graminum | Specialist | Fluid-feeding arthropods | Feeding efficiency | 25.00 | 25.00 | 105.1000000 | 170.4000000 | 78.6666666 | 146.5000000 | -1 | NA | NA | NA | NA | NA | NA |
| 63 | 12 | Costa | 2011 | Journal of Applied Entomology | Triticum aestivum | Triticum_aestivum | Annual | Poaceae | Greenbug (aphid) | Schizaphis graminum | Schizaphis_graminum | Specialist | Fluid-feeding arthropods | Feeding efficiency | 25.00 | 25.00 | 159.6000000 | 114.3333333 | 95.8333334 | 148.5000000 | 1 | NA | NA | NA | NA | NA | NA |
| 64 | 12 | Costa | 2011 | Journal of Applied Entomology | Triticum aestivum | Triticum_aestivum | Annual | Poaceae | Greenbug (aphid) | Schizaphis graminum | Schizaphis_graminum | Specialist | Fluid-feeding arthropods | Feeding efficiency | 25.00 | 25.00 | 88.0000000 | 44.0000000 | 33.3333333 | 50.0000000 | 1 | NA | NA | NA | NA | NA | NA |
| 65 | 13 | Dias | 2014 | Environmental Entomology | Triticum aestivum | Triticum_aestivum | Annual | Poaceae | Green aphid | Sitobion avenae | Sitobion_avenae | Specialist | Fluid-feeding arthropods | Growth / Development | 33.00 | 33.00 | 8.2000000 | 8.0000000 | 3.3318463 | 1.0340213 | -1 | NA | NA | NA | NA | NA | NA |
| 66 | 13 | Dias | 2014 | Environmental Entomology | Triticum aestivum | Triticum_aestivum | Annual | Poaceae | Green aphid | Sitobion avenae | Sitobion_avenae | Specialist | Fluid-feeding arthropods | Growth / Development | 30.00 | 30.00 | 9.4000000 | 11.6000000 | 1.3693064 | 1.7527122 | -1 | NA | NA | NA | NA | NA | NA |
| 67 | 13 | Dias | 2014 | Environmental Entomology | Triticum aestivum | Triticum_aestivum | Annual | Poaceae | Green aphid | Sitobion avenae | Sitobion_avenae | Specialist | Fluid-feeding arthropods | Reproduction | 33.00 | 33.00 | 9.1000000 | 5.2000000 | 2.1829338 | 4.4233132 | 1 | NA | NA | NA | NA | NA | NA |
| 68 | 13 | Dias | 2014 | Environmental Entomology | Triticum aestivum | Triticum_aestivum | Annual | Poaceae | Green aphid | Sitobion avenae | Sitobion_avenae | Specialist | Fluid-feeding arthropods | Reproduction | 30.00 | 30.00 | 12.2000000 | 10.0000000 | 14.7885091 | 10.5710454 | 1 | NA | NA | NA | NA | NA | NA |
| 69 | 13 | Dias | 2014 | Environmental Entomology | Triticum aestivum | Triticum_aestivum | Annual | Poaceae | Green aphid | Sitobion avenae | Sitobion_avenae | Specialist | Fluid-feeding arthropods | Growth / Development | 33.00 | 33.00 | 10.2000000 | 6.3000000 | 3.2744007 | 5.1126608 | 1 | NA | NA | NA | NA | NA | NA |
| 70 | 13 | Dias | 2014 | Environmental Entomology | Triticum aestivum | Triticum_aestivum | Annual | Poaceae | Green aphid | Sitobion avenae | Sitobion_avenae | Specialist | Fluid-feeding arthropods | Growth / Development | 30.00 | 30.00 | 14.3000000 | 11.0000000 | 14.9528258 | 10.8449066 | 1 | NA | NA | NA | NA | NA | NA |
| 71 | 13 | Dias | 2014 | Environmental Entomology | Triticum aestivum | Triticum_aestivum | Annual | Poaceae | Green aphid | Sitobion avenae | Sitobion_avenae | Specialist | Fluid-feeding arthropods | Reproduction | 33.00 | 33.00 | 26.8000000 | 14.8000000 | 16.7166773 | 10.8572234 | 1 | NA | NA | NA | NA | NA | NA |
| 72 | 13 | Dias | 2014 | Environmental Entomology | Triticum aestivum | Triticum_aestivum | Annual | Poaceae | Green aphid | Sitobion avenae | Sitobion_avenae | Specialist | Fluid-feeding arthropods | Reproduction | 30.00 | 30.00 | 29.3000000 | 23.6000000 | 37.5189952 | 27.0574943 | 1 | NA | NA | NA | NA | NA | NA |
| 73 | 13 | Dias | 2014 | Environmental Entomology | Triticum aestivum | Triticum_aestivum | Annual | Poaceae | Green aphid | Sitobion avenae | Sitobion_avenae | Specialist | Fluid-feeding arthropods | Reproduction | 33.00 | 33.00 | 3.1000000 | 3.2000000 | 1.3212494 | 1.0340213 | 1 | NA | NA | NA | NA | NA | NA |
| 74 | 13 | Dias | 2014 | Environmental Entomology | Triticum aestivum | Triticum_aestivum | Annual | Poaceae | Green aphid | Sitobion avenae | Sitobion_avenae | Specialist | Fluid-feeding arthropods | Reproduction | 30.00 | 30.00 | 2.5000000 | 2.6000000 | 0.9859006 | 1.3145341 | 1 | NA | NA | NA | NA | NA | NA |
| 75 | 13 | Dias | 2014 | Environmental Entomology | Triticum aestivum | Triticum_aestivum | Annual | Poaceae | Green aphid | Sitobion avenae | Sitobion_avenae | Specialist | Fluid-feeding arthropods | Mortality / Survivability | 33.00 | 33.00 | 0.6300000 | 0.7000000 | 0.5744563 | 0.2872281 | 1 | NA | NA | NA | NA | NA | NA |
| 76 | 13 | Dias | 2014 | Environmental Entomology | Triticum aestivum | Triticum_aestivum | Annual | Poaceae | Green aphid | Sitobion avenae | Sitobion_avenae | Specialist | Fluid-feeding arthropods | Mortality / Survivability | 30.00 | 30.00 | 0.6700000 | 0.7000000 | 2.9029296 | 0.5477226 | 1 | NA | NA | NA | NA | NA | NA |
| 77 | 13 | Dias | 2014 | Environmental Entomology | Triticum aestivum | Triticum_aestivum | Annual | Poaceae | Green aphid | Sitobion avenae | Sitobion_avenae | Specialist | Fluid-feeding arthropods | Abundance / Preference | 33.00 | 33.00 | 0.2500000 | 0.2200000 | 0.0574456 | 0.1148913 | 1 | NA | NA | NA | NA | NA | NA |
| 78 | 13 | Dias | 2014 | Environmental Entomology | Triticum aestivum | Triticum_aestivum | Annual | Poaceae | Green aphid | Sitobion avenae | Sitobion_avenae | Specialist | Fluid-feeding arthropods | Reproduction | 33.00 | 33.00 | 19.6000000 | 10.6000000 | 17.1762423 | 9.2487459 | 1 | NA | NA | NA | NA | NA | NA |
| 79 | 13 | Dias | 2014 | Environmental Entomology | Triticum aestivum | Triticum_aestivum | Annual | Poaceae | Green aphid | Sitobion avenae | Sitobion_avenae | Specialist | Fluid-feeding arthropods | Growth / Development | 33.00 | 33.00 | 11.8000000 | 10.9000000 | 2.7573901 | 3.8488570 | -1 | NA | NA | NA | NA | NA | NA |
| 80 | 13 | Dias | 2014 | Environmental Entomology | Triticum aestivum | Triticum_aestivum | Annual | Poaceae | Green aphid | Sitobion avenae | Sitobion_avenae | Specialist | Fluid-feeding arthropods | Abundance / Preference | 33.00 | 33.00 | 2.7400000 | 3.1900000 | 0.9191300 | 0.8042388 | -1 | NA | NA | NA | NA | NA | NA |
| 81 | 13 | Dias | 2014 | Environmental Entomology | Triticum aestivum | Triticum_aestivum | Annual | Poaceae | Green aphid | Sitobion avenae | Sitobion_avenae | Specialist | Fluid-feeding arthropods | Reproduction | 33.00 | 33.00 | 1.2900000 | 1.2400000 | 0.1148913 | 0.0574456 | 1 | NA | NA | NA | NA | NA | NA |
| 82 | 13 | Dias | 2014 | Environmental Entomology | Triticum aestivum | Triticum_aestivum | Annual | Poaceae | Green aphid | Sitobion avenae | Sitobion_avenae | Specialist | Fluid-feeding arthropods | Abundance / Preference | 30.00 | 30.00 | 0.2100000 | 0.1600000 | 0.0547723 | 0.0547723 | 1 | NA | NA | NA | NA | NA | NA |
| 83 | 13 | Dias | 2014 | Environmental Entomology | Triticum aestivum | Triticum_aestivum | Annual | Poaceae | Green aphid | Sitobion avenae | Sitobion_avenae | Specialist | Fluid-feeding arthropods | Reproduction | 30.00 | 30.00 | 21.4000000 | 13.4000000 | 23.9354758 | 12.7071633 | 1 | NA | NA | NA | NA | NA | NA |
| 84 | 13 | Dias | 2014 | Environmental Entomology | Triticum aestivum | Triticum_aestivum | Annual | Poaceae | Green aphid | Sitobion avenae | Sitobion_avenae | Specialist | Fluid-feeding arthropods | Growth / Development | 30.00 | 30.00 | 14.9000000 | 15.8000000 | 3.5054244 | 3.2315631 | -1 | NA | NA | NA | NA | NA | NA |
| 85 | 13 | Dias | 2014 | Environmental Entomology | Triticum aestivum | Triticum_aestivum | Annual | Poaceae | Green aphid | Sitobion avenae | Sitobion_avenae | Specialist | Fluid-feeding arthropods | Abundance / Preference | 30.00 | 30.00 | 3.3500000 | 4.1800000 | 0.9859006 | 1.4240786 | -1 | NA | NA | NA | NA | NA | NA |
| 86 | 13 | Dias | 2014 | Environmental Entomology | Triticum aestivum | Triticum_aestivum | Annual | Poaceae | Green aphid | Sitobion avenae | Sitobion_avenae | Specialist | Fluid-feeding arthropods | Reproduction | 30.00 | 30.00 | 1.2300000 | 1.1800000 | 0.0547723 | 0.0547723 | 1 | NA | NA | NA | NA | NA | NA |
| 87 | 14 | Gomes | 2005 | Scientia Agricola | Triticum aestivum | Triticum_aestivum | Annual | Poaceae | Greenbug (aphid) | Schizaphis graminum | Schizaphis_graminum | Specialist | Fluid-feeding arthropods | Reproduction | 10.00 | 10.00 | 0.3700000 | 0.2500000 | 0.0316228 | 0.0316228 | 1 | NA | NA | NA | NA | NA | NA |
| 88 | 14 | Gomes | 2005 | Scientia Agricola | Triticum aestivum | Triticum_aestivum | Annual | Poaceae | Greenbug (aphid) | Schizaphis graminum | Schizaphis_graminum | Specialist | Fluid-feeding arthropods | Abundance / Preference | 10.00 | 10.00 | 30.3000000 | 11.7000000 | 11.1206764 | 6.2296870 | 1 | NA | NA | NA | NA | NA | NA |
| 89 | 15 | Gomes | 2008 | Ciencia Rural | Solanum tuberosum | Solanum_tuberosum | Perennial | Non-Poaceae | Potato aphid | Myzus persicae | Myzus_persicae | Generalist | Fluid-feeding arthropods | Abundance / Preference | 7.00 | 7.00 | 31.1000000 | 26.1000000 | 7.6726788 | 11.8794234 | 1 | NA | NA | NA | NA | NA | NA |
| 90 | 15 | Gomes | 2008 | Ciencia Rural | Solanum tuberosum | Solanum_tuberosum | Perennial | Non-Poaceae | Potato aphid | Myzus persicae | Myzus_persicae | Generalist | Fluid-feeding arthropods | Reproduction | 7.00 | 7.00 | 220.9000000 | 94.6000000 | 72.4671284 | 24.3938271 | 1 | NA | NA | NA | NA | NA | NA |
| 91 | 16 | Gomes | 2008 | Neotropical Entomology | Solanum tuberosum | Solanum_tuberosum | Perennial | Non-Poaceae | Potato aphid | Myzus persicae | Myzus_persicae | Generalist | Fluid-feeding arthropods | Abundance / Preference | 8.00 | 8.00 | 2.0666667 | 2.1000000 | 0.6788225 | 0.7731034 | 1 | NA | NA | NA | NA | NA | NA |
| 92 | 16 | Gomes | 2008 | Neotropical Entomology | Solanum tuberosum | Solanum_tuberosum | Perennial | Non-Poaceae | Potato aphid | Myzus persicae | Myzus_persicae | Generalist | Fluid-feeding arthropods | Abundance / Preference | 8.00 | 8.00 | 4.0333333 | 4.5000000 | 1.0276619 | 1.2067956 | 1 | NA | NA | NA | NA | NA | NA |
| 93 | 16 | Gomes | 2008 | Neotropical Entomology | Solanum tuberosum | Solanum_tuberosum | Perennial | Non-Poaceae | Potato aphid | Myzus persicae | Myzus_persicae | Generalist | Fluid-feeding arthropods | Growth / Development | 8.00 | 8.00 | 6.5000000 | 7.3000000 | 0.2545584 | 0.4242641 | -1 | NA | NA | NA | NA | NA | NA |
| 94 | 16 | Gomes | 2008 | Neotropical Entomology | Solanum tuberosum | Solanum_tuberosum | Perennial | Non-Poaceae | Potato aphid | Myzus persicae | Myzus_persicae | Generalist | Fluid-feeding arthropods | Reproduction | 8.00 | 8.00 | 10.8000000 | 10.9000000 | 0.9616652 | 0.9899495 | 1 | NA | NA | NA | NA | NA | NA |
| 95 | 16 | Gomes | 2008 | Neotropical Entomology | Solanum tuberosum | Solanum_tuberosum | Perennial | Non-Poaceae | Potato aphid | Myzus persicae | Myzus_persicae | Generalist | Fluid-feeding arthropods | Reproduction | 8.00 | 8.00 | 0.4000000 | 0.3000000 | 0.0282843 | 0.0282843 | 1 | NA | NA | NA | NA | NA | NA |
| 96 | 16 | Gomes | 2008 | Neotropical Entomology | Solanum tuberosum | Solanum_tuberosum | Perennial | Non-Poaceae | Potato aphid | Myzus persicae | Myzus_persicae | Generalist | Fluid-feeding arthropods | Reproduction | 8.00 | 8.00 | 4.7000000 | 2.4000000 | 0.9899495 | 0.3394113 | 1 | NA | NA | NA | NA | NA | NA |
| 97 | 16 | Gomes | 2008 | Neotropical Entomology | Solanum tuberosum | Solanum_tuberosum | Perennial | Non-Poaceae | Potato aphid | Myzus persicae | Myzus_persicae | Generalist | Fluid-feeding arthropods | Reproduction | 8.00 | 8.00 | 43.4000000 | 23.9000000 | 7.2973420 | 3.4789654 | 1 | NA | NA | NA | NA | NA | NA |
| 98 | 16 | Gomes | 2008 | Neotropical Entomology | Solanum tuberosum | Solanum_tuberosum | Perennial | Non-Poaceae | Potato aphid | Myzus persicae | Myzus_persicae | Generalist | Fluid-feeding arthropods | Growth / Development | 8.00 | 8.00 | 17.3000000 | 19.4000000 | 0.9050967 | 2.1496046 | 1 | NA | NA | NA | NA | NA | NA |
| 99 | 17 | Goussain | 2002 | Neotropical Entomology | Zea mays | Zea_mays | Annual | Poaceae | Armyworm | Spodoptera frugiperda | Spodoptera_frugiperda | Generalist | Chewing arthropods | Mortality / Survivability | 20.00 | 20.00 | 3.2505000 | 6.7701000 | 4.4444087 | 5.7408809 | -1 | 20 | 20.000 | 1.0000000 | 1.400000 | 0.0894427 | 1.341641e-01 |
| 100 | 17 | Goussain | 2002 | Neotropical Entomology | Zea mays | Zea_mays | Annual | Poaceae | Armyworm | Spodoptera frugiperda | Spodoptera_frugiperda | Generalist | Chewing arthropods | Mortality / Survivability | 20.00 | 20.00 | 5.2153000 | 44.2692000 | 52.1728325 | 411.4150416 | -1 | 20 | 20.000 | 1.0000000 | 1.400000 | 0.0894427 | 1.341641e-01 |
| 101 | 17 | Goussain | 2002 | Neotropical Entomology | Zea mays | Zea_mays | Annual | Poaceae | Armyworm | Spodoptera frugiperda | Spodoptera_frugiperda | Generalist | Chewing arthropods | Growth / Development | 20.00 | 20.00 | 16.6000000 | 17.4000000 | 1.3416408 | 1.3863621 | -1 | 20 | 20.000 | 1.0000000 | 1.400000 | 0.0894427 | 1.341641e-01 |
| 102 | 17 | Goussain | 2002 | Neotropical Entomology | Zea mays | Zea_mays | Annual | Poaceae | Armyworm | Spodoptera frugiperda | Spodoptera_frugiperda | Generalist | Chewing arthropods | Growth / Development | 20.00 | 20.00 | 9.9000000 | 10.3000000 | 0.9838699 | 0.8944272 | -1 | 20 | 20.000 | 1.0000000 | 1.400000 | 0.0894427 | 1.341641e-01 |
| 103 | 17 | Goussain | 2002 | Neotropical Entomology | Zea mays | Zea_mays | Annual | Poaceae | Armyworm | Spodoptera frugiperda | Spodoptera_frugiperda | Generalist | Chewing arthropods | Mortality / Survivability | 20.00 | 20.00 | 35.0000000 | 39.2000000 | 33.3174129 | 38.4603692 | -1 | 20 | 20.000 | 1.0000000 | 1.400000 | 0.0894427 | 1.341641e-01 |
| 104 | 17 | Goussain | 2002 | Neotropical Entomology | Zea mays | Zea_mays | Annual | Poaceae | Armyworm | Spodoptera frugiperda | Spodoptera_frugiperda | Generalist | Chewing arthropods | Growth / Development | 20.00 | 20.00 | 0.3000000 | 0.3000000 | 0.0447214 | 0.0447214 | 1 | 20 | 20.000 | 1.0000000 | 1.400000 | 0.0894427 | 1.341641e-01 |
| 105 | 17 | Goussain | 2002 | Neotropical Entomology | Zea mays | Zea_mays | Annual | Poaceae | Armyworm | Spodoptera frugiperda | Spodoptera_frugiperda | Generalist | Chewing arthropods | Mortality / Survivability | 20.00 | 20.00 | 32.9000000 | 7.8000000 | 29.1136051 | 10.9567331 | -1 | 20 | 20.000 | 1.0000000 | 1.400000 | 0.0894427 | 1.341641e-01 |
| 106 | 17 | Goussain | 2002 | Neotropical Entomology | Zea mays | Zea_mays | Annual | Poaceae | Armyworm | Spodoptera frugiperda | Spodoptera_frugiperda | Generalist | Chewing arthropods | Mortality / Survivability | 20.00 | 20.00 | 14.5000000 | 36.3000000 | 23.7023206 | 21.4662526 | -1 | 20 | 20.000 | 1.0000000 | 1.400000 | 0.0894427 | 1.341641e-01 |
| 107 | 17 | Goussain | 2002 | Neotropical Entomology | Zea mays | Zea_mays | Annual | Poaceae | Armyworm | Spodoptera frugiperda | Spodoptera_frugiperda | Generalist | Chewing arthropods | Mortality / Survivability | 20.00 | 20.00 | 5.4000000 | 4.1000000 | 13.8636215 | 10.2859127 | -1 | 20 | 20.000 | 1.0000000 | 1.400000 | 0.0894427 | 1.341641e-01 |
| 108 | 17 | Goussain | 2002 | Neotropical Entomology | Zea mays | Zea_mays | Annual | Poaceae | Armyworm | Spodoptera frugiperda | Spodoptera_frugiperda | Generalist | Chewing arthropods | Mortality / Survivability | 20.00 | 20.00 | 4.2000000 | 10.8000000 | 13.0586370 | 20.5718254 | -1 | 20 | 20.000 | 1.0000000 | 1.400000 | 0.0894427 | 1.341641e-01 |
| 109 | 17 | Goussain | 2002 | Neotropical Entomology | Zea mays | Zea_mays | Annual | Poaceae | Armyworm | Spodoptera frugiperda | Spodoptera_frugiperda | Generalist | Chewing arthropods | Mortality / Survivability | 20.00 | 20.00 | 7.9000000 | 20.9000000 | 14.3108351 | 22.8526147 | -1 | 20 | 20.000 | 1.0000000 | 1.400000 | 0.0894427 | 1.341641e-01 |
| 110 | 18 | Goussain | 2005 | Neotropical Entomology | Triticum aestivum | Triticum_aestivum | Annual | Poaceae | Greenbug (aphid) | Schizaphis graminum | Schizaphis_graminum | Specialist | Fluid-feeding arthropods | Growth / Development | 10.00 | 10.00 | 6.4000000 | 6.6000000 | 0.6324555 | 0.3162278 | -1 | NA | NA | NA | NA | NA | NA |
| 111 | 18 | Goussain | 2005 | Neotropical Entomology | Triticum aestivum | Triticum_aestivum | Annual | Poaceae | Greenbug (aphid) | Schizaphis graminum | Schizaphis_graminum | Specialist | Fluid-feeding arthropods | Reproduction | 10.00 | 10.00 | 21.7000000 | 22.1000000 | 5.0596443 | 4.1109610 | 1 | NA | NA | NA | NA | NA | NA |
| 112 | 18 | Goussain | 2005 | Neotropical Entomology | Triticum aestivum | Triticum_aestivum | Annual | Poaceae | Greenbug (aphid) | Schizaphis graminum | Schizaphis_graminum | Specialist | Fluid-feeding arthropods | Growth / Development | 10.00 | 10.00 | 9.5000000 | 9.1000000 | 4.1109610 | 4.4271887 | 1 | NA | NA | NA | NA | NA | NA |
| 113 | 18 | Goussain | 2005 | Neotropical Entomology | Triticum aestivum | Triticum_aestivum | Annual | Poaceae | Greenbug (aphid) | Schizaphis graminum | Schizaphis_graminum | Specialist | Fluid-feeding arthropods | Growth / Development | 10.00 | 10.00 | 37.6000000 | 37.6000000 | 2.5298221 | 5.3758720 | 1 | NA | NA | NA | NA | NA | NA |
| 114 | 18 | Goussain | 2005 | Neotropical Entomology | Triticum aestivum | Triticum_aestivum | Annual | Poaceae | Greenbug (aphid) | Schizaphis graminum | Schizaphis_graminum | Specialist | Fluid-feeding arthropods | Reproduction | 10.00 | 10.00 | 89.4000000 | 72.2000000 | 20.5548048 | 21.1872603 | 1 | NA | NA | NA | NA | NA | NA |
| 115 | 18 | Goussain | 2005 | Neotropical Entomology | Triticum aestivum | Triticum_aestivum | Annual | Poaceae | Greenbug (aphid) | Schizaphis graminum | Schizaphis_graminum | Specialist | Fluid-feeding arthropods | Feeding efficiency | 14.00 | 14.00 | 28.1000000 | 19.7000000 | 16.8374582 | 11.5991379 | 1 | NA | NA | NA | NA | NA | NA |
| 116 | 19 | Griffin | 2015 | Journal of Pest Science | Triticum aestivum | Triticum_aestivum | Annual | Poaceae | Field slug | Deroceras reticulatrum | Deroceras_reticulatrum | Generalist | Rasping / grazing invertebrates | Feeding efficiency | 9.00 | 9.00 | 27.9186000 | 12.6447000 | 26.2911000 | 15.7746000 | 1 | 3 | 3.000 | 4.9900000 | 18.360000 | 1.2643971 | 3.256255e+00 |
| 117 | 19 | Griffin | 2015 | Journal of Pest Science | Triticum aestivum | Triticum_aestivum | Annual | Poaceae | Field slug | Deroceras reticulatrum | Deroceras_reticulatrum | Generalist | Rasping / grazing invertebrates | Feeding efficiency | 5.00 | 5.00 | 66.9700000 | 17.1100000 | 36.5373508 | 20.3705793 | 1 | 3 | 3.000 | 4.9900000 | 18.360000 | 1.2643971 | 3.256255e+00 |
| 118 | 20 | He | 2015 | Crop Protection | Oryza sativa | Oryza_sativa | Annual | Poaceae | Brown planthopper | Nilaparvata lugens | Nilaparvata_lugens | Specialist | Fluid-feeding arthropods | Reproduction | 10.00 | 10.00 | 109.9056000 | 65.5660000 | 73.0909885 | 55.3257869 | 1 | NA | NA | NA | NA | NA | NA |
| 119 | 20 | He | 2015 | Crop Protection | Oryza sativa | Oryza_sativa | Annual | Poaceae | Brown planthopper | Nilaparvata lugens | Nilaparvata_lugens | Specialist | Fluid-feeding arthropods | Mortality / Survivability | 10.00 | 10.00 | 3.4340000 | 2.7155000 | 1.0957292 | 2.2864849 | 1 | NA | NA | NA | NA | NA | NA |
| 120 | 20 | He | 2015 | Crop Protection | Oryza sativa | Oryza_sativa | Annual | Poaceae | Brown planthopper | Nilaparvata lugens | Nilaparvata_lugens | Specialist | Fluid-feeding arthropods | Growth / Development | 7.00 | 7.00 | 48.2820500 | 42.8571000 | 42.5807216 | 48.8004861 | 1 | NA | NA | NA | NA | NA | NA |
| 121 | 20 | He | 2015 | Crop Protection | Oryza sativa | Oryza_sativa | Annual | Poaceae | Brown planthopper | Nilaparvata lugens | Nilaparvata_lugens | Specialist | Fluid-feeding arthropods | Mortality / Survivability | 7.00 | 7.00 | 37.1950000 | 25.7545000 | 35.7758492 | 42.5238379 | 1 | NA | NA | NA | NA | NA | NA |
| 122 | 20 | He | 2015 | Crop Protection | Oryza sativa | Oryza_sativa | Annual | Poaceae | Brown planthopper | Nilaparvata lugens | Nilaparvata_lugens | Specialist | Fluid-feeding arthropods | Feeding efficiency | 15.00 | 15.00 | 12.0527500 | 3.8013500 | 18.3645251 | 6.2103288 | 1 | NA | NA | NA | NA | NA | NA |
| 123 | 20 | He | 2015 | Crop Protection | Oryza sativa | Oryza_sativa | Annual | Poaceae | Brown planthopper | Nilaparvata lugens | Nilaparvata_lugens | Specialist | Fluid-feeding arthropods | Abundance / Preference | 10.00 | 10.00 | 5.5950000 | 3.8050000 | 11.0047263 | 9.4646970 | 1 | NA | NA | NA | NA | NA | NA |
| 124 | 21 | Hogendorp | 2009 | Journal of Economic Entomology | Solenostemon scutellarioides | Solenostemon_scutellarioides | Perennial | Non-Poaceae | Citrus mealybug | Planococcus citri | Planococcus_citri | Generalist | Fluid-feeding arthropods | Reproduction | 15.00 | 15.00 | 206.8000000 | 199.5000000 | 22.4633034 | 23.2379001 | 1 | 12 | 12.000 | 1180.0000000 | 1046.000000 | 303.4553015 | 1.368320e+02 |
| 125 | 21 | Hogendorp | 2009 | Journal of Economic Entomology | Solenostemon scutellarioides | Solenostemon_scutellarioides | Perennial | Non-Poaceae | Citrus mealybug | Planococcus citri | Planococcus_citri | Generalist | Fluid-feeding arthropods | Growth / Development | 15.00 | 15.00 | 3.0000000 | 2.9000000 | 0.1161895 | 0.1161895 | 1 | 12 | 12.000 | 1180.0000000 | 1046.000000 | 303.4553015 | 1.368320e+02 |
| 126 | 21 | Hogendorp | 2009 | Journal of Economic Entomology | Solenostemon scutellarioides | Solenostemon_scutellarioides | Perennial | Non-Poaceae | Citrus mealybug | Planococcus citri | Planococcus_citri | Generalist | Fluid-feeding arthropods | Growth / Development | 15.00 | 15.00 | 34.5000000 | 35.7000000 | 0.7745967 | 1.1618950 | -1 | 12 | 12.000 | 1180.0000000 | 1046.000000 | 303.4553015 | 1.368320e+02 |
| 127 | 21 | Hogendorp | 2009 | Journal of Economic Entomology | Solenostemon scutellarioides | Solenostemon_scutellarioides | Perennial | Non-Poaceae | Citrus mealybug | Planococcus citri | Planococcus_citri | Generalist | Fluid-feeding arthropods | Reproduction | 12.00 | 12.00 | 244.8000000 | 223.1000000 | 19.7453792 | 19.3989690 | 1 | 12 | 12.000 | 1180.0000000 | 1046.000000 | 303.4553015 | 1.368320e+02 |
| 128 | 21 | Hogendorp | 2009 | Journal of Economic Entomology | Solenostemon scutellarioides | Solenostemon_scutellarioides | Perennial | Non-Poaceae | Citrus mealybug | Planococcus citri | Planococcus_citri | Generalist | Fluid-feeding arthropods | Growth / Development | 12.00 | 12.00 | 3.2000000 | 3.0000000 | 0.1732051 | 0.1732051 | 1 | 12 | 12.000 | 1180.0000000 | 1046.000000 | 303.4553015 | 1.368320e+02 |
| 129 | 21 | Hogendorp | 2009 | Journal of Economic Entomology | Solenostemon scutellarioides | Solenostemon_scutellarioides | Perennial | Non-Poaceae | Citrus mealybug | Planococcus citri | Planococcus_citri | Generalist | Fluid-feeding arthropods | Growth / Development | 12.00 | 12.00 | 35.7000000 | 35.3000000 | 0.6928203 | 0.6928203 | -1 | 12 | 12.000 | 1180.0000000 | 1046.000000 | 303.4553015 | 1.368320e+02 |
| 130 | 22 | Hogendorp | 2009 | HortScience | Ficus lyrata | Ficus_lyrata | Perennial | Non-Poaceae | Citrus mealybug | Planococcus citri | Planococcus_citri | Generalist | Fluid-feeding arthropods | Reproduction | 43.00 | 41.00 | 132.3000000 | 152.0000000 | 76.5908820 | 76.8374908 | 1 | 5 | 5.000 | 3774.9000000 | 6117.400000 | 296.0330395 | 2.028561e+02 |
| 131 | 22 | Hogendorp | 2009 | HortScience | Ficus lyrata | Ficus_lyrata | Perennial | Non-Poaceae | Citrus mealybug | Planococcus citri | Planococcus_citri | Generalist | Fluid-feeding arthropods | Growth / Development | 43.00 | 41.00 | 2.3000000 | 2.2000000 | 0.6557439 | 0.7043437 | 1 | 5 | 5.000 | 3774.9000000 | 6117.400000 | 296.0330395 | 2.028561e+02 |
| 132 | 22 | Hogendorp | 2009 | HortScience | Ficus lyrata | Ficus_lyrata | Perennial | Non-Poaceae | Citrus mealybug | Planococcus citri | Planococcus_citri | Generalist | Fluid-feeding arthropods | Growth / Development | 43.00 | 41.00 | 67.8000000 | 68.0000000 | 4.0656119 | 3.9699370 | -1 | 5 | 5.000 | 3774.9000000 | 6117.400000 | 296.0330395 | 2.028561e+02 |
| 133 | 23 | Hogendorp | 2010 | Journal of Entomological Sciences | Euphorbia pulcherrima | Euphorbia_pulcherrima | Perennial | Non-Poaceae | Greenhouse whitefly | Trialeurodes vaporariorum | Trialeurodes_vaporariorum | Generalist | Fluid-feeding arthropods | Abundance / Preference | 6.66 | 6.66 | 57.3333333 | 63.8666667 | 19.0498493 | 17.4498168 | 1 | 15 | 4.666 | 803.8000000 | 1073.750000 | 148.2190727 | 8.404920e+01 |
| 134 | 23 | Hogendorp | 2010 | Journal of Entomological Sciences | Euphorbia pulcherrima | Euphorbia_pulcherrima | Perennial | Non-Poaceae | Greenhouse whitefly | Trialeurodes vaporariorum | Trialeurodes_vaporariorum | Generalist | Fluid-feeding arthropods | Mortality / Survivability | 6.66 | 6.66 | 38.5333333 | 40.8666667 | 17.7508982 | 17.6653051 | 1 | 15 | 4.666 | 803.8000000 | 1073.500000 | 148.2190727 | 8.404920e+01 |
| 135 | 24 | Hou | 2010 | Journal of Economic Entomology | Oryza sativa | Oryza_sativa | Annual | Poaceae | Asiatic rice borer | Chilo suppressalis | Chilo_suppressalis | Generalist | Boring arthropods | Feeding efficiency | 25.00 | 25.00 | 20.2300000 | 33.1450000 | 25.0750000 | 36.6500000 | -1 | 3 | 3.000 | 51.9900000 | 65.450000 | 1.6454483 | 2.736640e+00 |
| 136 | 24 | Hou | 2010 | Journal of Economic Entomology | Oryza sativa | Oryza_sativa | Annual | Poaceae | Asiatic rice borer | Chilo suppressalis | Chilo_suppressalis | Generalist | Boring arthropods | Growth / Development | 25.00 | 25.00 | 5.4350000 | 3.3900000 | 3.6500000 | 2.7250000 | 1 | 3 | 3.000 | 51.9900000 | 65.450000 | 1.6454483 | 2.736640e+00 |
| 137 | 24 | Hou | 2010 | Journal of Economic Entomology | Oryza sativa | Oryza_sativa | Annual | Poaceae | Asiatic rice borer | Chilo suppressalis | Chilo_suppressalis | Generalist | Boring arthropods | Feeding efficiency | 25.00 | 25.00 | 46.2450000 | 27.3700000 | 27.5500000 | 17.3500000 | 1 | 3 | 3.000 | 51.9900000 | 65.450000 | 1.6454483 | 2.736640e+00 |
| 138 | 24 | Hou | 2010 | Journal of Economic Entomology | Oryza sativa | Oryza_sativa | Annual | Poaceae | Asiatic rice borer | Chilo suppressalis | Chilo_suppressalis | Generalist | Boring arthropods | Growth / Development | 6.00 | 6.00 | 41.0450000 | 17.3000000 | 6.4421580 | 5.6950637 | 1 | 3 | 3.000 | 51.9900000 | 65.450000 | 1.6454483 | 2.736640e+00 |
| 139 | 25 | Keeping | 2002 | Agriculturaland Forest Entomology | Saccharum spp. | Saccharum_spp. | Perennial | Poaceae | African stalk borer | Eldana saccharina | Eldana_saccharina | Specialist | Boring arthropods | Feeding efficiency | 8.00 | 8.00 | 113.9100000 | 86.6100000 | 28.0297128 | 28.8782409 | 1 | 6 | 6.000 | 0.1450000 | 0.305000 | 0.0367423 | 8.573210e-02 |
| 140 | 25 | Keeping | 2002 | Agriculturaland Forest Entomology | Saccharum spp. | Saccharum_spp. | Perennial | Poaceae | African stalk borer | Eldana saccharina | Eldana_saccharina | Specialist | Boring arthropods | Feeding efficiency | 8.00 | 8.00 | 111.6300000 | 87.8800000 | 22.7405541 | 21.8637417 | 1 | 6 | 6.000 | 0.1450000 | 0.305000 | 0.0367423 | 8.573210e-02 |
| 141 | 25 | Keeping | 2002 | Agriculturaland Forest Entomology | Saccharum spp. | Saccharum_spp. | Perennial | Poaceae | African stalk borer | Eldana saccharina | Eldana_saccharina | Specialist | Boring arthropods | Abundance / Preference | 8.00 | 8.00 | 109.6400000 | 7.8900000 | 259.1122089 | 22.7688384 | 1 | 6 | 6.000 | 0.1450000 | 0.305000 | 0.0367423 | 8.573210e-02 |
| 142 | 25 | Keeping | 2002 | Agriculturaland Forest Entomology | Saccharum spp. | Saccharum_spp. | Perennial | Poaceae | African stalk borer | Eldana saccharina | Eldana_saccharina | Specialist | Boring arthropods | Growth / Development | 8.00 | 8.00 | 114.6300000 | 91.8100000 | 24.9467272 | 24.4941789 | 1 | 6 | 6.000 | 0.1450000 | 0.305000 | 0.0367423 | 8.573210e-02 |
| 143 | 26 | Keeping | 2006 | Journal of Applied Entomology | Saccharum spp. | Saccharum_spp. | Perennial | Poaceae | African stalk borer | Eldana saccharina | Eldana_saccharina | Specialist | Boring arthropods | Feeding efficiency | 6.00 | 6.00 | 169.9825000 | 94.9950000 | 155.8304137 | 152.7073143 | 1 | 3 | 3.000 | 0.1700000 | 0.360000 | 0.0692820 | 1.039230e-01 |
| 144 | 26 | Keeping | 2006 | Journal of Applied Entomology | Saccharum spp. | Saccharum_spp. | Perennial | Poaceae | African stalk borer | Eldana saccharina | Eldana_saccharina | Specialist | Boring arthropods | Mortality / Survivability | 6.00 | 6.00 | 40.5800000 | 26.5450000 | 34.7460120 | 34.9848373 | 1 | 3 | 3.000 | 0.1700000 | 0.360000 | 0.0692820 | 1.039230e-01 |
| 145 | 26 | Keeping | 2006 | Journal of Applied Entomology | Saccharum spp. | Saccharum_spp. | Perennial | Poaceae | African stalk borer | Eldana saccharina | Eldana_saccharina | Specialist | Boring arthropods | Growth / Development | 6.00 | 6.00 | 2.4825000 | 1.5450000 | 2.4372423 | 2.4188711 | 1 | 3 | 3.000 | 0.1700000 | 0.360000 | 0.0692820 | 1.039230e-01 |
| 146 | 27 | Keeping | 2013 | Plant and Soil | Saccharum spp. | Saccharum_spp. | Perennial | Poaceae | African stalk borer | Eldana saccharina | Eldana_saccharina | Specialist | Boring arthropods | Feeding efficiency | 6.00 | 6.00 | 12.5026738 | 9.8823529 | 1.1614336 | 1.7727145 | 1 | 6 | 6.000 | 0.1155000 | 0.145300 | 0.0156767 | 1.935100e-02 |
| 147 | 27 | Keeping | 2013 | Plant and Soil | Saccharum spp. | Saccharum_spp. | Perennial | Poaceae | African stalk borer | Eldana saccharina | Eldana_saccharina | Specialist | Boring arthropods | Feeding efficiency | 6.00 | 6.00 | 2.8974937 | 2.2026106 | 0.5021609 | 0.5021220 | 1 | 6 | 6.000 | 0.1155000 | 0.145300 | 0.0156767 | 1.935100e-02 |
| 148 | 27 | Keeping | 2013 | Plant and Soil | Saccharum spp. | Saccharum_spp. | Perennial | Poaceae | African stalk borer | Eldana saccharina | Eldana_saccharina | Specialist | Boring arthropods | Abundance / Preference | 6.00 | 6.00 | 3.9052720 | 2.8003603 | 1.6809781 | 0.9823736 | 1 | 6 | 6.000 | 0.1155000 | 0.145300 | 0.0156767 | 1.935100e-02 |
| 149 | 28 | Keeping | 2014 | Frontiers in Plant Science | Saccharum spp. | Saccharum_spp. | Perennial | Poaceae | African stalk borer | Eldana saccharina | Eldana_saccharina | Specialist | Boring arthropods | Mortality / Survivability | 8.00 | 8.00 | 2.5225000 | 1.1060000 | 1.4467405 | 0.8499424 | 1 | 8 | 8.000 | 1.9000000 | 5.800000 | 0.2828427 | 3.111270e+00 |
| 150 | 28 | Keeping | 2014 | Frontiers in Plant Science | Saccharum spp. | Saccharum_spp. | Perennial | Poaceae | African stalk borer | Eldana saccharina | Eldana_saccharina | Specialist | Boring arthropods | Feeding efficiency | 8.00 | 8.00 | 6.9930000 | 2.7940000 | 4.2709250 | 1.4919953 | 1 | 8 | 8.000 | 1.9000000 | 5.800000 | 0.2828427 | 3.111270e+00 |
| 151 | 28 | Keeping | 2014 | Frontiers in Plant Science | Saccharum spp. | Saccharum_spp. | Perennial | Poaceae | Sugarcane thrips | Fulmekiola serrata | Fulmekiola_serrata | Specialist | Cell-feeding arthropods | Abundance / Preference | 8.00 | 8.00 | 23.9300000 | 22.2750000 | 7.2124892 | 6.7882251 | 1 | 8 | 8.000 | 1.9000000 | 5.800000 | 0.2828427 | 3.111270e+00 |
| 152 | 29 | Korndorfer | 2004 | Florida Entomologist | Oryza sativa | Oryza_sativa | Perennial | Poaceae | Tropical sod webworm | Herpetogramma phaeopteralis | Herpetogramma_phaeopteralis | Generalist | Chewing arthropods | Growth / Development | 10.00 | 10.00 | 30.9200000 | 30.0800000 | 33.6466343 | 33.4568976 | 1 | 15 | 15.000 | 0.6400000 | 1.160000 | 0.7513588 | 1.704113e+00 |
| 153 | 29 | Korndorfer | 2004 | Florida Entomologist | Oryza sativa | Oryza_sativa | Perennial | Poaceae | Tropical sod webworm | Herpetogramma phaeopteralis | Herpetogramma_phaeopteralis | Generalist | Chewing arthropods | Growth / Development | 15.00 | 15.00 | 27.6800000 | 24.1800000 | 51.8205172 | 54.6865248 | 1 | 15 | 15.000 | 0.6400000 | 1.160000 | 0.7513588 | 1.704113e+00 |
| 154 | 29 | Korndorfer | 2004 | Florida Entomologist | Oryza sativa | Oryza_sativa | Perennial | Poaceae | Tropical sod webworm | Herpetogramma phaeopteralis | Herpetogramma_phaeopteralis | Generalist | Chewing arthropods | Growth / Development | 15.00 | 15.00 | 11.4000000 | 11.8800000 | 4.4152010 | 6.1193137 | -1 | 15 | 15.000 | 0.6400000 | 1.160000 | 0.7513588 | 1.704113e+00 |
| 155 | 29 | Korndorfer | 2004 | Florida Entomologist | Oryza sativa | Oryza_sativa | Perennial | Poaceae | Tropical sod webworm | Herpetogramma phaeopteralis | Herpetogramma_phaeopteralis | Generalist | Chewing arthropods | Growth / Development | 15.00 | 15.00 | 17.4000000 | 17.6200000 | 4.4152010 | 6.4291524 | -1 | 15 | 15.000 | 0.6400000 | 1.160000 | 0.7513588 | 1.704113e+00 |
| 156 | 30 | Korndorfer | 2011 | Neotropical Entomology | Saccharum spp. | Saccharum_spp. | Perennial | Poaceae | Spittlebug | Mahanarva fimbriolata | Mahanarva_fimbriolata | Specialist | Fluid-feeding arthropods | Mortality / Survivability | 10.00 | 10.00 | 41.0000000 | 59.5000000 | 6.7356514 | 6.7356514 | -1 | 10 | 10.000 | 1.0500000 | 2.600000 | 4.0160926 | 4.016093e+00 |
| 157 | 30 | Korndorfer | 2011 | Neotropical Entomology | Saccharum spp. | Saccharum_spp. | Perennial | Poaceae | Spittlebug | Mahanarva fimbriolata | Mahanarva_fimbriolata | Specialist | Fluid-feeding arthropods | Growth / Development | 10.00 | 10.00 | 48.5000000 | 49.6000000 | 1.8341210 | 1.8341210 | -1 | 10 | 10.000 | 1.0500000 | 2.600000 | 4.0160926 | 4.016093e+00 |
| 158 | 30 | Korndorfer | 2011 | Neotropical Entomology | Saccharum spp. | Saccharum_spp. | Perennial | Poaceae | Spittlebug | Mahanarva fimbriolata | Mahanarva_fimbriolata | Specialist | Fluid-feeding arthropods | Growth / Development | 10.00 | 10.00 | 3.9000000 | 3.7000000 | 0.4427189 | 0.4427189 | -1 | 10 | 10.000 | 1.1000000 | 2.600000 | 4.0160926 | 4.016093e+00 |
| 159 | 30 | Korndorfer | 2011 | Neotropical Entomology | Saccharum spp. | Saccharum_spp. | Perennial | Poaceae | Spittlebug | Mahanarva fimbriolata | Mahanarva_fimbriolata | Specialist | Fluid-feeding arthropods | Reproduction | 10.00 | 10.00 | 376.0000000 | 18.4200000 | 1143.1633742 | 58.2491545 | 1 | 10 | 10.000 | 1.1000000 | 2.600000 | 4.0160926 | 4.016093e+00 |
| 160 | 30 | Korndorfer | 2011 | Neotropical Entomology | Saccharum spp. | Saccharum_spp. | Perennial | Poaceae | Spittlebug | Mahanarva fimbriolata | Mahanarva_fimbriolata | Specialist | Fluid-feeding arthropods | Mortality / Survivability | 10.00 | 10.00 | 88.2000000 | 5.2200000 | 243.4953798 | 16.5070894 | 1 | 10 | 10.000 | 1.1000000 | 2.600000 | 4.0160926 | 4.016093e+00 |
| 161 | 30 | Korndorfer | 2011 | Neotropical Entomology | Saccharum spp. | Saccharum_spp. | Perennial | Poaceae | Spittlebug | Mahanarva fimbriolata | Mahanarva_fimbriolata | Specialist | Fluid-feeding arthropods | Growth / Development | 10.00 | 10.00 | 13.9000000 | 12.5000000 | 1.0751744 | 1.0751744 | 1 | 10 | 10.000 | 1.1000000 | 2.600000 | 4.0160926 | 4.016093e+00 |
| 162 | 30 | Korndorfer | 2011 | Neotropical Entomology | Saccharum spp. | Saccharum_spp. | Perennial | Poaceae | Spittlebug | Mahanarva fimbriolata | Mahanarva_fimbriolata | Specialist | Fluid-feeding arthropods | Growth / Development | 10.00 | 10.00 | 15.2000000 | 13.2000000 | 1.0435516 | 1.0435516 | 1 | 10 | 10.000 | 1.1000000 | 2.600000 | 4.0160926 | 4.016093e+00 |
| 163 | 31 | Kvedaras | 2007 | Entomologia Experimentalis et Applicata | Saccharum spp. | Saccharum_spp. | Perennial | Poaceae | African stalk borer | Eldana saccharina | Eldana_saccharina | Specialist | Boring arthropods | Growth / Development | 6.00 | 6.00 | 0.0024380 | 0.0011000 | 0.0018518 | 0.0011023 | 1 | 6 | 6.000 | 0.1750000 | 0.330000 | 0.0379671 | 4.164130e-02 |
| 164 | 31 | Kvedaras | 2007 | Entomologia Experimentalis et Applicata | Saccharum spp. | Saccharum_spp. | Perennial | Poaceae | African stalk borer | Eldana saccharina | Eldana_saccharina | Specialist | Boring arthropods | Feeding efficiency | 6.00 | 6.00 | 0.6398750 | 0.3670750 | 0.2359471 | 0.2087578 | 1 | 6 | 6.000 | 0.1750000 | 0.330000 | 0.0379671 | 4.164130e-02 |
| 165 | 32 | Kvedaras | 2007 | Bulletinof Entomological Research | Saccharum spp. | Saccharum_spp. | Perennial | Poaceae | African stalk borer | Eldana saccharina | Eldana_saccharina | Specialist | Boring arthropods | Mortality / Survivability | 6.00 | 6.00 | 5.9891900 | 3.6465000 | 4.5070611 | 2.5359567 | 1 | 6 | 6.000 | 0.1540000 | 0.287000 | 0.0531539 | 5.339890e-02 |
| 166 | 32 | Kvedaras | 2007 | Bulletinof Entomological Research | Saccharum spp. | Saccharum_spp. | Perennial | Poaceae | African stalk borer | Eldana saccharina | Eldana_saccharina | Specialist | Boring arthropods | Feeding efficiency | 6.00 | 6.00 | 10.3626000 | 3.4994000 | 5.2066354 | 2.2988461 | 1 | 6 | 6.000 | 0.1540000 | 0.287000 | 0.5315393 | 5.339890e-02 |
| 167 | 33 | Kvedaras | 2007 | International Journal of Pest Management | Saccharum spp. | Saccharum_spp. | Perennial | Poaceae | African stalk borer | Eldana saccharina | Eldana_saccharina | Specialist | Boring arthropods | Growth / Development | 6.00 | 6.00 | 0.0589625 | 0.0503450 | 0.0542256 | 0.0423639 | 1 | 6 | 6.000 | 0.1809000 | 0.343400 | 0.0602574 | 6.221700e-02 |
| 168 | 33 | Kvedaras | 2007 | International Journal of Pest Management | Saccharum spp. | Saccharum_spp. | Perennial | Poaceae | African stalk borer | Eldana saccharina | Eldana_saccharina | Specialist | Boring arthropods | Feeding efficiency | 6.00 | 6.00 | 4.3358250 | 3.9231500 | 3.3697018 | 2.8055231 | 1 | 6 | 6.000 | 0.1809000 | 0.343400 | 0.0602574 | 6.221700e-02 |
| 169 | 34 | Massey | 2006 | Journal of AnimalEcology | Poa annua | Poa_annua | Annual | Poaceae | African army worm | Spodoptera exempta | Spodoptera_exempta | Specialist | Chewing arthropods | Feeding efficiency | 10.00 | 10.00 | 0.6800000 | 0.3200000 | 0.1581139 | 0.1581139 | 1 | 10 | 10.000 | 0.8100000 | 1.950000 | 0.4743416 | 6.957011e-01 |
| 170 | 34 | Massey | 2006 | Journal of AnimalEcology | Lolium perenne | Lolium_perenne | Perennial | Poaceae | African army worm | Spodoptera exempta | Spodoptera_exempta | Specialist | Chewing arthropods | Feeding efficiency | 10.00 | 10.00 | 0.6800000 | 0.3300000 | 0.1264911 | 0.1264911 | 1 | 10 | 10.000 | 0.5400000 | 4.680000 | 0.3162278 | 1.075174e+00 |
| 171 | 34 | Massey | 2006 | Journal of AnimalEcology | Festuca ovina | Festuca_ovina | Perennial | Poaceae | African army worm | Spodoptera exempta | Spodoptera_exempta | Specialist | Chewing arthropods | Feeding efficiency | 10.00 | 10.00 | 0.8300000 | 0.1800000 | 0.0632456 | 0.0948683 | 1 | 10 | 10.000 | 0.5200000 | 2.440000 | 0.1264911 | 5.059644e-01 |
| 172 | 34 | Massey | 2006 | Journal of AnimalEcology | Agrostis capillaris | Agrostis_capillaris | Perennial | Poaceae | African army worm | Spodoptera exempta | Spodoptera_exempta | Specialist | Chewing arthropods | Feeding efficiency | 10.00 | 10.00 | 0.7100000 | 0.2900000 | 0.0948683 | 0.0948683 | 1 | 10 | 10.000 | 0.4600000 | 2.510000 | 0.0948683 | 4.427189e-01 |
| 173 | 34 | Massey | 2006 | Journal of AnimalEcology | Brachypodium pinnatum | Brachypodium_pinnatum | Perennial | Poaceae | African army worm | Spodoptera exempta | Spodoptera_exempta | Specialist | Chewing arthropods | Feeding efficiency | 10.00 | 10.00 | 0.7400000 | 0.2700000 | 0.0948683 | 0.0948683 | 1 | 10 | 10.000 | 0.4700000 | 2.870000 | 0.0948683 | 4.427189e-01 |
| 174 | 34 | Massey | 2006 | Journal of AnimalEcology | Poa annua | Poa_annua | Annual | Poaceae | Desert locust | Schistocerca gregaria | Schistocerca_gregaria | Generalist | Chewing arthropods | Feeding efficiency | 10.00 | 10.00 | 0.6400000 | 0.3700000 | 0.1581139 | 0.1581139 | 1 | 10 | 10.000 | 0.8100000 | 1.950000 | 0.4743416 | 6.957011e-01 |
| 175 | 34 | Massey | 2006 | Journal of AnimalEcology | Lolium perenne | Lolium_perenne | Perennial | Poaceae | Desert locust | Schistocerca gregaria | Schistocerca_gregaria | Generalist | Chewing arthropods | Feeding efficiency | 10.00 | 10.00 | 0.6900000 | 0.3100000 | 0.0948683 | 0.1264911 | 1 | 10 | 10.000 | 0.5400000 | 4.680000 | 0.3162278 | 1.075174e+00 |
| 176 | 34 | Massey | 2006 | Journal of AnimalEcology | Festuca ovina | Festuca_ovina | Perennial | Poaceae | Desert locust | Schistocerca gregaria | Schistocerca_gregaria | Generalist | Chewing arthropods | Feeding efficiency | 10.00 | 10.00 | 0.7500000 | 0.2500000 | 0.1264911 | 0.1264911 | 1 | 10 | 10.000 | 0.5200000 | 2.440000 | 0.1264911 | 5.059644e-01 |
| 177 | 34 | Massey | 2006 | Journal of AnimalEcology | Agrostis capillaris | Agrostis_capillaris | Perennial | Poaceae | Desert locust | Schistocerca gregaria | Schistocerca_gregaria | Generalist | Chewing arthropods | Feeding efficiency | 10.00 | 10.00 | 0.6500000 | 0.3600000 | 0.0632456 | 0.0948683 | 1 | 10 | 10.000 | 0.4600000 | 2.510000 | 0.0948683 | 4.427189e-01 |
| 178 | 34 | Massey | 2006 | Journal of AnimalEcology | Brachypodium pinnatum | Brachypodium_pinnatum | Perennial | Poaceae | Desert locust | Schistocerca gregaria | Schistocerca_gregaria | Generalist | Chewing arthropods | Feeding efficiency | 10.00 | 10.00 | 0.8400000 | 0.1700000 | 0.1581139 | 0.1581139 | 1 | 10 | 10.000 | 0.4700000 | 2.870000 | 0.0948683 | 4.427189e-01 |
| 179 | 34 | Massey | 2006 | Journal of AnimalEcology | Poa annua | Poa_annua | Annual | Poaceae | Grain aphid | Sitobion avenae | Sitobion_avenae | Specialist | Fluid-feeding arthropods | Abundance / Preference | 10.00 | 10.00 | 12.7200000 | 20.2600000 | 7.4945981 | 16.1908616 | 1 | 10 | 10.000 | 0.8100000 | 1.950000 | 0.4743416 | 6.957011e-01 |
| 180 | 34 | Massey | 2006 | Journal of AnimalEcology | Lolium perenne | Lolium_perenne | Perennial | Poaceae | Grain aphid | Sitobion avenae | Sitobion_avenae | Specialist | Fluid-feeding arthropods | Abundance / Preference | 10.00 | 10.00 | 8.9200000 | 15.7200000 | 8.0954308 | 6.6407831 | 1 | 10 | 10.000 | 0.5400000 | 4.680000 | 0.3162278 | 1.075174e+00 |
| 181 | 34 | Massey | 2006 | Journal of AnimalEcology | Festuca ovina | Festuca_ovina | Perennial | Poaceae | Grain aphid | Sitobion avenae | Sitobion_avenae | Specialist | Fluid-feeding arthropods | Abundance / Preference | 10.00 | 10.00 | 5.7400000 | 4.0400000 | 2.2135944 | 2.4665766 | 1 | 10 | 10.000 | 0.5200000 | 2.440000 | 0.1264911 | 5.059644e-01 |
| 182 | 34 | Massey | 2006 | Journal of AnimalEcology | Agrostis capillaris | Agrostis_capillaris | Perennial | Poaceae | Grain aphid | Sitobion avenae | Sitobion_avenae | Specialist | Fluid-feeding arthropods | Abundance / Preference | 10.00 | 10.00 | 10.7800000 | 12.3900000 | 6.5142920 | 3.1939004 | 1 | 10 | 10.000 | 0.4600000 | 2.510000 | 0.0948683 | 4.427189e-01 |
| 183 | 34 | Massey | 2006 | Journal of AnimalEcology | Poa annua | Poa_annua | Annual | Poaceae | African army worm | Spodoptera exempta | Spodoptera_exempta | Specialist | Chewing arthropods | Growth / Development | 10.00 | 10.00 | 15.2000000 | 5.1100000 | 8.4432814 | 2.2135944 | 1 | 10 | 10.000 | 0.8100000 | 1.950000 | 0.4743416 | 6.957011e-01 |
| 184 | 34 | Massey | 2006 | Journal of AnimalEcology | Festuca ovina | Festuca_ovina | Perennial | Poaceae | African army worm | Spodoptera exempta | Spodoptera_exempta | Specialist | Chewing arthropods | Growth / Development | 10.00 | 10.00 | 13.5500000 | 6.0100000 | 6.4510464 | 1.4546477 | 1 | 10 | 10.000 | 0.5200000 | 2.440000 | 0.1264911 | 5.059644e-01 |
| 185 | 34 | Massey | 2006 | Journal of AnimalEcology | Agrostis capillaris | Agrostis_capillaris | Perennial | Poaceae | African army worm | Spodoptera exempta | Spodoptera_exempta | Specialist | Chewing arthropods | Growth / Development | 10.00 | 10.00 | 10.0700000 | 6.0900000 | 3.2255232 | 2.2135944 | 1 | 10 | 10.000 | 0.4600000 | 2.510000 | 0.0948683 | 4.427189e-01 |
| 186 | 34 | Massey | 2006 | Journal of AnimalEcology | Brachypodium pinnatum | Brachypodium_pinnatum | Perennial | Poaceae | African army worm | Spodoptera exempta | Spodoptera_exempta | Specialist | Chewing arthropods | Growth / Development | 10.00 | 10.00 | 6.2000000 | 3.0500000 | 1.9289894 | 1.2965338 | 1 | 10 | 10.000 | 0.4700000 | 2.870000 | 0.0948683 | 4.427189e-01 |
| 187 | 34 | Massey | 2006 | Journal of AnimalEcology | Poa annua | Poa_annua | Annual | Poaceae | Desert locust | Schistocerca gregaria | Schistocerca_gregaria | Generalist | Chewing arthropods | Growth / Development | 10.00 | 10.00 | 0.3600000 | 0.2500000 | 0.0948683 | 0.0632456 | 1 | 10 | 10.000 | 0.8100000 | 1.950000 | 0.4743416 | 6.957011e-01 |
| 188 | 34 | Massey | 2006 | Journal of AnimalEcology | Lolium perenne | Lolium_perenne | Perennial | Poaceae | Desert locust | Schistocerca gregaria | Schistocerca_gregaria | Generalist | Chewing arthropods | Growth / Development | 10.00 | 10.00 | 0.1500000 | 0.1000000 | 0.0316228 | 0.0316228 | 1 | 10 | 10.000 | 0.5400000 | 4.680000 | 0.3162278 | 1.075174e+00 |
| 189 | 34 | Massey | 2006 | Journal of AnimalEcology | Festuca ovina | Festuca_ovina | Perennial | Poaceae | Desert locust | Schistocerca gregaria | Schistocerca_gregaria | Generalist | Chewing arthropods | Growth / Development | 10.00 | 10.00 | 0.1200000 | 0.0900000 | 0.0316228 | 0.0316228 | 1 | 10 | 10.000 | 0.5200000 | 2.440000 | 0.1264911 | 5.059644e-01 |
| 190 | 34 | Massey | 2006 | Journal of AnimalEcology | Agrostis capillaris | Agrostis_capillaris | Perennial | Poaceae | Desert locust | Schistocerca gregaria | Schistocerca_gregaria | Generalist | Chewing arthropods | Growth / Development | 10.00 | 10.00 | 0.1100000 | 0.0900000 | 0.0316228 | 0.0316228 | 1 | 10 | 10.000 | 0.4600000 | 2.510000 | 0.0948683 | 4.427189e-01 |
| 191 | 34 | Massey | 2006 | Journal of AnimalEcology | Brachypodium pinnatum | Brachypodium_pinnatum | Perennial | Poaceae | Desert locust | Schistocerca gregaria | Schistocerca_gregaria | Generalist | Chewing arthropods | Growth / Development | 10.00 | 10.00 | 0.0900000 | 0.0700000 | 0.0316228 | 0.0316228 | 1 | 10 | 10.000 | 0.4700000 | 2.870000 | 0.0948683 | 4.427189e-01 |
| 192 | 34 | Massey | 2006 | Journal of AnimalEcology | Poa annua | Poa_annua | Annual | Poaceae | Grain aphid | Sitobion avenae | Sitobion_avenae | Specialist | Fluid-feeding arthropods | Abundance / Preference | 10.00 | 10.00 | 2.0000000 | 1.6300000 | 1.4546477 | 0.7589466 | 1 | 10 | 10.000 | 0.8100000 | 1.950000 | 0.4743416 | 6.957011e-01 |
| 193 | 34 | Massey | 2006 | Journal of AnimalEcology | Lolium perenne | Lolium_perenne | Perennial | Poaceae | Grain aphid | Sitobion avenae | Sitobion_avenae | Specialist | Fluid-feeding arthropods | Abundance / Preference | 10.00 | 10.00 | 0.4200000 | 0.5000000 | 0.1897367 | 0.2213594 | 1 | 10 | 10.000 | 0.5400000 | 4.680000 | 0.3162278 | 1.075174e+00 |
| 194 | 34 | Massey | 2006 | Journal of AnimalEcology | Festuca ovina | Festuca_ovina | Perennial | Poaceae | Grain aphid | Sitobion avenae | Sitobion_avenae | Specialist | Fluid-feeding arthropods | Abundance / Preference | 10.00 | 10.00 | 0.1000000 | 0.0900000 | 0.0632456 | 0.0632456 | 1 | 10 | 10.000 | 0.5200000 | 2.440000 | 0.1264911 | 5.059644e-01 |
| 195 | 34 | Massey | 2006 | Journal of AnimalEcology | Agrostis capillaris | Agrostis_capillaris | Perennial | Poaceae | Grain aphid | Sitobion avenae | Sitobion_avenae | Specialist | Fluid-feeding arthropods | Abundance / Preference | 10.00 | 10.00 | 0.1400000 | 0.1300000 | 0.0948683 | 0.1264911 | 1 | 10 | 10.000 | 0.4600000 | 2.510000 | 0.0948683 | 4.427189e-01 |
| 196 | 34 | Massey | 2006 | Journal of AnimalEcology | Brachypodium pinnatum | Brachypodium_pinnatum | Perennial | Poaceae | Grain aphid | Sitobion avenae | Sitobion_avenae | Specialist | Fluid-feeding arthropods | Abundance / Preference | 10.00 | 10.00 | 0.0600000 | 0.0500000 | 0.0632456 | 0.0316228 | 1 | 10 | 10.000 | 0.4700000 | 2.870000 | 0.0948683 | 4.427189e-01 |
| 197 | 34 | Massey | 2006 | Journal of AnimalEcology | Poa annua | Poa_annua | Annual | Poaceae | African army worm | Spodoptera exempta | Spodoptera_exempta | Specialist | Chewing arthropods | Feeding efficiency | 10.00 | 10.00 | 0.7300000 | 0.9800000 | 0.2213594 | 0.2213594 | 1 | 10 | 10.000 | 0.8100000 | 1.950000 | 0.4743416 | 6.957011e-01 |
| 198 | 34 | Massey | 2006 | Journal of AnimalEcology | Lolium perenne | Lolium_perenne | Perennial | Poaceae | African army worm | Spodoptera exempta | Spodoptera_exempta | Specialist | Chewing arthropods | Feeding efficiency | 10.00 | 10.00 | 0.4800000 | 0.9700000 | 0.3162278 | 0.2846050 | 1 | 10 | 10.000 | 0.5400000 | 4.680000 | 0.3162278 | 1.075174e+00 |
| 199 | 34 | Massey | 2006 | Journal of AnimalEcology | Festuca ovina | Festuca_ovina | Perennial | Poaceae | African army worm | Spodoptera exempta | Spodoptera_exempta | Specialist | Chewing arthropods | Feeding efficiency | 10.00 | 10.00 | 0.6100000 | 0.6300000 | 0.3162278 | 0.3478505 | 1 | 10 | 10.000 | 0.5200000 | 2.440000 | 0.1264911 | 5.059644e-01 |
| 200 | 34 | Massey | 2006 | Journal of AnimalEcology | Agrostis capillaris | Agrostis_capillaris | Perennial | Poaceae | African army worm | Spodoptera exempta | Spodoptera_exempta | Specialist | Chewing arthropods | Feeding efficiency | 10.00 | 10.00 | 1.2300000 | 1.0100000 | 0.4427189 | 0.4110961 | 1 | 10 | 10.000 | 0.4600000 | 2.510000 | 0.0948683 | 4.427189e-01 |
| 201 | 34 | Massey | 2006 | Journal of AnimalEcology | Brachypodium pinnatum | Brachypodium_pinnatum | Perennial | Poaceae | African army worm | Spodoptera exempta | Spodoptera_exempta | Specialist | Chewing arthropods | Feeding efficiency | 10.00 | 10.00 | 1.1500000 | 1.6000000 | 0.6640783 | 0.9170605 | 1 | 10 | 10.000 | 0.4700000 | 2.870000 | 0.0948683 | 4.427189e-01 |
| 202 | 34 | Massey | 2006 | Journal of AnimalEcology | Poa annua | Poa_annua | Annual | Poaceae | African army worm | Spodoptera exempta | Spodoptera_exempta | Specialist | Chewing arthropods | Feeding efficiency | 10.00 | 10.00 | 92.2300000 | 82.6200000 | 32.6030827 | 36.8721575 | 1 | 10 | 10.000 | 0.8100000 | 1.950000 | 0.4743416 | 6.957011e-01 |
| 203 | 34 | Massey | 2006 | Journal of AnimalEcology | Lolium perenne | Lolium_perenne | Perennial | Poaceae | African army worm | Spodoptera exempta | Spodoptera_exempta | Specialist | Chewing arthropods | Feeding efficiency | 10.00 | 10.00 | 71.3100000 | 34.6500000 | 40.6352679 | 29.3143139 | 1 | 10 | 10.000 | 0.5400000 | 4.680000 | 0.3162278 | 1.075174e+00 |
| 204 | 34 | Massey | 2006 | Journal of AnimalEcology | Festuca ovina | Festuca_ovina | Perennial | Poaceae | African army worm | Spodoptera exempta | Spodoptera_exempta | Specialist | Chewing arthropods | Feeding efficiency | 10.00 | 10.00 | 62.9400000 | 19.8600000 | 32.1287410 | 28.8083495 | 1 | 10 | 10.000 | 0.5200000 | 2.440000 | 0.1264911 | 5.059644e-01 |
| 205 | 34 | Massey | 2006 | Journal of AnimalEcology | Agrostis capillaris | Agrostis_capillaris | Perennial | Poaceae | African army worm | Spodoptera exempta | Spodoptera_exempta | Specialist | Chewing arthropods | Feeding efficiency | 10.00 | 10.00 | 48.9000000 | 43.4700000 | 19.3847621 | 17.4873955 | 1 | 10 | 10.000 | 0.4600000 | 2.510000 | 0.0948683 | 4.427189e-01 |
| 206 | 34 | Massey | 2006 | Journal of AnimalEcology | Brachypodium pinnatum | Brachypodium_pinnatum | Perennial | Poaceae | African army worm | Spodoptera exempta | Spodoptera_exempta | Specialist | Chewing arthropods | Feeding efficiency | 10.00 | 10.00 | 22.6000000 | 3.5800000 | 26.9426057 | 9.9295519 | 1 | 10 | 10.000 | 0.4700000 | 2.870000 | 0.0948683 | 4.427189e-01 |
| 207 | 34 | Massey | 2006 | Journal of AnimalEcology | Poa annua | Poa_annua | Annual | Poaceae | Desert locust | Schistocerca gregaria | Schistocerca_gregaria | Generalist | Chewing arthropods | Feeding efficiency | 10.00 | 10.00 | 0.6600000 | 1.0100000 | 0.2213594 | 0.6324555 | 1 | 10 | 10.000 | 0.8100000 | 1.950000 | 0.4743416 | 6.957011e-01 |
| 208 | 34 | Massey | 2006 | Journal of AnimalEcology | Lolium perenne | Lolium_perenne | Perennial | Poaceae | Desert locust | Schistocerca gregaria | Schistocerca_gregaria | Generalist | Chewing arthropods | Feeding efficiency | 10.00 | 10.00 | 1.2600000 | 2.2200000 | 0.7905694 | 0.8221922 | 1 | 10 | 10.000 | 0.5400000 | 4.680000 | 0.3162278 | 1.075174e+00 |
| 209 | 34 | Massey | 2006 | Journal of AnimalEcology | Festuca ovina | Festuca_ovina | Perennial | Poaceae | Desert locust | Schistocerca gregaria | Schistocerca_gregaria | Generalist | Chewing arthropods | Feeding efficiency | 10.00 | 10.00 | 0.7700000 | 1.1000000 | 0.3478505 | 0.6008328 | 1 | 10 | 10.000 | 0.5200000 | 2.440000 | 0.1264911 | 5.059644e-01 |
| 210 | 34 | Massey | 2006 | Journal of AnimalEcology | Agrostis capillaris | Agrostis_capillaris | Perennial | Poaceae | Desert locust | Schistocerca gregaria | Schistocerca_gregaria | Generalist | Chewing arthropods | Feeding efficiency | 10.00 | 10.00 | 0.6300000 | 1.5300000 | 0.2529822 | 0.5692100 | 1 | 10 | 10.000 | 0.4600000 | 2.510000 | 0.0948683 | 4.427189e-01 |
| 211 | 34 | Massey | 2006 | Journal of AnimalEcology | Brachypodium pinnatum | Brachypodium_pinnatum | Perennial | Poaceae | Desert locust | Schistocerca gregaria | Schistocerca_gregaria | Generalist | Chewing arthropods | Feeding efficiency | 10.00 | 10.00 | 0.8800000 | 1.5400000 | 0.3162278 | 0.6324555 | 1 | 10 | 10.000 | 0.4700000 | 2.870000 | 0.0948683 | 4.427189e-01 |
| 212 | 34 | Massey | 2006 | Journal of AnimalEcology | Poa annua | Poa_annua | Annual | Poaceae | Desert locust | Schistocerca gregaria | Schistocerca_gregaria | Generalist | Chewing arthropods | Feeding efficiency | 10.00 | 10.00 | 79.1800000 | 65.2800000 | 47.6555243 | 35.0380365 | 1 | 10 | 10.000 | 0.8100000 | 1.950000 | 0.4743416 | 6.957011e-01 |
| 213 | 34 | Massey | 2006 | Journal of AnimalEcology | Lolium perenne | Lolium_perenne | Perennial | Poaceae | Desert locust | Schistocerca gregaria | Schistocerca_gregaria | Generalist | Chewing arthropods | Feeding efficiency | 10.00 | 10.00 | 60.9700000 | 18.8400000 | 36.4610614 | 9.8030607 | 1 | 10 | 10.000 | 0.5400000 | 4.680000 | 0.3162278 | 1.075174e+00 |
| 214 | 34 | Massey | 2006 | Journal of AnimalEcology | Festuca ovina | Festuca_ovina | Perennial | Poaceae | Desert locust | Schistocerca gregaria | Schistocerca_gregaria | Generalist | Chewing arthropods | Feeding efficiency | 10.00 | 10.00 | 26.9400000 | 12.4400000 | 15.4319150 | 10.7517440 | 1 | 10 | 10.000 | 0.5200000 | 2.440000 | 0.1264911 | 5.059644e-01 |
| 215 | 34 | Massey | 2006 | Journal of AnimalEcology | Agrostis capillaris | Agrostis_capillaris | Perennial | Poaceae | Desert locust | Schistocerca gregaria | Schistocerca_gregaria | Generalist | Chewing arthropods | Feeding efficiency | 10.00 | 10.00 | 90.0100000 | 33.6900000 | 53.2843786 | 15.8746339 | 1 | 10 | 10.000 | 0.4600000 | 2.510000 | 0.0948683 | 4.427189e-01 |
| 216 | 34 | Massey | 2006 | Journal of AnimalEcology | Brachypodium pinnatum | Brachypodium_pinnatum | Perennial | Poaceae | Desert locust | Schistocerca gregaria | Schistocerca_gregaria | Generalist | Chewing arthropods | Feeding efficiency | 10.00 | 10.00 | 26.1300000 | 25.2300000 | 16.3489755 | 9.8030607 | 1 | 10 | 10.000 | 0.4700000 | 2.870000 | 0.0948683 | 4.427189e-01 |
| 217 | 34 | Massey | 2006 | Journal of AnimalEcology | Agrostis capillaris | Agrostis_capillaris | Perennial | Poaceae | African army worm | Spodoptera exempta | Spodoptera_exempta | Specialist | Chewing arthropods | Growth / Development | 5.00 | 3.50 | 22.8500000 | 26.1500000 | 1.3416408 | 1.4031215 | -1 | 10 | 10.000 | 0.4600000 | 2.510000 | 0.0948683 | 4.427189e-01 |
| 218 | 34 | Massey | 2006 | Journal of AnimalEcology | Brachypodium pinnatum | Brachypodium_pinnatum | Perennial | Poaceae | African army worm | Spodoptera exempta | Spodoptera_exempta | Specialist | Chewing arthropods | Growth / Development | 4.50 | 4.50 | 21.8000000 | 23.9000000 | 1.1667262 | 1.9091883 | -1 | 10 | 10.000 | 0.4700000 | 2.870000 | 0.0948683 | 4.427189e-01 |
| 219 | 34 | Massey | 2006 | Journal of AnimalEcology | Festuca ovina | Festuca_ovina | Perennial | Poaceae | African army worm | Spodoptera exempta | Spodoptera_exempta | Specialist | Chewing arthropods | Growth / Development | 4.00 | 4.00 | 22.1500000 | 24.5000000 | 2.8000000 | 2.2000000 | -1 | 10 | 10.000 | 0.5200000 | 2.440000 | 0.1264911 | 5.059644e-01 |
| 220 | 34 | Massey | 2006 | Journal of AnimalEcology | Poa annua | Poa_annua | Annual | Poaceae | African army worm | Spodoptera exempta | Spodoptera_exempta | Specialist | Chewing arthropods | Growth / Development | 5.00 | 5.00 | 18.6000000 | 18.7000000 | 2.0124612 | 4.0249224 | -1 | 10 | 10.000 | 0.8100000 | 1.950000 | 0.4743416 | 6.957011e-01 |
| 221 | 34 | Massey | 2006 | Journal of AnimalEcology | Brachypodium pinnatum | Brachypodium_pinnatum | Perennial | Poaceae | African army worm | Spodoptera exempta | Spodoptera_exempta | Specialist | Chewing arthropods | Growth / Development | 4.50 | 4.50 | 175.7500000 | 155.3000000 | 13.3643182 | 11.9854599 | 1 | 10 | 10.000 | 0.4700000 | 2.870000 | 0.0948683 | 4.427189e-01 |
| 222 | 34 | Massey | 2006 | Journal of AnimalEcology | Festuca ovina | Festuca_ovina | Perennial | Poaceae | African army worm | Spodoptera exempta | Spodoptera_exempta | Specialist | Chewing arthropods | Growth / Development | 4.00 | 4.00 | 215.7000000 | 191.7500000 | 15.6000000 | 33.2000000 | 1 | 10 | 10.000 | 0.5200000 | 2.440000 | 0.1264911 | 5.059644e-01 |
| 223 | 34 | Massey | 2006 | Journal of AnimalEcology | Poa annua | Poa_annua | Annual | Poaceae | African army worm | Spodoptera exempta | Spodoptera_exempta | Specialist | Chewing arthropods | Growth / Development | 5.00 | 5.00 | 223.4000000 | 136.2000000 | 33.5410197 | 27.7272429 | 1 | 10 | 10.000 | 0.8100000 | 1.950000 | 0.4743416 | 6.957011e-01 |
| 224 | 35 | Massey | 2009 | Journal of Animal Ecology | Deschampsia cespitosa | Deschampsia_cespitosa | Perennial | Poaceae | African army worm | Spodoptera exempta | Spodoptera_exempta | Specialist | Chewing arthropods | Growth / Development | 12.00 | 12.00 | 0.4000000 | 0.3200000 | 0.1385641 | 0.1732051 | 1 | 12 | 12.000 | 1.7900000 | 6.620000 | 0.7967434 | 1.524205e+00 |
| 225 | 35 | Massey | 2009 | Journal of Animal Ecology | Festuca ovina | Festuca_ovina | Perennial | Poaceae | African army worm | Spodoptera exempta | Spodoptera_exempta | Specialist | Chewing arthropods | Growth / Development | 12.00 | 12.00 | 0.4200000 | 0.3000000 | 0.0692820 | 0.1385641 | 1 | 12 | 12.000 | 0.5200000 | 2.440000 | 0.1385641 | 5.542563e-01 |
| 226 | 35 | Massey | 2009 | Journal of Animal Ecology | Lolium perenne | Lolium_perenne | Perennial | Poaceae | African army worm | Spodoptera exempta | Spodoptera_exempta | Specialist | Chewing arthropods | Growth / Development | 12.00 | 12.00 | 0.5400000 | 0.3600000 | 0.1039230 | 0.1039230 | 1 | 12 | 12.000 | 0.5400000 | 4.680000 | 0.3464102 | 1.177795e+00 |
| 227 | 35 | Massey | 2009 | Journal of Animal Ecology | Deschampsia cespitosa | Deschampsia_cespitosa | Perennial | Poaceae | African army worm | Spodoptera exempta | Spodoptera_exempta | Specialist | Chewing arthropods | Feeding efficiency | 12.00 | 12.00 | 78.7700000 | 34.9500000 | 64.1551619 | 90.2398471 | 1 | 12 | 12.000 | 1.7900000 | 6.620000 | 0.7967434 | 1.524205e+00 |
| 228 | 35 | Massey | 2009 | Journal of Animal Ecology | Festuca ovina | Festuca_ovina | Perennial | Poaceae | African army worm | Spodoptera exempta | Spodoptera_exempta | Specialist | Chewing arthropods | Feeding efficiency | 12.00 | 12.00 | 103.2800000 | 65.5600000 | 26.0846852 | 52.8968317 | 1 | 12 | 12.000 | 0.5200000 | 2.440000 | 0.1385641 | 5.542563e-01 |
| 229 | 35 | Massey | 2009 | Journal of Animal Ecology | Lolium perenne | Lolium_perenne | Perennial | Poaceae | African army worm | Spodoptera exempta | Spodoptera_exempta | Specialist | Chewing arthropods | Feeding efficiency | 12.00 | 12.00 | 128.8000000 | 60.7600000 | 38.7632971 | 73.3350312 | 1 | 12 | 12.000 | 0.5400000 | 4.680000 | 0.3464102 | 1.177795e+00 |
| 230 | 35 | Massey | 2009 | Journal of Animal Ecology | Deschampsia cespitosa | Deschampsia_cespitosa | Perennial | Poaceae | African army worm | Spodoptera exempta | Spodoptera_exempta | Specialist | Chewing arthropods | Feeding efficiency | 12.00 | 12.00 | 266.8300000 | 582.2800000 | 433.9826503 | 1993.9368897 | 1 | 12 | 12.000 | 1.7900000 | 6.620000 | 0.7967434 | 1.524205e+00 |
| 231 | 35 | Massey | 2009 | Journal of Animal Ecology | Festuca ovina | Festuca_ovina | Perennial | Poaceae | African army worm | Spodoptera exempta | Spodoptera_exempta | Specialist | Chewing arthropods | Feeding efficiency | 12.00 | 12.00 | 265.7400000 | 247.0700000 | 88.1267451 | 301.9310968 | 1 | 12 | 12.000 | 0.5200000 | 2.440000 | 0.1385641 | 5.542563e-01 |
| 232 | 35 | Massey | 2009 | Journal of Animal Ecology | Lolium perenne | Lolium_perenne | Perennial | Poaceae | African army worm | Spodoptera exempta | Spodoptera_exempta | Specialist | Chewing arthropods | Feeding efficiency | 12.00 | 12.00 | 507.9600000 | 225.9900000 | 559.9027441 | 534.6840843 | 1 | 12 | 12.000 | 0.5400000 | 4.680000 | 0.3464102 | 1.177795e+00 |
| 233 | 35 | Massey | 2009 | Journal of Animal Ecology | Deschampsia cespitosa | Deschampsia_cespitosa | Perennial | Poaceae | African army worm | Spodoptera exempta | Spodoptera_exempta | Specialist | Chewing arthropods | Feeding efficiency | 12.00 | 12.00 | 0.3400000 | 0.0600000 | 0.3117691 | 0.9006664 | 1 | 12 | 12.000 | 1.7900000 | 6.620000 | 0.7967434 | 1.524205e+00 |
| 234 | 35 | Massey | 2009 | Journal of Animal Ecology | Festuca ovina | Festuca_ovina | Perennial | Poaceae | African army worm | Spodoptera exempta | Spodoptera_exempta | Specialist | Chewing arthropods | Feeding efficiency | 12.00 | 12.00 | 0.3800000 | 0.3700000 | 0.0692820 | 0.2771281 | 1 | 12 | 12.000 | 0.5200000 | 2.440000 | 0.1385641 | 5.542563e-01 |
| 235 | 35 | Massey | 2009 | Journal of Animal Ecology | Lolium perenne | Lolium_perenne | Perennial | Poaceae | African army worm | Spodoptera exempta | Spodoptera_exempta | Specialist | Chewing arthropods | Feeding efficiency | 12.00 | 12.00 | 0.2300000 | 0.2000000 | 0.1732051 | 0.1385641 | 1 | 12 | 12.000 | 0.5400000 | 4.680000 | 0.3464102 | 1.177795e+00 |
| 236 | 35 | Massey | 2009 | Journal of Animal Ecology | Deschampsia cespitosa | Deschampsia_cespitosa | Perennial | Poaceae | African army worm | Spodoptera exempta | Spodoptera_exempta | Specialist | Chewing arthropods | Feeding efficiency | 12.00 | 12.00 | 0.1700000 | 0.2900000 | 0.1385641 | 0.3117691 | 1 | 12 | 12.000 | 1.7900000 | 6.620000 | 0.7967434 | 1.524205e+00 |
| 237 | 35 | Massey | 2009 | Journal of Animal Ecology | Festuca ovina | Festuca_ovina | Perennial | Poaceae | African army worm | Spodoptera exempta | Spodoptera_exempta | Specialist | Chewing arthropods | Feeding efficiency | 12.00 | 12.00 | 0.4400000 | 0.2300000 | 0.1039230 | 0.1385641 | 1 | 12 | 12.000 | 0.5200000 | 2.440000 | 0.1385641 | 5.542563e-01 |
| 238 | 35 | Massey | 2009 | Journal of Animal Ecology | Lolium perenne | Lolium_perenne | Perennial | Poaceae | African army worm | Spodoptera exempta | Spodoptera_exempta | Specialist | Chewing arthropods | Feeding efficiency | 12.00 | 12.00 | 0.1800000 | 0.2400000 | 0.1385641 | 0.1385641 | 1 | 12 | 12.000 | 0.5400000 | 4.680000 | 0.3464102 | 1.177795e+00 |
| 239 | 35 | Massey | 2009 | Journal of Animal Ecology | Deschampsia cespitosa | Deschampsia_cespitosa | Perennial | Poaceae | African army worm | Spodoptera exempta | Spodoptera_exempta | Specialist | Chewing arthropods | Feeding efficiency | 5.00 | 5.00 | 0.5100000 | 0.3300000 | 0.0223607 | 0.0670820 | 1 | 12 | 12.000 | 1.7900000 | 6.620000 | 0.7967434 | 1.524205e+00 |
| 240 | 35 | Massey | 2009 | Journal of Animal Ecology | Festuca ovina | Festuca_ovina | Perennial | Poaceae | African army worm | Spodoptera exempta | Spodoptera_exempta | Specialist | Chewing arthropods | Feeding efficiency | 5.00 | 5.00 | 0.5900000 | 0.5100000 | 0.0447214 | 0.0894427 | 1 | 12 | 12.000 | 0.5200000 | 2.440000 | 0.1385641 | 5.542563e-01 |
| 241 | 35 | Massey | 2009 | Journal of Animal Ecology | Lolium perenne | Lolium_perenne | Perennial | Poaceae | African army worm | Spodoptera exempta | Spodoptera_exempta | Specialist | Chewing arthropods | Feeding efficiency | 5.00 | 5.00 | 0.6500000 | 0.5600000 | 0.0223607 | 0.0447214 | 1 | 12 | 12.000 | 0.5400000 | 4.680000 | 0.3464102 | 1.177795e+00 |
| 242 | 35 | Massey | 2009 | Journal of Animal Ecology | Deschampsia cespitosa | Deschampsia_cespitosa | Perennial | Poaceae | African army worm | Spodoptera exempta | Spodoptera_exempta | Specialist | Chewing arthropods | Growth / Development | 10.00 | 10.00 | 3.4700000 | 4.2200000 | 0.5692100 | 0.3478505 | -1 | 12 | 12.000 | 1.7900000 | 6.620000 | 0.7967434 | 1.524205e+00 |
| 243 | 35 | Massey | 2009 | Journal of Animal Ecology | Festuca ovina | Festuca_ovina | Perennial | Poaceae | African army worm | Spodoptera exempta | Spodoptera_exempta | Specialist | Chewing arthropods | Growth / Development | 10.00 | 10.00 | 3.1700000 | 3.8300000 | 0.3794733 | 0.4110961 | -1 | 12 | 12.000 | 0.5200000 | 2.440000 | 0.1385641 | 5.542563e-01 |
| 244 | 35 | Massey | 2009 | Journal of Animal Ecology | Lolium perenne | Lolium_perenne | Perennial | Poaceae | African army worm | Spodoptera exempta | Spodoptera_exempta | Specialist | Chewing arthropods | Growth / Development | 10.00 | 10.00 | 2.8400000 | 3.4200000 | 0.2213594 | 0.3478505 | -1 | 12 | 12.000 | 0.5400000 | 4.680000 | 0.3464102 | 1.177795e+00 |
| 245 | 36 | Moraes | 2004 | Neotropical Entomology | Triticum aestivum | Triticum_aestivum | Annual | Poaceae | Greenbug (aphid) | Schizaphis graminum | Schizaphis_graminum | Specialist | Fluid-feeding arthropods | Abundance / Preference | 8.00 | 8.00 | 5.4333333 | 2.3000000 | 0.8485281 | 0.7542472 | 1 | NA | NA | NA | NA | NA | NA |
| 246 | 36 | Moraes | 2004 | Neotropical Entomology | Triticum aestivum | Triticum_aestivum | Annual | Poaceae | Greenbug (aphid) | Schizaphis graminum | Schizaphis_graminum | Specialist | Fluid-feeding arthropods | Abundance / Preference | 8.00 | 8.00 | 13.6000000 | 7.9000000 | 1.9798990 | 2.0647518 | 1 | NA | NA | NA | NA | NA | NA |
| 247 | 36 | Moraes | 2004 | Neotropical Entomology | Triticum aestivum | Triticum_aestivum | Annual | Poaceae | Greenbug (aphid) | Schizaphis graminum | Schizaphis_graminum | Specialist | Fluid-feeding arthropods | Growth / Development | 8.00 | 8.00 | 9.6000000 | 9.5000000 | 3.6769553 | 2.8284271 | -1 | NA | NA | NA | NA | NA | NA |
| 248 | 36 | Moraes | 2004 | Neotropical Entomology | Triticum aestivum | Triticum_aestivum | Annual | Poaceae | Greenbug (aphid) | Schizaphis graminum | Schizaphis_graminum | Specialist | Fluid-feeding arthropods | Growth / Development | 8.00 | 8.00 | 10.0000000 | 10.0000000 | 4.2426407 | 2.5455844 | -1 | NA | NA | NA | NA | NA | NA |
| 249 | 37 | Moraes | 2005 | Ci_encia e Agrotecnologia | Zea mays | Zea_mays | Annual | Poaceae | Corn leaf aphid | Rhopalosiphum maidis | Rhopalosiphum_maidis | Specialist | Fluid-feeding arthropods | Abundance / Preference | 12.00 | 12.00 | 3.0000000 | 3.8000000 | 1.0392305 | 1.3856406 | 1 | 6 | 6.000 | 1.0000000 | 2.500000 | 0.1714643 | 2.939388e-01 |
| 250 | 37 | Moraes | 2005 | Ci_encia e Agrotecnologia | Zea mays | Zea_mays | Annual | Poaceae | Corn leaf aphid | Rhopalosiphum maidis | Rhopalosiphum_maidis | Specialist | Fluid-feeding arthropods | Abundance / Preference | 6.00 | 6.00 | 3.1666667 | 3.5666667 | 2.2045408 | 1.5513435 | 1 | 6 | 6.000 | 1.0000000 | 2.500000 | 0.1714643 | 2.939388e-01 |
| 251 | 38 | Nabity | 2012 | Journal of Economic Entomology | Panicum virgatum | Panicum_virgatum | Perennial | Poaceae | Fall armyworm | Spodoptera frugiperda | Spodoptera_frugiperda | Generalist | Chewing arthropods | Feeding efficiency | 15.00 | 15.00 | 0.0560000 | 0.0640000 | 0.0154919 | 0.0116190 | 1 | 10 | 10.000 | 0.9000000 | 2.800000 | 0.3162278 | 6.324555e-01 |
| 252 | 38 | Nabity | 2012 | Journal of Economic Entomology | Panicum virgatum | Panicum_virgatum | Perennial | Poaceae | American grasshopper | Schistocerca americana | Schistocerca_americana | Generalist | Chewing arthropods | Feeding efficiency | 10.00 | 10.00 | 0.0650000 | 0.0800000 | 0.0022136 | 0.0442719 | 1 | 10 | 10.000 | 0.9000000 | 2.800000 | 0.3162278 | 6.324555e-01 |
| 253 | 38 | Nabity | 2012 | Journal of Economic Entomology | Panicum virgatum | Panicum_virgatum | Perennial | Poaceae | Fall armyworm | Spodoptera frugiperda | Spodoptera_frugiperda | Generalist | Chewing arthropods | Feeding efficiency | 15.00 | 15.00 | 10.2000000 | 9.5000000 | 6.9713700 | 6.5840717 | 1 | 10 | 10.000 | 0.9000000 | 2.800000 | 0.3162278 | 6.324555e-01 |
| 254 | 38 | Nabity | 2012 | Journal of Economic Entomology | Panicum virgatum | Panicum_virgatum | Perennial | Poaceae | American grasshopper | Schistocerca americana | Schistocerca_americana | Generalist | Chewing arthropods | Feeding efficiency | 10.00 | 10.00 | 30.7000000 | 19.1000000 | 15.8113883 | 11.3841996 | 1 | 10 | 10.000 | 0.9000000 | 2.800000 | 0.3162278 | 6.324555e-01 |
| 255 | 38 | Nabity | 2012 | Journal of Economic Entomology | Zea mays | Zea_mays | Annual | Poaceae | Fall armyworm | Spodoptera frugiperda | Spodoptera_frugiperda | Generalist | Chewing arthropods | Feeding efficiency | 15.00 | 15.00 | 0.0560000 | 0.0690000 | 0.0116190 | 0.0271109 | 1 | 8 | 9.000 | 0.6000000 | 2.500000 | 0.2828427 | 6.000000e-01 |
| 256 | 38 | Nabity | 2012 | Journal of Economic Entomology | Zea mays | Zea_mays | Annual | Poaceae | American grasshopper | Schistocerca americana | Schistocerca_americana | Generalist | Chewing arthropods | Feeding efficiency | 10.00 | 10.00 | 0.8800000 | 0.8500000 | 0.0284605 | 0.0221359 | 1 | 8 | 9.000 | 0.6000000 | 2.500000 | 0.2828427 | 6.000000e-01 |
| 257 | 38 | Nabity | 2012 | Journal of Economic Entomology | Miscanthus giganteus | Miscanthus_giganteus | Annual | Poaceae | Fall armyworm | Spodoptera frugiperda | Spodoptera_frugiperda | Generalist | Chewing arthropods | Feeding efficiency | 15.00 | 10.00 | 0.1010000 | 0.1460000 | 0.0542218 | 0.0948683 | 1 | 9 | 10.000 | 0.9000000 | 3.500000 | 0.3000000 | 1.581139e+00 |
| 258 | 38 | Nabity | 2012 | Journal of Economic Entomology | Miscanthus giganteus | Miscanthus_giganteus | Annual | Poaceae | American grasshopper | Schistocerca americana | Schistocerca_americana | Generalist | Chewing arthropods | Feeding efficiency | 15.00 | 10.00 | 0.0390000 | 0.0560000 | 0.0193649 | 0.0221359 | 1 | 9 | 10.000 | 0.9000000 | 3.500000 | 0.3000000 | 1.581139e+00 |
| 259 | 39 | Neri | 2005 | Cienciae Agrotecnologia | Zea mays | Zea_mays | Annual | Poaceae | Armyworm | Spodoptera frugiperda | Spodoptera_frugiperda | Generalist | Chewing arthropods | Abundance / Preference | 5.00 | 5.00 | 2.1000000 | 1.7000000 | 1.0062306 | 0.7826238 | 1 | NA | NA | NA | NA | NA | NA |
| 260 | 39 | Neri | 2005 | Cienciae Agrotecnologia | Zea mays | Zea_mays | Annual | Poaceae | Armyworm | Spodoptera frugiperda | Spodoptera_frugiperda | Generalist | Chewing arthropods | Mortality / Survivability | 5.00 | 5.00 | 23.0000000 | 9.0000000 | 12.0747671 | 4.2485292 | -1 | NA | NA | NA | NA | NA | NA |
| 261 | 39 | Neri | 2005 | Cienciae Agrotecnologia | Zea mays | Zea_mays | Annual | Poaceae | Armyworm | Spodoptera frugiperda | Spodoptera_frugiperda | Generalist | Chewing arthropods | Feeding efficiency | 5.00 | 5.00 | 0.3000000 | 0.5000000 | 0.1341641 | 0.0670820 | 1 | NA | NA | NA | NA | NA | NA |
| 262 | 39 | Neri | 2005 | Cienciae Agrotecnologia | Zea mays | Zea_mays | Annual | Poaceae | Armyworm | Spodoptera frugiperda | Spodoptera_frugiperda | Generalist | Chewing arthropods | Feeding efficiency | 5.00 | 5.00 | 6.7000000 | 7.9000000 | 0.2236068 | 1.1180340 | 1 | NA | NA | NA | NA | NA | NA |
| 263 | 39 | Neri | 2005 | Cienciae Agrotecnologia | Zea mays | Zea_mays | Annual | Poaceae | Armyworm | Spodoptera frugiperda | Spodoptera_frugiperda | Generalist | Chewing arthropods | Abundance / Preference | 5.00 | 5.00 | 3.0000000 | 2.8000000 | 0.2236068 | 0.4472136 | 1 | NA | NA | NA | NA | NA | NA |
| 264 | 39 | Neri | 2005 | Cienciae Agrotecnologia | Zea mays | Zea_mays | Annual | Poaceae | Armyworm | Spodoptera frugiperda | Spodoptera_frugiperda | Generalist | Chewing arthropods | Growth / Development | 5.00 | 5.00 | 0.0450000 | 0.0350000 | 0.0134164 | 0.0156525 | 1 | NA | NA | NA | NA | NA | NA |
| 265 | 40 | Jeer | 2020 | Silicon | Triticum aestivum | Triticum_aestivum | Annual | Poaceae | Pink stem borer | Sesamia inferens | Sesamia_inferens | Generalist | Boring arthropods | Feeding efficiency | 3.00 | 3.00 | 22.7500000 | 17.8300000 | 2.1650635 | 0.1732051 | 1 | 3 | 3.000 | 1.5600000 | 2.350000 | 0.0519615 | 1.732051e-01 |
| 266 | 40 | Jeer | 2020 | Silicon | Triticum aestivum | Triticum_aestivum | Annual | Poaceae | Pink stem borer | Sesamia inferens | Sesamia_inferens | Generalist | Boring arthropods | Feeding efficiency | 3.00 | 3.00 | 7.4500000 | 8.2300000 | 2.1823840 | 0.1905256 | 1 | 3 | 3.000 | 1.5600000 | 2.350000 | 0.0519615 | 1.732051e-01 |
| 267 | 41 | Nikpay | 2015 | Neotropical Entomology | Saccharum spp. | Saccharum_spp. | Perennial | Poaceae | Stalk borers | Sesamia spp. | Sesamia_spp. | Generalist | Boring arthropods | Feeding efficiency | 4.00 | 4.00 | 41.2000000 | 21.2000000 | 5.7800000 | 2.5000000 | 1 | NA | NA | NA | NA | NA | NA |
| 268 | 41 | Nikpay | 2015 | Neotropical Entomology | Saccharum spp. | Saccharum_spp. | Perennial | Poaceae | Stalk borers | Sesamia spp. | Sesamia_spp. | Generalist | Boring arthropods | Feeding efficiency | 4.00 | 4.00 | 6.4000000 | 1.6000000 | 1.6600000 | 0.4400000 | 1 | NA | NA | NA | NA | NA | NA |
| 269 | 41 | Nikpay | 2015 | Neotropical Entomology | Saccharum spp. | Saccharum_spp. | Perennial | Poaceae | Stalk borers | Sesamia spp. | Sesamia_spp. | Generalist | Boring arthropods | Feeding efficiency | 4.00 | 4.00 | 106.5000000 | 29.8000000 | 7.6800000 | 3.3200000 | 1 | NA | NA | NA | NA | NA | NA |
| 270 | 41 | Nikpay | 2015 | Neotropical Entomology | Saccharum spp. | Saccharum_spp. | Perennial | Poaceae | Stalk borers | Sesamia spp. | Sesamia_spp. | Generalist | Boring arthropods | Abundance / Preference | 4.00 | 4.00 | 37.9000000 | 20.8000000 | 3.7800000 | 2.0600000 | 1 | NA | NA | NA | NA | NA | NA |
| 271 | 41 | Nikpay | 2015 | Neotropical Entomology | Saccharum spp. | Saccharum_spp. | Perennial | Poaceae | Stalk borers | Sesamia spp. | Sesamia_spp. | Generalist | Boring arthropods | Mortality / Survivability | 4.00 | 4.00 | 1.0800000 | 0.1500000 | 0.1800000 | 0.0400000 | 1 | NA | NA | NA | NA | NA | NA |
| 272 | 42 | Ranganathan | 2006 | Biologia Plantarum | Oryza sativa | Oryza_sativa | Annual | Poaceae | Yellow stem borer | Scirpophaga incertulas | Scirpophaga_incertulas | Specialist | Boring arthropods | Feeding efficiency | 10.00 | 10.00 | 84.8000000 | 59.3000000 | 4.7434165 | 27.5118156 | 1 | NA | NA | NA | NA | NA | NA |
| 273 | 43 | Sidhu | 2013 | Bulletin of Entomological Research | Oryza sativa | Oryza_sativa | Annual | Poaceae | Sugarcane borer | Diatraea saccharalis | Diatraea_saccharalis | Specialist | Boring arthropods | Feeding efficiency | 5.00 | 5.00 | 64.3340000 | 40.6320000 | 17.4122613 | 28.5165749 | 1 | 5 | 5.000 | 1.9400000 | 1.440000 | 0.1565248 | 2.459675e-01 |
| 274 | 43 | Sidhu | 2013 | Bulletin of Entomological Research | Oryza sativa | Oryza_sativa | Annual | Poaceae | Sugarcane borer | Diatraea saccharalis | Diatraea_saccharalis | Specialist | Boring arthropods | Growth / Development | 5.00 | 5.00 | 0.2568000 | 0.2343000 | 0.0038013 | 0.0190066 | 1 | 5 | 5.000 | 1.9400000 | 1.440000 | 0.1565248 | 2.459675e-01 |
| 275 | 43 | Sidhu | 2013 | Bulletin of Entomological Research | Oryza sativa | Oryza_sativa | Annual | Poaceae | Sugarcane borer | Diatraea saccharalis | Diatraea_saccharalis | Specialist | Boring arthropods | Feeding efficiency | 5.00 | 5.00 | 76.9600000 | 47.9500000 | 9.5256496 | 9.6799383 | 1 | 5 | 5.000 | 1.9400000 | 1.440000 | 0.1565248 | 2.459675e-01 |
| 276 | 43 | Sidhu | 2013 | Bulletin of Entomological Research | Oryza sativa | Oryza_sativa | Annual | Poaceae | Sugarcane borer | Diatraea saccharalis | Diatraea_saccharalis | Specialist | Boring arthropods | Growth / Development | 5.00 | 5.00 | 0.2850000 | 0.2089000 | 0.0422617 | 0.0447214 | 1 | 5 | 5.000 | 1.9400000 | 1.440000 | 0.1565248 | 2.459675e-01 |
| 277 | 44 | Ye | 2013 | PNAS | Oryza sativa | Oryza_sativa | Annual | Poaceae | Rice leaffolder | Cnaphalocrocis medinalis | Cnaphalocrocis_medinalis | Specialist | Chewing arthropods | Growth / Development | 20.00 | 20.00 | 167.2000000 | 84.0000000 | 57.2433402 | 39.3547964 | 1 | NA | NA | NA | NA | NA | NA |
| 278 | 45 | Johnson | 2017 | Functional Ecology | Medicago sativa | Medicago_sativa | Perennial | Non-Poaceae | Pea aphid | Acyrthosiphon pisum | Acyrthosiphon_pisum | Specialist | Fluid-feeding arthropods | Abundance / Preference | 14.00 | 16.00 | 7.2100000 | 15.3100000 | 5.2757369 | 14.2400000 | 1 | 9 | 9.000 | 3.5077700 | 2.584440 | 1.1886000 | 1.422990e+00 |
| 279 | 46 | Frew | 2016 | Journal of Applied Ecology | Saccharum spp. | Saccharum_spp. | Perennial | Poaceae | Canegrub | Dermolepida albohirtum | Dermolepida_albohirtum | Specialist | Chewing arthropods | Growth / Development | 28.00 | 25.00 | 0.1088059 | 0.0481399 | 0.0583624 | 0.0608853 | 1 | 28 | 25.000 | 0.8008642 | 1.401641 | 0.4675534 | 4.565045e+00 |
| 280 | 46 | Frew | 2016 | Journal of Applied Ecology | Saccharum spp. | Saccharum_spp. | Perennial | Poaceae | Canegrub | Dermolepida albohirtum | Dermolepida_albohirtum | Specialist | Chewing arthropods | Feeding efficiency | 28.00 | 25.00 | 0.1323972 | 0.0461391 | 0.0916747 | 0.0589224 | 1 | 28 | 25.000 | 0.8008642 | 1.401641 | 0.4675534 | 4.565045e+00 |
| 281 | 46 | Frew | 2016 | Journal of Applied Ecology | Saccharum spp. | Saccharum_spp. | Perennial | Poaceae | Canegrub | Dermolepida albohirtum | Dermolepida_albohirtum | Specialist | Chewing arthropods | Growth / Development | 13.00 | 13.00 | 0.8227205 | 0.4877462 | 0.4043423 | 0.2871691 | 1 | 28 | 25.000 | 0.8008642 | 1.401641 | 0.4675534 | 4.565045e+00 |
| 282 | 47 | Dogramaci | 2013 | Florida Entomologist | Capsicum annum | Capsicum_annum | Perennial | Non-Poaceae | chilli thrip | Scirtothrips dorsalis | Scirtothrips_dorsalis | Generalist | Cell-feeding arthropods | Feeding efficiency | 5.00 | 5.00 | 85.3300000 | 82.3300000 | 17.5978550 | 16.0102467 | 1 | 3 | 3.000 | 0.1500000 | 0.440000 | 0.0692820 | 2.251666e-01 |
| 283 | 47 | Dogramaci | 2013 | Florida Entomologist | Capsicum annum | Capsicum_annum | Perennial | Non-Poaceae | chilli thrip | Scirtothrips dorsalis | Scirtothrips_dorsalis | Generalist | Cell-feeding arthropods | Abundance / Preference | 8.00 | 8.00 | 6.8700000 | 4.6100000 | 9.3055252 | 7.2124892 | 1 | 3 | 3.000 | 0.1500000 | 0.440000 | 0.0692820 | 2.251666e-01 |
| 284 | 47 | Dogramaci | 2013 | Florida Entomologist | Capsicum annum | Capsicum_annum | Perennial | Non-Poaceae | chilli thrip | Scirtothrips dorsalis | Scirtothrips_dorsalis | Generalist | Cell-feeding arthropods | Abundance / Preference | 8.00 | 8.00 | 10.8400000 | 6.3700000 | 14.2835570 | 10.2106219 | 1 | 3 | 3.000 | 0.1500000 | 0.440000 | 0.0692820 | 2.251666e-01 |
| 285 | 48 | Alvarenga | 2017 | Bulletin of Entomological Research | Zea mays | Zea_mays | Annual | Poaceae | fall armyworm | Spodoptera frugiperda | Spodoptera_frugiperda | Generalist | Chewing arthropods | Reproduction | 10.00 | 10.00 | 1593.0000000 | 1232.0000000 | 379.8844153 | 379.8844153 | 1 | 10 | 10.000 | 0.6700000 | 0.780000 | 0.1581139 | 9.486830e-02 |
| 286 | 48 | Alvarenga | 2017 | Bulletin of Entomological Research | Zea mays | Zea_mays | Annual | Poaceae | fall armyworm | Spodoptera frugiperda | Spodoptera_frugiperda | Generalist | Chewing arthropods | Feeding efficiency | 10.00 | 10.00 | 227.0000000 | 200.1000000 | 84.6541730 | 90.1565361 | 1 | 10 | 10.000 | 0.6700000 | 0.780000 | 0.1581139 | 9.486830e-02 |
| 287 | 48 | Alvarenga | 2017 | Bulletin of Entomological Research | Zea mays | Zea_mays | Annual | Poaceae | fall armyworm | Spodoptera frugiperda | Spodoptera_frugiperda | Generalist | Chewing arthropods | Growth / Development | 10.00 | 10.00 | 18.8800000 | 19.0800000 | 1.8024983 | 2.4665766 | -1 | 10 | 10.000 | 0.6700000 | 0.780000 | 0.1581139 | 9.486830e-02 |
| 288 | 48 | Alvarenga | 2017 | Bulletin of Entomological Research | Zea mays | Zea_mays | Annual | Poaceae | fall armyworm | Spodoptera frugiperda | Spodoptera_frugiperda | Generalist | Chewing arthropods | Growth / Development | 10.00 | 10.00 | 0.2010000 | 0.2000000 | 0.0316228 | 0.0316228 | 1 | 10 | 10.000 | 0.6700000 | 0.780000 | 0.1581139 | 9.486830e-02 |
| 289 | 48 | Alvarenga | 2017 | Bulletin of Entomological Research | Zea mays | Zea_mays | Annual | Poaceae | fall armyworm | Spodoptera frugiperda | Spodoptera_frugiperda | Generalist | Chewing arthropods | Mortality / Survivability | 10.00 | 10.00 | 76.0000000 | 76.0000000 | 15.7797655 | 15.7797655 | 1 | 10 | 10.000 | 0.6700000 | 0.780000 | 0.1581139 | 9.486830e-02 |
| 290 | 48 | Alvarenga | 2017 | Bulletin of Entomological Research | Zea mays | Zea_mays | Annual | Poaceae | fall armyworm | Spodoptera frugiperda | Spodoptera_frugiperda | Generalist | Chewing arthropods | Feeding efficiency | 10.00 | 10.00 | 180.5300000 | 186.6600000 | 32.9825560 | 35.0380365 | 1 | 10 | 10.000 | 0.6700000 | 0.780000 | 0.1581139 | 9.486830e-02 |
| 291 | 48 | Alvarenga | 2017 | Bulletin of Entomological Research | Zea mays | Zea_mays | Annual | Poaceae | fall armyworm | Spodoptera frugiperda | Spodoptera_frugiperda | Generalist | Chewing arthropods | Growth / Development | 10.00 | 10.00 | 10.7300000 | 10.3700000 | 0.5375872 | 0.6957011 | -1 | 10 | 10.000 | 0.6700000 | 0.780000 | 0.1581139 | 9.486830e-02 |
| 292 | 48 | Alvarenga | 2017 | Bulletin of Entomological Research | Zea mays | Zea_mays | Annual | Poaceae | fall armyworm | Spodoptera frugiperda | Spodoptera_frugiperda | Generalist | Chewing arthropods | Growth / Development | 10.00 | 10.00 | 0.2000000 | 0.1900000 | 0.0316228 | 0.0316228 | 1 | 10 | 10.000 | 0.6700000 | 0.780000 | 0.1581139 | 9.486830e-02 |
| 293 | 48 | Alvarenga | 2017 | Bulletin of Entomological Research | Zea mays | Zea_mays | Annual | Poaceae | fall armyworm | Spodoptera frugiperda | Spodoptera_frugiperda | Generalist | Chewing arthropods | Mortality / Survivability | 10.00 | 10.00 | 83.5000000 | 77.3300000 | 17.7403777 | 17.7403777 | 1 | 10 | 10.000 | 0.6700000 | 0.780000 | 0.1581139 | 9.486830e-02 |
| 294 | 48 | Alvarenga | 2017 | Bulletin of Entomological Research | Zea mays | Zea_mays | Annual | Poaceae | fall armyworm | Spodoptera frugiperda | Spodoptera_frugiperda | Generalist | Chewing arthropods | Growth / Development | 10.00 | 10.00 | 9.0600000 | 9.5400000 | 1.6443844 | 2.2768399 | 1 | 10 | 10.000 | 0.6700000 | 0.780000 | 0.1581139 | 9.486830e-02 |
| 295 | 48 | Alvarenga | 2017 | Bulletin of Entomological Research | Zea mays | Zea_mays | Annual | Poaceae | fall armyworm | Spodoptera frugiperda | Spodoptera_frugiperda | Generalist | Chewing arthropods | Growth / Development | 10.00 | 10.00 | 21.6600000 | 25.8300000 | 25.8041857 | 24.9819935 | -1 | 10 | 10.000 | 0.6700000 | 0.780000 | 0.1581139 | 9.486830e-02 |
| 296 | 48 | Alvarenga | 2017 | Bulletin of Entomological Research | Zea mays | Zea_mays | Annual | Poaceae | fall armyworm | Spodoptera frugiperda | Spodoptera_frugiperda | Generalist | Chewing arthropods | Reproduction | 10.00 | 10.00 | 1843.6000000 | 1306.2000000 | 966.5817896 | 1899.7699271 | 1 | 10 | 10.000 | 0.6700000 | 0.780000 | 0.1581139 | 9.486830e-02 |
| 297 | 49 | Jeer | 2017 | Journal of Applied Entomology | Oryza sativa | Oryza_sativa | Annual | Poaceae | Yellow stem borer | Scirpophaga incertulas | Scirpophaga_incertulas | Specialist | Boring arthropods | Mortality / Survivability | 4.00 | 4.00 | 14.1000000 | 14.3000000 | 9.4000000 | 8.6000000 | -1 | 4 | 4.000 | 29.8000000 | 45.000000 | 1.0200000 | 1.020000e+00 |
| 298 | 49 | Jeer | 2017 | Journal of Applied Entomology | Oryza sativa | Oryza_sativa | Annual | Poaceae | Yellow stem borer | Scirpophaga incertulas | Scirpophaga_incertulas | Specialist | Boring arthropods | Mortality / Survivability | 4.00 | 4.00 | 15.6000000 | 17.5000000 | 7.4000000 | 8.2000000 | -1 | 4 | 4.000 | 29.9000000 | 46.800000 | 1.1600000 | 1.160000e+00 |
| 299 | 49 | Jeer | 2017 | Journal of Applied Entomology | Oryza sativa | Oryza_sativa | Annual | Poaceae | Yellow stem borer | Scirpophaga incertulas | Scirpophaga_incertulas | Specialist | Boring arthropods | Growth / Development | 4.00 | 4.00 | 44.8600000 | 24.8200000 | 7.7600000 | 3.6800000 | 1 | 4 | 4.000 | 29.8000000 | 45.000000 | 1.0200000 | 1.020000e+00 |
| 300 | 49 | Jeer | 2017 | Journal of Applied Entomology | Oryza sativa | Oryza_sativa | Annual | Poaceae | Yellow stem borer | Scirpophaga incertulas | Scirpophaga_incertulas | Specialist | Boring arthropods | Growth / Development | 4.00 | 4.00 | 41.9800000 | 28.5800000 | 5.5600000 | 4.5600000 | 1 | 4 | 4.000 | 29.9000000 | 46.800000 | 1.1600000 | 1.160000e+00 |
| 301 | 49 | Jeer | 2017 | Journal of Applied Entomology | Oryza sativa | Oryza_sativa | Annual | Poaceae | Yellow stem borer | Scirpophaga incertulas | Scirpophaga_incertulas | Specialist | Boring arthropods | Growth / Development | 4.00 | 4.00 | 285.3000000 | 248.6000000 | 16.2000000 | 17.0000000 | 1 | 4 | 4.000 | 29.8000000 | 45.000000 | 1.0200000 | 1.020000e+00 |
| 302 | 49 | Jeer | 2017 | Journal of Applied Entomology | Oryza sativa | Oryza_sativa | Annual | Poaceae | Yellow stem borer | Scirpophaga incertulas | Scirpophaga_incertulas | Specialist | Boring arthropods | Growth / Development | 4.00 | 4.00 | 285.5000000 | 246.7000000 | 14.6000000 | 12.8000000 | 1 | 4 | 4.000 | 29.9000000 | 46.800000 | 1.1600000 | 1.160000e+00 |
| 303 | 50 | Almeida/Melo | 2016 | Revista De Cinencias Agroambientais | Dendranthema grandiflorum | Dendranthema_grandiflorum | Perennial | Non-Poaceae | Whitefly | Bemisia tabaci | Bemisia_tabaci | Generalist | Fluid-feeding arthropods | Reproduction | 10.00 | 10.00 | 18.8000000 | 11.8000000 | 34.1525987 | 15.8113883 | 1 | 10 | 10.000 | 0.2400000 | 0.210000 | 0.0664078 | 2.846050e-02 |
| 304 | 50 | Almeida/Melo | 2016 | Revista De Cinencias Agroambientais | Dendranthema grandiflorum | Dendranthema_grandiflorum | Perennial | Non-Poaceae | Whitefly | Bemisia tabaci | Bemisia_tabaci | Generalist | Fluid-feeding arthropods | Abundance / Preference | 10.00 | 10.00 | 4.8000000 | 2.9000000 | 6.1980642 | 4.3323204 | 1 | 10 | 10.000 | 0.2400000 | 0.210000 | 0.0664078 | 2.846050e-02 |
| 305 | 50 | Almeida/Melo | 2016 | Revista De Cinencias Agroambientais | Dendranthema grandiflorum | Dendranthema_grandiflorum | Perennial | Non-Poaceae | Whitefly | Bemisia tabaci | Bemisia_tabaci | Generalist | Fluid-feeding arthropods | Reproduction | 10.00 | 10.00 | 28.0000000 | 14.8000000 | 48.7306987 | 26.2469046 | 1 | 10 | 10.000 | 0.2400000 | 0.210000 | 0.0664078 | 2.846050e-02 |
| 306 | 50 | Almeida/Melo | 2016 | Revista De Cinencias Agroambientais | Dendranthema grandiflorum | Dendranthema_grandiflorum | Perennial | Non-Poaceae | Whitefly | Bemisia tabaci | Bemisia_tabaci | Generalist | Fluid-feeding arthropods | Abundance / Preference | 10.00 | 10.00 | 4.0000000 | 6.2000000 | 8.7911319 | 7.9056942 | 1 | 10 | 10.000 | 0.2400000 | 0.210000 | 0.0664078 | 2.846050e-02 |
| 307 | 50 | Almeida/Melo | 2016 | Revista De Cinencias Agroambientais | Dendranthema grandiflorum | Dendranthema_grandiflorum | Perennial | Non-Poaceae | Whitefly | Bemisia tabaci | Bemisia_tabaci | Generalist | Fluid-feeding arthropods | Mortality / Survivability | 10.00 | 10.00 | 93.0000000 | 91.6000000 | 16.0011250 | 22.3256803 | 1 | 10 | 10.000 | 0.2400000 | 0.210000 | 0.0664078 | 2.846050e-02 |
| 308 | 50 | Almeida/Melo | 2016 | Revista De Cinencias Agroambientais | Dendranthema grandiflorum | Dendranthema_grandiflorum | Perennial | Non-Poaceae | Whitefly | Bemisia tabaci | Bemisia_tabaci | Generalist | Fluid-feeding arthropods | Mortality / Survivability | 10.00 | 10.00 | 14.6000000 | 22.9000000 | 29.7254100 | 72.5110267 | 1 | 10 | 10.000 | 0.2400000 | 0.210000 | 0.0664078 | 2.846050e-02 |
| 309 | 50 | Almeida/Melo | 2016 | Revista De Cinencias Agroambientais | Dendranthema grandiflorum | Dendranthema_grandiflorum | Perennial | Non-Poaceae | Whitefly | Bemisia tabaci | Bemisia_tabaci | Generalist | Fluid-feeding arthropods | Mortality / Survivability | 10.00 | 10.00 | 37.5000000 | 43.7000000 | 61.2216955 | 10.6884985 | 1 | 10 | 10.000 | 0.2400000 | 0.210000 | 0.0664078 | 2.846050e-02 |
| 310 | 50 | Almeida/Melo | 2016 | Revista De Cinencias Agroambientais | Dendranthema grandiflorum | Dendranthema_grandiflorum | Perennial | Non-Poaceae | Whitefly | Bemisia tabaci | Bemisia_tabaci | Generalist | Fluid-feeding arthropods | Mortality / Survivability | 10.00 | 10.00 | 47.9000000 | 33.3000000 | 68.0838380 | 85.3814968 | 1 | 10 | 10.000 | 0.2400000 | 0.210000 | 0.0664078 | 2.846050e-02 |
| 311 | 51 | Vieira | 2016 | Fruitis | Citrus reticulata | Citrus_reticulata | Perennial | Non-Poaceae | Blackfly | Aleurocanthus woglumi | Aleurocanthus_woglumi | Generalist | Fluid-feeding arthropods | Growth / Development | 80.00 | 80.00 | 84.7500000 | 73.5000000 | 94.9881677 | 105.9001794 | -1 | NA | NA | NA | NA | NA | NA |
| 312 | 52 | Han | 2016 | Plos One | Oryza sativa | Oryza_sativa | Annual | Poaceae | Rice leaffolder | Cnaphalocrocis medinalis | Cnaphalocrocis_medinalis | Specialist | Chewing arthropods | Mortality / Survivability | 12.00 | 12.00 | 89.5400000 | 76.6100000 | 7.8635107 | 4.0529989 | 1 | 3 | 3.000 | 7.6777790 | 9.133750 | 0.0090933 | 3.971593e-01 |
| 313 | 52 | Han | 2016 | Plos One | Oryza sativa | Oryza_sativa | Annual | Poaceae | Rice leaffolder | Cnaphalocrocis medinalis | Cnaphalocrocis_medinalis | Specialist | Chewing arthropods | Mortality / Survivability | 12.00 | 12.00 | 77.7300000 | 76.4700000 | 7.8635107 | 3.0830504 | 1 | 3 | 3.000 | 7.6777790 | 9.133750 | 0.0090933 | 3.971593e-01 |
| 314 | 52 | Han | 2016 | Plos One | Oryza sativa | Oryza_sativa | Annual | Poaceae | Rice leaffolder | Cnaphalocrocis medinalis | Cnaphalocrocis_medinalis | Specialist | Chewing arthropods | Mortality / Survivability | 12.00 | 12.00 | 94.2100000 | 56.3500000 | 2.6327172 | 11.4315353 | 1 | 3 | 3.000 | 7.6777790 | 9.133750 | 0.0090933 | 3.971593e-01 |
| 315 | 52 | Han | 2016 | Plos One | Oryza sativa | Oryza_sativa | Annual | Poaceae | Rice leaffolder | Cnaphalocrocis medinalis | Cnaphalocrocis_medinalis | Specialist | Chewing arthropods | Mortality / Survivability | 12.00 | 12.00 | 76.0200000 | 70.5700000 | 3.3255376 | 9.0413052 | 1 | 3 | 3.000 | 7.6777790 | 9.133750 | 0.0090933 | 3.971593e-01 |
| 316 | 52 | Han | 2016 | Plos One | Oryza sativa | Oryza_sativa | Annual | Poaceae | Rice leaffolder | Cnaphalocrocis medinalis | Cnaphalocrocis_medinalis | Specialist | Chewing arthropods | Mortality / Survivability | 12.00 | 12.00 | 80.0700000 | 74.3500000 | 14.2720987 | 7.6210236 | 1 | 3 | 3.000 | 7.6777790 | 9.133750 | 0.0090933 | 3.971593e-01 |
| 317 | 53 | Ryalls | 2017 | Biology Letters | Phalaris aquatica | Phalaris_aquatica | Perennial | Poaceae | House cricket | Acheta domesticus | Acheta_domesticus | Generalist | Chewing arthropods | Feeding efficiency | 11.00 | 11.00 | 446.0364000 | 332.6636000 | 143.6897841 | 154.4031455 | 1 | 11 | 11.000 | 1.0016000 | 1.349800 | 0.2169073 | 3.635021e-01 |
| 318 | 53 | Ryalls | 2017 | Biology Letters | Phalaris aquatica | Phalaris_aquatica | Perennial | Poaceae | House cricket | Acheta domesticus | Acheta_domesticus | Generalist | Chewing arthropods | Feeding efficiency | 11.00 | 11.00 | 98.4000000 | 98.0500000 | 1.0281537 | 1.4593149 | 1 | 11 | 11.000 | 1.0016000 | 1.349800 | 0.2169073 | 3.635021e-01 |
| 319 | 53 | Ryalls | 2017 | Biology Letters | Phalaris aquatica | Phalaris_aquatica | Perennial | Poaceae | House cricket | Acheta domesticus | Acheta_domesticus | Generalist | Chewing arthropods | Growth / Development | 11.00 | 11.00 | 5.4636000 | 10.2000000 | 8.1370073 | 6.9198060 | -1 | 11 | 11.000 | 1.0016000 | 1.349800 | 0.2169073 | 3.635021e-01 |
| 320 | 53 | Ryalls | 2017 | Biology Letters | Phalaris aquatica | Phalaris_aquatica | Perennial | Poaceae | House cricket | Acheta domesticus | Acheta_domesticus | Generalist | Chewing arthropods | Feeding efficiency | 11.00 | 11.00 | 3.3727000 | 1.3364000 | 3.4904159 | 1.9355822 | 1 | 11 | 11.000 | 1.0016000 | 1.349800 | 0.2169073 | 3.635021e-01 |
| 321 | 54 | Frew | 2016 | Journal of Chemical Ecology | Saccharum spp. | Saccharum_spp. | Perennial | Poaceae | Canegrub | Dermolepida albohirtum | Dermolepida_albohirtum | Specialist | Chewing arthropods | Growth / Development | 14.00 | 12.00 | 0.0825071 | 0.3867500 | 0.2231366 | 0.1668286 | -1 | 14 | 12.000 | 0.7845714 | 1.360861 | 0.5490493 | 4.840788e-01 |
| 322 | 54 | Frew | 2016 | Journal of Chemical Ecology | Saccharum spp. | Saccharum_spp. | Perennial | Poaceae | Canegrub | Dermolepida albohirtum | Dermolepida_albohirtum | Specialist | Chewing arthropods | Feeding efficiency | 14.00 | 12.00 | 0.5004929 | 0.1741417 | 0.4169014 | 0.2598006 | 1 | 14 | 12.000 | 0.7845714 | 1.360861 | 0.5490493 | 4.840788e-01 |
| 323 | 55 | Frew | 2017 | Soil Biology & Biochemistry | Saccharum spp. | Saccharum_spp. | Perennial | Poaceae | Canegrub | Dermolepida albohirtum | Dermolepida_albohirtum | Specialist | Chewing arthropods | Growth / Development | 80.00 | 80.00 | 0.0015633 | 0.0740212 | 0.0474085 | 0.0614056 | -1 | 80 | 80.000 | 1.9778750 | 2.416663 | 1.3914818 | 6.361551e-01 |
| 324 | 55 | Frew | 2017 | Soil Biology & Biochemistry | Saccharum spp. | Saccharum_spp. | Perennial | Poaceae | Canegrub | Dermolepida albohirtum | Dermolepida_albohirtum | Specialist | Chewing arthropods | Feeding efficiency | 80.00 | 80.00 | 0.1163513 | 0.0336511 | 0.0927262 | 0.0283963 | 1 | 80 | 80.000 | 1.9778750 | 2.416663 | 1.3914818 | 6.361551e-01 |
| 325 | 55 | Frew | 2017 | Soil Biology & Biochemistry | Saccharum spp. | Saccharum_spp. | Perennial | Poaceae | Canegrub | Dermolepida albohirtum | Dermolepida_albohirtum | Specialist | Chewing arthropods | Growth / Development | 80.00 | 80.00 | 0.0084250 | 0.3024625 | 0.1855265 | 0.2403992 | -1 | 80 | 80.000 | 1.9778750 | 2.416663 | 1.3914818 | 6.361551e-01 |
| 326 | 55 | Frew | 2017 | Soil Biology & Biochemistry | Saccharum spp. | Saccharum_spp. | Perennial | Poaceae | Canegrub | Dermolepida albohirtum | Dermolepida_albohirtum | Specialist | Chewing arthropods | Growth / Development | 34.00 | 34.00 | 0.0709118 | 0.4265588 | 0.3367911 | 0.4664700 | -1 | 80 | 80.000 | 1.9778750 | 2.416663 | 1.3914818 | 6.361551e-01 |
| 327 | 56 | Han | 2015 | Plos One | Oryza sativa | Oryza_sativa | Annual | Poaceae | Rice leaffolder | Cnaphalocrocis medinalis | Cnaphalocrocis_medinalis | Specialist | Chewing arthropods | Growth / Development | 3.00 | 3.00 | 16.2000000 | 17.8400000 | 0.5022947 | 0.5022947 | -1 | 3 | 3.000 | 15.1000000 | 19.180000 | 0.7967434 | 1.558846e+00 |
| 328 | 56 | Han | 2015 | Plos One | Oryza sativa | Oryza_sativa | Annual | Poaceae | Rice leaffolder | Cnaphalocrocis medinalis | Cnaphalocrocis_medinalis | Specialist | Chewing arthropods | Growth / Development | 88.00 | 83.00 | 6.0200000 | 4.3000000 | 4.5027991 | 2.0042954 | 1 | 3 | 3.000 | 15.1000000 | 19.180000 | 0.7967434 | 1.558846e+00 |
| 329 | 56 | Han | 2015 | Plos One | Oryza sativa | Oryza_sativa | Annual | Poaceae | Rice leaffolder | Cnaphalocrocis medinalis | Cnaphalocrocis_medinalis | Specialist | Chewing arthropods | Mortality / Survivability | 3.00 | 3.00 | 40.1100000 | 16.8400000 | 6.0275368 | 2.3209481 | 1 | 3 | 3.000 | 15.1000000 | 19.180000 | 0.7967434 | 1.558846e+00 |
| 330 | 56 | Han | 2015 | Plos One | Oryza sativa | Oryza_sativa | Annual | Poaceae | Rice leaffolder | Cnaphalocrocis medinalis | Cnaphalocrocis_medinalis | Specialist | Chewing arthropods | Growth / Development | 121.00 | 59.00 | 5.3300000 | 5.3500000 | 0.9900000 | 0.6913031 | -1 | 3 | 3.000 | 15.1000000 | 19.180000 | 0.7967434 | 1.558846e+00 |
| 331 | 56 | Han | 2015 | Plos One | Oryza sativa | Oryza_sativa | Annual | Poaceae | Rice leaffolder | Cnaphalocrocis medinalis | Cnaphalocrocis_medinalis | Specialist | Chewing arthropods | Growth / Development | 21.00 | 22.00 | 22.4500000 | 16.6300000 | 3.2994545 | 3.0956744 | 1 | 3 | 3.000 | 15.1000000 | 19.180000 | 0.7967434 | 1.558846e+00 |
| 332 | 56 | Han | 2015 | Plos One | Oryza sativa | Oryza_sativa | Annual | Poaceae | Rice leaffolder | Cnaphalocrocis medinalis | Cnaphalocrocis_medinalis | Specialist | Chewing arthropods | Growth / Development | 3.00 | 3.00 | 32.6600000 | 15.6400000 | 7.5517415 | 2.2170250 | 1 | 3 | 3.000 | 15.1000000 | 19.180000 | 0.7967434 | 1.558846e+00 |
| 333 | 56 | Han | 2015 | Plos One | Oryza sativa | Oryza_sativa | Annual | Poaceae | Rice leaffolder | Cnaphalocrocis medinalis | Cnaphalocrocis_medinalis | Specialist | Chewing arthropods | Mortality / Survivability | 3.00 | 3.00 | 94.2800000 | 87.2800000 | 2.4248711 | 5.7677292 | 1 | 3 | 3.000 | 15.1000000 | 19.180000 | 0.7967434 | 1.558846e+00 |
| 334 | 56 | Han | 2015 | Plos One | Oryza sativa | Oryza_sativa | Annual | Poaceae | Rice leaffolder | Cnaphalocrocis medinalis | Cnaphalocrocis_medinalis | Specialist | Chewing arthropods | Reproduction | 26.00 | 13.00 | 118.2400000 | 95.4800000 | 35.9990778 | 31.0798520 | 1 | 3 | 3.000 | 15.1000000 | 19.180000 | 0.7967434 | 1.558846e+00 |
| 335 | 56 | Han | 2015 | Plos One | Oryza sativa | Oryza_sativa | Annual | Poaceae | Rice leaffolder | Cnaphalocrocis medinalis | Cnaphalocrocis_medinalis | Specialist | Chewing arthropods | Mortality / Survivability | 3.00 | 3.00 | 78.8900000 | 80.6800000 | 3.8105118 | 8.2792029 | 1 | 3 | 3.000 | 15.1000000 | 19.180000 | 0.7967434 | 1.558846e+00 |
| 336 | 56 | Han | 2015 | Plos One | Oryza sativa | Oryza_sativa | Annual | Poaceae | Rice leaffolder | Cnaphalocrocis medinalis | Cnaphalocrocis_medinalis | Specialist | Chewing arthropods | Feeding efficiency | 15.00 | 17.00 | 12.8400000 | 26.0100000 | 10.8443534 | 11.5446958 | 1 | 3 | 3.000 | 15.1000000 | 19.180000 | 0.7967434 | 1.558846e+00 |
| 337 | 56 | Han | 2015 | Plos One | Oryza sativa | Oryza_sativa | Annual | Poaceae | Rice leaffolder | Cnaphalocrocis medinalis | Cnaphalocrocis_medinalis | Specialist | Chewing arthropods | Feeding efficiency | 15.00 | 17.00 | 1.0000000 | 2.3400000 | 0.6971370 | 0.9070832 | 1 | 3 | 3.000 | 15.1000000 | 19.180000 | 0.7967434 | 1.558846e+00 |
| 338 | 56 | Han | 2015 | Plos One | Oryza sativa | Oryza_sativa | Annual | Poaceae | Rice leaffolder | Cnaphalocrocis medinalis | Cnaphalocrocis_medinalis | Specialist | Chewing arthropods | Growth / Development | 15.00 | 17.00 | 0.3500000 | 0.2700000 | 0.1936492 | 0.1649242 | 1 | 3 | 3.000 | 15.1000000 | 19.180000 | 0.7967434 | 1.558846e+00 |
| 339 | 56 | Han | 2015 | Plos One | Oryza sativa | Oryza_sativa | Annual | Poaceae | Rice leaffolder | Cnaphalocrocis medinalis | Cnaphalocrocis_medinalis | Specialist | Chewing arthropods | Feeding efficiency | 15.00 | 17.00 | 56.6500000 | 48.6300000 | 17.1573162 | 18.2653579 | 1 | 3 | 3.000 | 15.1000000 | 19.180000 | 0.7967434 | 1.558846e+00 |
| 340 | 56 | Han | 2015 | Plos One | Oryza sativa | Oryza_sativa | Annual | Poaceae | Rice leaffolder | Cnaphalocrocis medinalis | Cnaphalocrocis_medinalis | Specialist | Chewing arthropods | Feeding efficiency | 15.00 | 17.00 | 28.1300000 | 9.6800000 | 14.2913085 | 10.7200746 | 1 | 3 | 3.000 | 15.1000000 | 19.180000 | 0.7967434 | 1.558846e+00 |
| 341 | 56 | Han | 2015 | Plos One | Oryza sativa | Oryza_sativa | Annual | Poaceae | Rice leaffolder | Cnaphalocrocis medinalis | Cnaphalocrocis_medinalis | Specialist | Chewing arthropods | Feeding efficiency | 13.00 | 16.00 | 56.5400000 | 27.8300000 | 39.9855636 | 39.8400000 | 1 | 3 | 3.000 | 15.1000000 | 19.180000 | 0.7967434 | 1.558846e+00 |
| 342 | 57 | Wang | 2020 | Ecology and Evolution | Oryza sativa | Oryza_sativa | Annual | Poaceae | Rice striped stem borer | Chilo suppressalis | Chilo_suppressalis | Generalist | Boring arthropods | Feeding efficiency | 3.00 | 3.00 | 63.8500000 | 35.7060000 | 13.2467246 | 8.0575004 | 1 | 9 | 9.000 | 17.1690000 | 68.036000 | 10.0590000 | 9.714000e+00 |
| 343 | 57 | Wang | 2020 | Ecology and Evolution | Oryza sativa | Oryza_sativa | Annual | Poaceae | Rice striped stem borer | Chilo suppressalis | Chilo_suppressalis | Generalist | Boring arthropods | Growth / Development | 17.50 | 17.50 | 38.6810000 | 29.4590000 | 6.3753494 | 3.9448520 | 1 | 9 | 9.000 | 17.1690000 | 68.036000 | 10.0590000 | 9.714000e+00 |
| 344 | 58 | Gatarayiha | 2010 | International Journal of Pest Management | Solanum melongena | Solanum_melongena | Annual | Non-Poaceae | Two spotted spider mite | Tetranychus urticae | Tetranychus_urticae | Generalist | Cell-feeding arthropods | Mortality / Survivability | 15.00 | 15.00 | 1.3000000 | 3.7000000 | 0.3872983 | 1.5491933 | -1 | NA | NA | NA | NA | NA | NA |
| 345 | 58 | Gatarayiha | 2010 | International Journal of Pest Management | Phaseolus vulgaris | Phaseolus_vulgaris | Perennial | Non-Poaceae | Two spotted spider mite | Tetranychus urticae | Tetranychus_urticae | Generalist | Cell-feeding arthropods | Mortality / Survivability | 15.00 | 15.00 | 7.2000000 | 8.9000000 | 1.5491933 | 4.2602817 | -1 | NA | NA | NA | NA | NA | NA |
| 346 | 58 | Gatarayiha | 2010 | International Journal of Pest Management | Cucumis sativus | Cucumis_sativus | Annual | Non-Poaceae | Two spotted spider mite | Tetranychus urticae | Tetranychus_urticae | Generalist | Cell-feeding arthropods | Mortality / Survivability | 15.00 | 15.00 | 6.8000000 | 11.3000000 | 7.3586684 | 11.6189500 | -1 | NA | NA | NA | NA | NA | NA |
| 347 | 58 | Gatarayiha | 2010 | International Journal of Pest Management | Zea mays | Zea_mays | Annual | Poaceae | Two spotted spider mite | Tetranychus urticae | Tetranychus_urticae | Generalist | Cell-feeding arthropods | Mortality / Survivability | 15.00 | 15.00 | 2.6000000 | 3.1000000 | 2.3237900 | 3.4856850 | -1 | NA | NA | NA | NA | NA | NA |
| 348 | 59 | Ranger | 2009 | Environmental Entomology | Zinnia elegans | Zinnia_elegans | Annual | Non-Poaceae | Green peach aphid | Myzus persicae | Myzus_persicae | Generalist | Fluid-feeding arthropods | Growth / Development | 24.00 | 24.00 | 9.3900000 | 9.3800000 | 45.2665704 | 1.2247449 | -1 | 3 | 3.000 | 2.2400000 | 12.640000 | 0.4156922 | 2.875204e+00 |
| 349 | 59 | Ranger | 2009 | Environmental Entomology | Zinnia elegans | Zinnia_elegans | Annual | Non-Poaceae | Green peach aphid | Myzus persicae | Myzus_persicae | Generalist | Fluid-feeding arthropods | Abundance / Preference | 24.00 | 24.00 | 38.0000000 | 29.3000000 | 8.3282651 | 7.3484692 | 1 | 3 | 3.000 | 2.2400000 | 12.640000 | 0.4156922 | 2.875204e+00 |
| 350 | 60 | Teixeira | 2017 | Entomologia Experimentalis et Applicata | Brassica oleracea | Brassica_oleracea | Annual | Non-Poaceae | Diamondback moth | Plutella xylostella | Plutella_xylostella | Specialist | Chewing arthropods | Growth / Development | 10.00 | 10.00 | 7.9800000 | 7.2200000 | 1.2965338 | 1.8973666 | 1 | 10 | 10.000 | 0.0700000 | 0.080000 | 0.0100000 | 1.000000e-02 |
| 351 | 60 | Teixeira | 2017 | Entomologia Experimentalis et Applicata | Brassica oleracea | Brassica_oleracea | Annual | Non-Poaceae | Diamondback moth | Plutella xylostella | Plutella_xylostella | Specialist | Chewing arthropods | Reproduction | 10.00 | 10.00 | 141.3700000 | 100.3100000 | 68.8111619 | 58.6286278 | 1 | 10 | 10.000 | 0.0700000 | 0.080000 | 0.0100000 | 1.000000e-02 |
| 352 | 60 | Teixeira | 2017 | Entomologia Experimentalis et Applicata | Brassica oleracea | Brassica_oleracea | Annual | Non-Poaceae | Diamondback moth | Plutella xylostella | Plutella_xylostella | Specialist | Chewing arthropods | Reproduction | 10.00 | 10.00 | 54.0900000 | 38.6800000 | 31.6860222 | 38.7379013 | 1 | 10 | 10.000 | 0.0700000 | 0.080000 | 0.0100000 | 1.000000e-02 |
| 353 | 60 | Teixeira | 2017 | Entomologia Experimentalis et Applicata | Brassica oleracea | Brassica_oleracea | Annual | Non-Poaceae | Diamondback moth | Plutella xylostella | Plutella_xylostella | Specialist | Chewing arthropods | Abundance / Preference | 10.00 | 10.00 | 2.8000000 | 2.0800000 | 1.7708755 | 2.0554805 | 1 | 10 | 10.000 | 0.0700000 | 0.080000 | 0.0100000 | 1.000000e-02 |
| 354 | 60 | Teixeira | 2017 | Entomologia Experimentalis et Applicata | Brassica oleracea | Brassica_oleracea | Annual | Non-Poaceae | Diamondback moth | Plutella xylostella | Plutella_xylostella | Specialist | Chewing arthropods | Growth / Development | 9.00 | 9.00 | 10.4500000 | 10.1500000 | 0.2291400 | 0.3171000 | -1 | 10 | 10.000 | 0.0700000 | 0.080000 | 0.0100000 | 1.000000e-02 |
| 355 | 60 | Teixeira | 2017 | Entomologia Experimentalis et Applicata | Brassica oleracea | Brassica_oleracea | Annual | Non-Poaceae | Diamondback moth | Plutella xylostella | Plutella_xylostella | Specialist | Chewing arthropods | Mortality / Survivability | 9.00 | 9.00 | 76.4400000 | 76.3300000 | 9.3150000 | 12.4890000 | 1 | 10 | 10.000 | 0.0700000 | 0.080000 | 0.0100000 | 1.000000e-02 |
| 356 | 60 | Teixeira | 2017 | Entomologia Experimentalis et Applicata | Brassica oleracea | Brassica_oleracea | Annual | Non-Poaceae | Diamondback moth | Plutella xylostella | Plutella_xylostella | Specialist | Chewing arthropods | Growth / Development | 9.00 | 9.00 | 5.6890000 | 5.5780000 | 0.5733000 | 0.4236000 | 1 | 10 | 10.000 | 0.0700000 | 0.080000 | 0.0100000 | 1.000000e-02 |
| 357 | 60 | Teixeira | 2017 | Entomologia Experimentalis et Applicata | Brassica oleracea | Brassica_oleracea | Annual | Non-Poaceae | Diamondback moth | Plutella xylostella | Plutella_xylostella | Specialist | Chewing arthropods | Growth / Development | 9.00 | 9.00 | 19.3100000 | 18.8700000 | 0.3990000 | 0.4227000 | -1 | 10 | 10.000 | 0.0700000 | 0.080000 | 0.0100000 | 1.000000e-02 |
| 358 | 60 | Teixeira | 2017 | Entomologia Experimentalis et Applicata | Brassica oleracea | Brassica_oleracea | Annual | Non-Poaceae | Diamondback moth | Plutella xylostella | Plutella_xylostella | Specialist | Chewing arthropods | Reproduction | 10.00 | 10.00 | 38.5500000 | 60.4000000 | 56.3517879 | 56.3517879 | 1 | 10 | 10.000 | 0.0700000 | 0.080000 | 0.0100000 | 1.000000e-02 |
| 359 | 60 | Teixeira | 2017 | Entomologia Experimentalis et Applicata | Brassica oleracea | Brassica_oleracea | Annual | Non-Poaceae | Cabbage aphid | Brevicoryne brassicae | Brevicoryne_brassicae | Specialist | Fluid-feeding arthropods | Abundance / Preference | 8.00 | 8.00 | 0.2931000 | 0.2955000 | 0.0329300 | 0.0113400 | 1 | 10 | 10.000 | 0.0700000 | 0.080000 | 0.0100000 | 1.000000e-02 |
| 360 | 60 | Teixeira | 2017 | Entomologia Experimentalis et Applicata | Brassica oleracea | Brassica_oleracea | Annual | Non-Poaceae | Cabbage aphid | Brevicoryne brassicae | Brevicoryne_brassicae | Specialist | Fluid-feeding arthropods | Reproduction | 8.00 | 8.00 | 22.8300000 | 25.8300000 | 2.0190000 | 3.5020000 | 1 | 10 | 10.000 | 0.0700000 | 0.080000 | 0.0100000 | 1.000000e-02 |
| 361 | 60 | Teixeira | 2017 | Entomologia Experimentalis et Applicata | Brassica oleracea | Brassica_oleracea | Annual | Non-Poaceae | Cabbage aphid | Brevicoryne brassicae | Brevicoryne_brassicae | Specialist | Fluid-feeding arthropods | Abundance / Preference | 8.00 | 8.00 | 8.0950000 | 8.0560000 | 0.6299000 | 1.4460000 | -1 | 10 | 10.000 | 0.0700000 | 0.080000 | 0.0100000 | 1.000000e-02 |
| 362 | 60 | Teixeira | 2017 | Entomologia Experimentalis et Applicata | Brassica oleracea | Brassica_oleracea | Annual | Non-Poaceae | Cabbage aphid | Brevicoryne brassicae | Brevicoryne_brassicae | Specialist | Fluid-feeding arthropods | Abundance / Preference | 8.00 | 8.00 | 2.5000000 | 3.5000000 | 1.3780000 | 1.5120000 | 1 | 10 | 10.000 | 0.0700000 | 0.080000 | 0.0100000 | 1.000000e-02 |
| 363 | 61 | Yang | 2017 | Scientific Reports | Oryza sativa | Oryza_sativa | Annual | Poaceae | Brown planthopper | Nilaparvata lugens | Nilaparvata_lugens | Specialist | Fluid-feeding arthropods | Feeding efficiency | 13.00 | 13.00 | 54.3000000 | 26.2000000 | 24.8783038 | 19.8305320 | 1 | 3 | 3.000 | 14.0000000 | 20.000000 | 1.2817176 | 8.833459e-01 |
| 364 | 61 | Yang | 2017 | Scientific Reports | Oryza sativa | Oryza_sativa | Annual | Poaceae | Brown planthopper | Nilaparvata lugens | Nilaparvata_lugens | Specialist | Fluid-feeding arthropods | Feeding efficiency | 27.00 | 24.00 | 49.7500000 | 30.4800000 | 30.6053378 | 18.0282445 | 1 | 3 | 3.000 | 14.0000000 | 20.000000 | 1.2817176 | 8.833459e-01 |
| 365 | 61 | Yang | 2017 | Scientific Reports | Oryza sativa | Oryza_sativa | Annual | Poaceae | Brown planthopper | Nilaparvata lugens | Nilaparvata_lugens | Specialist | Fluid-feeding arthropods | Abundance / Preference | 30.00 | 30.00 | 37.8600000 | 18.9800000 | 13.4192027 | 9.4756002 | 1 | 3 | 3.000 | 14.0000000 | 20.000000 | 1.2817176 | 8.833459e-01 |
| 366 | 61 | Yang | 2017 | Scientific Reports | Oryza sativa | Oryza_sativa | Annual | Poaceae | Brown planthopper | Nilaparvata lugens | Nilaparvata_lugens | Specialist | Fluid-feeding arthropods | Growth / Development | 3.00 | 3.00 | 36.2000000 | 34.2000000 | 0.4849742 | 0.9526279 | -1 | 3 | 3.000 | 14.0000000 | 20.000000 | 1.2817176 | 8.833459e-01 |
| 367 | 62 | Wu | 2017 | Frontiers in Plant Science | Oryza sativa | Oryza_sativa | Annual | Poaceae | Brown planthopper | Nilaparvata lugens | Nilaparvata_lugens | Specialist | Fluid-feeding arthropods | Feeding efficiency | 20.00 | 20.00 | 64.8100000 | 46.5100000 | 17.1730021 | 13.5505719 | 1 | 6 | 6.000 | 7.2000000 | 29.160000 | 1.6166632 | 2.351510e+00 |
| 368 | 62 | Wu | 2017 | Frontiers in Plant Science | Oryza sativa | Oryza_sativa | Annual | Poaceae | Brown planthopper | Nilaparvata lugens | Nilaparvata_lugens | Specialist | Fluid-feeding arthropods | Mortality / Survivability | 20.00 | 20.00 | 1.6700000 | 3.4400000 | 7.3790243 | 7.1554175 | -1 | 6 | 6.000 | 7.2000000 | 29.160000 | 1.6166632 | 2.351510e+00 |
| 369 | 63 | Juma | 2015 | Bulletin of Entomological Research | Zea mays | Zea_mays | Annual | Poaceae | stem borer | Busseola fusca | Busseola_fusca | Specialist | Boring arthropods | Mortality / Survivability | 12.00 | 12.00 | 37.5000000 | 38.5000000 | 31.6618888 | 31.5579657 | 1 | 12 | 12.000 | 0.8500000 | 1.000000 | 0.1385641 | 1.385641e-01 |
| 370 | 63 | Juma | 2015 | Bulletin of Entomological Research | Zea mays | Zea_mays | Annual | Poaceae | stem borer | Busseola fusca | Busseola_fusca | Specialist | Boring arthropods | Growth / Development | 12.00 | 12.00 | 1.1000000 | 0.5000000 | 0.3810512 | 0.3117691 | 1 | 12 | 12.000 | 0.8500000 | 1.000000 | 0.1385641 | 1.385641e-01 |
| 371 | 64 | Vilela | 2014 | Revista Colombiana de Entomología | Saccharum spp. | Saccharum_spp. | Perennial | Poaceae | Sugarcane borer | Diatraea saccharalis | Diatraea_saccharalis | Specialist | Boring arthropods | Feeding efficiency | 10.00 | 10.00 | 14.0000000 | 9.2500000 | 32.2236094 | 20.9026553 | 1 | 6 | 6.000 | 0.1750000 | 0.190000 | 0.0122474 | 2.449490e-02 |
| 372 | 64 | Vilela | 2014 | Revista Colombiana de Entomología | Saccharum spp. | Saccharum_spp. | Perennial | Poaceae | Sugarcane borer | Diatraea saccharalis | Diatraea_saccharalis | Specialist | Boring arthropods | Feeding efficiency | 10.00 | 10.00 | 0.6000000 | 0.6000000 | 0.3952847 | 0.3478505 | 1 | 6 | 6.000 | 0.1750000 | 0.190000 | 0.0122474 | 2.449490e-02 |
| 373 | 64 | Vilela | 2014 | Revista Colombiana de Entomología | Saccharum spp. | Saccharum_spp. | Perennial | Poaceae | Sugarcane borer | Diatraea saccharalis | Diatraea_saccharalis | Specialist | Boring arthropods | Abundance / Preference | 10.00 | 10.00 | 32.0500000 | 36.0000000 | 28.2707623 | 30.1997517 | 1 | 6 | 6.000 | 0.1750000 | 0.190000 | 0.0122474 | 2.449490e-02 |
| 374 | 65 | Connick | 2011 | Mphil Thesis | Vitis vinifera | Vitis_vinifera | Perennial | Non-Poaceae | Light brown apple moth | Epiphyas postvittana | Epiphyas_postvittana | Generalist | Chewing arthropods | Feeding efficiency | 10.00 | 10.00 | 2.5370000 | 9.4900000 | 8.5128515 | 8.6330180 | 1 | NA | NA | NA | NA | NA | NA |
| 375 | 65 | Connick | 2011 | Mphil Thesis | Vitis vinifera | Vitis_vinifera | Perennial | Non-Poaceae | Light brown apple moth | Epiphyas postvittana | Epiphyas_postvittana | Generalist | Chewing arthropods | Growth / Development | 10.00 | 10.00 | 11.3130000 | 1.2590000 | 7.9689397 | 8.0606458 | -1 | NA | NA | NA | NA | NA | NA |
| 376 | 66 | Costa | 2009 | Ciênc. agrotec., Lavras | Triticum aestivum | Triticum_aestivum | Annual | Poaceae | Greenbug (aphid) | Schizaphis graminum | Schizaphis_graminum | Specialist | Fluid-feeding arthropods | Reproduction | 10.00 | 10.00 | 14.8900000 | 10.3300000 | 6.3561781 | 2.7828043 | 1 | NA | NA | NA | NA | NA | NA |
| 377 | 66 | Costa | 2009 | Ciênc. agrotec., Lavras | Triticum aestivum | Triticum_aestivum | Annual | Poaceae | Greenbug (aphid) | Schizaphis graminum | Schizaphis_graminum | Specialist | Fluid-feeding arthropods | Abundance / Preference | 10.00 | 10.00 | 23.6700000 | 17.0000000 | 15.0524417 | 10.0876657 | 1 | NA | NA | NA | NA | NA | NA |
| 378 | 66 | Costa | 2009 | Ciênc. agrotec., Lavras | Triticum aestivum | Triticum_aestivum | Annual | Poaceae | Greenbug (aphid) | Schizaphis graminum | Schizaphis_graminum | Specialist | Fluid-feeding arthropods | Abundance / Preference | 10.00 | 10.00 | 0.2200000 | 0.2000000 | 0.0316228 | 0.0316228 | 1 | NA | NA | NA | NA | NA | NA |
| 379 | 66 | Costa | 2009 | Ciênc. agrotec., Lavras | Triticum aestivum | Triticum_aestivum | Annual | Poaceae | Greenbug (aphid) | Schizaphis graminum | Schizaphis_graminum | Specialist | Fluid-feeding arthropods | Growth / Development | 10.00 | 10.00 | 28.4400000 | 20.0000000 | 6.3245553 | 7.5578436 | 1 | NA | NA | NA | NA | NA | NA |
| 380 | 66 | Costa | 2009 | Ciênc. agrotec., Lavras | Triticum aestivum | Triticum_aestivum | Annual | Poaceae | Greenbug (aphid) | Schizaphis graminum | Schizaphis_graminum | Specialist | Fluid-feeding arthropods | Feeding efficiency | 10.00 | 10.00 | 7.3400000 | 4.8000000 | 2.3717082 | 3.3203915 | 1 | NA | NA | NA | NA | NA | NA |
| 381 | 67 | Gomes | 2009 | Ciênc. agrotec., Lavras | Solanum tuberosum | Solanum_tuberosum | Perennial | Non-Poaceae | Cucurbit beetle | Diabrotica speciosa | Diabrotica_speciosa | Generalist | Chewing arthropods | Feeding efficiency | 4.00 | 4.00 | 5.0000000 | 2.6000000 | 0.3400000 | 0.3400000 | 1 | NA | NA | NA | NA | NA | NA |
| 382 | 67 | Gomes | 2009 | Ciênc. agrotec., Lavras | Solanum tuberosum | Solanum_tuberosum | Perennial | Non-Poaceae | Sepentine leaf miner | Liriomyza spp. | Liriomyza_spp. | Generalist | Leaf-mining arthropods | Feeding efficiency | 4.00 | 4.00 | 1.7500000 | 0.5000000 | 0.2600000 | 0.2800000 | 1 | NA | NA | NA | NA | NA | NA |
| 383 | 68 | Johnson | 2018 | Frontiers in Plant Science | Medicago sativa | Medicago_sativa | Perennial | Non-Poaceae | Pea aphid | Acyrthosiphon pisum | Acyrthosiphon_pisum | Specialist | Fluid-feeding arthropods | Abundance / Preference | 20.00 | 20.00 | 4.9000000 | 9.1500000 | 5.5901699 | 13.7392961 | 1 | 9 | 9.000 | 3.4360000 | 2.605000 | 1.3161000 | 2.064900e+00 |
| 384 | 69 | Massey | 2008 | Biology Letters | Deschampsia cespitosa | Deschampsia_cespitosa | Perennial | Poaceae | Field vole | Microtus agrestis | Microtus_agrestis | Generalist | Mammalian chewers | Growth / Development | 5.00 | 5.00 | 0.2610000 | 0.1070000 | 0.0402492 | 0.0581378 | 1 | 10 | 10.000 | 1.7900000 | 6.620000 | 0.2846050 | 3.162278e-01 |
| 385 | 70 | Massey | 2007 | Oecologia | Festuca ovina | Festuca_ovina | Perennial | Poaceae | Locusts | Schistocerca gregaria | Schistocerca_gregaria | Generalist | Chewing arthropods | Feeding efficiency | 10.00 | 10.00 | 1595.8000000 | 1078.0100000 | 625.0874251 | 551.5644695 | 1 | 10 | 10.000 | 0.7260000 | 1.695000 | 0.2055480 | 6.798897e-01 |
| 386 | 70 | Massey | 2007 | Oecologia | Lolium perenne | Lolium_perenne | Perennial | Poaceae | Locusts | Schistocerca gregaria | Schistocerca_gregaria | Generalist | Chewing arthropods | Feeding efficiency | 10.00 | 10.00 | 779.3400000 | 373.1000000 | 190.3311678 | 206.7813362 | 1 | 10 | 10.000 | 1.0550000 | 1.616000 | 0.2940918 | 4.300698e-01 |
| 387 | 70 | Massey | 2007 | Oecologia | Festuca ovina | Festuca_ovina | Perennial | Poaceae | Field vole | Microtus agrestis | Microtus_agrestis | Generalist | Mammalian chewers | Feeding efficiency | 10.00 | 10.00 | 1032.7900000 | 622.4000000 | 248.2071735 | 284.9465154 | 1 | 10 | 10.000 | 0.7260000 | 1.695000 | 0.2055480 | 6.798897e-01 |
| 388 | 70 | Massey | 2007 | Oecologia | Lolium perenne | Lolium_perenne | Perennial | Poaceae | Field vole | Microtus agrestis | Microtus_agrestis | Generalist | Mammalian chewers | Feeding efficiency | 10.00 | 10.00 | 1235.6700000 | 441.8570000 | 314.4252678 | 188.4085030 | 1 | 10 | 10.000 | 1.0550000 | 1.616000 | 0.2940918 | 4.300698e-01 |
| 389 | 71 | Massey | 2009 | Basic and Applied Ecology | Agrostis capillaris | Agrostis_capillaris | Perennial | Poaceae | Sheep | Ovis aries | Ovis_aries | Generalist | Mammalian chewers | Feeding efficiency | 10.00 | 10.00 | 1.0310000 | 0.8760000 | 0.1328157 | 0.0790569 | 1 | 12 | 12.000 | 0.4700000 | 1.820000 | 0.0692820 | 1.732051e-01 |
| 390 | 71 | Massey | 2009 | Basic and Applied Ecology | Brachypodium pinnatum | Brachypodium_pinnatum | Perennial | Poaceae | Sheep | Ovis aries | Ovis_aries | Generalist | Mammalian chewers | Feeding efficiency | 10.00 | 10.00 | 0.7780000 | 0.5960000 | 0.1581139 | 0.2055480 | 1 | 12 | 12.000 | 1.2900000 | 3.120000 | 0.4849742 | 1.108513e+00 |
| 391 | 71 | Massey | 2009 | Basic and Applied Ecology | Festuca ovina | Festuca_ovina | Perennial | Poaceae | Sheep | Ovis aries | Ovis_aries | Generalist | Mammalian chewers | Feeding efficiency | 10.00 | 10.00 | 1.1550000 | 0.9410000 | 0.1897367 | 0.2213594 | 1 | 12 | 12.000 | 0.2700000 | 1.740000 | 0.0692820 | 1.732051e-01 |
| 392 | 71 | Massey | 2009 | Basic and Applied Ecology | Lolium perenne | Lolium_perenne | Perennial | Poaceae | Sheep | Ovis aries | Ovis_aries | Generalist | Mammalian chewers | Feeding efficiency | 10.00 | 10.00 | 1.0150000 | 0.8680000 | 0.2403331 | 0.2181972 | 1 | 12 | 12.000 | 0.6300000 | 2.970000 | 0.0692820 | 3.117691e-01 |
| 393 | 71 | Massey | 2009 | Basic and Applied Ecology | Poa annua | Poa_annua | Annual | Poaceae | Sheep | Ovis aries | Ovis_aries | Generalist | Mammalian chewers | Feeding efficiency | 10.00 | 10.00 | 1.0840000 | 1.1320000 | 0.1454648 | 0.1075174 | 1 | 12 | 12.000 | 0.4300000 | 1.050000 | 0.1385641 | 1.732051e-01 |
| 394 | 72 | Massey | 2006 | Proceedings of the Royal Society | Festuca ovina | Festuca_ovina | Perennial | Poaceae | Field vole | Microtus agrestis | Microtus_agrestis | Generalist | Mammalian chewers | Feeding efficiency | 10.00 | 10.00 | 0.7980000 | 0.2020000 | 0.1075174 | 0.0885438 | 1 | 10 | 10.000 | 0.5200000 | 2.440000 | 0.1264911 | 5.375872e-01 |
| 395 | 72 | Massey | 2006 | Proceedings of the Royal Society | Lolium perenne | Lolium_perenne | Perennial | Poaceae | Field vole | Microtus agrestis | Microtus_agrestis | Generalist | Mammalian chewers | Feeding efficiency | 10.00 | 10.00 | 0.7300000 | 0.2700000 | 0.0632456 | 0.0758947 | 1 | 10 | 10.000 | 0.5400000 | 4.680000 | 0.3162278 | 1.075174e+00 |
| 396 | 72 | Massey | 2006 | Proceedings of the Royal Society | Festuca ovina | Festuca_ovina | Perennial | Poaceae | Field vole | Microtus agrestis | Microtus_agrestis | Generalist | Mammalian chewers | Growth / Development | 6.00 | 6.00 | 0.2300000 | 0.1300000 | 0.0416413 | 0.0416413 | 1 | 10 | 10.000 | 0.5200000 | 2.440000 | 0.1264911 | 5.375872e-01 |
| 397 | 72 | Massey | 2006 | Proceedings of the Royal Society | Lolium perenne | Lolium_perenne | Perennial | Poaceae | Field vole | Microtus agrestis | Microtus_agrestis | Generalist | Mammalian chewers | Growth / Development | 6.00 | 6.00 | 0.2800000 | 0.1800000 | 0.0391918 | 0.0538888 | 1 | 10 | 10.000 | 0.5400000 | 4.680000 | 0.3162278 | 1.075174e+00 |
| 398 | 72 | Massey | 2006 | Proceedings of the Royal Society | Festuca ovina | Festuca_ovina | Perennial | Poaceae | Field vole | Microtus agrestis | Microtus_agrestis | Generalist | Mammalian chewers | Growth / Development | 10.00 | 10.00 | 0.0080000 | 0.0030000 | 0.0063246 | 0.0094868 | 1 | 10 | 10.000 | 0.5200000 | 2.440000 | 0.1264911 | 5.375872e-01 |
| 399 | 72 | Massey | 2006 | Proceedings of the Royal Society | Lolium perenne | Lolium_perenne | Perennial | Poaceae | Field vole | Microtus agrestis | Microtus_agrestis | Generalist | Mammalian chewers | Growth / Development | 10.00 | 10.00 | 0.0110000 | 0.0060000 | 0.0031623 | 0.0094868 | 1 | 10 | 10.000 | 0.5400000 | 4.680000 | 0.3162278 | 1.075174e+00 |
| 400 | 72 | Massey | 2006 | Proceedings of the Royal Society | Festuca ovina | Festuca_ovina | Perennial | Poaceae | Field vole | Microtus agrestis | Microtus_agrestis | Generalist | Mammalian chewers | Feeding efficiency | 6.00 | 6.00 | 0.2020000 | 0.1490000 | 0.0538888 | 0.0563383 | 1 | 10 | 10.000 | 0.5200000 | 2.440000 | 0.1264911 | 5.375872e-01 |
| 401 | 72 | Massey | 2006 | Proceedings of the Royal Society | Lolium perenne | Lolium_perenne | Perennial | Poaceae | Field vole | Microtus agrestis | Microtus_agrestis | Generalist | Mammalian chewers | Feeding efficiency | 6.00 | 6.00 | 0.4360000 | 0.3650000 | 0.0440908 | 0.0440908 | 1 | 10 | 10.000 | 0.5400000 | 4.680000 | 0.3162278 | 1.075174e+00 |
| 402 | 72 | Massey | 2006 | Proceedings of the Royal Society | Festuca ovina | Festuca_ovina | Perennial | Poaceae | Field vole | Microtus agrestis | Microtus_agrestis | Generalist | Mammalian chewers | Feeding efficiency | 10.00 | 10.00 | 0.3230000 | 0.1990000 | 0.0695701 | 0.0758947 | 1 | 10 | 10.000 | 0.5200000 | 2.440000 | 0.1264911 | 5.375872e-01 |
| 403 | 72 | Massey | 2006 | Proceedings of the Royal Society | Lolium perenne | Lolium_perenne | Perennial | Poaceae | Field vole | Microtus agrestis | Microtus_agrestis | Generalist | Mammalian chewers | Feeding efficiency | 10.00 | 10.00 | 0.4130000 | 0.3380000 | 0.0664078 | 0.0664078 | 1 | 10 | 10.000 | 0.5400000 | 4.680000 | 0.3162278 | 1.075174e+00 |
| 404 | 73 | Peixoto | 2011 | Ciênc. agrotec., Lavras | Phaseolus vulgaris | Phaseolus_vulgaris | Perennial | Non-Poaceae | Whitefly | Bemisia tabaci | Bemisia_tabaci | Generalist | Fluid-feeding arthropods | Reproduction | 6.00 | 6.00 | 73.7000000 | 39.0000000 | 34.9297237 | 34.2928564 | 1 | NA | NA | NA | NA | NA | NA |
| 405 | 73 | Peixoto | 2011 | Ciênc. agrotec., Lavras | Phaseolus vulgaris | Phaseolus_vulgaris | Perennial | Non-Poaceae | Whitefly | Bemisia tabaci | Bemisia_tabaci | Generalist | Fluid-feeding arthropods | Reproduction | 6.00 | 6.00 | 28.3000000 | 10.4000000 | 9.4550304 | 7.5689233 | 1 | NA | NA | NA | NA | NA | NA |
| 406 | 73 | Peixoto | 2011 | Ciênc. agrotec., Lavras | Phaseolus vulgaris | Phaseolus_vulgaris | Perennial | Non-Poaceae | Whitefly | Bemisia tabaci | Bemisia_tabaci | Generalist | Fluid-feeding arthropods | Reproduction | 6.00 | 6.00 | 13.7000000 | 10.1000000 | 6.8340764 | 6.9320560 | 1 | NA | NA | NA | NA | NA | NA |
| 407 | 73 | Peixoto | 2011 | Ciênc. agrotec., Lavras | Phaseolus vulgaris | Phaseolus_vulgaris | Perennial | Non-Poaceae | Whitefly | Bemisia tabaci | Bemisia_tabaci | Generalist | Fluid-feeding arthropods | Reproduction | 6.00 | 6.00 | 4.7000000 | 6.7000000 | 3.1108520 | 7.3239743 | 1 | NA | NA | NA | NA | NA | NA |
| 408 | 74 | Frew | 2019 | Austral Ecology | Brassica napus | Brassica_napus | Annual | Non-Poaceae | Australian bollworm | Helicoverpa punctigera | Helicoverpa_punctigera | Generalist | Chewing arthropods | Growth / Development | 7.00 | 7.00 | 0.2869796 | 0.0908044 | 0.1568944 | 0.1443594 | 1 | 7 | 7.000 | 2619.0000000 | 3815.714000 | 1304.1992970 | 1.009543e+04 |
| 409 | 74 | Frew | 2019 | Austral Ecology | Brassica napus | Brassica_napus | Annual | Non-Poaceae | Australian bollworm | Helicoverpa punctigera | Helicoverpa_punctigera | Generalist | Chewing arthropods | Feeding efficiency | 7.00 | 7.00 | 1.4134760 | 2.8848400 | 0.6029016 | 3.2579128 | 1 | 7 | 7.000 | 2619.0000000 | 3815.714000 | 1304.1992970 | 1.009543e+04 |
| 410 | 74 | Frew | 2019 | Austral Ecology | Brassica napus | Brassica_napus | Annual | Non-Poaceae | Australian bollworm | Helicoverpa punctigera | Helicoverpa_punctigera | Generalist | Chewing arthropods | Growth / Development | 7.00 | 7.00 | 0.1384243 | 0.0111986 | 0.1206175 | 0.1243957 | 1 | 7 | 7.000 | 2619.0000000 | 3815.714000 | 1304.1992970 | 1.009543e+04 |
| 411 | 74 | Frew | 2019 | Austral Ecology | Cucumis sativus | Cucumis_sativus | Annual | Non-Poaceae | Australian bollworm | Helicoverpa punctigera | Helicoverpa_punctigera | Generalist | Chewing arthropods | Growth / Development | 7.00 | 7.00 | 0.5078855 | 0.1863805 | 0.2237721 | 0.2326654 | 1 | 7 | 7.000 | 2591.8570000 | 5640.857000 | 1153.3692480 | 1.014195e+03 |
| 412 | 74 | Frew | 2019 | Austral Ecology | Cucumis sativus | Cucumis_sativus | Annual | Non-Poaceae | Australian bollworm | Helicoverpa punctigera | Helicoverpa_punctigera | Generalist | Chewing arthropods | Feeding efficiency | 7.00 | 7.00 | 1.2495890 | 1.6857030 | 0.7707883 | 0.4717039 | 1 | 7 | 7.000 | 2591.8570000 | 5640.857000 | 1153.3692480 | 1.014195e+03 |
| 413 | 74 | Frew | 2019 | Austral Ecology | Cucumis sativus | Cucumis_sativus | Annual | Non-Poaceae | Australian bollworm | Helicoverpa punctigera | Helicoverpa_punctigera | Generalist | Chewing arthropods | Growth / Development | 7.00 | 7.00 | 0.1680929 | 0.0333586 | 0.0933402 | 0.1438153 | 1 | 7 | 7.000 | 2591.8570000 | 5640.857000 | 1153.3692480 | 1.014195e+03 |
| 414 | 74 | Frew | 2019 | Austral Ecology | Sorghum bicolor | Sorghum_bicolor | Annual | Poaceae | Australian bollworm | Helicoverpa punctigera | Helicoverpa_punctigera | Generalist | Chewing arthropods | Growth / Development | 7.00 | 7.00 | 0.0044018 | 0.1559319 | 0.0769214 | 0.0973747 | -1 | 7 | 7.000 | 4052.7140000 | 5752.429000 | 1345.2909898 | 8.253554e+02 |
| 415 | 74 | Frew | 2019 | Austral Ecology | Sorghum bicolor | Sorghum_bicolor | Annual | Poaceae | Australian bollworm | Helicoverpa punctigera | Helicoverpa_punctigera | Generalist | Chewing arthropods | Feeding efficiency | 7.00 | 7.00 | 0.8403215 | 1.5730069 | 0.5059383 | 0.4346001 | 1 | 7 | 7.000 | 4052.7140000 | 5752.429000 | 1345.2909898 | 8.253554e+02 |
| 416 | 74 | Frew | 2019 | Austral Ecology | Sorghum bicolor | Sorghum_bicolor | Annual | Poaceae | Australian bollworm | Helicoverpa punctigera | Helicoverpa_punctigera | Generalist | Chewing arthropods | Growth / Development | 7.00 | 7.00 | 0.2158557 | 0.0768957 | 0.1356176 | 0.1301313 | 1 | 7 | 7.000 | 4052.7140000 | 5752.429000 | 1345.2909898 | 8.253554e+02 |
| 417 | 75 | Alcanta | 2011 | Revista Brasileira De Entomologia | Gossypium hirsutum | Gossypium_hirsutum | Perennial | Non-Poaceae | Cotton aphid | Aphis gossypii | Aphis_gossypii | Generalist | Fluid-feeding arthropods | Abundance / Preference | 10.00 | 10.00 | 45.9300000 | 45.2700000 | 11.9534096 | 11.9850323 | -1 | NA | NA | NA | NA | NA | NA |
| 418 | 76 | Camargo | 2008 | Ciencia E Agrotecnologia | Pinus taeda | Pinus_taeda | Perennial | Non-Poaceae | Giant conifer aphid | Cinara atlantica | Cinara_atlantica | Specialist | Fluid-feeding arthropods | Growth / Development | 20.00 | 20.00 | 10.5500000 | 15.8300000 | 1.7441330 | 5.9926622 | -1 | NA | NA | NA | NA | NA | NA |
| 419 | 76 | Camargo | 2008 | Ciencia E Agrotecnologia | Pinus taeda | Pinus_taeda | Perennial | Non-Poaceae | Giant conifer aphid | Cinara atlantica | Cinara_atlantica | Specialist | Fluid-feeding arthropods | Growth / Development | 20.00 | 20.00 | 4.3500000 | 1.8300000 | 2.1466253 | 3.2646592 | -1 | NA | NA | NA | NA | NA | NA |
| 420 | 76 | Camargo | 2008 | Ciencia E Agrotecnologia | Pinus taeda | Pinus_taeda | Perennial | Non-Poaceae | Giant conifer aphid | Cinara atlantica | Cinara_atlantica | Specialist | Fluid-feeding arthropods | Reproduction | 20.00 | 20.00 | 21.3500000 | 3.4100000 | 9.0784360 | 8.4970583 | 1 | NA | NA | NA | NA | NA | NA |
| 421 | 76 | Camargo | 2008 | Ciencia E Agrotecnologia | Pinus taeda | Pinus_taeda | Perennial | Non-Poaceae | Giant conifer aphid | Cinara atlantica | Cinara_atlantica | Specialist | Fluid-feeding arthropods | Growth / Development | 20.00 | 20.00 | 10.2500000 | 4.2500000 | 6.0821049 | 10.0623059 | 1 | NA | NA | NA | NA | NA | NA |
| 422 | 76 | Camargo | 2008 | Ciencia E Agrotecnologia | Pinus taeda | Pinus_taeda | Perennial | Non-Poaceae | Giant conifer aphid | Cinara atlantica | Cinara_atlantica | Specialist | Fluid-feeding arthropods | Growth / Development | 20.00 | 20.00 | 36.4000000 | 9.5000000 | 13.9977855 | 18.5146429 | 1 | NA | NA | NA | NA | NA | NA |
| 423 | 76 | Camargo | 2008 | Ciencia E Agrotecnologia | Pinus taeda | Pinus_taeda | Perennial | Non-Poaceae | Giant conifer aphid | Cinara atlantica | Cinara_atlantica | Specialist | Fluid-feeding arthropods | Mortality / Survivability | 20.00 | 20.00 | 80.0000000 | 70.0000000 | 3.5777088 | 4.6957428 | 1 | NA | NA | NA | NA | NA | NA |
| 424 | 76 | Camargo | 2008 | Ciencia E Agrotecnologia | Pinus taeda | Pinus_taeda | Perennial | Non-Poaceae | Giant conifer aphid | Cinara atlantica | Cinara_atlantica | Specialist | Fluid-feeding arthropods | Reproduction | 20.00 | 20.00 | 1.1400000 | 0.7000000 | 0.5813777 | 1.4310835 | 1 | NA | NA | NA | NA | NA | NA |
| 425 | 76 | Camargo | 2008 | Ciencia E Agrotecnologia | Pinus taeda | Pinus_taeda | Perennial | Non-Poaceae | Giant conifer aphid | Cinara atlantica | Cinara_atlantica | Specialist | Fluid-feeding arthropods | Mortality / Survivability | 20.00 | 20.00 | 91.0000000 | 62.0000000 | 1.7888544 | 5.2323991 | 1 | NA | NA | NA | NA | NA | NA |
| 426 | 76 | Camargo | 2008 | Ciencia E Agrotecnologia | Pinus taeda | Pinus_taeda | Perennial | Non-Poaceae | Giant conifer aphid | Cinara atlantica | Cinara_atlantica | Specialist | Fluid-feeding arthropods | Mortality / Survivability | 20.00 | 20.00 | 90.0000000 | 73.0000000 | 2.0124612 | 4.3826932 | 1 | NA | NA | NA | NA | NA | NA |
| 427 | 76 | Camargo | 2008 | Ciencia E Agrotecnologia | Pinus taeda | Pinus_taeda | Perennial | Non-Poaceae | Giant conifer aphid | Cinara atlantica | Cinara_atlantica | Specialist | Fluid-feeding arthropods | Mortality / Survivability | 20.00 | 20.00 | 90.0000000 | 64.0000000 | 2.0124612 | 5.1429563 | 1 | NA | NA | NA | NA | NA | NA |
| 428 | 77 | Ferreira | 2011 | Neotropical Entomology | Glycine max | Glycine_max | Annual | Non-Poaceae | Silverleaf whitefly | Bemisia tabaci | Bemisia_tabaci | Generalist | Fluid-feeding arthropods | Reproduction | 10.00 | 10.00 | 142.6000000 | 125.6000000 | 120.8938749 | 86.8361445 | 1 | NA | NA | NA | NA | NA | NA |
| 429 | 77 | Ferreira | 2011 | Neotropical Entomology | Glycine max | Glycine_max | Annual | Non-Poaceae | Silverleaf whitefly | Bemisia tabaci | Bemisia_tabaci | Generalist | Fluid-feeding arthropods | Abundance / Preference | 10.00 | 10.00 | 101.0000000 | 74.2000000 | 76.7484788 | 56.2885424 | 1 | NA | NA | NA | NA | NA | NA |
| 430 | 77 | Ferreira | 2011 | Neotropical Entomology | Glycine max | Glycine_max | Annual | Non-Poaceae | Silverleaf whitefly | Bemisia tabaci | Bemisia_tabaci | Generalist | Fluid-feeding arthropods | Reproduction | 8.00 | 8.00 | 75.3000000 | 55.3000000 | 30.3207388 | 24.1547676 | 1 | NA | NA | NA | NA | NA | NA |
| 431 | 77 | Ferreira | 2011 | Neotropical Entomology | Glycine max | Glycine_max | Annual | Non-Poaceae | Silverleaf whitefly | Bemisia tabaci | Bemisia_tabaci | Generalist | Fluid-feeding arthropods | Abundance / Preference | 8.00 | 8.00 | 49.6000000 | 32.4000000 | 18.3564920 | 16.0654661 | 1 | NA | NA | NA | NA | NA | NA |
| 432 | 77 | Ferreira | 2011 | Neotropical Entomology | Glycine max | Glycine_max | Annual | Non-Poaceae | Silverleaf whitefly | Bemisia tabaci | Bemisia_tabaci | Generalist | Fluid-feeding arthropods | Mortality / Survivability | 18.00 | 18.00 | 71.1000000 | 57.3000000 | 17.2675476 | 24.0769859 | 1 | NA | NA | NA | NA | NA | NA |
| 433 | 78 | Fornoff | 2014 | Oecologia | Myriophyllum spicatum | Myriophyllum_spicatum | Perennial | Non-Poaceae | watermilfoil moth | Acentria ephemerella | Acentria_ephemerella | Generalist | Chewing arthropods | Growth / Development | 16.00 | 16.00 | 0.1930000 | 0.1430000 | 0.2800000 | 0.2400000 | 1 | 10 | 10.000 | 0.7700000 | 0.800000 | 0.4743416 | 6.008328e-01 |
| 434 | 79 | Villegas | 2017 | Plants | Oryza sativa | Oryza_sativa | Annual | Poaceae | rice water weevil | Lissorhoptrus oryzophilus | Lissorhoptrus_oryzophilus | Specialist | Chewing arthropods | Abundance / Preference | 4.00 | 4.00 | 13.2650000 | 13.4200000 | 6.4700000 | 5.8100000 | 1 | NA | NA | NA | NA | NA | NA |
| 435 | 79 | Villegas | 2017 | Plants | Oryza sativa | Oryza_sativa | Annual | Poaceae | rice water weevil | Lissorhoptrus oryzophilus | Lissorhoptrus_oryzophilus | Specialist | Chewing arthropods | Feeding efficiency | 4.00 | 4.00 | 3.4000000 | 3.8800000 | 2.4000000 | 3.1800000 | 1 | NA | NA | NA | NA | NA | NA |
| 436 | 80 | Ramachandran | 1991 | Journal of Chemical Ecology | Oryza sativa | Oryza_sativa | Annual | Poaceae | rice leafroller | Cnaphalocrocis medinalis | Cnaphalocrocis_medinalis | Specialist | Chewing arthropods | Feeding efficiency | 6.00 | 6.00 | 16.0000000 | 32.0000000 | 4.8989795 | 14.6969385 | 1 | 3 | 3.000 | 4.4800000 | 5.210000 | 0.2424871 | 1.160474e+00 |
| 437 | 80 | Ramachandran | 1991 | Journal of Chemical Ecology | Oryza sativa | Oryza_sativa | Annual | Poaceae | rice leafroller | Cnaphalocrocis medinalis | Cnaphalocrocis_medinalis | Specialist | Chewing arthropods | Growth / Development | 10.00 | 10.00 | 1.3000000 | 21.1000000 | 12.0166551 | 12.6491106 | 1 | 3 | 3.000 | 4.4800000 | 5.210000 | 0.2424871 | 1.160474e+00 |
| 438 | 80 | Ramachandran | 1991 | Journal of Chemical Ecology | Oryza sativa | Oryza_sativa | Annual | Poaceae | rice leafroller | Cnaphalocrocis medinalis | Cnaphalocrocis_medinalis | Specialist | Chewing arthropods | Mortality / Survivability | 10.00 | 10.00 | 12.0000000 | 40.0000000 | 9.4868330 | 18.9736660 | 1 | 3 | 3.000 | 4.4800000 | 5.210000 | 0.2424871 | 1.160474e+00 |
| 439 | 81 | Teixeira | 2020 | Journal of Agronomy and Crop Science | Brassica oleracea | Brassica_oleracea | Annual | Non-Poaceae | Diamondback moth | Plutella xylostella | Plutella_xylostella | Specialist | Chewing arthropods | Mortality / Survivability | 10.00 | 10.00 | 88.8500000 | 80.4900000 | 12.2380145 | 23.2427408 | 1 | 10 | 10.000 | 0.0900000 | 0.120000 | 0.0632456 | 3.162280e-02 |
| 440 | 81 | Teixeira | 2020 | Journal of Agronomy and Crop Science | Brassica oleracea | Brassica_oleracea | Annual | Non-Poaceae | Diamondback moth | Plutella xylostella | Plutella_xylostella | Specialist | Chewing arthropods | Growth / Development | 10.00 | 10.00 | 8.6800000 | 8.8400000 | 1.7076299 | 1.1384200 | -1 | 10 | 10.000 | 0.0900000 | 0.120000 | 0.0632456 | 3.162280e-02 |
| 441 | 81 | Teixeira | 2020 | Journal of Agronomy and Crop Science | Brassica oleracea | Brassica_oleracea | Annual | Non-Poaceae | Diamondback moth | Plutella xylostella | Plutella_xylostella | Specialist | Chewing arthropods | Growth / Development | 10.00 | 10.00 | 6.8000000 | 6.2900000 | 0.9170605 | 0.8854377 | 1 | 10 | 10.000 | 0.0900000 | 0.120000 | 0.0632456 | 3.162280e-02 |
| 442 | 81 | Teixeira | 2020 | Journal of Agronomy and Crop Science | Brassica oleracea | Brassica_oleracea | Annual | Non-Poaceae | Diamondback moth | Plutella xylostella | Plutella_xylostella | Specialist | Chewing arthropods | Reproduction | 10.00 | 10.00 | 117.0900000 | 98.1400000 | 46.0427627 | 68.7479163 | 1 | 10 | 10.000 | 0.0900000 | 0.120000 | 0.0632456 | 3.162280e-02 |
| 443 | 81 | Teixeira | 2020 | Journal of Agronomy and Crop Science | Brassica oleracea | Brassica_oleracea | Annual | Non-Poaceae | Diamondback moth | Plutella xylostella | Plutella_xylostella | Specialist | Chewing arthropods | Reproduction | 10.00 | 10.00 | 47.7200000 | 34.4200000 | 7.5262208 | 11.9534096 | 1 | 10 | 10.000 | 0.0900000 | 0.120000 | 0.0632456 | 3.162280e-02 |
| 444 | 81 | Teixeira | 2020 | Journal of Agronomy and Crop Science | Brassica oleracea | Brassica_oleracea | Annual | Non-Poaceae | Diamondback moth | Plutella xylostella | Plutella_xylostella | Specialist | Chewing arthropods | Abundance / Preference | 10.00 | 10.00 | 3.3300000 | 2.3300000 | 0.8221922 | 0.6957011 | 1 | 10 | 10.000 | 0.0900000 | 0.120000 | 0.0632456 | 3.162280e-02 |
| 445 | 82 | Hall | 2019 | Functional Ecology | Brachypodium distachyon | Brachypodium_distachyon | Annual | Poaceae | Cotton bollworm | Helicoverpa armigera | Helicoverpa_armigera | Generalist | Chewing arthropods | Feeding efficiency | 10.00 | 10.00 | 0.1140000 | 0.0510000 | 0.0790569 | 0.0316228 | 1 | NA | NA | NA | NA | NA | NA |
| 446 | 82 | Hall | 2019 | Functional Ecology | Brachypodium distachyon | Brachypodium_distachyon | Annual | Poaceae | Cotton bollworm | Helicoverpa armigera | Helicoverpa_armigera | Generalist | Chewing arthropods | Growth / Development | 10.00 | 10.00 | 0.1370000 | 0.0400000 | 0.0917061 | 0.0284605 | 1 | NA | NA | NA | NA | NA | NA |
| 447 | 83 | Johnson | 2019 | Functional Ecology | Phalaris aquatica | Phalaris_aquatica | Perennial | Poaceae | Cotton bollworm | Helicoverpa armigera | Helicoverpa_armigera | Generalist | Chewing arthropods | Growth / Development | 12.00 | 12.00 | 0.0900000 | 0.0500000 | 0.0346410 | 0.0346410 | 1 | 12 | 12.000 | 1.2820000 | 1.353000 | 0.2217025 | 1.420282e-01 |
| 448 | 84 | Johnson | 2019 | Biology Letters | Triticum aestivum | Triticum_aestivum | Annual | Poaceae | Cotton bollworm | Helicoverpa armigera | Helicoverpa_armigera | Generalist | Chewing arthropods | Growth / Development | 9.00 | 9.00 | 0.0548000 | 0.1413900 | 0.0834000 | 0.1179000 | 1 | 10 | 8.000 | 0.0876200 | 0.335375 | 0.2504524 | 2.548413e-01 |
| 449 | 84 | Johnson | 2019 | Biology Letters | Triticum aestivum | Triticum_aestivum | Annual | Poaceae | Cotton bollworm | Helicoverpa armigera | Helicoverpa_armigera | Generalist | Chewing arthropods | Feeding efficiency | 10.00 | 10.00 | 0.4600000 | 0.5000000 | 0.2213594 | 0.2529822 | 1 | 10 | 8.000 | 0.0876200 | 0.335375 | 0.2504524 | 2.548413e-01 |
| 450 | 84 | Johnson | 2019 | Biology Letters | Triticum aestivum | Triticum_aestivum | Annual | Poaceae | Pruinose Scarab | Sericesthis geminata | Sericesthis_geminata | Generalist | Chewing arthropods | Growth / Development | 10.00 | 10.00 | 6.9600000 | 2.8700000 | 10.4355163 | 16.7600716 | -1 | 10 | 8.000 | 0.0876200 | 0.335375 | 0.2504524 | 2.548413e-01 |
| 451 | 85 | Mir | 2019 | Frontiers in Plant Science | Bromus catharticus | Bromus_catharticus | Annual | Poaceae | Common grasshopper | Oxya grandis | Oxya_grandis | Generalist | Chewing arthropods | Feeding efficiency | 25.00 | 25.00 | 1.4000000 | 0.5400000 | 1.2000000 | 0.6000000 | 1 | 25 | 25.000 | 0.1300000 | 1.180000 | 0.2500000 | 3.000000e-01 |
| 452 | 86 | Jeong | 2012 | Korean Journal of Horticultural Science & Technology | Dendranthema grandiflorum | Dendranthema_grandiflorum | Annual | Non-Poaceae | Chrysanthemum aphid | Macrosiphoniellas anborni | Macrosiphoniellas_anborni | Specialist | Fluid-feeding arthropods | Abundance / Preference | 3.00 | 3.00 | 49.3500000 | 29.6800000 | 5.8716522 | 9.1971898 | 1 | 3 | 3.000 | 3.3600000 | 7.020000 | 0.0866025 | 1.922576e+00 |
| 453 | 87 | Rowe | 2019 | Journal of Pest Science | Triticum aestivum | Triticum_aestivum | Annual | Poaceae | Corn leaf aphid | Rhopalosiphum maidis | Rhopalosiphum_maidis | Specialist | Fluid-feeding arthropods | Growth / Development | 18.00 | 19.00 | 20.8300000 | 17.3700000 | 6.9452028 | 4.6640219 | 1 | 10 | 10.000 | 0.5426000 | 2.145600 | 0.1454648 | 5.663639e-01 |
| 454 | 87 | Rowe | 2019 | Journal of Pest Science | Triticum aestivum | Triticum_aestivum | Annual | Poaceae | Corn leaf aphid | Rhopalosiphum maidis | Rhopalosiphum_maidis | Specialist | Fluid-feeding arthropods | Reproduction | 20.00 | 13.00 | 3.6000000 | 2.6150000 | 2.7995571 | 1.3232373 | 1 | 10 | 10.000 | 0.5426000 | 2.145600 | 0.1454648 | 5.663639e-01 |
| 455 | 87 | Rowe | 2019 | Journal of Pest Science | Triticum aestivum | Triticum_aestivum | Annual | Poaceae | Corn leaf aphid | Rhopalosiphum maidis | Rhopalosiphum_maidis | Specialist | Fluid-feeding arthropods | Abundance / Preference | 13.00 | 17.00 | 0.1055000 | 0.0712000 | 0.0594916 | 0.0577235 | 1 | 10 | 10.000 | 0.5426000 | 2.145600 | 0.1454648 | 5.663639e-01 |
| 456 | 87 | Rowe | 2019 | Journal of Pest Science | Triticum aestivum | Triticum_aestivum | Annual | Poaceae | Corn leaf aphid | Rhopalosiphum maidis | Rhopalosiphum_maidis | Specialist | Fluid-feeding arthropods | Growth / Development | 18.00 | 19.00 | 15.1700000 | 15.9100000 | 2.1849600 | 3.3297629 | -1 | 10 | 10.000 | 0.5426000 | 2.145600 | 0.1454648 | 5.663639e-01 |
| 457 | 87 | Rowe | 2019 | Journal of Pest Science | Triticum aestivum | Triticum_aestivum | Annual | Poaceae | Corn leaf aphid | Rhopalosiphum maidis | Rhopalosiphum_maidis | Specialist | Fluid-feeding arthropods | Feeding efficiency | 13.00 | 15.00 | 5.9300000 | 7.5400000 | 2.8123300 | 4.2602817 | -1 | 10 | 10.000 | 0.5426000 | 2.145600 | 0.1454648 | 5.663639e-01 |
| 458 | 87 | Rowe | 2019 | Journal of Pest Science | Triticum aestivum | Triticum_aestivum | Annual | Poaceae | Corn leaf aphid | Rhopalosiphum maidis | Rhopalosiphum_maidis | Specialist | Fluid-feeding arthropods | Feeding efficiency | 13.00 | 15.00 | 280.2000000 | 268.1400000 | 57.2561543 | 63.0521689 | -1 | 10 | 10.000 | 0.5426000 | 2.145600 | 0.1454648 | 5.663639e-01 |
| 459 | 87 | Rowe | 2019 | Journal of Pest Science | Triticum aestivum | Triticum_aestivum | Annual | Poaceae | Corn leaf aphid | Rhopalosiphum maidis | Rhopalosiphum_maidis | Specialist | Fluid-feeding arthropods | Feeding efficiency | 13.00 | 15.00 | 5.0700000 | 6.6900000 | 2.8844410 | 4.3764712 | -1 | 10 | 10.000 | 0.5426000 | 2.145600 | 0.1454648 | 5.663639e-01 |
| 460 | 87 | Rowe | 2019 | Journal of Pest Science | Triticum aestivum | Triticum_aestivum | Annual | Poaceae | Corn leaf aphid | Rhopalosiphum maidis | Rhopalosiphum_maidis | Specialist | Fluid-feeding arthropods | Feeding efficiency | 4.00 | 3.00 | 31.8400000 | 24.7400000 | 36.7600000 | 21.4254685 | 1 | 10 | 10.000 | 0.5426000 | 2.145600 | 0.1454648 | 5.663639e-01 |
| 461 | 87 | Rowe | 2019 | Journal of Pest Science | Triticum aestivum | Triticum_aestivum | Annual | Poaceae | Russian aphid | Diuraphis noxia | Diuraphis_noxia | Specialist | Fluid-feeding arthropods | Growth / Development | 20.00 | 20.00 | 32.7500000 | 24.6000000 | 5.5901699 | 9.7492564 | 1 | 10 | 10.000 | 0.5426000 | 2.145600 | 0.1454648 | 5.663639e-01 |
| 462 | 87 | Rowe | 2019 | Journal of Pest Science | Triticum aestivum | Triticum_aestivum | Annual | Poaceae | Russian aphid | Diuraphis noxia | Diuraphis_noxia | Specialist | Fluid-feeding arthropods | Reproduction | 20.00 | 18.00 | 18.7500000 | 15.0560000 | 3.8370926 | 7.4398947 | 1 | 10 | 10.000 | 0.5426000 | 2.145600 | 0.1454648 | 5.663639e-01 |
| 463 | 87 | Rowe | 2019 | Journal of Pest Science | Triticum aestivum | Triticum_aestivum | Annual | Poaceae | Russian aphid | Diuraphis noxia | Diuraphis_noxia | Specialist | Fluid-feeding arthropods | Abundance / Preference | 18.00 | 20.00 | 0.3564000 | 0.3080000 | 0.0585484 | 0.4092004 | 1 | 10 | 10.000 | 0.5426000 | 2.145600 | 0.1454648 | 5.663639e-01 |
| 464 | 87 | Rowe | 2019 | Journal of Pest Science | Triticum aestivum | Triticum_aestivum | Annual | Poaceae | Russian aphid | Diuraphis noxia | Diuraphis_noxia | Specialist | Fluid-feeding arthropods | Growth / Development | 20.00 | 19.00 | 10.7300000 | 11.2600000 | 1.3997786 | 1.0722891 | -1 | 10 | 10.000 | 0.5426000 | 2.145600 | 0.1454648 | 5.663639e-01 |
| 465 | 87 | Rowe | 2019 | Journal of Pest Science | Triticum aestivum | Triticum_aestivum | Annual | Poaceae | Russian aphid | Diuraphis noxia | Diuraphis_noxia | Specialist | Fluid-feeding arthropods | Feeding efficiency | 15.00 | 14.00 | 3.1400000 | 3.5300000 | 2.5174392 | 2.7688265 | -1 | 10 | 10.000 | 0.5426000 | 2.145600 | 0.1454648 | 5.663639e-01 |
| 466 | 87 | Rowe | 2019 | Journal of Pest Science | Triticum aestivum | Triticum_aestivum | Annual | Poaceae | Russian aphid | Diuraphis noxia | Diuraphis_noxia | Specialist | Fluid-feeding arthropods | Feeding efficiency | 15.00 | 14.00 | 328.5700000 | 343.3500000 | 44.4618488 | 14.7795467 | -1 | 10 | 10.000 | 0.5426000 | 2.145600 | 0.1454648 | 5.663639e-01 |
| 467 | 87 | Rowe | 2019 | Journal of Pest Science | Triticum aestivum | Triticum_aestivum | Annual | Poaceae | Russian aphid | Diuraphis noxia | Diuraphis_noxia | Specialist | Fluid-feeding arthropods | Feeding efficiency | 15.00 | 14.00 | 2.1400000 | 2.5300000 | 2.5174392 | 2.7688265 | -1 | 10 | 10.000 | 0.5426000 | 2.145600 | 0.1454648 | 5.663639e-01 |
| 468 | 87 | Rowe | 2019 | Journal of Pest Science | Triticum aestivum | Triticum_aestivum | Annual | Poaceae | Russian aphid | Diuraphis noxia | Diuraphis_noxia | Specialist | Fluid-feeding arthropods | Feeding efficiency | 15.00 | 14.00 | 192.3600000 | 185.9100000 | 199.1100738 | 179.5995546 | 1 | 10 | 10.000 | 0.5426000 | 2.145600 | 0.1454648 | 5.663639e-01 |
| 469 | 88 | Johnson | 2019 | Bulletin of Entomological Research | Glycine max | Glycine_max | Annual | Non-Poaceae | Australian bollworm | Helicoverpa punctigera | Helicoverpa_punctigera | Generalist | Chewing arthropods | Growth / Development | 10.00 | 10.00 | 0.2099100 | 0.1236700 | 0.0575851 | 0.0810176 | 1 | 10 | 10.000 | 0.3472000 | 0.462000 | 0.0316228 | 6.324560e-02 |
| 470 | 88 | Johnson | 2019 | Bulletin of Entomological Research | Glycine max | Glycine_max | Annual | Non-Poaceae | Australian bollworm | Helicoverpa punctigera | Helicoverpa_punctigera | Generalist | Chewing arthropods | Growth / Development | 10.00 | 10.00 | 0.1696613 | 0.1138064 | 0.0972027 | 0.0957203 | 1 | 10 | 10.000 | 0.3473000 | 0.465400 | 0.0632456 | 3.162280e-02 |
| 471 | 88 | Johnson | 2019 | Bulletin of Entomological Research | Glycine max | Glycine_max | Annual | Non-Poaceae | Australian bollworm | Helicoverpa punctigera | Helicoverpa_punctigera | Generalist | Chewing arthropods | Growth / Development | 10.00 | 10.00 | 0.5164295 | 0.3490563 | 0.1800358 | 0.1895065 | 1 | 10 | 10.000 | 0.2494000 | 0.445000 | 0.0632456 | 6.324560e-02 |
| 472 | 89 | Rowe | 2019 | Mres Thesis | Brachypodium distachyon | Brachypodium_distachyon | Annual | Poaceae | Corn leaf aphid | Rhopalosiphum maidis | Rhopalosiphum_maidis | Specialist | Fluid-feeding arthropods | Growth / Development | 16.00 | 17.00 | 24.0600000 | 23.2900000 | 11.8280000 | 9.7181600 | 1 | 10 | 10.000 | 1.0377000 | 2.102200 | 0.2087103 | 3.364663e-01 |
| 473 | 89 | Rowe | 2019 | Mres Thesis | Brachypodium distachyon | Brachypodium_distachyon | Annual | Poaceae | Corn leaf aphid | Rhopalosiphum maidis | Rhopalosiphum_maidis | Specialist | Fluid-feeding arthropods | Reproduction | 14.00 | 15.00 | 11.9290000 | 13.2670000 | 8.3813125 | 8.6367529 | 1 | 10 | 10.000 | 1.0377000 | 2.102200 | 0.2087103 | 3.364663e-01 |
| 474 | 89 | Rowe | 2019 | Mres Thesis | Brachypodium distachyon | Brachypodium_distachyon | Annual | Poaceae | Corn leaf aphid | Rhopalosiphum maidis | Rhopalosiphum_maidis | Specialist | Fluid-feeding arthropods | Abundance / Preference | 14.00 | 14.00 | 0.1798000 | 0.1929000 | 0.0931673 | 0.0969089 | 1 | 10 | 10.000 | 1.0377000 | 2.102200 | 0.2087103 | 3.364663e-01 |
| 475 | 89 | Rowe | 2019 | Mres Thesis | Brachypodium distachyon | Brachypodium_distachyon | Annual | Poaceae | Corn leaf aphid | Rhopalosiphum maidis | Rhopalosiphum_maidis | Specialist | Fluid-feeding arthropods | Growth / Development | 15.00 | 16.00 | 13.1556000 | 12.6670000 | 1.8783969 | 1.8216000 | -1 | 10 | 10.000 | 1.0377000 | 2.102200 | 0.2087103 | 3.364663e-01 |
| 476 | 89 | Rowe | 2019 | Mres Thesis | Brachypodium distachyon | Brachypodium_distachyon | Annual | Poaceae | Corn leaf aphid | Rhopalosiphum maidis | Rhopalosiphum_maidis | Specialist | Fluid-feeding arthropods | Feeding efficiency | 17.00 | 18.00 | 7.7100000 | 7.3300000 | 5.8135789 | 4.7517576 | -1 | 10 | 10.000 | 1.0377000 | 2.102200 | 0.2087103 | 3.364663e-01 |
| 477 | 89 | Rowe | 2019 | Mres Thesis | Brachypodium distachyon | Brachypodium_distachyon | Annual | Poaceae | Corn leaf aphid | Rhopalosiphum maidis | Rhopalosiphum_maidis | Specialist | Fluid-feeding arthropods | Feeding efficiency | 17.00 | 18.00 | 321.1600000 | 280.3800000 | 32.2014549 | 85.0225194 | -1 | 10 | 10.000 | 1.0377000 | 2.102200 | 0.2087103 | 3.364663e-01 |
| 478 | 89 | Rowe | 2019 | Mres Thesis | Brachypodium distachyon | Brachypodium_distachyon | Annual | Poaceae | Corn leaf aphid | Rhopalosiphum maidis | Rhopalosiphum_maidis | Specialist | Fluid-feeding arthropods | Feeding efficiency | 15.00 | 17.00 | 6.7600000 | 6.8800000 | 5.5770960 | 4.5766472 | -1 | 10 | 10.000 | 1.0377000 | 2.102200 | 0.2087103 | 3.364663e-01 |
| 479 | 89 | Rowe | 2019 | Mres Thesis | Brachypodium distachyon | Brachypodium_distachyon | Annual | Poaceae | Corn leaf aphid | Rhopalosiphum maidis | Rhopalosiphum_maidis | Specialist | Fluid-feeding arthropods | Feeding efficiency | 7.00 | 10.00 | 186.0500000 | 86.7500000 | 46.4064780 | 61.3481866 | 1 | 10 | 10.000 | 1.0377000 | 2.102200 | 0.2087103 | 3.364663e-01 |
| 480 | 89 | Rowe | 2019 | Mres Thesis | Brachypodium distachyon | Brachypodium_distachyon | Annual | Poaceae | Corn leaf aphid | Rhopalosiphum maidis | Rhopalosiphum_maidis | Specialist | Fluid-feeding arthropods | Feeding efficiency | 9.00 | 8.00 | 106.0900000 | 54.9800000 | 104.9700000 | 45.3396868 | -1 | 10 | 10.000 | 1.0377000 | 2.102200 | 0.2087103 | 3.364663e-01 |
| 481 | 89 | Rowe | 2019 | Mres Thesis | Brachypodium distachyon | Brachypodium_distachyon | Annual | Poaceae | Russian aphid | Diuraphis noxia | Diuraphis_noxia | Specialist | Fluid-feeding arthropods | Growth / Development | 15.00 | 19.00 | 17.2000000 | 15.3700000 | 11.6189500 | 10.4177685 | 1 | 10 | 10.000 | 1.0377000 | 2.102200 | 0.2087103 | 3.364663e-01 |
| 482 | 89 | Rowe | 2019 | Mres Thesis | Brachypodium distachyon | Brachypodium_distachyon | Annual | Poaceae | Russian aphid | Diuraphis noxia | Diuraphis_noxia | Specialist | Fluid-feeding arthropods | Reproduction | 9.00 | 12.00 | 12.1100000 | 9.3300000 | 6.9180000 | 8.2826670 | 1 | 10 | 10.000 | 1.0377000 | 2.102200 | 0.2087103 | 3.364663e-01 |
| 483 | 89 | Rowe | 2019 | Mres Thesis | Brachypodium distachyon | Brachypodium_distachyon | Annual | Poaceae | Russian aphid | Diuraphis noxia | Diuraphis_noxia | Specialist | Fluid-feeding arthropods | Abundance / Preference | 9.00 | 10.00 | 0.1401000 | 0.1420000 | 0.0597000 | 0.7621089 | 1 | 10 | 10.000 | 1.0377000 | 2.102200 | 0.2087103 | 3.364663e-01 |
| 484 | 89 | Rowe | 2019 | Mres Thesis | Brachypodium distachyon | Brachypodium_distachyon | Annual | Poaceae | Russian aphid | Diuraphis noxia | Diuraphis_noxia | Specialist | Fluid-feeding arthropods | Growth / Development | 10.00 | 13.00 | 15.2000000 | 13.9487000 | 2.6066655 | 1.0351538 | -1 | 10 | 10.000 | 1.0377000 | 2.102200 | 0.2087103 | 3.364663e-01 |
| 485 | 89 | Rowe | 2019 | Mres Thesis | Brachypodium distachyon | Brachypodium_distachyon | Annual | Poaceae | Russian aphid | Diuraphis noxia | Diuraphis_noxia | Specialist | Fluid-feeding arthropods | Feeding efficiency | 15.00 | 19.00 | 11.6000000 | 9.1100000 | 3.8342535 | 6.8434713 | -1 | 10 | 10.000 | 1.0377000 | 2.102200 | 0.2087103 | 3.364663e-01 |
| 486 | 89 | Rowe | 2019 | Mres Thesis | Brachypodium distachyon | Brachypodium_distachyon | Annual | Poaceae | Russian aphid | Diuraphis noxia | Diuraphis_noxia | Specialist | Fluid-feeding arthropods | Feeding efficiency | 15.00 | 19.00 | 273.4800000 | 285.9900000 | 47.1729372 | 53.6144570 | -1 | 10 | 10.000 | 1.0377000 | 2.102200 | 0.2087103 | 3.364663e-01 |
| 487 | 89 | Rowe | 2019 | Mres Thesis | Brachypodium distachyon | Brachypodium_distachyon | Annual | Poaceae | Russian aphid | Diuraphis noxia | Diuraphis_noxia | Specialist | Fluid-feeding arthropods | Feeding efficiency | 15.00 | 19.00 | 11.6000000 | 9.1100000 | 3.8342535 | 6.8434713 | -1 | 10 | 10.000 | 1.0377000 | 2.102200 | 0.2087103 | 3.364663e-01 |
| 488 | 89 | Rowe | 2019 | Mres Thesis | Brachypodium distachyon | Brachypodium_distachyon | Annual | Poaceae | Russian aphid | Diuraphis noxia | Diuraphis_noxia | Specialist | Fluid-feeding arthropods | Feeding efficiency | 14.00 | 16.00 | 36.8100000 | 89.1600000 | 25.2936039 | 79.4400000 | -1 | 10 | 10.000 | 1.0377000 | 2.102200 | 0.2087103 | 3.364663e-01 |
| 489 | 90 | Johnson | 2020 | Ecology | Brachypodium distachyon | Brachypodium_distachyon | Annual | Poaceae | Bird cherry oat aphid | Rhopalosiphum padi | Rhopalosiphum_padi | Specialist | Fluid-feeding arthropods | Abundance / Preference | 10.00 | 10.00 | 15.4000000 | 12.9000000 | 6.1136314 | 4.3576186 | 1 | NA | NA | NA | NA | NA | NA |
| 490 | 90 | Johnson | 2020 | Ecology | Brachypodium distachyon | Brachypodium_distachyon | Annual | Poaceae | Cotton bollworm | Helicoverpa armigera | Helicoverpa_armigera | Generalist | Chewing arthropods | Growth / Development | 10.00 | 10.00 | 0.3172900 | 0.0728100 | 0.1382548 | 0.0445249 | 1 | NA | NA | NA | NA | NA | NA |
| 491 | 91 | Cahenzli | 2012 | Animal Behaviour | Festuca rubra | Festuca_rubra | Annual | Poaceae | Small heath butterfly | Coenonympha pamphilus | Coenonympha_pamphilus | Specialist | Chewing arthropods | Growth / Development | 35.00 | 35.00 | 27.7100000 | 32.0000000 | 3.4313263 | 5.4132130 | -1 | 9 | 9.000 | 0.9100000 | 3.170000 | 0.1800000 | 3.600000e-01 |
| 492 | 91 | Cahenzli | 2012 | Animal Behaviour | Festuca rubra | Festuca_rubra | Annual | Poaceae | Small heath butterfly | Coenonympha pamphilus | Coenonympha_pamphilus | Specialist | Chewing arthropods | Growth / Development | 35.00 | 35.00 | 68.8500000 | 66.3200000 | 7.4542605 | 1.0353140 | 1 | 9 | 9.000 | 0.9100000 | 3.170000 | 0.1800000 | 3.600000e-01 |
| 493 | 91 | Cahenzli | 2012 | Animal Behaviour | Festuca rubra | Festuca_rubra | Annual | Poaceae | Small heath butterfly | Coenonympha pamphilus | Coenonympha_pamphilus | Specialist | Chewing arthropods | Growth / Development | 35.00 | 35.00 | 13.1900000 | 12.7150000 | 0.7690904 | 0.7395100 | 1 | 9 | 9.000 | 0.9100000 | 3.170000 | 0.1800000 | 3.600000e-01 |
| 494 | 92 | Calatayud | 2016 | Agriculture Ecosystems & Environment | Zea mays | Zea_mays | Annual | Poaceae | Spotted stalk borer | Chilo partellus | Chilo_partellus | Generalist | Boring arthropods | Mortality / Survivability | 10.00 | 10.00 | 52.2000000 | 25.6000000 | 25.2982213 | 9.4868330 | 1 | 10 | 10.000 | 0.5400000 | 0.923000 | 0.1264911 | 1.264911e-01 |
| 495 | 92 | Calatayud | 2016 | Agriculture Ecosystems & Environment | Zea mays | Zea_mays | Annual | Poaceae | Spotted stalk borer | Chilo partellus | Chilo_partellus | Generalist | Boring arthropods | Growth / Development | 10.00 | 10.00 | 0.1000000 | 0.1200000 | 0.0031623 | 0.0632456 | 1 | 10 | 10.000 | 0.5400000 | 0.923000 | 0.1264911 | 1.264911e-01 |
| 496 | 92 | Calatayud | 2016 | Agriculture Ecosystems & Environment | Zea mays | Zea_mays | Annual | Poaceae | African maize stalkborer | Busseola fusca | Busseola_fusca | Specialist | Boring arthropods | Mortality / Survivability | 10.00 | 10.00 | 16.3000000 | 6.7000000 | 6.6407831 | 4.1109610 | 1 | 10 | 10.000 | 0.5400000 | 0.923000 | 0.1264911 | 1.264911e-01 |
| 497 | 92 | Calatayud | 2016 | Agriculture Ecosystems & Environment | Zea mays | Zea_mays | Annual | Poaceae | African maize stalkborer | Busseola fusca | Busseola_fusca | Specialist | Boring arthropods | Growth / Development | 10.00 | 10.00 | 0.2200000 | 0.0600000 | 0.0948683 | 0.0316228 | 1 | 10 | 10.000 | 0.5400000 | 0.923000 | 0.1264911 | 1.264911e-01 |
| 498 | 93 | Cotterill | 2007 | Pest Management Science | Triticum aestivum | Triticum_aestivum | Annual | Poaceae | European Rabbit | Oryctolagus cuniculus | Oryctolagus_cuniculus | Generalist | Mammalian chewers | Feeding efficiency | 3.00 | 3.00 | 46.9100000 | 20.8800000 | 11.8472275 | 7.4478185 | 1 | NA | NA | NA | NA | NA | NA |
| 499 | 94 | Han | 2018 | Journal of Integrative Agriculture | Oryza sativa | Oryza_sativa | Annual | Poaceae | Rice stem borer | Chilo suppressalis | Chilo_suppressalis | Generalist | Boring arthropods | Growth / Development | 3.00 | 3.00 | 64.9600000 | 49.2000000 | 4.1049604 | 3.4121401 | 1 | NA | NA | NA | NA | NA | NA |
| 500 | 94 | Han | 2018 | Journal of Integrative Agriculture | Oryza sativa | Oryza_sativa | Annual | Poaceae | Rice stem borer | Chilo suppressalis | Chilo_suppressalis | Generalist | Boring arthropods | Growth / Development | 3.00 | 3.00 | 56.0800000 | 39.7100000 | 5.2134729 | 4.6592167 | 1 | NA | NA | NA | NA | NA | NA |
| 501 | 95 | Hogan | 2018 | Scandinavian Journal of Forest Research | Picea sitchensis | Picea_sitchensis | Perennial | Non-Poaceae | Large pine weevil | Hylobius abietis | Hylobius_abietis | Generalist | Chewing arthropods | Feeding efficiency | 6.00 | 6.00 | 711.1000000 | 672.8600000 | 495.6500000 | 443.8500000 | 1 | 15 | 15.000 | 593.9500000 | 714.050000 | 283.8400000 | 4.344200e+02 |
| 502 | 96 | Nascimento | 2018 | Journal of Applied Entomology | Oryza sativa | Oryza_sativa | Annual | Poaceae | fall armyworm | Spodoptera frugiperda | Spodoptera_frugiperda | Generalist | Chewing arthropods | Growth / Development | 10.00 | 10.00 | 32.7000000 | 32.7000000 | 1.2649111 | 2.4033310 | -1 | 10 | 10.000 | 0.7500000 | 1.540000 | 0.5059644 | 6.324555e-01 |
| 503 | 96 | Nascimento | 2018 | Journal of Applied Entomology | Oryza sativa | Oryza_sativa | Annual | Poaceae | fall armyworm | Spodoptera frugiperda | Spodoptera_frugiperda | Generalist | Chewing arthropods | Mortality / Survivability | 10.00 | 10.00 | 76.0000000 | 52.0000000 | 18.3728332 | 23.4641002 | 1 | 10 | 10.000 | 0.7500000 | 1.540000 | 0.5059644 | 6.324555e-01 |
| 504 | 96 | Nascimento | 2018 | Journal of Applied Entomology | Oryza sativa | Oryza_sativa | Annual | Poaceae | fall armyworm | Spodoptera frugiperda | Spodoptera_frugiperda | Generalist | Chewing arthropods | Feeding efficiency | 10.00 | 10.00 | 180.1000000 | 159.8000000 | 12.4909968 | 22.1359436 | 1 | 10 | 10.000 | 0.7500000 | 1.540000 | 0.5059644 | 6.324555e-01 |
| 505 | 96 | Nascimento | 2018 | Journal of Applied Entomology | Oryza sativa | Oryza_sativa | Annual | Poaceae | fall armyworm | Spodoptera frugiperda | Spodoptera_frugiperda | Generalist | Chewing arthropods | Growth / Development | 10.00 | 10.00 | 10.5000000 | 10.5000000 | 0.6008328 | 0.3478505 | -1 | 10 | 10.000 | 0.7500000 | 1.540000 | 0.5059644 | 6.324555e-01 |
| 506 | 96 | Nascimento | 2018 | Journal of Applied Entomology | Oryza sativa | Oryza_sativa | Annual | Poaceae | fall armyworm | Spodoptera frugiperda | Spodoptera_frugiperda | Generalist | Chewing arthropods | Growth / Development | 10.00 | 10.00 | 12.7000000 | 8.0750000 | 1.2332883 | 4.2690748 | 1 | 10 | 10.000 | 0.7500000 | 1.540000 | 0.5059644 | 6.324555e-01 |
| 507 | 96 | Nascimento | 2018 | Journal of Applied Entomology | Oryza sativa | Oryza_sativa | Annual | Poaceae | fall armyworm | Spodoptera frugiperda | Spodoptera_frugiperda | Generalist | Chewing arthropods | Reproduction | 10.00 | 10.00 | 600.6000000 | 212.7000000 | 52.7151686 | 93.6350415 | 1 | 10 | 10.000 | 0.7500000 | 1.540000 | 0.5059644 | 6.324555e-01 |
| 508 | 96 | Nascimento | 2018 | Journal of Applied Entomology | Oryza sativa | Oryza_sativa | Annual | Poaceae | fall armyworm | Spodoptera frugiperda | Spodoptera_frugiperda | Generalist | Chewing arthropods | Mortality / Survivability | 10.00 | 10.00 | 80.0000000 | 38.7500000 | 10.7517440 | 34.2474671 | 1 | 10 | 10.000 | 0.7500000 | 1.540000 | 0.5059644 | 6.324555e-01 |
| 509 | 97 | Ramirez-Godoy | 2018 | Notulae Botanicae Horti Agrobotanici Cluj-Napoca | Citrus latifolia | Citrus_latifolia | Perennial | Non-Poaceae | Asian citrus psyllid | Diaphorina citri | Diaphorina_citri | Specialist | Fluid-feeding arthropods | Abundance / Preference | 3.00 | 3.00 | 1.3600000 | 0.7200000 | 0.6062178 | 0.3637307 | 1 | NA | NA | NA | NA | NA | NA |
| 510 | 97 | Ramirez-Godoy | 2018 | Notulae Botanicae Horti Agrobotanici Cluj-Napoca | Citrus latifolia | Citrus_latifolia | Perennial | Non-Poaceae | Asian citrus psyllid | Diaphorina citri | Diaphorina_citri | Specialist | Fluid-feeding arthropods | Abundance / Preference | 3.00 | 3.00 | 7.7200000 | 3.3300000 | 2.9444864 | 1.5242047 | 1 | NA | NA | NA | NA | NA | NA |
| 511 | 97 | Ramirez-Godoy | 2018 | Notulae Botanicae Horti Agrobotanici Cluj-Napoca | Citrus latifolia | Citrus_latifolia | Perennial | Non-Poaceae | Asian citrus psyllid | Diaphorina citri | Diaphorina_citri | Specialist | Fluid-feeding arthropods | Abundance / Preference | 3.00 | 3.00 | 11.6100000 | 4.8500000 | 3.1176915 | 1.2817176 | 1 | NA | NA | NA | NA | NA | NA |
| 512 | 98 | Yang | 2018 | Ecology and Evolution | Oryza sativa | Oryza_sativa | Annual | Poaceae | Brown planthopper | Nilaparvata lugens | Nilaparvata_lugens | Specialist | Fluid-feeding arthropods | Feeding efficiency | 20.00 | 20.00 | 29.9500000 | 26.8400000 | 10.0175845 | 7.8262379 | 1 | NA | NA | NA | NA | NA | NA |
| 513 | 99 | Jeer | 2019 | Field Crops Research | Oryza sativa | Oryza_sativa | Annual | Poaceae | Yellow Stem Borer | Scirpophaga incertulas | Scirpophaga_incertulas | Specialist | Boring arthropods | Feeding efficiency | 4.00 | 4.00 | 39.4000000 | 30.7000000 | 1.8000000 | 1.6000000 | 1 | NA | NA | NA | NA | NA | NA |
| 514 | 99 | Jeer | 2019 | Field Crops Research | Oryza sativa | Oryza_sativa | Annual | Poaceae | Yellow Stem Borer | Scirpophaga incertulas | Scirpophaga_incertulas | Specialist | Boring arthropods | Abundance / Preference | 4.00 | 4.00 | 12.1000000 | 11.8000000 | 1.5000000 | 2.3000000 | 1 | NA | NA | NA | NA | NA | NA |
| 515 | 99 | Jeer | 2019 | Field Crops Research | Oryza sativa | Oryza_sativa | Annual | Poaceae | Yellow Stem Borer | Scirpophaga incertulas | Scirpophaga_incertulas | Specialist | Boring arthropods | Growth / Development | 8.00 | 8.00 | 291.8000000 | 251.2000000 | 3.0000000 | 2.0000000 | 1 | NA | NA | NA | NA | NA | NA |
| 516 | 100 | Ramirez-Godoy | 2018 | Horttechnology | Citrus latifolia | Citrus_latifolia | Perennial | Non-Poaceae | Asian Citris Psyllid | Diaphorina citri | Diaphorina_citri | Specialist | Fluid-feeding arthropods | Reproduction | 6.00 | 6.00 | 6.6000000 | 3.2000000 | 1.8371173 | 1.4207041 | 1 | 5 | 5.000 | 1.2200000 | 2.470000 | 0.7379024 | 4.695743e-01 |
| 517 | 100 | Ramirez-Godoy | 2018 | Horttechnology | Citrus latifolia | Citrus_latifolia | Perennial | Non-Poaceae | Asian Citris Psyllid | Diaphorina citri | Diaphorina_citri | Specialist | Fluid-feeding arthropods | Abundance / Preference | 5.00 | 5.00 | 1.5000000 | 0.7500000 | 0.6260990 | 1.1180340 | 1 | 4 | 4.000 | 1.8100000 | 1.740000 | 4.6600000 | 6.800000e-01 |
| 518 | 100 | Ramirez-Godoy | 2018 | Horttechnology | Citrus latifolia | Citrus_latifolia | Perennial | Non-Poaceae | Asian Citris Psyllid | Diaphorina citri | Diaphorina_citri | Specialist | Fluid-feeding arthropods | Abundance / Preference | 5.00 | 5.00 | 4.2500000 | 1.0000000 | 2.4596748 | 0.8944272 | 1 | 4 | 4.000 | 1.8100000 | 1.740000 | 4.6600000 | 6.800000e-01 |
| 519 | 100 | Ramirez-Godoy | 2018 | Horttechnology | Citrus latifolia | Citrus_latifolia | Perennial | Non-Poaceae | Asian Citris Psyllid | Diaphorina citri | Diaphorina_citri | Specialist | Fluid-feeding arthropods | Reproduction | 5.00 | 5.00 | 11.7500000 | 6.2500000 | 1.6770510 | 2.4596748 | 1 | 4 | 4.000 | 1.8100000 | 1.740000 | 4.6600000 | 6.800000e-01 |
| 520 | 101 | Ruffino | 2017 | Ecology and Evolution | Deschampsia cespitosa | Deschampsia_cespitosa | Perennial | Poaceae | NA | Microtus agrestis | Microtus_agrestis | Generalist | Mammalian chewers | Growth / Development | 38.00 | 12.00 | 23.0600000 | 23.3800000 | 5.0548195 | 5.4732806 | 1 | NA | NA | NA | NA | NA | NA |
| 521 | 101 | Ruffino | 2017 | Ecology and Evolution | Deschampsia cespitosa | Deschampsia_cespitosa | Perennial | Poaceae | NA | Microtus agrestis | Microtus_agrestis | Generalist | Mammalian chewers | Growth / Development | 76.00 | 40.00 | 28.1400000 | 32.6800000 | 5.4050347 | 5.9450820 | 1 | NA | NA | NA | NA | NA | NA |
| 522 | 102 | Chen | 2019 | Pest Management Science | Triticum aestivum | Triticum_aestivum | Annual | Poaceae | Green aphid | Sitobion avenae | Sitobion_avenae | Specialist | Fluid-feeding arthropods | Reproduction | 30.00 | 30.00 | 37.5000000 | 21.9400000 | 7.8214781 | 8.5937669 | 1 | 5 | 5.000 | 0.4780000 | 0.669000 | 0.1341641 | 4.472140e-02 |
| 523 | 102 | Chen | 2019 | Pest Management Science | Triticum aestivum | Triticum_aestivum | Annual | Poaceae | Green aphid | Sitobion avenae | Sitobion_avenae | Specialist | Fluid-feeding arthropods | Abundance / Preference | 20.00 | 20.00 | 37.5000000 | 21.9400000 | 6.3862101 | 7.0167813 | 1 | 5 | 5.000 | 0.4780000 | 0.669000 | 0.1341641 | 4.472140e-02 |
| 524 | 103 | Chen | 2019 | Crop Protection | Triticum aestivum | Triticum_aestivum | Annual | Poaceae | Green aphid | Sitobion avenae | Sitobion_avenae | Specialist | Fluid-feeding arthropods | Abundance / Preference | 20.00 | 20.00 | 407.5000000 | 274.5000000 | 145.7916321 | 65.7403985 | 1 | NA | NA | NA | NA | NA | NA |
| 525 | 103 | Chen | 2019 | Crop Protection | Triticum aestivum | Triticum_aestivum | Annual | Poaceae | Green aphid | Sitobion avenae | Sitobion_avenae | Specialist | Fluid-feeding arthropods | Abundance / Preference | 20.00 | 20.00 | 407.5000000 | 290.3000000 | 145.7916321 | 99.8627959 | 1 | NA | NA | NA | NA | NA | NA |
| 526 | 103 | Chen | 2019 | Crop Protection | Triticum aestivum | Triticum_aestivum | Annual | Poaceae | Green aphid | Sitobion avenae | Sitobion_avenae | Specialist | Fluid-feeding arthropods | Abundance / Preference | 20.00 | 20.00 | 407.5000000 | 293.8000000 | 145.7916321 | 113.1003183 | 1 | NA | NA | NA | NA | NA | NA |
| 527 | 103 | Chen | 2019 | Crop Protection | Triticum aestivum | Triticum_aestivum | Annual | Poaceae | Green aphid | Sitobion avenae | Sitobion_avenae | Specialist | Fluid-feeding arthropods | Abundance / Preference | 3.00 | 3.00 | 3.8400000 | 7.6010000 | 2.4508519 | 7.5413492 | 1 | NA | NA | NA | NA | NA | NA |
| 528 | 103 | Chen | 2019 | Crop Protection | Triticum aestivum | Triticum_aestivum | Annual | Poaceae | Green aphid | Sitobion avenae | Sitobion_avenae | Specialist | Fluid-feeding arthropods | Abundance / Preference | 3.00 | 3.00 | 8.2110000 | 10.0080000 | 7.8496543 | 8.4056426 | 1 | NA | NA | NA | NA | NA | NA |
| 529 | 103 | Chen | 2019 | Crop Protection | Triticum aestivum | Triticum_aestivum | Annual | Poaceae | Green aphid | Sitobion avenae | Sitobion_avenae | Specialist | Fluid-feeding arthropods | Abundance / Preference | 3.00 | 3.00 | 108.7410000 | 46.6750000 | 44.4167109 | 25.3849366 | 1 | NA | NA | NA | NA | NA | NA |
| 530 | 103 | Chen | 2019 | Crop Protection | Triticum aestivum | Triticum_aestivum | Annual | Poaceae | Green aphid | Sitobion avenae | Sitobion_avenae | Specialist | Fluid-feeding arthropods | Reproduction | 20.00 | 20.00 | 27.5000000 | 24.6500000 | 5.5901699 | 6.7082039 | 1 | NA | NA | NA | NA | NA | NA |
| 531 | 103 | Chen | 2019 | Crop Protection | Triticum aestivum | Triticum_aestivum | Annual | Poaceae | Green aphid | Sitobion avenae | Sitobion_avenae | Specialist | Fluid-feeding arthropods | Reproduction | 20.00 | 20.00 | 27.5000000 | 21.9400000 | 5.5901699 | 5.8137767 | 1 | NA | NA | NA | NA | NA | NA |
| 532 | 103 | Chen | 2019 | Crop Protection | Triticum aestivum | Triticum_aestivum | Annual | Poaceae | Green aphid | Sitobion avenae | Sitobion_avenae | Specialist | Fluid-feeding arthropods | Reproduction | 20.00 | 20.00 | 27.5000000 | 22.7600000 | 5.5901699 | 7.8262379 | 1 | NA | NA | NA | NA | NA | NA |
| 533 | 104 | Lin | 2019 | Frontiers in Plant Science | Oryza sativa | Oryza_sativa | Annual | Poaceae | Leaf folder | Cnaphalocrocis medinalis | Cnaphalocrocis_medinalis | Specialist | Chewing arthropods | Growth / Development | 20.00 | 20.00 | 6.2840000 | 3.4140000 | 2.1063760 | 1.8425200 | 1 | NA | NA | NA | NA | NA | NA |
| 534 | 105 | Martin | 2019 | Proceedings of the Royal Society | Phyllostachys aureosulcata | Phyllostachys_aureosulcata | Perennial | Poaceae | Guinea pig | Cavia porcellus | Cavia_porcellus | Generalist | Mammalian chewers | Growth / Development | 6.00 | 6.00 | 353.0000000 | 292.0000000 | 66.1362231 | 31.8433667 | 1 | NA | NA | NA | NA | NA | NA |
| 535 | 105 | Martin | 2019 | Proceedings of the Royal Society | Phyllostachys aureosulcata | Phyllostachys_aureosulcata | Perennial | Poaceae | Guinea pig | Cavia porcellus | Cavia_porcellus | Generalist | Mammalian chewers | Growth / Development | 6.00 | 6.00 | 349.0000000 | 274.0000000 | 51.4392846 | 51.4392846 | 1 | NA | NA | NA | NA | NA | NA |
| 536 | 106 | Moise | 2019 | Entomologia Experimentalis et Applicata | Zea mays | Zea_mays | Annual | Poaceae | True armyworm | Pseudaletia unipuncta | Pseudaletia_unipuncta | Generalist | Chewing arthropods | Mortality / Survivability | 10.00 | 10.00 | 34.9040000 | 23.5290000 | 18.4993243 | 15.4129413 | 1 | 8 | 8.000 | 0.3310000 | 0.857000 | 0.1272792 | 4.525483e-01 |
| 537 | 106 | Moise | 2019 | Entomologia Experimentalis et Applicata | Zea mays | Zea_mays | Annual | Poaceae | True armyworm | Pseudaletia unipuncta | Pseudaletia_unipuncta | Generalist | Chewing arthropods | Growth / Development | 10.00 | 10.00 | 21.5560000 | 20.2020000 | 1.1668805 | 1.1415822 | -1 | 8 | 8.000 | 0.3310000 | 0.857000 | 0.1272792 | 4.525483e-01 |
| 538 | 106 | Moise | 2019 | Entomologia Experimentalis et Applicata | Zea mays | Zea_mays | Annual | Poaceae | True armyworm | Pseudaletia unipuncta | Pseudaletia_unipuncta | Generalist | Chewing arthropods | Growth / Development | 10.00 | 10.00 | 373.0750000 | 364.3300000 | 28.5205822 | 51.3838497 | 1 | 8 | 8.000 | 0.3310000 | 0.857000 | 0.1272792 | 4.525483e-01 |
| 539 | 106 | Moise | 2019 | Entomologia Experimentalis et Applicata | Zea mays | Zea_mays | Annual | Poaceae | True armyworm | Pseudaletia unipuncta | Pseudaletia_unipuncta | Generalist | Chewing arthropods | Feeding efficiency | 20.00 | 20.00 | 0.4040000 | 0.2950000 | 0.1654690 | 0.3443545 | 1 | 8 | 8.000 | 0.3310000 | 0.857000 | 0.1272792 | 4.525483e-01 |
| 540 | 106 | Moise | 2019 | Entomologia Experimentalis et Applicata | Zea mays | Zea_mays | Annual | Poaceae | True armyworm | Pseudaletia unipuncta | Pseudaletia_unipuncta | Generalist | Chewing arthropods | Feeding efficiency | 20.00 | 20.00 | 10.9890000 | 39.4780000 | 52.0914396 | 53.9160711 | 1 | 8 | 8.000 | 0.3310000 | 0.857000 | 0.1272792 | 4.525483e-01 |
| 541 | 106 | Moise | 2019 | Entomologia Experimentalis et Applicata | Zea mays | Zea_mays | Annual | Poaceae | True armyworm | Pseudaletia unipuncta | Pseudaletia_unipuncta | Generalist | Chewing arthropods | Feeding efficiency | 20.00 | 20.00 | 21.6530000 | 50.8510000 | 28.6082537 | 40.4012762 | 1 | 8 | 8.000 | 0.3310000 | 0.857000 | 0.1272792 | 4.525483e-01 |
| 542 | 107 | Villalba | 2019 | Applied Animal Behaviour Science | Taeniatherum caput-medusae | Taeniatherum_caput-medusae | Annual | Poaceae | Sheep | Ovis aries | Ovis_aries | Generalist | Mammalian chewers | Feeding efficiency | 3.00 | 3.00 | 5.2480000 | 0.5160000 | 0.9474318 | 0.0571577 | 1 | NA | NA | NA | NA | NA | NA |
| 543 | 107 | Villalba | 2019 | Applied Animal Behaviour Science | Taeniatherum caput-medusae | Taeniatherum_caput-medusae | Annual | Poaceae | Sheep | Ovis aries | Ovis_aries | Generalist | Mammalian chewers | Feeding efficiency | 3.00 | 3.00 | 6.2010000 | 0.7670000 | 2.5201339 | 0.3238935 | 1 | NA | NA | NA | NA | NA | NA |
| 544 | 107 | Villalba | 2019 | Applied Animal Behaviour Science | Taeniatherum caput-medusae | Taeniatherum_caput-medusae | Annual | Poaceae | Sheep | Ovis aries | Ovis_aries | Generalist | Mammalian chewers | Feeding efficiency | 3.00 | 3.00 | 3.8010000 | 0.6040000 | 2.5080096 | 0.3827832 | 1 | NA | NA | NA | NA | NA | NA |
| 545 | 107 | Villalba | 2019 | Applied Animal Behaviour Science | Taeniatherum caput-medusae | Taeniatherum_caput-medusae | Annual | Poaceae | Sheep | Ovis aries | Ovis_aries | Generalist | Mammalian chewers | Feeding efficiency | 3.00 | 3.00 | 9.5370000 | 0.1500000 | 2.5010814 | 0.1177795 | 1 | NA | NA | NA | NA | NA | NA |
| 546 | 107 | Villalba | 2019 | Applied Animal Behaviour Science | Taeniatherum caput-medusae | Taeniatherum_caput-medusae | Annual | Poaceae | Sheep | Ovis aries | Ovis_aries | Generalist | Mammalian chewers | Feeding efficiency | 3.00 | 3.00 | 24.2480000 | 0.3420000 | 3.0345530 | 0.3533384 | 1 | NA | NA | NA | NA | NA | NA |
| 547 | 108 | Islam | 2020 | Ecological Entomology | Cucumis sativus | Cucumis_sativus | Annual | Non-Poaceae | Cotton bollworm | Helicoverpa armigera | Helicoverpa_armigera | Generalist | Chewing arthropods | Growth / Development | 30.00 | 30.00 | 0.5710000 | 0.4310000 | 0.2629068 | 0.2902930 | 1 | NA | NA | NA | NA | NA | NA |
| 548 | 108 | Islam | 2020 | Ecological Entomology | Cucumis sativus | Cucumis_sativus | Annual | Non-Poaceae | Cotton bollworm | Helicoverpa armigera | Helicoverpa_armigera | Generalist | Chewing arthropods | Feeding efficiency | 30.00 | 30.00 | 0.8950000 | 0.8220000 | 0.4491325 | 0.2464752 | 1 | NA | NA | NA | NA | NA | NA |
| 549 | 108 | Islam | 2020 | Ecological Entomology | Cucumis sativus | Cucumis_sativus | Annual | Non-Poaceae | Cotton bollworm | Helicoverpa armigera | Helicoverpa_armigera | Generalist | Chewing arthropods | Growth / Development | 30.00 | 30.00 | 1.0560000 | 0.4030000 | 1.2433302 | 0.4327008 | 1 | NA | NA | NA | NA | NA | NA |
| 550 | 108 | Islam | 2020 | Ecological Entomology | Cucumis sativus | Cucumis_sativus | Annual | Non-Poaceae | Cotton bollworm | Helicoverpa armigera | Helicoverpa_armigera | Generalist | Chewing arthropods | Feeding efficiency | 30.00 | 30.00 | 2.5290000 | 1.4760000 | 2.3716387 | 1.2214213 | 1 | NA | NA | NA | NA | NA | NA |
| 551 | 109 | Hall | 2020 | Oecologia (in review) | Phalaris aquatica | Phalaris_aquatica | Perennial | Poaceae | Rhopalosiphum | Rhopalosiphum padi | Rhopalosiphum_padi | Specialist | Fluid-feeding arthropods | Abundance / Preference | 9.00 | 9.00 | 345.7000000 | 278.6000000 | 204.5000000 | 154.2000000 | 1 | NA | NA | NA | NA | NA | NA |
| 552 | 109 | Hall | 2020 | Oecologia (in review) | Phalaris aquatica | Phalaris_aquatica | Perennial | Poaceae | Rhopalosiphum | Rhopalosiphum padi | Rhopalosiphum_padi | Specialist | Fluid-feeding arthropods | Growth / Development | 8.00 | 8.00 | 0.6140000 | 0.5790000 | 0.0197990 | 0.0593970 | 1 | NA | NA | NA | NA | NA | NA |
| 553 | 110 | Hall | 2020 | TBC | Phalaris aquatica | Phalaris_aquatica | Perennial | Poaceae | Cotton bollworm | Helicoverpa armigera | Helicoverpa_armigera | Generalist | Chewing arthropods | Growth / Development | 9.00 | 9.00 | 0.7270000 | 0.0720000 | 0.3160000 | 0.1610000 | 1 | NA | NA | NA | NA | NA | NA |
| 554 | 111 | Brown | 2019 | MSc Thesis | Zea mays | Zea_mays | Annual | Poaceae | fall armyworm | Spodoptera frugiperda | Spodoptera_frugiperda | Generalist | Chewing arthropods | Feeding efficiency | 51.00 | 51.00 | 87.3340000 | 90.7500000 | 29.5797966 | 28.7156837 | 1 | 5 | 5.000 | 0.3080000 | 0.841000 | 0.0760263 | 2.012461e-01 |
| 555 | 111 | Brown | 2019 | MSc Thesis | Zea mays | Zea_mays | Annual | Poaceae | fall armyworm | Spodoptera frugiperda | Spodoptera_frugiperda | Generalist | Chewing arthropods | Growth / Development | 51.00 | 51.00 | 227.2710000 | 251.0410000 | 86.8683354 | 77.2345485 | 1 | 5 | 5.000 | 0.3080000 | 0.841000 | 0.0760263 | 2.012461e-01 |
| 556 | 111 | Brown | 2019 | MSc Thesis | Zea mays | Zea_mays | Annual | Poaceae | fall armyworm | Spodoptera frugiperda | Spodoptera_frugiperda | Generalist | Chewing arthropods | Growth / Development | 51.00 | 51.00 | 2.6810000 | 2.6870000 | 0.3070814 | 0.3356471 | 1 | 5 | 5.000 | 0.3080000 | 0.841000 | 0.0760263 | 2.012461e-01 |
| 557 | 111 | Brown | 2019 | MSc Thesis | Zea mays | Zea_mays | Annual | Poaceae | fall armyworm | Spodoptera frugiperda | Spodoptera_frugiperda | Generalist | Chewing arthropods | Growth / Development | 33.00 | 32.00 | 172.5840000 | 151.2820000 | 35.7311797 | 24.0190031 | 1 | 5 | 5.000 | 0.3080000 | 0.841000 | 0.0760263 | 2.012461e-01 |
| 558 | 112 | Vandegeer | 2020 | Functional Ecology | Festuca arundinacea | Festuca_arundinacea | Perennial | Poaceae | Cotton bollworm | Helicoverpa armigera | Helicoverpa_armigera | Generalist | Chewing arthropods | Growth / Development | 11.00 | 12.00 | 0.2620000 | 0.0510000 | 0.1127652 | 0.0623538 | 1 | NA | NA | NA | NA | NA | NA |
| 559 | 113 | Jadhao | 2020 | Scientific Reports | Eleusine coracana | Eleusine_coracana | Annual | Poaceae | Pink stem borer | Sesamia inferens | Sesamia_inferens | Generalist | Boring arthropods | Feeding efficiency | 5.00 | 5.00 | 13.4050000 | 5.4160000 | 0.2280000 | 0.2960000 | 1 | 3 | 3.000 | 6.6640000 | 16.106000 | 0.3160000 | 4.210000e-01 |
| 560 | 113 | Jadhao | 2020 | Scientific Reports | Eleusine coracana | Eleusine_coracana | Annual | Poaceae | Pink stem borer | Sesamia inferens | Sesamia_inferens | Generalist | Boring arthropods | Feeding efficiency | 5.00 | 5.00 | 82.2540000 | 37.0680000 | 1.9700000 | 1.9700000 | 1 | 3 | 3.000 | 6.6640000 | 16.106000 | 0.3160000 | 4.210000e-01 |
| 561 | 113 | Jadhao | 2020 | Scientific Reports | Eleusine coracana | Eleusine_coracana | Annual | Poaceae | Pink stem borer | Sesamia inferens | Sesamia_inferens | Generalist | Boring arthropods | Feeding efficiency | 5.00 | 5.00 | 75.6830000 | 33.7320000 | 4.2200000 | 2.3910000 | 1 | 3 | 3.000 | 6.6640000 | 16.106000 | 0.3160000 | 4.210000e-01 |
| 562 | 114 | Cougnon | 2017 | Grass and Forage Science | Festuca arundinacea | Festuca_arundinacea | Perennial | Poaceae | Sheep | Ovis aries | Ovis_aries | Generalist | Mammalian chewers | Feeding efficiency | 3.00 | 3.00 | 6.2470000 | 3.9480000 | 1.0220000 | 1.5430000 | 1 | 3 | 3.000 | 4.1060000 | 7.475000 | 0.7230000 | 1.173000e+00 |
| 563 | 115 | de Oliveira | 2020 | Plos One | Triticum aestivum | Triticum_aestivum | Annual | Poaceae | Bird cherry oat aphid | Rhopalosiphum padi | Rhopalosiphum_padi | Specialist | Fluid-feeding arthropods | Abundance / Preference | 8.00 | 8.00 | 1.5090000 | 0.2820000 | 1.3519882 | 0.4497199 | 1 | NA | NA | NA | NA | NA | NA |
| 564 | 115 | de Oliveira | 2020 | Plos One | Triticum aestivum | Triticum_aestivum | Annual | Poaceae | Bird cherry oat aphid | Rhopalosiphum padi | Rhopalosiphum_padi | Specialist | Fluid-feeding arthropods | Abundance / Preference | 12.00 | 12.00 | 97.5200000 | 89.5140000 | 33.9932291 | 24.3595626 | 1 | NA | NA | NA | NA | NA | NA |
| 565 | 116 | Andama | 2020 | Plant, Cell and Environment | Oryza sativa | Oryza_sativa | Annual | Poaceae | Rice skipper | Parnara guttata | Parnara_guttata | Specialist | Chewing arthropods | Feeding efficiency | 40.00 | 40.00 | 14.4900000 | 1.1890000 | 21.4465671 | 3.0231374 | 1 | NA | NA | NA | NA | NA | NA |
| 566 | 117 | Sampaio | 2020 | Neotropical Entomology | Sorghum bicolor | Sorghum_bicolor | Annual | Poaceae | Greenbug (aphid) | Schizaphis graminum | Schizaphis_graminum | Specialist | Fluid-feeding arthropods | Reproduction | 6.00 | 6.00 | 9.5000000 | 8.5000000 | 1.2982296 | 0.5388877 | 1 | 6 | 6.000 | 0.8000000 | 1.300000 | 0.0734847 | 1.469694e-01 |
| 567 | 117 | Sampaio | 2020 | Neotropical Entomology | Sorghum bicolor | Sorghum_bicolor | Annual | Poaceae | Greenbug (aphid) | Schizaphis graminum | Schizaphis_graminum | Specialist | Fluid-feeding arthropods | Abundance / Preference | 6.00 | 6.00 | 67.7000000 | 61.7000000 | 27.2383259 | 27.3363055 | 1 | NA | NA | NA | NA | NA | NA |
| 568 | 117 | Sampaio | 2020 | Neotropical Entomology | Sorghum bicolor | Sorghum_bicolor | Annual | Poaceae | Greenbug (aphid) | Schizaphis graminum | Schizaphis_graminum | Specialist | Fluid-feeding arthropods | Abundance / Preference | 6.00 | 6.00 | 419.2000000 | 374.4000000 | 73.7541362 | 66.9690496 | 1 | NA | NA | NA | NA | NA | NA |
| 569 | 117 | Sampaio | 2020 | Neotropical Entomology | Sorghum bicolor | Sorghum_bicolor | Annual | Poaceae | Greenbug (aphid) | Schizaphis graminum | Schizaphis_graminum | Specialist | Fluid-feeding arthropods | Growth / Development | 15.00 | 15.00 | 0.6500000 | 0.6500000 | 0.0232379 | 0.0348569 | 1 | 15 | 15.000 | 0.5000000 | 1.100000 | 0.1936492 | 1.161895e-01 |
| 570 | 117 | Sampaio | 2020 | Neotropical Entomology | Sorghum bicolor | Sorghum_bicolor | Annual | Poaceae | Greenbug (aphid) | Schizaphis graminum | Schizaphis_graminum | Specialist | Fluid-feeding arthropods | Growth / Development | 15.00 | 15.00 | 0.3400000 | 0.3500000 | 0.0348569 | 0.0426028 | 1 | 15 | 15.000 | 0.5000000 | 1.100000 | 0.1936492 | 1.161895e-01 |
| 571 | 117 | Sampaio | 2020 | Neotropical Entomology | Sorghum bicolor | Sorghum_bicolor | Annual | Poaceae | Greenbug (aphid) | Schizaphis graminum | Schizaphis_graminum | Specialist | Fluid-feeding arthropods | Growth / Development | 10.00 | 10.00 | 0.1400000 | 0.1200000 | 0.0189737 | 0.0126491 | 1 | 10 | 10.000 | 0.3000000 | 1.500000 | 0.0316228 | 1.264911e-01 |
| 572 | 118 | Hall | 2020 | Plants | Brachypodium distachyon | Brachypodium_distachyon | Annual | Poaceae | House cricket | Acheta domesticus | Acheta_domesticus | Generalist | Chewing arthropods | Growth / Development | 12.00 | 12.00 | 14.2550000 | 6.5450000 | 13.0527349 | 3.9386835 | 1 | NA | NA | NA | NA | NA | NA |
| 573 | 118 | Hall | 2020 | Plants | Brachypodium distachyon | Brachypodium_distachyon | Annual | Poaceae | Cotton bollworm | Helicoverpa armigera | Helicoverpa_armigera | Generalist | Chewing arthropods | Growth / Development | 12.00 | 12.00 | 26.8330000 | 149.5410000 | 30.0046625 | 21.0814832 | -1 | NA | NA | NA | NA | NA | NA |
| 574 | 119 | Boer | 2019 | Bulletin of Entomological Research | Zea mays | Zea_mays | Annual | Poaceae | Corn leaf aphid | Rhopalosiphum maidis | Rhopalosiphum_maidis | Specialist | Fluid-feeding arthropods | Abundance / Preference | 9.00 | 9.00 | 122.4000000 | 92.3000000 | 132.9600000 | 57.8100000 | 1 | 5 | 5.000 | 2.3000000 | 2.900000 | 0.2906888 | 3.130495e-01 |
| 575 | 119 | Boer | 2019 | Bulletin of Entomological Research | Zea mays | Zea_mays | Annual | Poaceae | Corn leaf aphid | Rhopalosiphum maidis | Rhopalosiphum_maidis | Specialist | Fluid-feeding arthropods | Abundance / Preference | 9.00 | 9.00 | 17.4000000 | 15.3000000 | 12.3600000 | 9.9000000 | 1 | 5 | 5.000 | 2.3000000 | 2.900000 | 0.2906888 | 3.130495e-01 |
| 576 | 119 | Boer | 2019 | Bulletin of Entomological Research | Zea mays | Zea_mays | Annual | Poaceae | Corn leaf aphid | Rhopalosiphum maidis | Rhopalosiphum_maidis | Specialist | Fluid-feeding arthropods | Abundance / Preference | 9.00 | 9.00 | 109.9000000 | 54.0000000 | 111.5400000 | 47.3100000 | 1 | 5 | 5.000 | 2.3000000 | 2.900000 | 0.2906888 | 3.130495e-01 |
| 577 | 120 | Cibils-Stewart | 2021 | PHD Thesis | Festuca arundinacea | Festuca_arundinacea | Perennial | Poaceae | bird-cherry oat aphid | Rhopalosiphum padi | Rhopalosiphum_padi | Specialist | Fluid-feeding arthropods | Abundance / Preference | 8.00 | 11.00 | 236.0000000 | 183.6666667 | 49.1133892 | 121.5993421 | 1 | 10 | 10.000 | 0.4206667 | 1.355000 | 0.1637946 | 6.033200e-02 |
| 578 | 120 | Cibils-Stewart | 2021 | PHD Thesis | Festuca arundinacea | Festuca_arundinacea | Perennial | Poaceae | bird-cherry oat aphid | Rhopalosiphum padi | Rhopalosiphum_padi | Specialist | Fluid-feeding arthropods | Abundance / Preference | 15.00 | 15.00 | 0.0258795 | 0.0319543 | 0.0025370 | 0.0054717 | 1 | 10 | 10.000 | 0.4206667 | 1.355000 | 0.1637946 | 6.033200e-02 |
| 579 | 120 | Cibils-Stewart | 2021 | PHD Thesis | Festuca arundinacea | Festuca_arundinacea | Perennial | Poaceae | bird-cherry oat aphid | Rhopalosiphum padi | Rhopalosiphum_padi | Specialist | Fluid-feeding arthropods | Growth / Development | 15.00 | 15.00 | 0.1774250 | 0.1684303 | 0.0197077 | 0.0179334 | -1 | 10 | 10.000 | 0.4206667 | 1.355000 | 0.1637946 | 6.033200e-02 |
| 580 | 121 | Oliva | 2021 | Journal of Soil Science and Plant Nutrition | Saccharum officinarum | Saccharum_officinarum | Perennial | Poaceae | stalkborer | Diatraea saccharalis | Diatraea_saccharalis | Specialist | Boring arthropods | Feeding efficiency | 10.00 | 10.00 | 15.4411765 | 10.5882353 | 1.9411765 | 1.1470588 | 1 | 12 | 12.000 | 0.9292035 | 2.044248 | 0.3716814 | 4.026549e-01 |
| 581 | 121 | Oliva | 2021 | Journal of Soil Science and Plant Nutrition | Saccharum officinarum | Saccharum_officinarum | Perennial | Poaceae | stalkborer | Diatraea saccharalis | Diatraea_saccharalis | Specialist | Boring arthropods | Feeding efficiency | 10.00 | 10.00 | 15.4411765 | 7.9411765 | 1.9411765 | 0.8823529 | 1 | 12 | 12.000 | 0.9292035 | 3.128319 | 0.3716814 | 3.716814e-01 |
| 582 | 121 | Oliva | 2021 | Journal of Soil Science and Plant Nutrition | Saccharum officinarum | Saccharum_officinarum | Perennial | Poaceae | stalkborer | Diatraea saccharalis | Diatraea_saccharalis | Specialist | Boring arthropods | Feeding efficiency | 10.00 | 10.00 | 15.4411765 | 5.3823529 | 1.9411765 | 0.9705882 | 1 | 12 | 12.000 | 0.9292035 | 4.738938 | 0.3716814 | 3.716814e-01 |
| 583 | 121 | Oliva | 2021 | Journal of Soil Science and Plant Nutrition | Saccharum officinarum | Saccharum_officinarum | Perennial | Poaceae | stalkborer | Diatraea saccharalis | Diatraea_saccharalis | Specialist | Boring arthropods | Feeding efficiency | 10.00 | 10.00 | 15.4411765 | 4.3235294 | 1.9411765 | 1.3235294 | 1 | 12 | 12.000 | 0.9292035 | 5.792035 | 0.3716814 | 3.716814e-01 |
| 584 | 122 | Acevedo | 2021 | Frontiers in Plant Science | Zea mays | Zea_mays | Annual | Poaceae | fall armyworm | Spodoptera frugiperda | Spodoptera_frugiperda | Generalist | Chewing arthropods | Growth / Development | 29.00 | 29.00 | 20.4081633 | 10.4591837 | 11.6770155 | 6.8688327 | 1 | 29 | 29.000 | 189.6373057 | 450.777202 | 33.4828900 | 1.171901e+02 |
| 585 | 122 | Acevedo | 2021 | Frontiers in Plant Science | Zea mays | Zea_mays | Annual | Poaceae | fall armyworm | Spodoptera frugiperda | Spodoptera_frugiperda | Generalist | Chewing arthropods | Growth / Development | 29.00 | 29.00 | 24.0277778 | 19.5833333 | 9.7232142 | 11.2190933 | 1 | 29 | 29.000 | 189.6373057 | 450.777202 | 33.4828900 | 1.171901e+02 |
| 586 | 122 | Acevedo | 2021 | Frontiers in Plant Science | Glycine max | Glycine_max | Annual | Non-Poaceae | fall armyworm | Spodoptera frugiperda | Spodoptera_frugiperda | Generalist | Chewing arthropods | Growth / Development | 29.00 | 29.00 | 108.5714286 | 84.2857143 | 50.0051018 | 50.0051018 | 1 | 29 | 29.000 | 156.1403509 | 287.719298 | 47.2382878 | 2.834297e+01 |
| 587 | 122 | Acevedo | 2021 | Frontiers in Plant Science | Glycine max | Glycine_max | Annual | Non-Poaceae | fall armyworm | Spodoptera frugiperda | Spodoptera_frugiperda | Generalist | Chewing arthropods | Growth / Development | 29.00 | 29.00 | 19.1666667 | 18.4722222 | 7.4793956 | 8.9752747 | 1 | 29 | 29.000 | 156.1403509 | 287.719298 | 47.2382878 | 2.834297e+01 |
| 588 | 122 | Acevedo | 2021 | Frontiers in Plant Science | Solanum lycopersicum | Solanum_lycopersicum | Perennial | Non-Poaceae | fall armyworm | Spodoptera frugiperda | Spodoptera_frugiperda | Generalist | Chewing arthropods | Growth / Development | 29.00 | 29.00 | 39.6373057 | 36.2694301 | 15.3463246 | 18.1365654 | 1 | 29 | 29.000 | 55.1724138 | 72.183908 | 22.2834406 | 9.903751e+00 |
| 589 | 122 | Acevedo | 2021 | Frontiers in Plant Science | Solanum lycopersicum | Solanum_lycopersicum | Perennial | Non-Poaceae | fall armyworm | Spodoptera frugiperda | Spodoptera_frugiperda | Generalist | Chewing arthropods | Growth / Development | 29.00 | 29.00 | 6.6168224 | 5.8691589 | 5.2341789 | 4.0262914 | 1 | 29 | 29.000 | 55.1724138 | 72.183908 | 22.2834406 | 9.903751e+00 |
| 590 | 122 | Acevedo | 2021 | Frontiers in Plant Science | Zea mays | Zea_mays | Annual | Poaceae | fall armyworm | Spodoptera frugiperda | Spodoptera_frugiperda | Generalist | Chewing arthropods | Growth / Development | 29.00 | 29.00 | 14.6683673 | 6.1224490 | 6.8688327 | 2.7475331 | 1 | 29 | 29.000 | 220.7253886 | 506.735751 | 33.4828900 | 1.339316e+02 |
| 591 | 122 | Acevedo | 2021 | Frontiers in Plant Science | Zea mays | Zea_mays | Annual | Poaceae | fall armyworm | Spodoptera frugiperda | Spodoptera_frugiperda | Generalist | Chewing arthropods | Growth / Development | 29.00 | 29.00 | 26.3888889 | 18.8888889 | 11.2190933 | 14.9587911 | 1 | 29 | 29.000 | 220.7253886 | 506.735751 | 33.4828900 | 1.339316e+02 |
| 592 | 122 | Acevedo | 2021 | Frontiers in Plant Science | Glycine max | Glycine_max | Annual | Non-Poaceae | fall armyworm | Spodoptera frugiperda | Spodoptera_frugiperda | Generalist | Chewing arthropods | Growth / Development | 29.00 | 29.00 | 69.2857143 | 56.4285714 | 53.8516481 | 26.9258240 | 1 | 29 | 29.000 | 129.8245614 | 236.842105 | 18.8953151 | 6.613360e+01 |
| 593 | 122 | Acevedo | 2021 | Frontiers in Plant Science | Glycine max | Glycine_max | Annual | Non-Poaceae | fall armyworm | Spodoptera frugiperda | Spodoptera_frugiperda | Generalist | Chewing arthropods | Growth / Development | 29.00 | 29.00 | 23.8888889 | 26.8055556 | 9.7232142 | 9.7232142 | 1 | 29 | 29.000 | 129.8245614 | 236.842105 | 18.8953151 | 6.613360e+01 |
| 594 | 122 | Acevedo | 2021 | Frontiers in Plant Science | Solanum lycopersicum | Solanum_lycopersicum | Perennial | Non-Poaceae | fall armyworm | Spodoptera frugiperda | Spodoptera_frugiperda | Generalist | Chewing arthropods | Growth / Development | 29.00 | 29.00 | 4.4041451 | 4.9222798 | 2.7902408 | 2.7902408 | 1 | 29 | 29.000 | 49.1954023 | 71.724138 | 14.8556271 | 2.475938e+00 |
| 595 | 122 | Acevedo | 2021 | Frontiers in Plant Science | Solanum lycopersicum | Solanum_lycopersicum | Perennial | Non-Poaceae | fall armyworm | Spodoptera frugiperda | Spodoptera_frugiperda | Generalist | Chewing arthropods | Growth / Development | 29.00 | 29.00 | 3.3271028 | 3.1775701 | 3.6236623 | 2.4157749 | 1 | 29 | 29.000 | 49.1954023 | 71.724138 | 14.8556271 | 2.475938e+00 |
| 596 | 123 | Haq | 2022 | Silicon | Zea mays | Zea_mays | Annual | Poaceae | fall armyworm | Spodoptera frugiperda | Spodoptera_frugiperda | Generalist | Chewing arthropods | Abundance / Preference | 50.00 | 50.00 | 0.1250000 | 0.1050000 | 0.0353553 | 0.0424264 | 1 | 15 | 15.000 | 2621.8300000 | 3198.330000 | 14.8556271 | 2.475938e+00 |
| 597 | 123 | Haq | 2022 | Silicon | Zea mays | Zea_mays | Annual | Poaceae | fall armyworm | Spodoptera frugiperda | Spodoptera_frugiperda | Generalist | Chewing arthropods | Reproduction | 50.00 | 50.00 | 1.1300000 | 1.1100000 | 0.0353553 | 0.0424264 | 1 | 15 | 15.000 | 2621.8300000 | 3198.330000 | 14.8556271 | 2.475938e+00 |
| 598 | 123 | Haq | 2022 | Silicon | Zea mays | Zea_mays | Annual | Poaceae | fall armyworm | Spodoptera frugiperda | Spodoptera_frugiperda | Generalist | Chewing arthropods | Reproduction | 50.00 | 50.00 | 83.5600000 | 49.6400000 | 102.9547473 | 75.9432683 | 1 | 15 | 15.000 | 2621.8300000 | 3198.330000 | 14.8556271 | 2.475938e+00 |
| 599 | 123 | Haq | 2022 | Silicon | Zea mays | Zea_mays | Annual | Poaceae | fall armyworm | Spodoptera frugiperda | Spodoptera_frugiperda | Generalist | Chewing arthropods | Growth / Development | 50.00 | 50.00 | 35.3400000 | 36.9900000 | 1.9091883 | 1.2727922 | 1 | 15 | 15.000 | 2621.8300000 | 3198.330000 | 14.8556271 | 2.475938e+00 |
| 600 | 123 | Haq | 2022 | Silicon | Zea mays | Zea_mays | Annual | Poaceae | fall armyworm | Spodoptera frugiperda | Spodoptera_frugiperda | Generalist | Chewing arthropods | Reproduction | 50.00 | 50.00 | 91.3000000 | 71.4300000 | 108.6823123 | 99.6313455 | 1 | 15 | 15.000 | 2621.8300000 | 3198.330000 | 14.8556271 | 2.475938e+00 |
| 601 | 123 | Haq | 2022 | Silicon | Zea mays | Zea_mays | Annual | Poaceae | fall armyworm | Spodoptera frugiperda | Spodoptera_frugiperda | Generalist | Chewing arthropods | Reproduction | 50.00 | 50.00 | 208.9000000 | 165.4700000 | 28.8499567 | 18.6676190 | 1 | 15 | 15.000 | 2621.8300000 | 3198.330000 | 14.8556271 | 2.475938e+00 |
| 602 | 123 | Haq | 2022 | Silicon | Zea mays | Zea_mays | Annual | Poaceae | fall armyworm | Spodoptera frugiperda | Spodoptera_frugiperda | Generalist | Chewing arthropods | Abundance / Preference | 50.00 | 50.00 | 0.1250000 | 0.0900000 | 0.0353553 | 0.0353553 | 1 | 15 | 15.000 | 2621.8300000 | 3183.660000 | 14.8556271 | 2.475938e+00 |
| 603 | 123 | Haq | 2022 | Silicon | Zea mays | Zea_mays | Annual | Poaceae | fall armyworm | Spodoptera frugiperda | Spodoptera_frugiperda | Generalist | Chewing arthropods | Reproduction | 50.00 | 50.00 | 1.1300000 | 1.1000000 | 0.0353553 | 0.0424264 | 1 | 15 | 15.000 | 2621.8300000 | 3183.660000 | 14.8556271 | 2.475938e+00 |
| 604 | 123 | Haq | 2022 | Silicon | Zea mays | Zea_mays | Annual | Poaceae | fall armyworm | Spodoptera frugiperda | Spodoptera_frugiperda | Generalist | Chewing arthropods | Reproduction | 50.00 | 50.00 | 83.5600000 | 46.0400000 | 102.9547473 | 71.2056529 | 1 | 15 | 15.000 | 2621.8300000 | 3183.660000 | 14.8556271 | 2.475938e+00 |
| 605 | 123 | Haq | 2022 | Silicon | Zea mays | Zea_mays | Annual | Poaceae | fall armyworm | Spodoptera frugiperda | Spodoptera_frugiperda | Generalist | Chewing arthropods | Growth / Development | 50.00 | 50.00 | 35.3400000 | 39.4600000 | 1.9091883 | 2.1920310 | 1 | 15 | 15.000 | 2621.8300000 | 3183.660000 | 14.8556271 | 2.475938e+00 |
| 606 | 123 | Haq | 2022 | Silicon | Zea mays | Zea_mays | Annual | Poaceae | fall armyworm | Spodoptera frugiperda | Spodoptera_frugiperda | Generalist | Chewing arthropods | Reproduction | 50.00 | 50.00 | 91.3000000 | 75.1500000 | 108.6823123 | 102.0355085 | 1 | 15 | 15.000 | 2621.8300000 | 3183.660000 | 14.8556271 | 2.475938e+00 |
| 607 | 123 | Haq | 2022 | Silicon | Zea mays | Zea_mays | Annual | Poaceae | fall armyworm | Spodoptera frugiperda | Spodoptera_frugiperda | Generalist | Chewing arthropods | Reproduction | 50.00 | 50.00 | 208.9000000 | 153.4700000 | 28.8499567 | 31.4662518 | 1 | 15 | 15.000 | 2621.8300000 | 3183.660000 | 14.8556271 | 2.475938e+00 |
| 608 | 124 | Haq | 2022 | JOURNAL OF KING SAUD UNIVERSITY SCIENCE | Zea mays | Zea_mays | Annual | Poaceae | fall armyworm | Spodoptera frugiperda | Spodoptera_frugiperda | Generalist | Chewing arthropods | Mortality / Survivability | 3.00 | 3.00 | 3.6700000 | 11.7800000 | 1.4202817 | 11.5527789 | -1 | NA | NA | NA | NA | NA | NA |
| 609 | 124 | Haq | 2022 | JOURNAL OF KING SAUD UNIVERSITY SCIENCE | Zea mays | Zea_mays | Annual | Poaceae | fall armyworm | Spodoptera frugiperda | Spodoptera_frugiperda | Generalist | Chewing arthropods | Mortality / Survivability | 3.00 | 3.00 | 1.0000000 | 7.3300000 | 1.8879354 | 8.2965234 | -1 | NA | NA | NA | NA | NA | NA |
| 610 | 124 | Haq | 2022 | JOURNAL OF KING SAUD UNIVERSITY SCIENCE | Zea mays | Zea_mays | Annual | Poaceae | fall armyworm | Spodoptera frugiperda | Spodoptera_frugiperda | Generalist | Chewing arthropods | Mortality / Survivability | 3.00 | 3.00 | 4.6700000 | 22.8900000 | 1.7840123 | 25.0454547 | -1 | NA | NA | NA | NA | NA | NA |
| 611 | 124 | Haq | 2022 | JOURNAL OF KING SAUD UNIVERSITY SCIENCE | Zea mays | Zea_mays | Annual | Poaceae | fall armyworm | Spodoptera frugiperda | Spodoptera_frugiperda | Generalist | Chewing arthropods | Growth / Development | 3.00 | 3.00 | 95.3300000 | 77.1100000 | 1.7840123 | 25.0454547 | 1 | NA | NA | NA | NA | NA | NA |
| 612 | 124 | Haq | 2022 | JOURNAL OF KING SAUD UNIVERSITY SCIENCE | Zea mays | Zea_mays | Annual | Poaceae | fall armyworm | Spodoptera frugiperda | Spodoptera_frugiperda | Generalist | Chewing arthropods | Reproduction | 3.00 | 3.00 | 192.5000000 | 175.0000000 | 4.9883063 | 24.7336855 | 1 | NA | NA | NA | NA | NA | NA |
| 613 | 124 | Haq | 2022 | JOURNAL OF KING SAUD UNIVERSITY SCIENCE | Zea mays | Zea_mays | Annual | Poaceae | fall armyworm | Spodoptera frugiperda | Spodoptera_frugiperda | Generalist | Chewing arthropods | Growth / Development | 3.00 | 3.00 | 3.8300000 | 3.6700000 | 0.6928203 | 0.8660254 | -1 | NA | NA | NA | NA | NA | NA |
| 614 | 125 | Leroy | 2022 | JOURNAL OF CHEMICAL ECOLOGY | Zea mays | Zea_mays | Annual | Poaceae | beet armyworm | Spodoptera exigua | Spodoptera_exigua | Generalist | Chewing arthropods | Reproduction | 15.00 | 15.00 | 152.0000000 | 140.0000000 | 104.5705503 | 116.1895004 | 1 | 3 | 3.000 | 0.3100000 | 9.560000 | 0.0692820 | 5.196152e-01 |
| 615 | 126 | Pelosi | 2022 | Arthropod-Plant Interactions | Oryza sativa | Oryza_sativa | Annual | Poaceae | Sugarcane borer | Diatraea saccharalis | Diatraea_saccharalis | Specialist | Chewing arthropods | Mortality / Survivability | 5.00 | 5.00 | 2.7533333 | 2.0800000 | 0.5888312 | 1.4161764 | 1 | 5 | 5.000 | 1.1900000 | 1.890000 | 0.1788854 | 2.906888e-01 |
| 616 | 126 | Pelosi | 2022 | Arthropod-Plant Interactions | Oryza sativa | Oryza_sativa | Annual | Poaceae | Sugarcane borer | Diatraea saccharalis | Diatraea_saccharalis | Specialist | Chewing arthropods | Growth / Development | 5.00 | 5.00 | 0.0443333 | 0.0163333 | 0.0216153 | 0.0104350 | 1 | 5 | 5.000 | 1.1900000 | 1.890000 | 0.1788854 | 2.906888e-01 |
| 617 | 126 | Pelosi | 2022 | Arthropod-Plant Interactions | Oryza sativa | Oryza_sativa | Annual | Poaceae | Sugarcane borer | Diatraea saccharalis | Diatraea_saccharalis | Specialist | Chewing arthropods | Growth / Development | 5.00 | 5.00 | 1.4933333 | 1.3133333 | 0.0372678 | 0.1863390 | 1 | 5 | 5.000 | 1.1900000 | 1.890000 | 0.1788854 | 2.906888e-01 |
| 618 | 126 | Pelosi | 2022 | Arthropod-Plant Interactions | Oryza sativa | Oryza_sativa | Annual | Poaceae | Sugarcane borer | Diatraea saccharalis | Diatraea_saccharalis | Specialist | Chewing arthropods | Feeding efficiency | 5.00 | 5.00 | 67.1100000 | 60.9766667 | 22.7184507 | 31.3794873 | 1 | 5 | 5.000 | 1.1900000 | 1.890000 | 0.1788854 | 2.906888e-01 |
| 619 | 126 | Pelosi | 2022 | Arthropod-Plant Interactions | Oryza sativa | Oryza_sativa | Annual | Poaceae | Sugarcane borer | Diatraea saccharalis | Diatraea_saccharalis | Specialist | Chewing arthropods | Feeding efficiency | 5.00 | 5.00 | 35.7500000 | 28.9100000 | 5.9479408 | 6.3951544 | 1 | 5 | 5.000 | 1.1900000 | 1.890000 | 0.1788854 | 2.906888e-01 |
| 620 | 127 | Abbasi | 2022 | Sustainability | Gossypium hirsutum | Gossypium_hirsutum | Perennial | Non-Poaceae | Whitefly | Bemisia tabaci | Bemisia_tabaci | Generalist | Fluid-feeding arthropods | Reproduction | 9.00 | 9.00 | 119.7900000 | 71.4700000 | 14.3100000 | 6.7800000 | 1 | 9 | 9.000 | 85.3500000 | 793.830000 | 53.6400000 | 1.106400e+02 |
| 621 | 127 | Abbasi | 2022 | Sustainability | Gossypium hirsutum | Gossypium_hirsutum | Perennial | Non-Poaceae | Whitefly | Bemisia tabaci | Bemisia_tabaci | Generalist | Fluid-feeding arthropods | Growth / Development | 9.00 | 9.00 | 1.8700000 | 2.8500000 | 0.3000000 | 0.3600000 | -1 | 9 | 9.000 | 85.3500000 | 793.830000 | 53.6400000 | 1.106400e+02 |
| 622 | 127 | Abbasi | 2022 | Sustainability | Gossypium hirsutum | Gossypium_hirsutum | Perennial | Non-Poaceae | Whitefly | Bemisia tabaci | Bemisia_tabaci | Generalist | Fluid-feeding arthropods | Growth / Development | 9.00 | 9.00 | 2.0600000 | 3.0000000 | 0.3000000 | 0.3000000 | -1 | 9 | 9.000 | 85.3500000 | 793.830000 | 53.6400000 | 1.106400e+02 |
| 623 | 127 | Abbasi | 2022 | Sustainability | Gossypium hirsutum | Gossypium_hirsutum | Perennial | Non-Poaceae | Whitefly | Bemisia tabaci | Bemisia_tabaci | Generalist | Fluid-feeding arthropods | Growth / Development | 9.00 | 9.00 | 2.1700000 | 2.8300000 | 0.3900000 | 0.1500000 | -1 | 9 | 9.000 | 85.3500000 | 793.830000 | 53.6400000 | 1.106400e+02 |
| 624 | 127 | Abbasi | 2022 | Sustainability | Gossypium hirsutum | Gossypium_hirsutum | Perennial | Non-Poaceae | Whitefly | Bemisia tabaci | Bemisia_tabaci | Generalist | Fluid-feeding arthropods | Growth / Development | 9.00 | 9.00 | 9.8800000 | 14.0200000 | 0.5700000 | 1.0200000 | -1 | 9 | 9.000 | 85.3500000 | 793.830000 | 53.6400000 | 1.106400e+02 |
| 625 | 128 | Nuambote-Yobila | 2022 | PHYTOPARASITICA | Zea mays | Zea_mays | Annual | Poaceae | fall armyworm | Spodoptera frugiperda | Spodoptera_frugiperda | Generalist | Chewing arthropods | Growth / Development | 12.00 | 12.00 | 1.4900000 | 1.5700000 | 0.7274613 | 1.7320508 | 1 | 12 | 12.000 | 0.6820000 | 1.157000 | 0.2424871 | 2.771281e-01 |
| 626 | 128 | Nuambote-Yobila | 2022 | PHYTOPARASITICA | Zea mays | Zea_mays | Annual | Poaceae | fall armyworm | Spodoptera frugiperda | Spodoptera_frugiperda | Generalist | Chewing arthropods | Growth / Development | 12.00 | 12.00 | 7.1900000 | 5.4900000 | 3.2908965 | 2.3902301 | 1 | 12 | 12.000 | 0.6820000 | 1.157000 | 0.2424871 | 2.771281e-01 |
| 627 | 128 | Nuambote-Yobila | 2022 | PHYTOPARASITICA | Zea mays | Zea_mays | Annual | Poaceae | fall armyworm | Spodoptera frugiperda | Spodoptera_frugiperda | Generalist | Chewing arthropods | Mortality / Survivability | 12.00 | 12.00 | 7.9900000 | 4.5700000 | 7.9674337 | 6.0275368 | -1 | 12 | 12.000 | 0.6820000 | 1.157000 | 0.2424871 | 2.771281e-01 |
| 628 | 128 | Nuambote-Yobila | 2022 | PHYTOPARASITICA | Zea mays | Zea_mays | Annual | Poaceae | fall armyworm | Spodoptera frugiperda | Spodoptera_frugiperda | Generalist | Chewing arthropods | Mortality / Survivability | 12.00 | 12.00 | 6.7200000 | 5.6900000 | 5.8889727 | 3.1176915 | -1 | 12 | 12.000 | 0.6820000 | 1.157000 | 0.2424871 | 2.771281e-01 |
| 629 | 129 | Vu | 2022 | Insects | Oryza sativa | Oryza_sativa | Annual | Poaceae | Brown planthopper | Nilaparvata lugens | Nilaparvata_lugens | Specialist | Fluid-feeding arthropods | Mortality / Survivability | 6.00 | 6.00 | 0.8900000 | 0.7300000 | 0.1224745 | 0.1714643 | 1 | NA | NA | NA | NA | NA | NA |
| 630 | 129 | Vu | 2022 | Insects | Oryza sativa | Oryza_sativa | Annual | Poaceae | Brown planthopper | Nilaparvata lugens | Nilaparvata_lugens | Specialist | Fluid-feeding arthropods | Growth / Development | 6.00 | 6.00 | 6.6100000 | 5.1300000 | 2.2045408 | 1.2982296 | 1 | NA | NA | NA | NA | NA | NA |
| 631 | 129 | Vu | 2022 | Insects | Oryza sativa | Oryza_sativa | Annual | Poaceae | Brown planthopper | Nilaparvata lugens | Nilaparvata_lugens | Specialist | Fluid-feeding arthropods | Reproduction | 6.00 | 6.00 | 120.9300000 | 94.5900000 | 38.9468869 | 36.5218921 | 1 | NA | NA | NA | NA | NA | NA |
| 632 | 129 | Vu | 2022 | Insects | Oryza sativa | Oryza_sativa | Annual | Poaceae | Brown planthopper | Nilaparvata lugens | Nilaparvata_lugens | Specialist | Fluid-feeding arthropods | Feeding efficiency | 6.00 | 6.00 | 0.9000000 | 0.3000000 | 0.0244949 | 0.0244949 | 1 | NA | NA | NA | NA | NA | NA |
| 633 | 129 | Vu | 2022 | Insects | Oryza sativa | Oryza_sativa | Annual | Poaceae | Brown planthopper | Nilaparvata lugens | Nilaparvata_lugens | Specialist | Fluid-feeding arthropods | Abundance / Preference | 6.00 | 6.00 | 0.0900000 | 0.0700000 | 0.0734847 | 0.0489898 | 1 | NA | NA | NA | NA | NA | NA |
| 634 | 129 | Vu | 2022 | Insects | Oryza sativa | Oryza_sativa | Annual | Poaceae | Brown planthopper | Nilaparvata lugens | Nilaparvata_lugens | Specialist | Fluid-feeding arthropods | Growth / Development | 6.00 | 6.00 | 2.0600000 | 1.7800000 | 1.3717143 | 0.7838367 | 1 | NA | NA | NA | NA | NA | NA |
| 635 | 129 | Vu | 2022 | Insects | Oryza sativa | Oryza_sativa | Annual | Poaceae | Brown planthopper | Nilaparvata lugens | Nilaparvata_lugens | Specialist | Fluid-feeding arthropods | Mortality / Survivability | 6.00 | 6.00 | 0.6700000 | 0.0300000 | 0.5143928 | 0.1224745 | 1 | NA | NA | NA | NA | NA | NA |
| 636 | 129 | Vu | 2022 | Insects | Oryza sativa | Oryza_sativa | Annual | Poaceae | Brown planthopper | Nilaparvata lugens | Nilaparvata_lugens | Specialist | Fluid-feeding arthropods | Reproduction | 6.00 | 6.00 | 30.3200000 | 51.1400000 | 30.4716524 | 32.2352850 | 1 | NA | NA | NA | NA | NA | NA |
| 637 | 129 | Vu | 2022 | Insects | Oryza sativa | Oryza_sativa | Annual | Poaceae | Brown planthopper | Nilaparvata lugens | Nilaparvata_lugens | Specialist | Fluid-feeding arthropods | Abundance / Preference | 6.00 | 6.00 | 0.4100000 | 0.1400000 | 0.0979796 | 0.1714643 | 1 | NA | NA | NA | NA | NA | NA |
| 638 | 129 | Vu | 2022 | Insects | Oryza sativa | Oryza_sativa | Annual | Poaceae | Whitebacked plant hopper | Sogatella furcifera | Sogatella_furcifera | Specialist | Fluid-feeding arthropods | Mortality / Survivability | 6.00 | 6.00 | 0.5200000 | 0.5400000 | 0.3184337 | 0.2939388 | 1 | NA | NA | NA | NA | NA | NA |
| 639 | 129 | Vu | 2022 | Insects | Oryza sativa | Oryza_sativa | Annual | Poaceae | Whitebacked plant hopper | Sogatella furcifera | Sogatella_furcifera | Specialist | Fluid-feeding arthropods | Growth / Development | 6.00 | 6.00 | 0.6150000 | 0.6700000 | 0.3551760 | 0.1347219 | 1 | NA | NA | NA | NA | NA | NA |
| 640 | 129 | Vu | 2022 | Insects | Oryza sativa | Oryza_sativa | Annual | Poaceae | Whitebacked plant hopper | Sogatella furcifera | Sogatella_furcifera | Specialist | Fluid-feeding arthropods | Reproduction | 6.00 | 6.00 | 68.0000000 | 28.3350000 | 74.5012305 | 50.6064581 | 1 | NA | NA | NA | NA | NA | NA |
| 641 | 129 | Vu | 2022 | Insects | Oryza sativa | Oryza_sativa | Annual | Poaceae | Whitebacked plant hopper | Sogatella furcifera | Sogatella_furcifera | Specialist | Fluid-feeding arthropods | Abundance / Preference | 6.00 | 6.00 | 3.7500000 | 5.0000000 | 1.7758801 | 3.1475943 | 1 | NA | NA | NA | NA | NA | NA |
| 642 | 129 | Vu | 2022 | Insects | Oryza sativa | Oryza_sativa | Annual | Poaceae | Whitebacked plant hopper | Sogatella furcifera | Sogatella_furcifera | Specialist | Fluid-feeding arthropods | Abundance / Preference | 6.00 | 6.00 | 0.0450000 | 0.0550000 | 0.0244949 | 0.0367423 | 1 | NA | NA | NA | NA | NA | NA |
| 643 | 129 | Vu | 2022 | Insects | Oryza sativa | Oryza_sativa | Annual | Poaceae | Whitebacked plant hopper | Sogatella furcifera | Sogatella_furcifera | Specialist | Fluid-feeding arthropods | Reproduction | 6.00 | 6.00 | 44.1650000 | 11.6650000 | 56.8771518 | 28.6957723 | 1 | NA | NA | NA | NA | NA | NA |
| 644 | 129 | Vu | 2022 | Insects | Oryza sativa | Oryza_sativa | Annual | Poaceae | Green leaf hopper | Nephotettix virescens | Nephotettix_virescens | Specialist | Fluid-feeding arthropods | Mortality / Survivability | 6.00 | 6.00 | 0.3500000 | 0.3700000 | 0.1224745 | 0.2327015 | 1 | NA | NA | NA | NA | NA | NA |
| 645 | 129 | Vu | 2022 | Insects | Oryza sativa | Oryza_sativa | Annual | Poaceae | Green leaf hopper | Nephotettix virescens | Nephotettix_virescens | Specialist | Fluid-feeding arthropods | Growth / Development | 6.00 | 6.00 | 1.8350000 | 1.6600000 | 0.6736097 | 0.5388877 | 1 | NA | NA | NA | NA | NA | NA |
| 646 | 129 | Vu | 2022 | Insects | Oryza sativa | Oryza_sativa | Annual | Poaceae | Green leaf hopper | Nephotettix virescens | Nephotettix_virescens | Specialist | Fluid-feeding arthropods | Abundance / Preference | 6.00 | 6.00 | 5.7500000 | 6.0000000 | 2.0943137 | 2.1433035 | 1 | NA | NA | NA | NA | NA | NA |
| 647 | 130 | Annamalai | 2022 | Silicon | Oryza sativa | Oryza_sativa | Annual | Poaceae | Yellow stem borer | Scirpophaga incertulas | Scirpophaga_incertulas | Specialist | Boring arthropods | Feeding efficiency | 3.00 | 3.00 | 86.9700000 | 53.1300000 | 14.2547781 | 15.5884573 | 1 | 5 | 5.000 | 228.0500000 | 370.770000 | 23.7470419 | 1.826868e+01 |
| 648 | 130 | Annamalai | 2022 | Silicon | Oryza sativa | Oryza_sativa | Annual | Poaceae | Yellow stem borer | Scirpophaga incertulas | Scirpophaga_incertulas | Specialist | Boring arthropods | Feeding efficiency | 3.00 | 3.00 | 86.9700000 | 47.0000000 | 14.5838678 | 14.5838678 | 1 | 5 | 5.000 | 225.5800000 | 347.900000 | 25.5582570 | 1.462388e+01 |
| 649 | 130 | Annamalai | 2022 | Silicon | Oryza sativa | Oryza_sativa | Annual | Poaceae | Yellow stem borer | Scirpophaga incertulas | Scirpophaga_incertulas | Specialist | Boring arthropods | Feeding efficiency | 10.00 | 10.00 | 86.7300000 | 46.5000000 | 26.0571679 | 27.2904562 | 1 | 5 | 5.000 | 228.0500000 | 370.770000 | 23.7470419 | 1.826868e+01 |
| 650 | 130 | Annamalai | 2022 | Silicon | Oryza sativa | Oryza_sativa | Annual | Poaceae | Yellow stem borer | Scirpophaga incertulas | Scirpophaga_incertulas | Specialist | Boring arthropods | Feeding efficiency | 10.00 | 10.00 | 86.7300000 | 33.4700000 | 27.2904562 | 27.2588334 | 1 | 5 | 5.000 | 225.5800000 | 347.900000 | 25.5582570 | 1.462388e+01 |
| 651 | 130 | Annamalai | 2022 | Silicon | Oryza sativa | Oryza_sativa | Annual | Poaceae | Yellow stem borer | Scirpophaga incertulas | Scirpophaga_incertulas | Specialist | Boring arthropods | Growth / Development | 5.00 | 5.00 | 0.0900000 | 0.0200000 | 0.0447214 | 0.0223607 | 1 | 5 | 5.000 | 228.0500000 | 370.770000 | 23.7470419 | 1.826868e+01 |
| 652 | 130 | Annamalai | 2022 | Silicon | Oryza sativa | Oryza_sativa | Annual | Poaceae | Yellow stem borer | Scirpophaga incertulas | Scirpophaga_incertulas | Specialist | Boring arthropods | Growth / Development | 5.00 | 5.00 | 0.1900000 | 0.0500000 | 0.0447214 | 0.0223607 | 1 | 5 | 5.000 | 225.5800000 | 347.900000 | 25.5582570 | 1.462388e+01 |
| 653 | 130 | Annamalai | 2022 | Silicon | Oryza sativa | Oryza_sativa | Annual | Poaceae | Yellow stem borer | Scirpophaga incertulas | Scirpophaga_incertulas | Specialist | Boring arthropods | Mortality / Survivability | 3.00 | 3.00 | 11.2000000 | 77.8500000 | 17.7535208 | 17.7535208 | -1 | 5 | 5.000 | 228.0500000 | 370.770000 | 23.7470419 | 1.826868e+01 |
| 654 | 130 | Annamalai | 2022 | Silicon | Oryza sativa | Oryza_sativa | Annual | Poaceae | Yellow stem borer | Scirpophaga incertulas | Scirpophaga_incertulas | Specialist | Boring arthropods | Mortality / Survivability | 3.00 | 3.00 | 11.2000000 | 88.8600000 | 17.7535208 | 17.7535208 | -1 | 5 | 5.000 | 225.5800000 | 347.900000 | 25.5582570 | 1.462388e+01 |
| 655 | 131 | Rizwan | 2022 | Pakistan Journal of Zoology | Oryza sativa | Oryza_sativa | Annual | Poaceae | Rice leaffolder | Cnaphalocrocis medinalis | Cnaphalocrocis_medinalis | Specialist | Chewing arthropods | Growth / Development | 3.00 | 3.00 | 18.0900000 | 15.1500000 | 26.2405697 | 1.5934867 | -1 | NA | NA | NA | NA | NA | NA |
| 656 | 131 | Rizwan | 2022 | Pakistan Journal of Zoology | Oryza sativa | Oryza_sativa | Annual | Poaceae | Rice leaffolder | Cnaphalocrocis medinalis | Cnaphalocrocis_medinalis | Specialist | Chewing arthropods | Mortality / Survivability | 3.00 | 3.00 | 39.5200000 | 23.1700000 | 5.9928958 | 2.9964479 | 1 | NA | NA | NA | NA | NA | NA |
| 657 | 131 | Rizwan | 2022 | Pakistan Journal of Zoology | Oryza sativa | Oryza_sativa | Annual | Poaceae | Rice leaffolder | Cnaphalocrocis medinalis | Cnaphalocrocis_medinalis | Specialist | Chewing arthropods | Growth / Development | 3.00 | 3.00 | 55.0500000 | 21.5800000 | 0.9006664 | 1.1951151 | 1 | NA | NA | NA | NA | NA | NA |
| 658 | 131 | Rizwan | 2022 | Pakistan Journal of Zoology | Oryza sativa | Oryza_sativa | Annual | Poaceae | Rice leaffolder | Cnaphalocrocis medinalis | Cnaphalocrocis_medinalis | Specialist | Chewing arthropods | Growth / Development | 3.00 | 3.00 | 5.0000000 | 5.1100000 | 0.0692820 | 0.0692820 | -1 | NA | NA | NA | NA | NA | NA |
| 659 | 131 | Rizwan | 2022 | Pakistan Journal of Zoology | Oryza sativa | Oryza_sativa | Annual | Poaceae | Rice leaffolder | Cnaphalocrocis medinalis | Cnaphalocrocis_medinalis | Specialist | Chewing arthropods | Growth / Development | 3.00 | 3.00 | 55.0900000 | 32.9400000 | 9.1105872 | 4.1915630 | 1 | NA | NA | NA | NA | NA | NA |
| 660 | 131 | Rizwan | 2022 | Pakistan Journal of Zoology | Oryza sativa | Oryza_sativa | Annual | Poaceae | Rice leaffolder | Cnaphalocrocis medinalis | Cnaphalocrocis_medinalis | Specialist | Chewing arthropods | Growth / Development | 3.00 | 3.00 | 21.5600000 | 17.5700000 | 0.3810512 | 1.0045895 | 1 | NA | NA | NA | NA | NA | NA |
| 661 | 131 | Rizwan | 2022 | Pakistan Journal of Zoology | Oryza sativa | Oryza_sativa | Annual | Poaceae | Rice leaffolder | Cnaphalocrocis medinalis | Cnaphalocrocis_medinalis | Specialist | Chewing arthropods | Feeding efficiency | 3.00 | 3.00 | 10.0900000 | 24.8200000 | 4.0183579 | 4.0183579 | 1 | NA | NA | NA | NA | NA | NA |
| 662 | 132 | Leroy | 2022 | Entomologia Generalis | Zea mays | Zea_mays | Annual | Poaceae | fall armyworm | Spodoptera frugiperda | Spodoptera_frugiperda | Generalist | Chewing arthropods | Feeding efficiency | 50.00 | 50.00 | 0.8600000 | 0.8000000 | 0.7071068 | 0.6363961 | 1 | NA | NA | NA | NA | NA | NA |
| 663 | 132 | Leroy | 2022 | Entomologia Generalis | Zea mays | Zea_mays | Annual | Poaceae | fall armyworm | Spodoptera frugiperda | Spodoptera_frugiperda | Generalist | Chewing arthropods | Feeding efficiency | 50.00 | 50.00 | 7.3300000 | 4.9800000 | 3.4648232 | 2.4041631 | 1 | 5 | 5.000 | 0.2130000 | 8.459000 | 0.0670820 | 1.366237e+00 |
| 664 | 132 | Leroy | 2022 | Entomologia Generalis | Zea mays | Zea_mays | Annual | Poaceae | fall armyworm | Spodoptera frugiperda | Spodoptera_frugiperda | Generalist | Chewing arthropods | Growth / Development | 50.00 | 50.00 | 0.5700000 | 0.3800000 | 0.2828427 | 0.2828427 | 1 | 5 | 5.000 | 0.2130000 | 8.459000 | 0.0670820 | 1.366237e+00 |
| 665 | 132 | Leroy | 2022 | Entomologia Generalis | Zea mays | Zea_mays | Annual | Poaceae | fall armyworm | Spodoptera frugiperda | Spodoptera_frugiperda | Generalist | Chewing arthropods | Feeding efficiency | 50.00 | 50.00 | 0.8900000 | 0.8900000 | 0.0707107 | 0.0707107 | 1 | 5 | 5.000 | 0.2130000 | 8.459000 | 0.0670820 | 1.366237e+00 |
| 666 | 132 | Leroy | 2022 | Entomologia Generalis | Zea mays | Zea_mays | Annual | Poaceae | fall armyworm | Spodoptera frugiperda | Spodoptera_frugiperda | Generalist | Chewing arthropods | Growth / Development | 29.00 | 20.00 | 18.0700000 | 21.3900000 | 1.5616978 | 0.9391486 | -1 | 5 | 5.000 | 0.2130000 | 8.459000 | 0.0670820 | 1.366237e+00 |
| 667 | 132 | Leroy | 2022 | Entomologia Generalis | Zea mays | Zea_mays | Annual | Poaceae | fall armyworm | Spodoptera frugiperda | Spodoptera_frugiperda | Generalist | Chewing arthropods | Growth / Development | 38.00 | 39.00 | 75.5400000 | 60.8900000 | 9.9863507 | 16.7990446 | 1 | 5 | 5.000 | 0.2130000 | 8.459000 | 0.0670820 | 1.366237e+00 |
| 668 | 132 | Leroy | 2022 | Entomologia Generalis | Zea mays | Zea_mays | Annual | Poaceae | fall armyworm | Spodoptera frugiperda | Spodoptera_frugiperda | Generalist | Chewing arthropods | Growth / Development | 38.00 | 39.00 | 9.4100000 | 9.5700000 | 0.8630180 | 1.6861495 | -1 | 5 | 5.000 | 0.2130000 | 8.459000 | 0.0670820 | 1.366237e+00 |
| 669 | 132 | Leroy | 2022 | Entomologia Generalis | Zea mays | Zea_mays | Annual | Poaceae | fall armyworm | Spodoptera frugiperda | Spodoptera_frugiperda | Generalist | Chewing arthropods | Growth / Development | 29.00 | 20.00 | 23.6000000 | 19.4200000 | 3.2310989 | 2.5043961 | 1 | 5 | 5.000 | 0.2130000 | 8.459000 | 0.0670820 | 1.366237e+00 |
| 670 | 133 | Sousa | 2022 | Journal of Pest Science | Zea mays | Zea_mays | Annual | Poaceae | fall armyworm | Spodoptera frugiperda | Spodoptera_frugiperda | Generalist | Chewing arthropods | Feeding efficiency | 20.00 | 20.00 | 7.1600000 | 5.9500000 | 6.1715476 | 7.3790243 | 1 | 3 | 3.000 | 1.4000000 | 1.900000 | 0.1039230 | 1.039230e-01 |
| 671 | 133 | Sousa | 2022 | Journal of Pest Science | Zea mays | Zea_mays | Annual | Poaceae | fall armyworm | Spodoptera frugiperda | Spodoptera_frugiperda | Generalist | Chewing arthropods | Feeding efficiency | 20.00 | 20.00 | 5.6700000 | 5.7500000 | 1.6994117 | 2.1913466 | 1 | NA | NA | NA | NA | NA | NA |
| 672 | 134 | Lin | 2022 | Journal of Pest Science | Oryza sativa | Oryza_sativa | Annual | Poaceae | Brown planthopper | Nilaparvata lugens | Nilaparvata_lugens | Specialist | Fluid-feeding arthropods | Abundance / Preference | 20.00 | 20.00 | 9.3400000 | 5.9100000 | 2.6385602 | 3.2199379 | 1 | 6 | 6.000 | 9.3500000 | 30.600000 | 0.8573214 | 1.347219e+00 |
| 673 | 134 | Lin | 2022 | Journal of Pest Science | Oryza sativa | Oryza_sativa | Annual | Poaceae | Brown planthopper | Nilaparvata lugens | Nilaparvata_lugens | Specialist | Fluid-feeding arthropods | Feeding efficiency | 10.00 | 10.00 | 9.5300000 | 5.2700000 | 3.5733738 | 1.2649111 | 1 | 6 | 6.000 | 9.3500000 | 30.600000 | 0.8573214 | 1.347219e+00 |
| 674 | 134 | Lin | 2022 | Journal of Pest Science | Oryza sativa | Oryza_sativa | Annual | Poaceae | Brown planthopper | Nilaparvata lugens | Nilaparvata_lugens | Specialist | Fluid-feeding arthropods | Reproduction | 5.00 | 5.00 | 85.8300000 | 34.4800000 | 7.3119423 | 6.1939083 | 1 | 6 | 6.000 | 9.3500000 | 30.600000 | 0.8573214 | 1.347219e+00 |
| 675 | 135 | Perdomo | 2022 | BRAGANTIA | Zea mays | Zea_mays | Annual | Poaceae | fall armyworm | Spodoptera frugiperda | Spodoptera_frugiperda | Generalist | Chewing arthropods | Feeding efficiency | 4.00 | 4.00 | 1.7000000 | 0.6700000 | 0.5000000 | 0.2400000 | 1 | NA | NA | NA | NA | NA | NA |
| 676 | 135 | Perdomo | 2022 | BRAGANTIA | Zea mays | Zea_mays | Annual | Poaceae | fall armyworm | Spodoptera frugiperda | Spodoptera_frugiperda | Generalist | Chewing arthropods | Feeding efficiency | 4.00 | 4.00 | 0.8900000 | 1.0500000 | 0.6000000 | 0.2800000 | 1 | NA | NA | NA | NA | NA | NA |
| 677 | 135 | Perdomo | 2022 | BRAGANTIA | Zea mays | Zea_mays | Annual | Poaceae | fall armyworm | Spodoptera frugiperda | Spodoptera_frugiperda | Generalist | Chewing arthropods | Feeding efficiency | 4.00 | 4.00 | 3.8800000 | 1.8400000 | 0.2200000 | 0.1200000 | 1 | NA | NA | NA | NA | NA | NA |
| 678 | 135 | Perdomo | 2022 | BRAGANTIA | Zea mays | Zea_mays | Annual | Poaceae | fall armyworm | Spodoptera frugiperda | Spodoptera_frugiperda | Generalist | Chewing arthropods | Feeding efficiency | 4.00 | 4.00 | 2.0300000 | 1.4500000 | 1.4400000 | 0.3600000 | 1 | NA | NA | NA | NA | NA | NA |
| 679 | 136 | Bhavanam | 2021 | Plants | Oryza sativa | Oryza_sativa | Annual | Poaceae | fall armyworm | Spodoptera frugiperda | Spodoptera_frugiperda | Generalist | Chewing arthropods | Growth / Development | 8.00 | 8.00 | 119.6100000 | 89.1200000 | 46.8953217 | 41.4081731 | 1 | NA | NA | NA | NA | NA | NA |
| 680 | 136 | Bhavanam | 2021 | Plants | Oryza sativa | Oryza_sativa | Annual | Poaceae | fall armyworm | Spodoptera frugiperda | Spodoptera_frugiperda | Generalist | Chewing arthropods | Growth / Development | 8.00 | 8.00 | 81.8100000 | 48.8500000 | 19.9121270 | 7.3256263 | 1 | NA | NA | NA | NA | NA | NA |
| 681 | 137 | Santos-Cividanes | 2022 | Journal of Pest Science | Saccharum spp. | Saccharum_spp. | Perennial | Poaceae | Sugarcane borer | Diatraea saccharalis | Diatraea_saccharalis | Specialist | Boring arthropods | Feeding efficiency | 5.00 | 5.00 | 6.0000000 | 4.3800000 | 1.5205262 | 1.0285913 | 1 | 12 | 12.000 | 0.2300000 | 0.280000 | 0.0346410 | 3.464100e-02 |
| 682 | 137 | Santos-Cividanes | 2022 | Journal of Pest Science | Saccharum spp. | Saccharum_spp. | Perennial | Poaceae | Sugarcane borer | Diatraea saccharalis | Diatraea_saccharalis | Specialist | Boring arthropods | Feeding efficiency | 5.00 | 5.00 | 6.3200000 | 5.0100000 | 1.6099689 | 1.2298374 | 1 | 12 | 12.000 | 0.2200000 | 0.210000 | 0.0692820 | 6.928200e-02 |
| 683 | 137 | Santos-Cividanes | 2022 | Journal of Pest Science | Saccharum spp. | Saccharum_spp. | Perennial | Poaceae | Sugarcane borer | Diatraea saccharalis | Diatraea_saccharalis | Specialist | Boring arthropods | Feeding efficiency | 5.00 | 5.00 | 18.1800000 | 13.6200000 | 2.9963311 | 2.1242646 | 1 | 12 | 12.000 | 0.2300000 | 0.280000 | 0.0346410 | 3.464100e-02 |
| 684 | 137 | Santos-Cividanes | 2022 | Journal of Pest Science | Saccharum spp. | Saccharum_spp. | Perennial | Poaceae | Sugarcane borer | Diatraea saccharalis | Diatraea_saccharalis | Specialist | Boring arthropods | Feeding efficiency | 5.00 | 5.00 | 16.5100000 | 13.6500000 | 2.3031500 | 1.7217723 | 1 | 12 | 12.000 | 0.2200000 | 0.210000 | 0.0692820 | 6.928200e-02 |
| 685 | 137 | Santos-Cividanes | 2022 | Journal of Pest Science | Saccharum spp. | Saccharum_spp. | Perennial | Poaceae | Sugarcane borer | Diatraea saccharalis | Diatraea_saccharalis | Specialist | Boring arthropods | Growth / Development | 5.00 | 5.00 | 49.9000000 | 57.1100000 | 15.5183118 | 9.8163384 | 1 | NA | NA | NA | NA | NA | NA |
| 686 | 137 | Santos-Cividanes | 2022 | Journal of Pest Science | Saccharum spp. | Saccharum_spp. | Perennial | Poaceae | Sugarcane borer | Diatraea saccharalis | Diatraea_saccharalis | Specialist | Boring arthropods | Growth / Development | 5.00 | 5.00 | 16.6000000 | 16.4500000 | 1.4310835 | 1.4534442 | 1 | NA | NA | NA | NA | NA | NA |
| 687 | 138 | Javvaji | 2021 | Entomologia Experimentalis et Applicata | Oryza sativa | Oryza_sativa | Annual | Poaceae | Rice leaffolder | Cnaphalocrocis medinalis | Cnaphalocrocis_medinalis | Specialist | Chewing arthropods | Feeding efficiency | 3.00 | 3.00 | 465.2100000 | 107.2200000 | 2.7712813 | 2.1131020 | 1 | NA | NA | NA | NA | NA | NA |
| 688 | 138 | Javvaji | 2021 | Entomologia Experimentalis et Applicata | Oryza sativa | Oryza_sativa | Annual | Poaceae | Rice leaffolder | Cnaphalocrocis medinalis | Cnaphalocrocis_medinalis | Specialist | Chewing arthropods | Reproduction | 3.00 | 3.00 | 157.6100000 | 62.0500000 | 8.1233183 | 3.2562555 | 1 | NA | NA | NA | NA | NA | NA |
| 689 | 138 | Javvaji | 2021 | Entomologia Experimentalis et Applicata | Oryza sativa | Oryza_sativa | Annual | Poaceae | Rice leaffolder | Cnaphalocrocis medinalis | Cnaphalocrocis_medinalis | Specialist | Chewing arthropods | Reproduction | 3.00 | 3.00 | 178.0100000 | 92.3600000 | 20.5940841 | 5.4213190 | 1 | NA | NA | NA | NA | NA | NA |
| 690 | 138 | Javvaji | 2021 | Entomologia Experimentalis et Applicata | Oryza sativa | Oryza_sativa | Annual | Poaceae | Rice leaffolder | Cnaphalocrocis medinalis | Cnaphalocrocis_medinalis | Specialist | Chewing arthropods | Abundance / Preference | 5.00 | 5.00 | 33.7400000 | 19.7600000 | 0.8049845 | 1.2521981 | 1 | NA | NA | NA | NA | NA | NA |
| 691 | 138 | Javvaji | 2021 | Entomologia Experimentalis et Applicata | Oryza sativa | Oryza_sativa | Annual | Poaceae | Rice leaffolder | Cnaphalocrocis medinalis | Cnaphalocrocis_medinalis | Specialist | Chewing arthropods | Feeding efficiency | 3.00 | 3.00 | 1.2000000 | 3.1300000 | 0.0866025 | 0.0346410 | -1 | NA | NA | NA | NA | NA | NA |
| 692 | 138 | Javvaji | 2021 | Entomologia Experimentalis et Applicata | Oryza sativa | Oryza_sativa | Annual | Poaceae | Rice leaffolder | Cnaphalocrocis medinalis | Cnaphalocrocis_medinalis | Specialist | Chewing arthropods | Feeding efficiency | 3.00 | 3.00 | 3.7200000 | 2.2800000 | 0.1212436 | 0.1558846 | 1 | NA | NA | NA | NA | NA | NA |
| 693 | 138 | Javvaji | 2021 | Entomologia Experimentalis et Applicata | Oryza sativa | Oryza_sativa | Annual | Poaceae | Rice leaffolder | Cnaphalocrocis medinalis | Cnaphalocrocis_medinalis | Specialist | Chewing arthropods | Feeding efficiency | 3.00 | 3.00 | 26.7100000 | 41.5300000 | 1.8879354 | 1.1258330 | -1 | NA | NA | NA | NA | NA | NA |
| 694 | 138 | Javvaji | 2021 | Entomologia Experimentalis et Applicata | Oryza sativa | Oryza_sativa | Annual | Poaceae | Rice leaffolder | Cnaphalocrocis medinalis | Cnaphalocrocis_medinalis | Specialist | Chewing arthropods | Growth / Development | 3.00 | 3.00 | 32.0800000 | 35.1500000 | 3.4814221 | 4.3128065 | -1 | NA | NA | NA | NA | NA | NA |
| 695 | 139 | Pereira | 2021 | Neotropical Entomology | Zea mays | Zea_mays | Annual | Poaceae | fall armyworm | Spodoptera frugiperda | Spodoptera_frugiperda | Generalist | Chewing arthropods | Reproduction | 15.00 | 15.00 | 307.3700000 | 124.0700000 | 308.4831235 | 97.3668013 | 1 | NA | NA | NA | NA | NA | NA |
| 696 | 139 | Pereira | 2021 | Neotropical Entomology | Zea mays | Zea_mays | Annual | Poaceae | fall armyworm | Spodoptera frugiperda | Spodoptera_frugiperda | Generalist | Chewing arthropods | Mortality / Survivability | 13.00 | 13.00 | 5.3400000 | 30.6800000 | 5.6246600 | 12.8357625 | -1 | NA | NA | NA | NA | NA | NA |
| 697 | 139 | Pereira | 2021 | Neotropical Entomology | Zea mays | Zea_mays | Annual | Poaceae | fall armyworm | Spodoptera frugiperda | Spodoptera_frugiperda | Generalist | Chewing arthropods | Feeding efficiency | 10.00 | 10.00 | 0.4700000 | 0.4100000 | 0.3162278 | 0.1897367 | 1 | NA | NA | NA | NA | NA | NA |
| 698 | 139 | Pereira | 2021 | Neotropical Entomology | Zea mays | Zea_mays | Annual | Poaceae | fall armyworm | Spodoptera frugiperda | Spodoptera_frugiperda | Generalist | Chewing arthropods | Growth / Development | 13.00 | 13.00 | 0.8100000 | 0.3700000 | 0.2884441 | 0.3605551 | 1 | NA | NA | NA | NA | NA | NA |
| 699 | 140 | Johnson | 2022 | PROCEEDINGS OF THE ROYAL SOCIETY B-BIOLOGICAL SCIENCES | Triticum aestivum | Triticum_aestivum | Annual | Poaceae | Cotton bollworm | Helicoverpa armigera | Helicoverpa_armigera | Generalist | Chewing arthropods | Growth / Development | 9.50 | 9.50 | 0.6400000 | 0.3400000 | 0.2773986 | 0.2465766 | 1 | NA | NA | NA | NA | NA | NA |
| 700 | 140 | Johnson | 2022 | PROCEEDINGS OF THE ROYAL SOCIETY B-BIOLOGICAL SCIENCES | Triticum aestivum | Triticum_aestivum | Annual | Poaceae | Cotton bollworm | Helicoverpa armigera | Helicoverpa_armigera | Generalist | Chewing arthropods | Feeding efficiency | 7.50 | 7.50 | 0.1420000 | 0.1840000 | 0.0273861 | 0.0328634 | -1 | NA | NA | NA | NA | NA | NA |
| 701 | 141 | Johnson | 2022 | Journal of Ecology | Brachypodium distachyon | Brachypodium_distachyon | Annual | Poaceae | Cotton bollworm | Helicoverpa armigera | Helicoverpa_armigera | Generalist | Chewing arthropods | Growth / Development | 8.00 | 8.00 | 0.1670000 | 0.0490000 | 0.0644011 | 0.0446197 | 1 | 8 | 8.000 | 0.5765000 | 3.785250 | 0.1253464 | 3.800127e-01 |
| 702 | 142 | Waterman | 2021 | Ecology | Brachypodium distachyon | Brachypodium_distachyon | Annual | Poaceae | Cotton bollworm | Helicoverpa armigera | Helicoverpa_armigera | Generalist | Chewing arthropods | Feeding efficiency | 8.00 | 9.00 | 1.6076005 | 0.3865872 | 1.1624400 | 0.3199000 | 1 | 8 | 9.000 | 0.6171250 | 2.899667 | 0.0833124 | 6.890093e-01 |
| 703 | 142 | Waterman | 2021 | Ecology | Brachypodium distachyon | Brachypodium_distachyon | Annual | Poaceae | Cotton bollworm | Helicoverpa armigera | Helicoverpa_armigera | Generalist | Chewing arthropods | Growth / Development | 8.00 | 8.00 | 0.4062986 | 0.1885370 | 0.2449772 | 0.1784583 | 1 | 8 | 9.000 | 0.6171250 | 2.899667 | 0.0833124 | 6.890093e-01 |
| 704 | 142 | Waterman | 2021 | Ecology | Brachypodium distachyon | Brachypodium_distachyon | Annual | Poaceae | Cotton bollworm | Helicoverpa armigera | Helicoverpa_armigera | Generalist | Chewing arthropods | Feeding efficiency | 8.00 | 8.00 | 0.0412514 | 0.0178748 | 0.0306089 | 0.0189307 | 1 | 8 | 9.000 | 0.6171250 | 2.899667 | 0.0833124 | 6.890093e-01 |
| 705 | 142 | Waterman | 2021 | Ecology | Brachypodium distachyon | Brachypodium_distachyon | Annual | Poaceae | Cotton bollworm | Helicoverpa armigera | Helicoverpa_armigera | Generalist | Chewing arthropods | Feeding efficiency | 4.00 | 4.00 | 0.7500000 | 3.1250000 | 0.8660254 | 1.1086779 | -1 | 8 | 9.000 | 0.6171250 | 2.899667 | 0.0833124 | 6.890093e-01 |
| 706 | 143 | Cibils-Stewart | 2021 | Functional Ecology | Festuca arundinacea | Festuca_arundinacea | Perennial | Poaceae | Cotton bollworm | Helicoverpa armigera | Helicoverpa_armigera | Generalist | Chewing arthropods | Growth / Development | 11.00 | 8.00 | 1.0030560 | 0.1512610 | 0.3172710 | 0.1739660 | 1 | 24 | 27.000 | 0.4550000 | 0.836800 | 0.2206170 | 1.361070e-01 |
| 707 | 143 | Cibils-Stewart | 2021 | Functional Ecology | Lolium perenne | Lolium_perenne | Perennial | Poaceae | Cotton bollworm | Helicoverpa armigera | Helicoverpa_armigera | Generalist | Chewing arthropods | Growth / Development | 11.00 | 14.00 | 0.8961040 | 0.5654260 | 0.4104300 | 0.3173730 | 1 | 28 | 31.000 | 0.6860000 | 0.675000 | 0.8523350 | 1.100070e-01 |
| 708 | 144 | Biru | 2022 | Entomologia Experimentalis et Applicata | Brachypodium distachyon | Brachypodium_distachyon | Annual | Poaceae | House cricket | Acheta domesticus | Acheta_domesticus | Generalist | Chewing arthropods | Growth / Development | 8.00 | 10.00 | 12.6000000 | 5.7777778 | 12.1307690 | 6.7040618 | 1 | NA | NA | NA | NA | NA | NA |
| 709 | 144 | Biru | 2022 | Entomologia Experimentalis et Applicata | Brachypodium distachyon | Brachypodium_distachyon | Annual | Poaceae | House cricket | Acheta domesticus | Acheta_domesticus | Generalist | Chewing arthropods | Feeding efficiency | 10.00 | 9.00 | 0.7410000 | 0.5444444 | 0.6659238 | 0.2603897 | 1 | NA | NA | NA | NA | NA | NA |
| 710 | 145 | Biru | 2021 | Global Change Biology | Brachypodium distachyon | Brachypodium_distachyon | Annual | Poaceae | Cotton bollworm | Helicoverpa armigera | Helicoverpa_armigera | Generalist | Chewing arthropods | Growth / Development | 8.00 | 8.00 | 2.2519701 | 1.4111300 | 0.5786732 | 0.5361999 | 1 | NA | NA | NA | NA | NA | NA |
| 711 | 146 | Islam | 2022 | Ecological Entomology | Brachypodium distachyon | Brachypodium_distachyon | Annual | Poaceae | Cotton bollworm | Helicoverpa armigera | Helicoverpa_armigera | Generalist | Chewing arthropods | Growth / Development | 10.00 | 10.00 | 0.8900000 | 0.4200000 | 0.4110961 | 0.1264911 | 1 | NA | NA | NA | NA | NA | NA |
| 712 | 146 | Islam | 2022 | Ecological Entomology | Brachypodium distachyon | Brachypodium_distachyon | Annual | Poaceae | Bird cherry oat aphid | Rhopalosiphum padi | Rhopalosiphum_padi | Specialist | Fluid-feeding arthropods | Abundance / Preference | 10.00 | 10.00 | 126.0000000 | 173.0000000 | 27.1007195 | 61.6644144 | 1 | NA | NA | NA | NA | NA | NA |
| 713 | 147 | Islam | 2023 | Journal of Pest Science | Brachypodium distachyon | Brachypodium_distachyon | Annual | Poaceae | Cotton bollworm | Helicoverpa armigera | Helicoverpa_armigera | Generalist | Chewing arthropods | Growth / Development | 40.00 | 40.00 | 1.4400000 | 0.4600000 | 0.4427189 | 0.3162278 | 1 | NA | NA | NA | NA | NA | NA |
| 714 | 147 | Islam | 2023 | Journal of Pest Science | Brachypodium distachyon | Brachypodium_distachyon | Annual | Poaceae | Cotton bollworm | Helicoverpa armigera | Helicoverpa_armigera | Generalist | Chewing arthropods | Feeding efficiency | 20.00 | 20.00 | 36.1000000 | 5.3500000 | 19.1407419 | 2.9963311 | 1 | NA | NA | NA | NA | NA | NA |
| 715 | 147 | Islam | 2023 | Journal of Pest Science | Brachypodium distachyon | Brachypodium_distachyon | Annual | Poaceae | Cotton bollworm | Helicoverpa armigera | Helicoverpa_armigera | Generalist | Chewing arthropods | Growth / Development | 20.00 | 20.00 | 390.6000000 | 100.2000000 | 170.2989372 | 49.7748732 | 1 | NA | NA | NA | NA | NA | NA |
| 716 | 147 | Islam | 2023 | Journal of Pest Science | Brachypodium distachyon | Brachypodium_distachyon | Annual | Poaceae | Cotton bollworm | Helicoverpa armigera | Helicoverpa_armigera | Generalist | Chewing arthropods | Growth / Development | 20.00 | 20.00 | 4.4300000 | 4.2000000 | 0.9838699 | 0.8944272 | 1 | NA | NA | NA | NA | NA | NA |
| 717 | 148 | Islam | 2022 | Journal of Pest Science | Phaseolus vulgaris | Phaseolus_vulgaris | Perennial | Non-Poaceae | Two spotted spider mite | Tetranychus urticae | Tetranychus_urticae | Generalist | Cell-feeding arthropods | Reproduction | 10.00 | 10.00 | 277.3000000 | 183.0000000 | 79.6577743 | 36.5243070 | 1 | NA | NA | NA | NA | NA | NA |
| 718 | 148 | Islam | 2022 | Journal of Pest Science | Phaseolus vulgaris | Phaseolus_vulgaris | Perennial | Non-Poaceae | Two spotted spider mite | Tetranychus urticae | Tetranychus_urticae | Generalist | Cell-feeding arthropods | Abundance / Preference | 10.00 | 10.00 | 334.9000000 | 202.8000000 | 89.3659667 | 65.3010337 | 1 | NA | NA | NA | NA | NA | NA |
| 719 | 148 | Islam | 2022 | Journal of Pest Science | Phaseolus vulgaris | Phaseolus_vulgaris | Perennial | Non-Poaceae | Two spotted spider mite | Tetranychus urticae | Tetranychus_urticae | Generalist | Cell-feeding arthropods | Feeding efficiency | 10.00 | 10.00 | 355.8600000 | 287.1900000 | 34.0893532 | 14.5781000 | 1 | NA | NA | NA | NA | NA | NA |
| 720 | 149 | Malik | 2023 | Plant Physiology and Biochemistry | Triticum aestivum | Triticum_aestivum | Annual | Poaceae | Common grasshopper | Oxya grandis | Oxya_grandis | Generalist | Chewing arthropods | Feeding efficiency | 5.00 | 5.00 | 51.0700000 | 11.8300000 | 7.8709593 | 2.9068884 | 1 | 5 | 5.000 | 4.0800000 | 12.410000 | 0.2012461 | 2.236068e-01 |
| 721 | 150 | Abbasi | 2020 | Arthropod-Plant Interactions | Gossypium hirsutum | Gossypium_hirsutum | Perennial | Non-Poaceae | Whitefly | Bemisia tabaci | Bemisia_tabaci | Generalist | Fluid-feeding arthropods | Abundance / Preference | 9.00 | 9.00 | 76.6300000 | 56.7300000 | 11.8200000 | 11.8200000 | 1 | NA | NA | NA | NA | NA | NA |
A. Effect: ID for effect sizes
B. Study: ID for publications (studies)
C. Author*: First author’s family name
D. Year: The year of publication of the study.
E. Journal*: Journal name
F. Plant_Species: Plant species name
G. Plant_Phylogeny: Plant Latin name for constructing phylogeny
H. Plant_lifespan: Whether the plant is annual or perennial
I. Poaceae_or_Non: Whether the plant is a Poaceae species or not
J. Herbivore_common_name*: Common names for herbivore species
K. Herbivore_Latin_name: Herbivore species’ Latin names
L. Herbivore_Phylogeny: Herbivore Latin name for constructing phylogeny
M. Herbivore_diet_breadth: : Whether a focal herbivore is a generalist or a specialist feeder
N. Feeding_guild: How the herbivore feeds on the plant
O. Performance_parameter*: Broad classification of herbivore performance parameter
P. Nc: Sample size for the control group
Q. Ne: Sample size for the experimental group
R. Xc: Mean value for the control group
S. Xe: Mean value for the experimental group
T. Dev_c: Standard deviation for the control group
U. Dev_e: Standard deviation for the experimental group
V. Negative: the vector of 1 or -1 which indicate if corresponding effect sizes should be reverse in its sign
W. Si_Nc: Sample size for the control group (for silicon uptake)
X. Si_Ne: Sample size for the experimental group (for silicon uptake)
Y. Si_Xc: Mean value for the control group (for silicon uptake)
Z. Si_Xe: Mean value for the experimental group (for silicon uptake)
AA. Si_Dev_c: Standard deviation for the control group (for silicon uptake)
AB. Si_Dev_e: Standard deviation for the experimental group (for silicon uptake)
Table of sample sizes
# selecting out variables, which we used for our analysis
dat <- full_data %>% select(-Author, -Journal, -Herbivore_common_name, -Performance_parameter)
# making a table of sample sizes for different variables
dat %>%
summarise(
`Effect sizes` = n_distinct(Effect),
Studies = n_distinct(Study),
`Plant species` = n_distinct(Plant_Species),
`Herbivore species` = n_distinct(Herbivore_Latin_name),
`Effect sizes (Silicon uptake)` = n_distinct(Effect[!is.na(Si_Nc)]),
`Studies (Silicon uptake)` = n_distinct(Study[!is.na(Si_Nc)]),
`Annual plants (Plant_lifespan)` = sum(Plant_lifespan == "Annual", na.rm = T), # na.rm is important when NA exists
`Perennial plants (Plant_lifespan)` = sum(Plant_lifespan == "Perennial", na.rm = T),
`Poaceae (Poaceae_or_Non)` = sum(Poaceae_or_Non == "Poaceae", na.rm = T),
`Non-Poaceae (Poaceae_or_Non)` = sum(Poaceae_or_Non == "Non-Poaceae", na.rm = T),
`Specialist (Herbivore_diet_breadth)` = sum(Herbivore_diet_breadth == "Specialist", na.rm = T),
`Generalist (Herbivore_diet_breadth)` = sum(Herbivore_diet_breadth == "Generalist", na.rm = T),
`Boring arthropods (Feeding_guild)` = sum(Feeding_guild == "Boring arthropods", na.rm = T),
`Cell-feeding arthropods (Feeding_guild)` = sum(Feeding_guild == "Cell-feeding arthropods", na.rm = T),
`Chewing arthropods (Feeding_guild)` = sum(Feeding_guild == "Chewing arthropods", na.rm = T),
`Fluid-feeding arthropods (Feeding_guild)` = sum(Feeding_guild == "Fluid-feeding arthropods", na.rm = T),
`Leaf-mining arthropods (Feeding_guild)` = sum(Feeding_guild == "Leaf-mining arthropods", na.rm = T),
`Mammalian chewers (Feeding_guild)` = sum(Feeding_guild == "Mammalian chewers", na.rm = T),
`Rasping/grazing invertebrates (Feeding_guild)` = sum(Feeding_guild == "Rasping / grazing invertebrates", na.rm = T)
) -> n_table1
# transposing the table and creating that table and adding a correct number of the papers for `Combined`
# n_authors <- n_distinct(dat$authors) # the total number of papers
n_table2 <- t(n_table1)
colnames(n_table2) <- "n (sample size)"
n_table2 %>%
as_tibble(rownames = "Number") %>%
rename("Number of" = "Number") %>%
kable() %>% kable_styling("striped", position = "left") %>%
scroll_box(width = "60%", height = "300px")| Number of | n (sample size) |
|---|---|
| Effect sizes | 721 |
| Studies | 150 |
| Plant species | 47 |
| Herbivore species | 62 |
| Effect sizes (Silicon uptake) | 467 |
| Studies (Silicon uptake) | 89 |
| Annual plants (Plant_lifespan) | 466 |
| Perennial plants (Plant_lifespan) | 255 |
| Poaceae (Poaceae_or_Non) | 574 |
| Non-Poaceae (Poaceae_or_Non) | 147 |
| Specialist (Herbivore_diet_breadth) | 408 |
| Generalist (Herbivore_diet_breadth) | 313 |
| Boring arthropods (Feeding_guild) | 77 |
| Cell-feeding arthropods (Feeding_guild) | 12 |
| Chewing arthropods (Feeding_guild) | 331 |
| Fluid-feeding arthropods (Feeding_guild) | 269 |
| Leaf-mining arthropods (Feeding_guild) | 1 |
| Mammalian chewers (Feeding_guild) | 29 |
| Rasping/grazing invertebrates (Feeding_guild) | 2 |
#pander(split.cell = 40, split.table = Inf) # not as nice as kableMissing data patterns
The only missing values were studies that reported herbivore performance without quantifying plant silicon content.
# summering missingness in our dataset
# funs(sum(is.na(.))) needs to be in funs as is.na has "." = each column
dat %>% summarise_all(~sum(is.na(.))) %>% # map(~sum(is.na(.)) # this is an alterantive way
t() %>% as_tibble(rownames = "Variable") %>%
rename("Number of missing data (n)" = "V1") %>%
#pander(split.cell = 40, split.table = Inf)
kable() %>% kable_styling("striped", position = "left") %>%
scroll_box(width = "60%", height = "300px")| Variable | Number of missing data (n) |
|---|---|
| Effect | 0 |
| Study | 0 |
| Year | 0 |
| Plant_Species | 0 |
| Plant_Phylogeny | 0 |
| Plant_lifespan | 0 |
| Poaceae_or_Non | 0 |
| Herbivore_Latin_name | 0 |
| Herbivore_Phylogeny | 0 |
| Herbivore_diet_breadth | 0 |
| Feeding_guild | 0 |
| Nc | 0 |
| Ne | 0 |
| Xc | 0 |
| Xe | 0 |
| Dev_c | 0 |
| Dev_e | 0 |
| Negative | 0 |
| Si_Nc | 254 |
| Si_Ne | 254 |
| Si_Xc | 254 |
| Si_Xe | 254 |
| Si_Dev_c | 254 |
| Si_Dev_e | 254 |
Meta-analysis: the Effect of Silicon on Herbivores
Choosing effect size statistics: checking the mean-variance relationship
We checked the mean-variance relationship in our data. If there is such a relationship, it is preferable to use the logarithm of response ratio, lnRR (Hedges, Gurevitch, and Curtis 1999) rather than standardized mean difference, SMD (often known as Cohen’s d or Hedges’ g) because the latter assumes the homogeneity of variance (see below).
Figure E1
# A)
cor_1 <- round(with(dat,cor(log(Xe), log(Dev_e))), 3)
mod_1 <- lm(log(Dev_e) ~ log(Xe), data = dat)
plot_exp <- ggplot(dat, aes(log(Xe), log(Dev_e))) + geom_point() +
geom_smooth(method = "lm") +
labs(x = "ln(mean[experiment])",
y = "ln(SD[experiment])",
title = "Herbivore performance (experimental)") +
xlim(-9, 9) + ylim(-9, 9) +
annotate('text',x = 7.5, y = -8, label = paste("r = ", cor_1)) +
annotate('text',x = 7.5, y = -6, label = paste("b = ", round(mod_1$coefficients[[2]],3)))
# B)
cor_2 <- round(with(dat,cor(log(Xc), log(Dev_c))), 3)
mod_2 <- lm(log(Dev_c) ~ log(Xc), data = dat)
plot_con <- ggplot(dat, aes(log(Xc), log(Dev_c))) + geom_point() +
geom_smooth(method = "lm") +
labs(x = "ln(mean[control])",
y = "ln(SD[control])",
title = "Herbivore performance (control)")+
xlim(-9, 9) + ylim(-9, 9) +
annotate('text',x = 7.5, y = -8, label = paste("r = ", cor_2)) +
annotate('text',x = 7.5, y = -6, label = paste("b = ", round(mod_2$coefficients[[2]],3)))
# c)
cor_3 <- round(with(dat,cor.test(log(Si_Xe), log(Si_Dev_e)))$estimate[[1]], 3)
mod_3 <- lm(log(Si_Dev_e) ~ log(Si_Xe), data = dat)
Si_plot_exp <- ggplot(dat, aes(log(Si_Xe), log(Si_Dev_e))) + geom_point() +
geom_smooth(method = "lm") +
labs(x = "ln(mean[experiment])",
y = "ln(SD[experiment])",
title = "Silicon content (experimental)") +
xlim(-9, 9) + ylim(-9, 9) +
annotate('text',x = 7.5, y = -8, label = paste("r = ", cor_3)) +
annotate('text',x = 7.5, y = -6, label = paste("b = ", round(mod_3$coefficients[[2]],3)))
# D)
cor_4 <- round(with(dat,cor.test(log(Si_Xc), log(Si_Dev_c)))$estimate[[1]], 3)
mod_4 <- lm(log(Si_Dev_c) ~ log(Si_Xc), data = dat)
Si_plot_con <- ggplot(dat, aes(log(Si_Xc), log(Si_Dev_c))) + geom_point() +
geom_smooth(method = "lm") +
labs(x = "ln(mean[control])",
y = "ln(SD[control])",
title = "Silicon content (control)")+
xlim(-9, 9) + ylim(-9, 9) +
annotate('text',x = 7.5, y = -8, label = paste("r = ", cor_4)) +
annotate('text',x = 7.5, y = -6, label = paste("b = ", round(mod_4$coefficients[[2]],3)))
Si_mean_SD <- Si_plot_exp + Si_plot_con +
plot_annotation(tag_levels = "A", tag_suffix = ")")
mean_SD <- (plot_exp | plot_con) / (Si_plot_exp | Si_plot_con) +
plot_annotation(tag_levels = "A", tag_suffix = ")")
mean_SD
# saving the figure for Scott
# ggsave(here("fig", "mean_SD.png"),
# width = 20,
# height = 20,
# units = "cm")
# getting correlations
# Checking the slopes Figure E1: Mean-variance relationships for (A and B) herbivore performance and (C and D) silicon content in experimental (i.e. Si supplemented) and control plants.
Calculating effect sizes
# calculating effect size - think about turning into function
dat <- escalc(measure = "ROM", m1i = Xe, m2i = Xc, sd1i = Dev_e, sd2i = Dev_c, n1i = Nc, n2i = Ne, data = dat, var.names = c("lnRR", "varlnRR"))
dat <- escalc(measure = "VR", m1i = Xe, m2i = Xc, sd1i = Dev_e, sd2i = Dev_c, n1i = Nc, n2i = Ne, data = dat, var.names = c("lnVR", "varlnVR"))
# for Silicon accumulation
dat <- escalc(
measure = "ROM", m1i = Si_Xe, m2i = Si_Xc, sd1i = Si_Dev_c, sd2i = Si_Dev_c, n1i = Si_Nc, n2i = Si_Ne,
data = dat, var.names = c("Si_lnRR", "Si_varlnRR")
)
dat <- escalc(
measure = "VR", m1i = Si_Xe, m2i = Si_Xc, sd1i = Si_Dev_e, sd2i = Si_Dev_c, n1i = Si_Nc, n2i = Si_Ne,
data = dat, var.names = c("Si_lnVR", "Si_varlnVR")
)
# flipping the sign for effect sizes (lnRR not lnVR) when small means improvement (better)
dat %>%
mutate(lnRR = Negative * lnRR) %>%
as_tibble() %>% data.frame -> datPhylogenetic tree and correlation matrix
Figure E2
herbiv_tree <- read.tree(file = here("phylo/plants_herbivtree_binary_JMW.tre"))
plant_tree <- read.tree(file = here("phylo/plants_planttree_binary.tre"))
#plot(herbiv_tree)
#plot(plant_tree)
htree <- compute.brlen(herbiv_tree)
ptree <- compute.brlen(plant_tree)
#is.ultrametric(htree)
#is.ultrametric(ptree)
par(mfrow = c(1,2))
plot(ptree)
mtext("Plant phylogeny", side = 1, cex = 1.5, line = 1)
plot(htree)
mtext("Herbivore phylogeny", side = 1, cex = 1.5, line =1)
cor_htree <- vcv(htree,corr=T) # correlation matrix for Herbivores
cor_ptree <- vcv(ptree,corr=T) # correlation matrix for Plants
# saving for Scott
# pdf(here("fig", "tree.pdf"), height=15, width=12)
# plot(tree)
# dev.off() Figure E2: Phylogenetic trees for plants (left panel) and herbviores (right panel).
Meta-analytic models: lnRR and lnVR
levels(dat$Plant_Phylogeny)[levels(dat$Plant_Phylogeny) == "Saccharum_officinarum"] <- "Saccharum_spp."
levels(dat$Herbivore_Phylogeny)[levels(dat$Herbivore_Phylogeny) == "Sesamia_spp."] <- "Sesamia_inferens"
levels(dat$Herbivore_Phylogeny)[levels(dat$Herbivore_Phylogeny) == "Liriomyza_spp."] <- "Liriomyza_puella"
levels(dat$Herbivore_Phylogeny)[levels(dat$Herbivore_Phylogeny) == "Chlosyne_lacinia_saundersii"] <- "Chlosyne_lacinia"
levels(dat$Herbivore_Phylogeny)[levels(dat$Herbivore_Phylogeny) == "Deroceras_reticulatrum"] <- "Deroceras_reticulatum"
levels(dat$Herbivore_Phylogeny)[levels(dat$Herbivore_Phylogeny) == "Macrosiphoniellas_anborni"] <- "Macrosiphoniella_sanborni"
levels(dat$Herbivore_Phylogeny)[levels(dat$Herbivore_Phylogeny) == "Pseudaletia_unipuncta"] <- "Mythimna_unipuncta"
#lnRR
# matrix for controlling for correlated errors
VCV_lnRR <- make_VCV_matrix(dat, V = "varlnRR", cluster = "Study", obs = "Effect")
ma_lnRR <- rma.mv(
yi = lnRR,
V = VCV_lnRR,
random = list(~1|Plant_Phylogeny, ~1|Plant_Species,
~1|Herbivore_Phylogeny, ~1|Herbivore_Latin_name,
~1|Study, ~1|Effect),
R= list(Plant_Phylogeny = cor_ptree, Herbivore_Phylogeny = cor_htree),
test = "t",
data = dat)
# lnVR
# matrix for controlling for correlated errors
VCV_lnVR <- make_VCV_matrix(dat, V = "varlnVR", cluster = "Study", obs = "Effect")
ma_lnVR <- rma.mv(
yi = lnVR,
V = VCV_lnVR,
random = list(~1|Plant_Phylogeny, ~1|Plant_Species,
~1|Herbivore_Phylogeny, ~1|Herbivore_Latin_name,
~1|Study, ~1|Effect),
R= list(Plant_Phylogeny = cor_ptree, Herbivore_Phylogeny = cor_htree),
test = "t",
data = dat,
control=list(optimizer="optim", optmethod="BFGS"))
# control=list(optimizer="optim", optmethod="Nelder-Mead")Table E1
Table E1: Overall effects (meta-analytic means), 95% confidence intervals (CIs) and 95% prediction intervals (95%) (Nakagawa et al. 2020).
# getting a table of CI and PI
tma_lnRR <- get_pred1(ma_lnRR, mod = "Int")
tma_lnVR <- get_pred1(ma_lnVR, mod = "Int")
# Drawing a table for meta-analyses
tibble(`Effect size` = c("lnRR","lnVR"),
`Overall mean` = c(tma_lnRR$estimate, tma_lnVR$estimate),
`Lower CI [0.025]` = c(tma_lnRR$lowerCL, tma_lnVR$lowerCL),
`Upper CI [0.975]` = c(tma_lnRR$upperCL,tma_lnVR$upperCL),
`P value` = c(tma_lnRR$pval, tma_lnVR$pval),
`Lower PI [0.025]` = c(tma_lnRR$lowerPR, tma_lnVR$lowerPR),
`Upper PI [0.975]` = c(tma_lnRR$upperPR, tma_lnVR$upperPR)) %>%
kable("html", digits = 3) %>%
kable_styling("striped", position = "left")| Effect size | Overall mean | Lower CI [0.025] | Upper CI [0.975] | P value | Lower PI [0.025] | Upper PI [0.975] |
|---|---|---|---|---|---|---|
| lnRR | -0.402 | -0.593 | -0.210 | 0.000 | -1.325 | 0.521 |
| lnVR | -0.081 | -0.175 | 0.012 | 0.088 | -1.235 | 1.072 |
Table E2
Table E2: Variance components (V) and heterogeneity,
I2 (I2) (Higgins et al.
2003) from the metafor model Note that in these
models, I2[total] is the sum of variance
components of Plant_Phylogeny, Plant_Species,
Herbivore_Phylogeny, Study and
Effect – [see (Nakagawa and
Santos 2012; Senior et al. 2016)].
# getting I2
I2_lnRR <- i2_ml(ma_lnRR)
I2_lnVR <- i2_ml(ma_lnVR)
# getting sigma2 = V
V_lnRR <- c(sum(ma_lnRR$sigma2), ma_lnRR$sigma2)
V_lnVR <-c(sum(ma_lnVR$sigma2), ma_lnVR$sigma2)
# Drawing a table for variance components V and I2
tibble(`Effect size` = c("lnRR (*I*^2^)","lnVR (*I*^2^)", "lnRR (*V*)", "lnVR (*V*)"),
Total = c(I2_lnRR[1], I2_lnVR[1], V_lnRR[1], V_lnVR[1]),
`Plant phylogeny` = c(I2_lnRR[2], I2_lnVR[2],V_lnRR[2], V_lnVR[2]),
`Plant species` = c(I2_lnRR[3], I2_lnVR[3], V_lnRR[3],V_lnVR[3]),
`Herbivore phylogeny` = c(I2_lnRR[4], I2_lnVR[4], V_lnRR[4], V_lnVR[4]),
`Herbivore species` = c(I2_lnRR[5], I2_lnVR[5], V_lnRR[5], V_lnVR[5]),
Study = c(I2_lnRR[6], I2_lnVR[6], V_lnRR[6], V_lnVR[6]),
`Effect (within-study)` = c(I2_lnRR[7], I2_lnVR[7], V_lnRR[7], V_lnVR[7]),
) %>%
kable("html", digits = 3) %>%
kable_styling("striped", position = "left")| Effect size | Total | Plant phylogeny | Plant species | Herbivore phylogeny | Herbivore species | Study | Effect (within-study) |
|---|---|---|---|---|---|---|---|
| lnRR (I2) | 99.042 | 1.652 | 0 | 11.219 | 13.415 | 25.283 | 47.473 |
| lnVR (I2) | 80.838 | 0.000 | 0 | 0.000 | 10.109 | 0.001 | 70.728 |
| lnRR (V) | 0.212 | 0.004 | 0 | 0.024 | 0.029 | 0.054 | 0.101 |
| lnVR (V) | 0.343 | 0.000 | 0 | 0.000 | 0.043 | 0.000 | 0.300 |
Figure 2A-B
## Figure 1A
p1 <- orchard_plot(ma_lnRR, data = dat, mod="1",group= 'Study', xlab = "log(Response Ratio) (lnRR)", alpha = 0.2, angle = 45) +
scale_fill_manual(values="blue") +
scale_colour_manual(values="blue") +
annotate(geom="text", x=0.8, y=1.5, label="p < 0.001",
color="black")
## Figure 1B
p2 <- orchard_plot(ma_lnVR, data = dat, mod="1",group= 'Study', xlab = "log(Variability Ratio) (lnVR)", alpha = 0.2, angle = 45) +
scale_fill_manual(values="green") +
scale_colour_manual(values="green") +
annotate(geom="text", x=0.8, y=1.5, label="p = 0.106",
color="black")
#ggsave(here("fig", "Overall.png"))Selecting random effects for meta-regression
#lnRR
ma_lnRR2 <- rma.mv(
yi = lnRR,
V = VCV_lnRR,
random = list( ~1|Plant_Species,
~1|Herbivore_Phylogeny, ~1|Herbivore_Latin_name,
~1|Study, ~1|Effect),
R= list(Herbivore_Phylogeny = cor_htree),
method = "ML",
test = "t",
data = dat)
# we take this model
ma_lnRR3 <- rma.mv(
yi = lnRR,
V = VCV_lnRR,
random = list(~1|Herbivore_Phylogeny, ~1|Herbivore_Latin_name,
~1|Study, ~1|Effect),
R= list(Herbivore_Phylogeny = cor_htree),
method = "ML",
test = "t",
data = dat)
ma_lnRR4 <- rma.mv(
yi = lnRR,
V = VCV_lnRR,
random = list(~1|Herbivore_Phylogeny,
~1|Study, ~1|Effect),
R= list(Herbivore_Phylogeny = cor_htree),
method = "ML",
test = "t",
data = dat)
# TODO - these arent running due to estimation differences between models, do we need to fix?
#pval_lnRR1 <- round(anova(ma_lnRR, ma_lnRR2)$pval, 3)
#pval_lnRR2 <- round(anova(ma_lnRR2, ma_lnRR3)$pval, 3)
#pval_lnRR3 <- round(anova(ma_lnRR3, ma_lnRR4)$pval, 3)
#lnVR
ma_lnVR2 <- rma.mv(
yi = lnVR,
V = VCV_lnVR,
random = list( ~1|Plant_Species,
~1|Herbivore_Phylogeny, ~1|Herbivore_Latin_name,
~1|Study, ~1|Effect),
R= list(Herbivore_Phylogeny = cor_htree),
method = "ML",
test = "t",
data = dat)
# we take this model
ma_lnVR3 <- rma.mv(
yi = lnVR,
V = VCV_lnVR,
random = list(~1|Herbivore_Phylogeny, ~1|Herbivore_Latin_name,
~1|Study, ~1|Effect),
R= list(Herbivore_Phylogeny = cor_htree),
method = "ML",
test = "t",
data = dat)
ma_lnVR4 <- rma.mv(
yi = lnVR,
V = VCV_lnVR,
random = list(~1|Herbivore_Phylogeny,
~1|Study, ~1|Effect),
R= list(Herbivore_Phylogeny = cor_htree),
method = "ML",
test = "t",
data = dat)
#pval_lnVR1 <- round(anova(ma_lnVR, ma_lnVR2)$pval, 3)
#pval_lnVR2 <- round(anova(ma_lnVR2, ma_lnVR3)$pval, 3)
#pval_lnVR3 <- round(anova(ma_lnVR3, ma_lnVR4)$pval, 3)Based on 3 criteria: 1) the amounts of variance each random effects
explain, 2) the results of log-likelihood ratio tests, and 3) retaining
some clustering factor(s) regardless of its effect, we decided to keep
Herbivore_Phylogeny, Herbivore_Latin_name,
Study, Effect for lnRR and lnVR.
Meta-analysis of Silicon Content
# creating the smaller dataset - only one value per
sdat <- dat %>% group_by(Study) %>%
summarise(Si_lnRR = first(Si_lnRR),
Si_varlnRR = first(Si_varlnRR),
Si_lnVR = first(Si_lnVR),
Si_varlnVR = first(Si_varlnVR),
Plant_Phylogeny = first(Plant_Phylogeny),
Plant_Species = first(Plant_Species)
)
#lnRR
ma_lnRR_Si <- rma.mv(
yi = Si_lnRR,
V = Si_varlnRR,
random = list(~1|Plant_Phylogeny, ~1|Plant_Species, ~1|Study),
R= list(Phylogeny = cor_ptree),
test = "t",
data = sdat)
#summary(ma_lnRR_Si)
#lnVR
ma_lnVR_Si <- rma.mv(
yi = Si_lnVR,
V = Si_varlnVR,
random = list(~1|Plant_Phylogeny, ~1|Plant_Species, ~1|Study),
R= list(Phylogeny = cor_ptree),
est = "t",
data = sdat)
#summary(ma_lnCVR_Si)Table E3
Table E3: Overall effects (meta-analytic means), 95% confidence intervals (CIs) and 95% prediction intervals (95%).
# getting a table of CI and PI
tma_lnRR_Si <- get_pred1(ma_lnRR_Si, mod = "Int")
tma_lnVR_Si <- get_pred1(ma_lnVR_Si, mod = "Int")
# Drawing a table for meta-analyses
tibble(`Effect size` = c("lnRR", "lnVR"),
`Overall mean` = c(tma_lnRR_Si$estimate, tma_lnVR_Si$estimate),
`Lower CI [0.025]` = c(tma_lnRR_Si$lowerCL, tma_lnVR_Si$lowerCL),
`Upper CI [0.975]` = c(tma_lnRR_Si$upperCL, tma_lnVR_Si$upperCL),
`P value` = c(tma_lnRR_Si$pval, tma_lnVR_Si$pval),
`Lower PI [0.025]` = c(tma_lnRR_Si$lowerPR, tma_lnVR_Si$lowerPR),
`Upper PI [0.975]` = c(tma_lnRR_Si$upperPR, tma_lnVR_Si$upperPR)
) %>%
kable("html", digits = 3) %>%
kable_styling("striped", position = "left")| Effect size | Overall mean | Lower CI [0.025] | Upper CI [0.975] | P value | Lower PI [0.025] | Upper PI [0.975] |
|---|---|---|---|---|---|---|
| lnRR | 0.727 | 0.557 | 0.898 | 0 | -0.514 | 1.969 |
| lnVR | 0.386 | 0.198 | 0.573 | 0 | -1.178 | 1.949 |
Table E4
Table E4: Variance components (V) and heterogeneity,
I2 (I2) (Higgins et al.
2003) from the metafor model Note that in these
models, I2[total] is the sum of variance
components of Plant phylogeny, Plant species
and Study.
# getting I2
I2_lnRR_Si <- i2_ml(ma_lnRR_Si)
I2_lnVR_Si <- i2_ml(ma_lnVR_Si)
# getting sigma2 = V
V_lnRR_Si <- c(sum(ma_lnRR_Si$sigma2), ma_lnRR_Si$sigma2)
V_lnVR_Si <-c(sum(ma_lnVR_Si$sigma2), ma_lnVR_Si$sigma2)
# Drawing a table for variance components V and I2
tibble(`Effect size` = c("lnRR (*I*^2^)", "lnVR (*I*^2^)", "lnRR (*V*)", "lnVR (*V*)"),
Total = c(I2_lnRR_Si[1],I2_lnVR_Si[1], V_lnRR_Si[1], V_lnVR_Si[1]),
`Plant phylogeny` = c(I2_lnRR_Si[2], I2_lnVR_Si[2], V_lnRR_Si[2], V_lnVR_Si[2]),
`Plant species` = c(I2_lnRR_Si[3], I2_lnVR_Si[3], V_lnRR_Si[3], V_lnVR_Si[3]),
Study = c(I2_lnRR_Si[4], I2_lnVR_Si[4], V_lnRR_Si[4], V_lnVR_Si[4])
) %>%
kable("html", digits = 3) %>%
kable_styling("striped", position = "left")| Effect size | Total | Plant phylogeny | Plant species | Study |
|---|---|---|---|---|
| lnRR (I2) | 99.955 | 14.732 | 0 | 85.223 |
| lnVR (I2) | 84.092 | 0.000 | 0 | 84.092 |
| lnRR (V) | 0.383 | 0.056 | 0 | 0.326 |
| lnVR (V) | 0.627 | 0.000 | 0 | 0.627 |
Figure 2C-D
## Remove NAs for orchardplot to work
sdat2<-na.omit(sdat)
## Figure 1C
p4 <- orchard_plot(ma_lnRR_Si, data = sdat2, mod="1",group= 'Study', xlab = "log(Response Ratio) (lnRR)", alpha = 0.2, angle = 45) +
scale_fill_manual(values="blue") +
scale_colour_manual(values="blue") +
annotate(geom="text", x=0.8, y=1.5, label="p < 0.001",
color="black")
## Figure 1D
p5 <- orchard_plot(ma_lnVR_Si, data = sdat2, mod="1",group= 'Study', xlab = "log(Variability Ratio) (lnVR)", alpha = 0.2, angle = 45) +
scale_fill_manual(values="green") +
scale_colour_manual(values="green") +
annotate(geom="text", x=0.8, y=1.5, label="p < 0.001",
color="black")
#ggsave(here("fig", "Overall_Silicon.png"))Bivariate Meta-analysis: Testing for Correlations Between Silicon Content and Herbivore Performance
We conducted bivariate meta-analytic models for lnRR (mean effect size) and lnVR (absolute variance) to determine any relationship between silicon content and herbivore performance. We expected that these two effects will be negatively correlated (i.e. more plant silicon results in worse herbivore performance); we did not have any expectation for the corresponding lnVR pairings.
For this complex model, we only considered Study as a
random effect, excluding Species and
Phylogeny.
# turning data into a long format to ran bivariate models
dat %>%
select(Effect, Study, Year, Feeding_guild,
lnRR, varlnRR,
lnVR, varlnVR) -> dat2 # main effects
dat %>%
select(Effect, Study, Year, Feeding_guild,
lnRR = Si_lnRR, varlnRR = Si_varlnRR,
lnVR = Si_lnVR, varlnVR = Si_varlnVR) -> dat3 # silicon uptakes
# long format
ldat <- rbind(dat2, dat3) %>%
mutate(Number = 1:(2*nrow(dat)),
RR = rep(c("lnRR", "Si_lnRR"), each = nrow(dat)),
VR = rep(c("lnVR", "Si_lnVR"), each = nrow(dat)))
# lnRR
bi_ma_lnRR <- rma.mv(yi = lnRR, V = varlnRR,
mods = ~ RR - 1,
random =list(~ RR | Study), struct="UN", data=ldat) # only takes 2 random factor
#summary(bi_ma_lnRR)
#cor_lnRR <- round(bi_ma_lnRR$rho, 3)
#saveRDS(bi_ma_lnRR, file = here("Rdata", "bi_ma_lnRR.rds"))
# takes very long to run
ci_cor_lnRR <- confint(bi_ma_lnRR)
saveRDS(ci_cor_lnRR, file = here("Rdata", "ci_cor_lnRR.rds"))
# lnVR
bi_ma_lnVR <- rma.mv(yi = lnVR, V = varlnVR,
mods = ~ VR - 1,
random =list(~ VR | Study), struct="UN", data=ldat) # only takes 2 random factor
#summary(bi_ma_lnVR)
#cor_lnRR <- round(bi_ma_lnRR$rho, 3)
#saveRDS(bi_ma_lnVR, file = here("Rdata", "bi_ma_lnVR.rds"))
ci_cor_lnVR <- confint(bi_ma_lnVR)
saveRDS(ci_cor_lnVR, file = here("Rdata", "ci_cor_lnVR.rds"))# we probably rewrite here
# bivariate correlations
ci_cor_lnRR <- readRDS(file = here("Rdata", "ci_cor_lnRR.rds"))
ci_cor_lnVR <- readRDS(file = here("Rdata", "ci_cor_lnVR.rds"))We found a correlation of -0.14 (95% CI = [-0.371 , 0.109]) between the two lnRRs, and a correlation of -0.129 (95% CI = [-0.389 , 0.154]) between the two lnVRs.
Run above correlations but only with chewing arthropods
chew_ldat<-subset(ldat, Feeding_guild == 'Chewing arthropods')
# lnRR
bi_ma_lnRR_chew <- rma.mv(yi = lnRR, V = varlnRR,
mods = ~ RR - 1,
random =list(~ RR | Study), struct="UN", data=chew_ldat) # only takes 2 random factor
# takes very long to run
ci_cor_lnRR_chew <- confint(bi_ma_lnRR_chew)
saveRDS(ci_cor_lnRR_chew, file = here("Rdata", "ci_cor_lnRR_chew.rds"))
#lnVR
bi_ma_lnVR_chew <- rma.mv(yi = lnVR, V = varlnVR,
mods = ~ VR - 1,
random =list(~ VR | Study), struct="UN", data=chew_ldat) # only takes 2 random factor
#summary(bi_ma_lnVR)
#cor_lnRR <- round(bi_ma_lnRR$rho, 3)
#saveRDS(bi_ma_lnVR, file = here("Rdata", "bi_ma_lnVR.rds"))
ci_cor_lnVR_chew <- confint(bi_ma_lnVR_chew)
saveRDS(ci_cor_lnVR_chew, file = here("Rdata", "ci_cor_lnVR_chew.rds"))# bivariate correlations chewing arthropods only
ci_cor_lnRR_chew <- readRDS(file = here("Rdata", "ci_cor_lnRR_chew.rds"))
ci_cor_lnVR_chew <- readRDS(file = here("Rdata", "ci_cor_lnVR_chew.rds"))We found a correlation of -0.175 (95% CI = [-0.5 , 0.199]) between the two lnRRs, and a correlation of 0.113 (95% CI = [-0.374 , 0.543]) between the two lnVRs.
Meta-regression: the Effect of Silicon and Herbivore Performance
Univariate (uni-predictor) analyses
We ran a univariate meta-regression model for each of the following
moderators: 1) Poaceae_or_Non, 2)
Plant_lifespan, 3) Herbivore_diet_breadth, and
4) Feeding_guild (see the meta-data above).
Plant groups: Poaceae vs. Non-Poaceae
Mean change: lnRR
# reordering
dat$Poaceae_or_Non <- factor(dat$Poaceae_or_Non,
levels = rev(c("Poaceae", "Non-Poaceae")),
labels = rev(c("Poaceae", "Non-Poaceae")))
# meta-regression: multiple intercepts
mr_mdcot_lnRR1 <- rma.mv(
yi = lnRR,
V = VCV_lnRR,
mods = ~ Poaceae_or_Non - 1,
test = "t",
random = list(~1|Herbivore_Phylogeny, ~1|Herbivore_Latin_name,
~1|Study, ~1|Effect),
data = dat
)
# getting marginal R2
r2_mr_mdcot_lnRR1 <- r2_ml(mr_mdcot_lnRR1)
# getting the level names out
level_names <- levels(dat$Poaceae_or_Non)
# helper function to run metafor meta-regression
run_rma <- function(name) {
rma.mv(yi = lnRR,
V = VCV_lnRR,
mods = ~ relevel(Poaceae_or_Non, ref = name),
test = "t",
random = list(~1|Herbivore_Phylogeny, ~1|Herbivore_Latin_name,
~1|Study, ~1|Effect),
data = dat)
}
# results of meta-regression including all contrast results; taking the last level out ([-length(level_names)])
mr_mdcot_lnRR <- map(level_names[-length(level_names)], run_rma)Table E5
Table E5: Regression coefficients (estimate), 95%
confidence intervals (CIs), P values, and variance explained,
R2[marginal] (R2) = 2.06% from the
meta-regression of lnRR with Poaceae_or_Non_Poaceae.
# getting estimates
res_mr_mdcot_lnRR1 <- get_pred1(mr_mdcot_lnRR1, mod = "Poaceae_or_Non")
#res_mr_annuality_lnRR2 <- map(mr_annuality_lnRR, ~ get_est2(.x, mod = "Annuality"))
res_mr_mdcot_lnRR <- map(mr_mdcot_lnRR, ~ get_pred2(.x, mod = "Poaceae_or_Non"))
# a list of the numbers to take out unnecessary contrasts
contra_list <- Map(seq, from=1, to=1:1)
# you need to flatten twice: first to make it a list and make it a vector
# I guess we can make it a loop (or vectorise)
res_mr_mdcot_lnRR2 <- map2_dfr(res_mr_mdcot_lnRR, contra_list, ~.x[-(.y), ])
# creating a table
mr_results(res_mr_mdcot_lnRR1, res_mr_mdcot_lnRR2) %>%
kable("html", digits = 3) %>%
kable_styling("striped", position = "left") %>%
scroll_box(width = "100%")| Fixed effect | Estimate | Lower CI [0.025] | Upper CI [0.975] | P value | Lower PI [0.025] | Upper PI [0.975] |
|---|---|---|---|---|---|---|
| Non-Poaceae | -0.219 | -0.363 | -0.076 | 0.003 | -1.113 | 0.674 |
| Poaceae | -0.381 | -0.479 | -0.283 | 0.000 | -1.268 | 0.506 |
| Non-Poaceae-Poaceae | -0.162 | -0.322 | -0.001 | 0.049 | -1.058 | 0.735 |
Change in SD: lnVR
# meta-regression: multiple intercepts
mr_mdcot_lnVR1 <- rma.mv(
yi = lnVR,
V = VCV_lnVR,
mods = ~ Poaceae_or_Non - 1,
test = "t",
random = list(~1|Herbivore_Phylogeny, ~1|Herbivore_Latin_name,
~1|Study, ~1|Effect),
data = dat
)
# getting marginal R2
r2_mr_mdcot_lnVR1 <- r2_ml(mr_mdcot_lnVR1)
# getting the level names out
level_names <- levels(dat$Poaceae_or_Non)
# helper function to run metafor meta-regression
run_rma <- function(name) {
rma.mv(yi = lnVR,
V = VCV_lnVR,
mods = ~ relevel(Poaceae_or_Non, ref = name),
test = "t",
random = list(~1|Herbivore_Phylogeny, ~1|Herbivore_Latin_name,
~1|Study, ~1|Effect),
data = dat)
}
# results of meta-regression including all contrast results; taking the last level out ([-length(level_names)])
mr_mdcot_lnVR <- map(level_names[-length(level_names)], run_rma)Table E6
Table E6: Regression coefficients (estimate), 95%
confidence intervals (CIs), P values, and variance explained,
R2[marginal] (R2) = 0.78% from the
meta-regression of lnVR with Poaceae_or_Non_Poaceae.
# getting estimates
res_mr_mdcot_lnVR1 <- get_pred1(mr_mdcot_lnVR1, mod = "Poaceae_or_Non")
#res_mr_annuality_lnRR2 <- map(mr_annuality_lnRR, ~ get_est2(.x, mod = "Annuality"))
res_mr_mdcot_lnVR <- map(mr_mdcot_lnVR, ~ get_pred2(.x, mod = "Poaceae_or_Non"))
# a list of the numbers to take out unnecessary contrasts
contra_list <- Map(seq, from=1, to=1:1)
# you need to flatten twice: first to make it a list and make it a vector
# I guess we can make it a loop (or vectorise)
res_mr_mdcot_lnVR2 <- map2_dfr(res_mr_mdcot_lnVR, contra_list, ~.x[-(.y), ])
# creating a table
mr_results(res_mr_mdcot_lnVR1, res_mr_mdcot_lnVR2) %>%
kable("html", digits = 3) %>%
kable_styling("striped", position = "left") %>%
scroll_box(width = "100%")| Fixed effect | Estimate | Lower CI [0.025] | Upper CI [0.975] | P value | Lower PI [0.025] | Upper PI [0.975] |
|---|---|---|---|---|---|---|
| Non-Poaceae | 0.014 | -0.149 | 0.176 | 0.868 | -1.147 | 1.174 |
| Poaceae | -0.115 | -0.221 | -0.008 | 0.035 | -1.269 | 1.039 |
| Non-Poaceae-Poaceae | -0.129 | -0.312 | 0.055 | 0.169 | -1.292 | 1.035 |
Figure E3
p10 <- orchard_plot(mr_mdcot_lnRR1, data = dat, mod="Poaceae_or_Non",group= 'Study', xlab = "log(Response Ratio) (lnRR)", alpha = 0.2, angle = 45)
p11 <- orchard_plot(mr_mdcot_lnVR1, data = dat, mod="Poaceae_or_Non",group= 'Study', xlab = "log(Variabilty Ratio) (lnVR)", alpha = 0.2, angle = 45)
p10/p11
#ggsave(here("fig", "Monocot_Dicot.png"))Figure E3: Mean change (lnRR) and change in SD (lnVR) in herbivore performance when feeding on Poaceae and non-Poaceae plants. The orchard plot shows the meta-analytic mean (mean effect size) with its 95% confidence interval (thick line) and 95% prediction interval (thin line), with observed effect sizes based on sample sizes.
Plant lifespan: annual vs. perennial
Mean change: lnRR
# getting the level names out
level_names <- levels(dat$Plant_lifespan)
# lnRR
# meta-regression: multiple intercepts
mr_annuality_lnRR1 <- rma.mv(
yi = lnRR,
V = VCV_lnRR,
mods = ~ Plant_lifespan - 1,
test = "t",
random = list(~1|Herbivore_Phylogeny, ~1|Herbivore_Latin_name,
~1|Study, ~1|Effect),
data = dat
)
# getting marginal R2
r2_mr_annuality_lnRR1 <- r2_ml(mr_annuality_lnRR1)
# meta-regression: contrasts
# helper function to run metafor meta-regression
run_rma <- function(name) {
rma.mv(yi = lnRR,
V = VCV_lnRR,
mods = ~ relevel(Plant_lifespan, ref = name),
test = "t",
random = list(~1|Herbivore_Phylogeny, ~1|Herbivore_Latin_name,
~1|Study, ~1|Effect),
data = dat)
}
# results of meta-regression including all contrast results; taking the last level out ([-length(level_names)])
mr_annuality_lnRR <- map(level_names[-length(level_names)], run_rma)Table E7
Table E7: Regression coefficients (estimate), 95%
confidence intervals (CIs), and variance explained,
R2[marginal] (R2) = 0% from the
meta-regression of lnRR with Plant_lifespan.
# getting estimates
res_mr_annuality_lnRR1 <- get_pred1(mr_annuality_lnRR1, mod = "Plant_lifespan")
res_mr_annuality_lnRR <- map(mr_annuality_lnRR, ~ get_pred2(.x, mod = "Plant_lifespan"))
# a list of the numbers to take out unnecessary contrasts
contra_list <- Map(seq, from=1, to=1:1)
# you need to flatten twice: first to make it a list and make it a vector
# I guess we can make it a loop (or vectorise)
res_mr_annuality_lnRR2 <- map2_dfr(res_mr_annuality_lnRR, contra_list, ~.x[-(.y), ])
# creating a table
mr_results(res_mr_annuality_lnRR1, res_mr_annuality_lnRR2) %>%
kable("html", digits = 3) %>%
kable_styling("striped", position = "left") %>%
scroll_box(width = "100%")| Fixed effect | Estimate | Lower CI [0.025] | Upper CI [0.975] | P value | Lower PI [0.025] | Upper PI [0.975] |
|---|---|---|---|---|---|---|
| Annual | -0.336 | -0.436 | -0.237 | 0.000 | -1.227 | 0.555 |
| Perennial | -0.332 | -0.441 | -0.224 | 0.000 | -1.224 | 0.560 |
| Annual-Perennial | 0.004 | -0.109 | 0.117 | 0.949 | -0.889 | 0.896 |
Change in SD: lnVR
# getting the level names out
level_names <- levels(dat$Plant_lifespan)
# lnRR
# meta-regression: multiple intercepts
mr_annuality_lnVR1 <- rma.mv(
yi = lnVR,
V = VCV_lnVR,
mods = ~ Plant_lifespan - 1,
test = "t",
random = list(~1|Herbivore_Phylogeny, ~1|Herbivore_Latin_name,
~1|Study, ~1|Effect),
data = dat
)
# getting marginal R2
r2_mr_annuality_lnVR1 <- r2_ml(mr_annuality_lnVR1)
# meta-regression: contrasts
# helper function to run metafor meta-regression
run_rma <- function(name) {
rma.mv(yi = lnVR,
V = VCV_lnVR,
mods = ~ relevel(Plant_lifespan, ref = name),
test = "t",
random = list(~1|Herbivore_Phylogeny, ~1|Herbivore_Latin_name,
~1|Study, ~1|Effect),
data = dat)
}
# results of meta-regression including all contrast results; taking the last level out ([-length(level_names)])
mr_annuality_lnVR <- map(level_names[-length(level_names)], run_rma)Table E8
Table E8: Regression coefficients (estimate), 95%
confidence intervals (CIs), and variance explained,
R2[marginal] (R2) = 0.39% from the
meta-regression of lnVR with Plant_lifespan.
# getting estimates
res_mr_annuality_lnVR1 <- get_pred1(mr_annuality_lnVR1, mod = "Plant_lifespan")
res_mr_annuality_lnVR <- map(mr_annuality_lnVR, ~ get_pred2(.x, mod = "Plant_lifespan"))
# a list of the numbers to take out unnecessary contrasts
contra_list <- Map(seq, from=1, to=1:1)
# you need to flatten twice: first to make it a list and make it a vector
# I guess we can make it a loop (or vectorise)
res_mr_annuality_lnVR2 <- map2_dfr(res_mr_annuality_lnVR, contra_list, ~.x[-(.y), ])
# creating a table
mr_results(res_mr_annuality_lnVR1, res_mr_annuality_lnVR2) %>%
kable("html", digits = 3) %>%
kable_styling("striped", position = "left") %>%
scroll_box(width = "100%")| Fixed effect | Estimate | Lower CI [0.025] | Upper CI [0.975] | P value | Lower PI [0.025] | Upper PI [0.975] |
|---|---|---|---|---|---|---|
| Annual | -0.112 | -0.227 | 0.003 | 0.057 | -1.274 | 1.051 |
| Perennial | -0.035 | -0.164 | 0.094 | 0.594 | -1.199 | 1.129 |
| Annual-Perennial | 0.077 | -0.072 | 0.226 | 0.311 | -1.089 | 1.243 |
Figure E4
p7 <- orchard_plot(mr_annuality_lnRR1, data = dat, mod="Plant_lifespan",group= 'Study', xlab = "log(Response Ratio) (lnRR)", alpha = 0.2, angle = 45)
p8 <- orchard_plot(mr_annuality_lnVR1, data = dat, mod="Plant_lifespan",group= 'Study', xlab = "log(Variability Ratio) (lnVR)", alpha = 0.2, angle = 45)
p7/p8
#ggsave(here("fig", "Annuality.png"))Figure E4: Mean change (lnRR), and change in SD (lnVR) in herbivore performance when feeding on annual or perennial plants. The orchard plot shows the meta-analytic mean (mean effect size) with its 95% confidence interval (thick line) and 95% prediction interval (thin line), with observed effect sizes based on sample sizes.
Herbivore distinction: generalists vs. specialists
Mean change: lnRR
# meta-regression: multiple intercepts
mr_specgen_lnRR1 <- rma.mv(
yi = lnRR,
V = VCV_lnRR,
mods = ~ Herbivore_diet_breadth - 1,
test = "t",
random = list(~1|Herbivore_Phylogeny, ~1|Herbivore_Latin_name,
~1|Study, ~1|Effect),
data = dat
)
# getting marginal R2
r2_mr_specgen_lnRR1 <- r2_ml(mr_specgen_lnRR1)
# getting the level names out
level_names <- levels(dat$Herbivore_diet_breadth)
# helper function to run metafor meta-regression
run_rma <- function(name) {
rma.mv(yi = lnRR,
V = VCV_lnRR,
mods = ~ relevel(Herbivore_diet_breadth, ref = name),
test = "t",
random = list(~1|Herbivore_Phylogeny, ~1|Herbivore_Latin_name,
~1|Study, ~1|Effect),
data = dat)
}
# results of meta-regression including all contrast results; taking the last level out ([-length(level_names)])
mr_specgen_lnRR <- map(level_names[-length(level_names)], run_rma)Table E9
Table E9: Regression coefficients (estimate), 95%
confidence intervals (CIs), P values and variance explained,
R2[marginal] (R2) = 1.25% from the
meta-regression of lnRR with Herbivore_diet_breadth.
# getting estimates
res_mr_specgen_lnRR1 <- get_pred1(mr_specgen_lnRR1, mod = "Herbivore_diet_breadth")
#res_mr_annuality_lnRR2 <- map(mr_annuality_lnRR, ~ get_est2(.x, mod = "Annuality"))
res_mr_specgen_lnRR <- map(mr_specgen_lnRR, ~ get_pred2(.x, mod = "Herbivore_diet_breadth"))
# a list of the numbers to take out unnecessary contrasts
contra_list <- Map(seq, from=1, to=1:1)
# you need to flatten twice: first to make it a list and make it a vector
# I guess we can make it a loop (or vectorise)
res_mr_specgen_lnRR2 <- map2_dfr(res_mr_specgen_lnRR, contra_list, ~.x[-(.y), ])
# creating a table
mr_results(res_mr_specgen_lnRR1, res_mr_specgen_lnRR2) %>%
kable("html", digits = 3) %>%
kable_styling("striped", position = "left") %>%
scroll_box(width = "100%")| Fixed effect | Estimate | Lower CI [0.025] | Upper CI [0.975] | P value | Lower PI [0.025] | Upper PI [0.975] |
|---|---|---|---|---|---|---|
| Generalist | -0.388 | -0.509 | -0.266 | 0.000 | -1.277 | 0.502 |
| Specialist | -0.286 | -0.404 | -0.167 | 0.000 | -1.175 | 0.603 |
| Generalist-Specialist | 0.102 | -0.065 | 0.269 | 0.232 | -0.795 | 0.999 |
####Change in SD: lnVR
# meta-regression: multiple intercepts
mr_specgen_lnVR1 <- rma.mv(
yi = lnVR,
V = VCV_lnVR,
mods = ~ Herbivore_diet_breadth - 1,
test = "t",
random = list(~1|Herbivore_Phylogeny, ~1|Herbivore_Latin_name,
~1|Study, ~1|Effect),
data = dat
)
# getting marginal R2
r2_mr_specgen_lnVR1 <- r2_ml(mr_specgen_lnVR1)
# getting the level names out
level_names <- levels(dat$Herbivore_diet_breadth)
# helper function to run metafor meta-regression
run_rma <- function(name) {
rma.mv(yi = lnVR,
V = VCV_lnVR,
mods = ~ relevel(Herbivore_diet_breadth, ref = name),
test = "t",
random = list(~1|Herbivore_Phylogeny, ~1|Herbivore_Latin_name,
~1|Study, ~1|Effect),
data = dat)
}
# results of meta-regression including all contrast results; taking the last level out ([-length(level_names)])
mr_specgen_lnVR <- map(level_names[-length(level_names)], run_rma)Table E10
Table E10: Regression coefficients (estimate), 95%
confidence intervals (CIs), P values and variance explained,
R2[marginal] (R2) = 0.14% from the
meta-regression of lnVR with Herbivore_diet_breadth.
# getting estimates
res_mr_specgen_lnVR1 <- get_pred1(mr_specgen_lnVR1, mod = "Herbivore_diet_breadth")
#res_mr_annuality_lnVR2 <- map(mr_annuality_lnVR, ~ get_est2(.x, mod = "Annuality"))
res_mr_specgen_lnVR <- map(mr_specgen_lnVR, ~ get_pred2(.x, mod = "Herbivore_diet_breadth"))
# a list of the numbers to take out unnecessary contrasts
contra_list <- Map(seq, from=1, to=1:1)
# you need to flatten twice: first to make it a list and make it a vector
# I guess we can make it a loop (or vectorise)
res_mr_specgen_lnVR2 <- map2_dfr(res_mr_specgen_lnVR, contra_list, ~.x[-(.y), ])
# creating a table
mr_results(res_mr_specgen_lnVR1, res_mr_specgen_lnVR2) %>%
kable("html", digits = 3) %>%
kable_styling("striped", position = "left") %>%
scroll_box(width = "100%")| Fixed effect | Estimate | Lower CI [0.025] | Upper CI [0.975] | P value | Lower PI [0.025] | Upper PI [0.975] |
|---|---|---|---|---|---|---|
| Generalist | -0.103 | -0.242 | 0.037 | 0.149 | -1.269 | 1.063 |
| Specialist | -0.059 | -0.189 | 0.072 | 0.379 | -1.224 | 1.106 |
| Generalist-Specialist | 0.044 | -0.145 | 0.233 | 0.646 | -1.129 | 1.217 |
Figure E5
p13 <- orchard_plot(mr_specgen_lnRR1, data = dat, mod="Herbivore_diet_breadth",group= 'Study', xlab = "log(Response Ratio) (lnRR)", alpha = 0.2, angle = 45)
p14 <- orchard_plot(mr_specgen_lnVR1, data = dat, mod="Herbivore_diet_breadth",group= 'Study', xlab = "log(Variability Ratio) (lnVR)", alpha = 0.2, angle = 45)
p13/p14
#ggsave(here("fig", "SpecGen.png"))Figure E5: Mean change (lnRR) and change in SD (lnVR) in herbivore performance comparing herbivore diet breadth (generalist or specialist). The orchard plot shows the meta-analytic mean (mean effect size) with its 95% confidence interval (thick line) and 95% prediction interval (thin line), with observed effect sizes based on sample sizes.
Feeding guilds (7 groups)
Mean change: lnRR
# reordering
dat$Feeding_guild <- factor(dat$Feeding_guild,
levels = rev(c("Fluid-feeding arthropods",
"Chewing arthropods", "Boring arthropods",
"Mammalian chewers", "Cell-feeding arthropods",
"Leaf-mining arthropods", "Rasping / grazing invertebrates")),
labels = rev(c("Fluid-feeding arthropods",
"Chewing arthropods", "Boring arthropods",
"Mammalian chewers", "Cell-feeding arthropods",
"Leaf-mining arthropods", "Rasping / grazing invertebrates")))
# meta-regression: multiple intercepts
mr_feeding_lnRR1 <- rma.mv(
yi = lnRR,
V = VCV_lnRR,
mods = ~ Feeding_guild - 1,
test = "t",
random = list(~1|Herbivore_Phylogeny, ~1|Herbivore_Latin_name,
~1|Study, ~1|Effect),
data = dat
)
# getting marginal R2
r2_mr_feeding_lnRR1 <- r2_ml(mr_feeding_lnRR1)
# # meta-regression: contrasts x 7
# getting the level names out
level_names <- levels(dat$Feeding_guild)
# helper function to run metafor meta-regression
run_feeding_lnRR <- function(name) {
rma.mv(yi = lnRR,
V = VCV_lnRR,
mods = ~ relevel(Feeding_guild, ref = name),
test = "t",
random = list(~1|Herbivore_Phylogeny, ~1|Herbivore_Latin_name,
~1|Study, ~1|Effect),
data = dat)
}
# results of meta-regression including all contrast results; taking the last level out ([-length(level_names)])
mr_feeding_lnRR <- map(level_names[-length(level_names)], run_feeding_lnRR)Table E11
Table E11: Regression coefficients (estimate), 95%
confidence intervals (CIs), and variance explained,
R2[marginal] (R2) = 10.04% from the
meta-regression of lnRR with Feeding_guild.
# getting estimates
res_mr_feeding_lnRR1 <- get_pred1(mr_feeding_lnRR1, mod = "Feeding_guild")
res_mr_feeding_lnRR <- map(mr_feeding_lnRR, ~ get_pred2(.x, mod = "Feeding_guild"))
# a list of the numbers to take out unnecessary contrasts
contra_list <- Map(seq, from=1, to=1:6)
# I guess we can make it a loop (or vectorise)
res_mr_feeding_lnRR2 <- map2_dfr(res_mr_feeding_lnRR, contra_list, ~.x[-(.y), ])
# creating a table
mr_results(res_mr_feeding_lnRR1, res_mr_feeding_lnRR2) %>%
kable("html", digits = 3) %>%
kable_styling("striped", position = "left") %>%
scroll_box(width = "100%", height = "300px")| Fixed effect | Estimate | Lower CI [0.025] | Upper CI [0.975] | P value | Lower PI [0.025] | Upper PI [0.975] |
|---|---|---|---|---|---|---|
| Rasping / grazing invertebrates | -1.038 | -2.225 | 0.148 | 0.086 | -2.500 | 0.423 |
| Leaf-mining arthropods | -1.165 | -2.152 | -0.178 | 0.021 | -2.470 | 0.140 |
| Cell-feeding arthropods | -0.358 | -0.759 | 0.043 | 0.080 | -1.301 | 0.585 |
| Mammalian chewers | -0.602 | -0.908 | -0.295 | 0.000 | -1.508 | 0.305 |
| Boring arthropods | -0.495 | -0.691 | -0.299 | 0.000 | -1.371 | 0.380 |
| Chewing arthropods | -0.385 | -0.513 | -0.257 | 0.000 | -1.248 | 0.478 |
| Fluid-feeding arthropods | -0.155 | -0.285 | -0.025 | 0.020 | -1.018 | 0.708 |
| Rasping / grazing invertebrates-Leaf-mining arthropods | -0.127 | -1.670 | 1.417 | 0.872 | -1.890 | 1.637 |
| Rasping / grazing invertebrates-Cell-feeding arthropods | 0.681 | -0.572 | 1.933 | 0.286 | -0.835 | 2.196 |
| Rasping / grazing invertebrates-Mammalian chewers | 0.437 | -0.789 | 1.663 | 0.484 | -1.057 | 1.930 |
| Rasping / grazing invertebrates-Boring arthropods | 0.543 | -0.660 | 1.746 | 0.376 | -0.932 | 2.018 |
| Rasping / grazing invertebrates-Chewing arthropods | 0.653 | -0.541 | 1.847 | 0.283 | -0.814 | 2.121 |
| Rasping / grazing invertebrates-Fluid-feeding arthropods | 0.884 | -0.310 | 2.078 | 0.147 | -0.584 | 2.351 |
| Leaf-mining arthropods-Cell-feeding arthropods | 0.807 | -0.258 | 1.872 | 0.137 | -0.557 | 2.172 |
| Leaf-mining arthropods-Mammalian chewers | 0.564 | -0.470 | 1.597 | 0.285 | -0.776 | 1.904 |
| Leaf-mining arthropods-Boring arthropods | 0.670 | -0.336 | 1.676 | 0.191 | -0.649 | 1.989 |
| Leaf-mining arthropods-Chewing arthropods | 0.780 | -0.210 | 1.769 | 0.122 | -0.527 | 2.087 |
| Leaf-mining arthropods-Fluid-feeding arthropods | 1.010 | 0.015 | 2.005 | 0.047 | -0.301 | 2.321 |
| Cell-feeding arthropods-Mammalian chewers | -0.244 | -0.748 | 0.261 | 0.343 | -1.235 | 0.748 |
| Cell-feeding arthropods-Boring arthropods | -0.138 | -0.580 | 0.305 | 0.542 | -1.099 | 0.824 |
| Cell-feeding arthropods-Chewing arthropods | -0.028 | -0.448 | 0.393 | 0.898 | -0.979 | 0.924 |
| Cell-feeding arthropods-Fluid-feeding arthropods | 0.203 | -0.219 | 0.624 | 0.345 | -0.749 | 1.155 |
| Mammalian chewers-Boring arthropods | 0.106 | -0.258 | 0.470 | 0.566 | -0.821 | 1.034 |
| Mammalian chewers-Chewing arthropods | 0.216 | -0.114 | 0.547 | 0.200 | -0.699 | 1.132 |
| Mammalian chewers-Fluid-feeding arthropods | 0.447 | 0.114 | 0.779 | 0.009 | -0.469 | 1.363 |
| Boring arthropods-Chewing arthropods | 0.110 | -0.121 | 0.340 | 0.349 | -0.774 | 0.994 |
| Boring arthropods-Fluid-feeding arthropods | 0.340 | 0.105 | 0.576 | 0.005 | -0.545 | 1.226 |
| Chewing arthropods-Fluid-feeding arthropods | 0.230 | 0.052 | 0.409 | 0.011 | -0.641 | 1.102 |
Change in SD: lnVR
# meta-regression: multiple intercepts
mr_feeding_lnVR1 <- rma.mv(
yi = lnVR,
V = VCV_lnVR,
mods = ~ Feeding_guild - 1,
test = "t",
random = list(~1|Herbivore_Phylogeny, ~1|Herbivore_Latin_name,
~1|Study, ~1|Effect),
data = dat
)
# getting marginal R2
r2_mr_feeding_lnVR1 <- r2_ml(mr_feeding_lnVR1)
# # meta-regression: contrasts x 7
# getting the level names out
level_names <- levels(dat$Feeding_guild)
# helper function to run metafor meta-regression
run_feeding_lnVR <- function(name) {
rma.mv(yi = lnVR,
V = VCV_lnVR,
mods = ~ relevel(Feeding_guild, ref = name),
test = "t",
random = list(~1|Herbivore_Phylogeny, ~1|Herbivore_Latin_name,
~1|Study, ~1|Effect),
data = dat)
}
# results of meta-regression including all contrast results; taking the last level out ([-length(level_names)])
mr_feeding_lnVR <- map(level_names[-length(level_names)], run_feeding_lnVR)Table E12
Table E12: Regression coefficients (estimate), 95%
confidence intervals (CIs), and variance explained,
R2[marginal] (R2) = 2.04% from the
meta-regression of lnVR with Feeding_guild.
# getting estimates
res_mr_feeding_lnVR1 <- get_pred1(mr_feeding_lnVR1, mod = "Feeding_guild")
res_mr_feeding_lnVR <- map(mr_feeding_lnVR, ~ get_pred2(.x, mod = "Feeding_guild"))
# a list of the numbers to take out unnecessary contrasts
contra_list <- Map(seq, from=1, to=1:6)
# I guess we can make it a loop (or vectorise)
res_mr_feeding_lnVR2 <- map2_dfr(res_mr_feeding_lnVR, contra_list, ~.x[-(.y), ])
# creating a table
mr_results(res_mr_feeding_lnVR1, res_mr_feeding_lnVR2) %>%
kable("html", digits = 3) %>%
kable_styling("striped", position = "left") %>%
scroll_box(width = "100%", height = "300px")| Fixed effect | Estimate | Lower CI [0.025] | Upper CI [0.975] | P value | Lower PI [0.025] | Upper PI [0.975] |
|---|---|---|---|---|---|---|
| Rasping / grazing invertebrates | -0.542 | -1.674 | 0.590 | 0.348 | -2.163 | 1.080 |
| Leaf-mining arthropods | 0.053 | -1.520 | 1.627 | 0.947 | -1.902 | 2.009 |
| Cell-feeding arthropods | 0.134 | -0.372 | 0.639 | 0.604 | -1.132 | 1.399 |
| Mammalian chewers | -0.219 | -0.638 | 0.200 | 0.305 | -1.453 | 1.015 |
| Boring arthropods | -0.248 | -0.510 | 0.014 | 0.064 | -1.438 | 0.942 |
| Chewing arthropods | -0.086 | -0.236 | 0.064 | 0.263 | -1.256 | 1.085 |
| Fluid-feeding arthropods | -0.005 | -0.163 | 0.152 | 0.949 | -1.176 | 1.166 |
| Rasping / grazing invertebrates-Leaf-mining arthropods | 0.595 | -1.344 | 2.534 | 0.547 | -1.664 | 2.854 |
| Rasping / grazing invertebrates-Cell-feeding arthropods | 0.675 | -0.565 | 1.915 | 0.285 | -1.023 | 2.374 |
| Rasping / grazing invertebrates-Mammalian chewers | 0.323 | -0.885 | 1.530 | 0.600 | -1.352 | 1.998 |
| Rasping / grazing invertebrates-Boring arthropods | 0.294 | -0.868 | 1.456 | 0.620 | -1.349 | 1.936 |
| Rasping / grazing invertebrates-Chewing arthropods | 0.456 | -0.686 | 1.598 | 0.433 | -1.172 | 2.085 |
| Rasping / grazing invertebrates-Fluid-feeding arthropods | 0.537 | -0.607 | 1.680 | 0.357 | -1.092 | 2.166 |
| Leaf-mining arthropods-Cell-feeding arthropods | 0.080 | -1.572 | 1.733 | 0.924 | -1.939 | 2.100 |
| Leaf-mining arthropods-Mammalian chewers | -0.272 | -1.901 | 1.356 | 0.743 | -2.272 | 1.727 |
| Leaf-mining arthropods-Boring arthropods | -0.301 | -1.896 | 1.294 | 0.711 | -2.274 | 1.672 |
| Leaf-mining arthropods-Chewing arthropods | -0.139 | -1.716 | 1.438 | 0.863 | -2.097 | 1.819 |
| Leaf-mining arthropods-Fluid-feeding arthropods | -0.058 | -1.640 | 1.523 | 0.942 | -2.020 | 1.903 |
| Cell-feeding arthropods-Mammalian chewers | -0.353 | -1.009 | 0.304 | 0.292 | -1.686 | 0.981 |
| Cell-feeding arthropods-Boring arthropods | -0.382 | -0.949 | 0.186 | 0.187 | -1.673 | 0.910 |
| Cell-feeding arthropods-Chewing arthropods | -0.219 | -0.746 | 0.308 | 0.415 | -1.494 | 1.056 |
| Cell-feeding arthropods-Fluid-feeding arthropods | -0.139 | -0.668 | 0.391 | 0.607 | -1.414 | 1.137 |
| Mammalian chewers-Boring arthropods | -0.029 | -0.524 | 0.466 | 0.908 | -1.291 | 1.233 |
| Mammalian chewers-Chewing arthropods | 0.133 | -0.311 | 0.578 | 0.556 | -1.109 | 1.376 |
| Mammalian chewers-Fluid-feeding arthropods | 0.214 | -0.234 | 0.662 | 0.349 | -1.030 | 1.458 |
| Boring arthropods-Chewing arthropods | 0.162 | -0.137 | 0.462 | 0.287 | -1.036 | 1.361 |
| Boring arthropods-Fluid-feeding arthropods | 0.243 | -0.063 | 0.549 | 0.120 | -0.957 | 1.443 |
| Chewing arthropods-Fluid-feeding arthropods | 0.080 | -0.135 | 0.296 | 0.463 | -1.100 | 1.261 |
Figure E6
p17 <- orchard_plot(mr_feeding_lnRR1, data = dat, mod="Feeding_guild",group= 'Study', xlab = "log(Response Ratio) (lnRR)", alpha = 0.2, angle = 45)
p18 <- orchard_plot(mr_feeding_lnVR1, data = dat, mod="Feeding_guild",group= 'Study', xlab = "log(Variability Ratio) (lnVR)", alpha = 0.2, angle = 45)
# p11|p12
# ggsave(here("fig", "Feeding_guild.png"))
p17/p18
#ggsave(here("fig", "Feeding_guild2.png"))Figure E6: Mean change (lnRR), and change in SD (lnVR) in herbivore performance comparing herbivore feeding guild. The orchard plot shows the meta-analytic mean (mean effect size) with its 95% confidence interval (thick line) and 95% prediction interval (thin line), with observed effect sizes based on sample sizes.
Figure 3A-B (N < 10 taken out)
# TODO the esiest to jsut run models without these groups
# TODO - I cannot subset the data properly, I have tried a few alternative techniques, they work, but as the dataset from the model is has more 'Feeding_guild' levels than the subset dataframe so there is an NA row in the figure.
datlowrm<-dat[!(dat$Feeding_guild=="Rasping / grazing invertebrates" | dat$Feeding_guild=="Leaf-mining arthropods" | dat$Feeding_guild=="Cell-feeding arthropods"),]
# matrix for controlling for correlated errors
VCV_lnRR_lrem <- make_VCV_matrix(datlowrm, V = "varlnRR", cluster = "Study", obs = "Effect")
#lnRR - run models removing groups with low effect sizes
# meta-regression: multiple intercepts
mr_feeding_lnRR1_1 <- rma.mv(
yi = lnRR,
V = VCV_lnRR_lrem,
mods = ~ Feeding_guild - 1,
test = "t",
random = list(~1|Herbivore_Phylogeny, ~1|Herbivore_Latin_name,
~1|Study, ~1|Effect),
data = datlowrm
)
# getting marginal R2
r2_mr_feeding_lnRR1_1 <- r2_ml(mr_feeding_lnRR1_1)
# # meta-regression: contrasts x 7
# helper function to run metafor meta-regression
run_feeding_lnRR1 <- function(name) {
rma.mv(yi = lnRR,
V = VCV_lnRR_lrem,
mods = ~ relevel(Feeding_guild, ref = name),
test = "t",
random = list(~1|Herbivore_Phylogeny, ~1|Herbivore_Latin_name,
~1|Study, ~1|Effect),
data = datlowrm)
}
# results of meta-regression including all contrast results; taking the last level out ([-length(level_names)])
#op_feeding_lnRR <- orchaRd::mod_results(mr_feeding_lnRR1, mod = "Feeding_guild", data = dat, group = "Study",
# at = list(Feeding_guild = c("Fluid-feeding arthropods", 'Chewing arthropods','Boring arthropods','Mammalian chewers'), subset = TRUE))
#<- mod_results(mr_feeding_lnRR1, data = dat,mod = "Feeding_guild", group = 'Study')
# taking out some groups N < 10
#op_feeding_lnRR$mod_table <- op_feeding_lnRR$mod_table[-which(op_feeding_lnRR$mod_table$name == "Leaf-mining arthropods" | #op_feeding_lnRR$mod_table$name == "Rasping / grazing invertebrates" | op_feeding_lnRR$mod_table$name == "Cell-feeding arthropods"), ]
#op_feeding_lnRR$data <- op_feeding_lnRR$data[-which(op_feeding_lnRR$data$moderator == "Leaf-mining arthropods" | op_feeding_lnRR$data$moderator == "Rasping / grazing invertebrates" | op_feeding_lnRR$data$moderator == "Cell-feeding arthropods"), ]
m1 <- orchard_plot(mr_feeding_lnRR1_1 , data= datlowrm,mod="Feeding_guild", group= 'Study', xlab = "log(Response Ratio) (lnRR)", alpha = 0.2, angle = 45)
#lnVR
# matrix for controlling for correlated errors
VCV_lnVR_lrem <- make_VCV_matrix(datlowrm, V = "varlnVR", cluster = "Study", obs = "Effect")
#lnRR - run models removing groups with low effect sizes
# meta-regression: multiple intercepts
mr_feeding_lnVR1_1 <- rma.mv(
yi = lnVR,
V = VCV_lnVR_lrem,
mods = ~ Feeding_guild - 1,
test = "t",
random = list(~1|Herbivore_Phylogeny, ~1|Herbivore_Latin_name,
~1|Study, ~1|Effect),
data = datlowrm
)
# getting marginal R2
r2_mr_feeding_lnVR1_1 <- r2_ml(mr_feeding_lnVR1_1)
# helper function to run metafor meta-regression
run_feeding_lnRR1 <- function(name) {
rma.mv(yi = lnRR,
V = VCV_lnRR_lrem,
mods = ~ relevel(Feeding_guild, ref = name),
test = "t",
random = list(~1|Herbivore_Phylogeny, ~1|Herbivore_Latin_name,
~1|Study, ~1|Effect),
data = datlowrm)
}
#op_feeding_lnVR <- mod_results(mr_feeding_lnVR1, mod = "Feeding_guild")
# taking out some groups N < 10
#op_feeding_lnVR$mod_table <- op_feeding_lnVR$mod_table[-which(op_feeding_lnVR$mod_table$name == "Leaf-mining arthropods" | op_feeding_lnVR$mod_table$name == "Rasping / grazing invertebrates" | op_feeding_lnVR$mod_table$name == "Cell-feeding arthropods"), ]
#op_feeding_lnVR$data <- op_feeding_lnVR$data[-which(op_feeding_lnVR$data$moderator == "Leaf-mining arthropods" | op_feeding_lnVR$data$moderator == "Rasping / grazing invertebrates" | op_feeding_lnVR$data$moderator == "Cell-feeding arthropods"), ]
m2 <- orchard_plot(mr_feeding_lnVR1_1 , data= datlowrm,mod="Feeding_guild", group= 'Study', xlab = "log(Response Ratio) (lnVR)", alpha = 0.2, angle = 45)
#ggsave(here("fig", "Feeding_guild2m.png"))Interaction effects (subset of guilds with “Chewing arthropods” and “Fluid-feeding arthropods”)
We ran a univariate meta-regression model for each of the following
moderators:: 1) Lifespan_guild, 2)
Poaceae_guild, and 3) Diet_guild (see the
meta-data above).
We with the 3 other categorical variables:
#
dat %>% filter(Feeding_guild == "Chewing arthropods" | Feeding_guild == "Fluid-feeding arthropods") %>%
mutate(Lifespan_guild = factor(str_c(Plant_lifespan, Feeding_guild, sep = ":")),
Poaceae_guild = factor(str_c(Poaceae_or_Non, Feeding_guild, sep = ":")),
Diet_guild = factor(str_c(Herbivore_diet_breadth, Feeding_guild, sep = ":")) ) -> gdatThe combined effect of plant lifespan and guilds (Chewing arthropods/ Fluid-feeding arthropods)
Mean change: lnRR
# reordering
gdat$Lifespan_guild <- factor(gdat$Lifespan_guild,
levels = c("Annual:Chewing arthropods","Perennial:Chewing arthropods", "Annual:Fluid-feeding arthropods", "Perennial:Fluid-feeding arthropods"),
labels = c("Annual:Chewing arthropods","Perennial:Chewing arthropods", "Annual:Fluid-feeding arthropods", "Perennial:Fluid-feeding arthropods"))
# vcv matrix
VCV_lnRR2 <- make_VCV_matrix(gdat, V = "varlnRR", cluster = "Study", obs = "Effect")
# meta-regression: multiple intercepts
mr_Annuality_guild_lnRR1 <- rma.mv(
yi = lnRR,
V = VCV_lnRR2,
mods = ~ Lifespan_guild - 1,
test = "t",
random = list(~1|Herbivore_Phylogeny, ~1|Herbivore_Latin_name,
~1|Study, ~1|Effect),
data = gdat
)
# getting marginal R2
r2_mr_Annuality_guild_lnRR1 <- r2_ml(mr_Annuality_guild_lnRR1)
# # meta-regression: contrasts x 7
# getting the level names out
level_names <- levels(gdat$Lifespan_guild)
# helper function to run metafor meta-regression
run_Annuality_guild_lnRR <- function(name) {
rma.mv(yi = lnRR,
V = VCV_lnRR2,
mods = ~ relevel(Lifespan_guild, ref = name),
test = "t",
random = list(~1|Herbivore_Phylogeny, ~1|Herbivore_Latin_name,
~1|Study, ~1|Effect),
data = gdat)
}
# results of meta-regression including all contrast results; taking the last level out ([-length(level_names)])
mr_Annuality_guild_lnRR <- map(level_names[-length(level_names)], run_Annuality_guild_lnRR)Table E13
Table E13: Regression coefficients (estimate), 95%
confidence intervals (CIs), and variance explained,
R2[marginal] (R2) = 6.06% from the
meta-regression of lnRR with Lifespan_guild.
# getting estimates
res_mr_Annuality_guild_lnRR1 <- get_pred1(mr_Annuality_guild_lnRR1, mod = "Lifespan_guild")
res_mr_Annuality_guild_lnRR <- map(mr_Annuality_guild_lnRR, ~ get_pred2(.x, mod = "Lifespan_guild"))
# a list of the numbers to take out unnecessary contrasts
contra_list <- Map(seq, from=1, to=1:3)
res_mr_Annuality_guild_lnRR2 <- map2_dfr(res_mr_Annuality_guild_lnRR, contra_list, ~.x[-(.y), ])
# creating a table
mr_results(res_mr_Annuality_guild_lnRR1, res_mr_Annuality_guild_lnRR2) %>%
kable("html", digits = 3) %>%
kable_styling("striped", position = "left") %>%
scroll_box(width = "100%", height = "300px")| Fixed effect | Estimate | Lower CI [0.025] | Upper CI [0.975] | P value | Lower PI [0.025] | Upper PI [0.975] |
|---|---|---|---|---|---|---|
| Annual:Chewing arthropods | -0.336 | -0.468 | -0.204 | 0.000 | -1.145 | 0.473 |
| Perennial:Chewing arthropods | -0.404 | -0.563 | -0.245 | 0.000 | -1.218 | 0.410 |
| Annual:Fluid-feeding arthropods | -0.170 | -0.320 | -0.019 | 0.027 | -0.982 | 0.643 |
| Perennial:Fluid-feeding arthropods | -0.129 | -0.286 | 0.029 | 0.109 | -0.942 | 0.685 |
| Annual:Chewing arthropods-Perennial:Chewing arthropods | -0.068 | -0.218 | 0.082 | 0.372 | -0.880 | 0.744 |
| Annual:Chewing arthropods-Annual:Fluid-feeding arthropods | 0.166 | -0.034 | 0.366 | 0.103 | -0.656 | 0.989 |
| Annual:Chewing arthropods-Perennial:Fluid-feeding arthropods | 0.207 | 0.005 | 0.410 | 0.045 | -0.616 | 1.031 |
| Perennial:Chewing arthropods-Annual:Fluid-feeding arthropods | 0.235 | 0.015 | 0.454 | 0.036 | -0.593 | 1.062 |
| Perennial:Chewing arthropods-Perennial:Fluid-feeding arthropods | 0.275 | 0.056 | 0.495 | 0.014 | -0.552 | 1.103 |
| Annual:Fluid-feeding arthropods-Perennial:Fluid-feeding arthropods | 0.041 | -0.143 | 0.224 | 0.662 | -0.778 | 0.860 |
Change in SD: lnVR
# vcv matrix
VCV_lnVR2 <- make_VCV_matrix(gdat, V = "varlnVR", cluster = "Study", obs = "Effect")
# meta-regression: multiple intercepts
mr_Annuality_guild_lnVR1 <- rma.mv(
yi = lnVR,
V = VCV_lnVR2,
mods = ~ Lifespan_guild - 1,
test = "t",
random = list(~1|Herbivore_Phylogeny, ~1|Herbivore_Latin_name,
~1|Study, ~1|Effect),
data = gdat
)
# getting marginal R2
r2_mr_Annuality_guild_lnVR1 <- r2_ml(mr_Annuality_guild_lnVR1)
# # meta-regression: contrasts x 7
# getting the level names out
level_names <- levels(gdat$Lifespan_guild)
# helper function to run metafor meta-regression
run_Annuality_guild_lnVR <- function(name) {
rma.mv(yi = lnVR,
V = VCV_lnVR2,
mods = ~ relevel(Lifespan_guild, ref = name),
test = "t",
random = list(~1|Herbivore_Phylogeny, ~1|Herbivore_Latin_name,
~1|Study, ~1|Effect),
data = gdat)
}
# results of meta-regression including all contrast results; taking the last level out ([-length(level_names)])
mr_Annuality_guild_lnVR <- map(level_names[-length(level_names)], run_Annuality_guild_lnVR)Table E14
Table E14: Regression coefficients (estimate), 95%
confidence intervals (CIs), and variance explained,
R2[marginal] (R2) = 1.24% from the
meta-regression of lnVR with Lifespan_guild.
# getting estimates
res_mr_Annuality_guild_lnVR1 <- get_pred1(mr_Annuality_guild_lnVR1, mod = "Lifespan_guild")
res_mr_Annuality_guild_lnVR <- map(mr_Annuality_guild_lnVR, ~ get_pred2(.x, mod = "Lifespan_guild"))
# a list of the numbers to take out unnecessary contrasts
contra_list <- Map(seq, from=1, to=1:3)
res_mr_Annuality_guild_lnVR2 <- map2_dfr(res_mr_Annuality_guild_lnVR, contra_list, ~.x[-(.y), ])
# creating a table
mr_results(res_mr_Annuality_guild_lnVR1, res_mr_Annuality_guild_lnVR2) %>%
kable("html", digits = 3) %>%
kable_styling("striped", position = "left") %>%
scroll_box(width = "100%", height = "300px")| Fixed effect | Estimate | Lower CI [0.025] | Upper CI [0.975] | P value | Lower PI [0.025] | Upper PI [0.975] |
|---|---|---|---|---|---|---|
| Annual:Chewing arthropods | -0.121 | -0.287 | 0.045 | 0.152 | -1.292 | 1.049 |
| Perennial:Chewing arthropods | -0.016 | -0.219 | 0.186 | 0.874 | -1.193 | 1.160 |
| Annual:Fluid-feeding arthropods | -0.069 | -0.259 | 0.121 | 0.475 | -1.243 | 1.105 |
| Perennial:Fluid-feeding arthropods | 0.075 | -0.135 | 0.286 | 0.483 | -1.102 | 1.253 |
| Annual:Chewing arthropods-Perennial:Chewing arthropods | 0.105 | -0.110 | 0.320 | 0.339 | -1.074 | 1.283 |
| Annual:Chewing arthropods-Annual:Fluid-feeding arthropods | 0.052 | -0.199 | 0.303 | 0.685 | -1.134 | 1.238 |
| Annual:Chewing arthropods-Perennial:Fluid-feeding arthropods | 0.196 | -0.068 | 0.461 | 0.145 | -0.992 | 1.385 |
| Perennial:Chewing arthropods-Annual:Fluid-feeding arthropods | -0.053 | -0.332 | 0.226 | 0.710 | -1.245 | 1.139 |
| Perennial:Chewing arthropods-Perennial:Fluid-feeding arthropods | 0.092 | -0.195 | 0.378 | 0.530 | -1.102 | 1.285 |
| Annual:Fluid-feeding arthropods-Perennial:Fluid-feeding arthropods | 0.144 | -0.109 | 0.398 | 0.264 | -1.042 | 1.331 |
Figure 4C-D
## Figure 3C
p20 <- orchard_plot(mr_Annuality_guild_lnRR1, data= gdat, group = 'Study',mod="Lifespan_guild", xlab = "log(Response Ratio) (lnRR)", alpha = 0.2, angle = 45)
## Figure 3D
p21 <- orchard_plot(mr_Annuality_guild_lnVR1, data= gdat, group = 'Study',mod="Lifespan_guild", xlab = "log(Variability Ratio) (lnVR)", alpha = 0.2, angle = 45)
## Figure 3D
#ggsave(here("fig", "Annuality_guild.png"))The combined effect of flowering divisions (Poaceae vs Non-Poaceae) and guilds (Chewing arthropods / Fluid-feeding arthropods)
Mean change: lnRR
# reordering
gdat$Poaceae_guild <- factor(gdat$Poaceae_guild,
levels = c("Non-Poaceae:Chewing arthropods","Poaceae:Chewing arthropods", "Non-Poaceae:Fluid-feeding arthropods", "Poaceae:Fluid-feeding arthropods"),
labels = c("Non-Poaceae:Chewing arthropods","Poaceae:Chewing arthropods", "Non-Poaceae:Fluid-feeding arthropods", "Poaceae:Fluid-feeding arthropods"))
# meta-regression: multiple intercepts
mr_Monocot_Dicot_guild_lnRR1 <- rma.mv(
yi = lnRR,
V = VCV_lnRR2,
mods = ~ Poaceae_guild - 1,
test = "t",
random = list(~1|Herbivore_Phylogeny, ~1|Herbivore_Latin_name,
~1|Study, ~1|Effect),
data = gdat
)
# getting marginal R2
r2_mr_Monocot_Dicot_guild_lnRR1 <- r2_ml(mr_Monocot_Dicot_guild_lnRR1)
# # meta-regression: contrasts x 7
# getting the level names out
level_names <- levels(gdat$Poaceae_guild)
# helper function to run metafor meta-regression
run_Monocot_Dicot_guild_lnRR <- function(name) {
rma.mv(yi = lnRR,
V = VCV_lnRR2,
mods = ~ relevel(Poaceae_guild, ref = name),
test = "t",
random = list(~1|Herbivore_Phylogeny, ~1|Herbivore_Latin_name,
~1|Study, ~1|Effect),
data = gdat)
}
# results of meta-regression including all contrast results; taking the last level out ([-length(level_names)])
mr_Monocot_Dicot_guild_lnRR <- map(level_names[-length(level_names)], run_Monocot_Dicot_guild_lnRR)Table E15
Table E15: Regression coefficients (estimate), 95%
confidence intervals (CIs), and variance explained,
R2[marginal] (R2) = 9.44% from the
meta-regression of lnRR with Poaceae_guild.
# getting estimates
res_mr_Monocot_Dicot_guild_lnRR1 <- get_pred1(mr_Monocot_Dicot_guild_lnRR1, mod = "Poaceae_guild")
#res_mr_annuality_lnRR2 <- map(mr_annuality_lnRR, ~ get_est2(.x, mod = "Annuality"))
res_mr_Monocot_Dicot_guild_lnRR <- map(mr_Monocot_Dicot_guild_lnRR, ~ get_pred2(.x, mod = "Poaceae_guild"))
# a list of the numbers to take out unnecessary contrasts
contra_list <- Map(seq, from=1, to=1:3)
# you need to flatten twice: first to make it a list and make it a vector
# I guess we can make it a loop (or vectorise)
res_mr_Monocot_Dicot_guild_lnRR2 <- map2_dfr(res_mr_Monocot_Dicot_guild_lnRR, contra_list, ~.x[-(.y), ])
# creating a table
mr_results(res_mr_Monocot_Dicot_guild_lnRR1, res_mr_Monocot_Dicot_guild_lnRR2) %>%
kable("html", digits = 3) %>%
kable_styling("striped", position = "left") %>%
scroll_box(width = "100%", height = "300px")| Fixed effect | Estimate | Lower CI [0.025] | Upper CI [0.975] | P value | Lower PI [0.025] | Upper PI [0.975] |
|---|---|---|---|---|---|---|
| Non-Poaceae:Chewing arthropods | -0.185 | -0.388 | 0.018 | 0.073 | -1.011 | 0.641 |
| Poaceae:Chewing arthropods | -0.410 | -0.543 | -0.277 | 0.000 | -1.222 | 0.401 |
| Non-Poaceae:Fluid-feeding arthropods | -0.170 | -0.354 | 0.014 | 0.071 | -0.991 | 0.652 |
| Poaceae:Fluid-feeding arthropods | -0.129 | -0.301 | 0.042 | 0.140 | -0.948 | 0.690 |
| Non-Poaceae:Chewing arthropods-Poaceae:Chewing arthropods | -0.225 | -0.432 | -0.018 | 0.033 | -1.052 | 0.602 |
| Non-Poaceae:Chewing arthropods-Non-Poaceae:Fluid-feeding arthropods | 0.016 | -0.255 | 0.286 | 0.910 | -0.830 | 0.861 |
| Non-Poaceae:Chewing arthropods-Poaceae:Fluid-feeding arthropods | 0.056 | -0.209 | 0.321 | 0.677 | -0.787 | 0.900 |
| Poaceae:Chewing arthropods-Non-Poaceae:Fluid-feeding arthropods | 0.241 | 0.014 | 0.467 | 0.037 | -0.592 | 1.073 |
| Poaceae:Chewing arthropods-Poaceae:Fluid-feeding arthropods | 0.281 | 0.066 | 0.496 | 0.011 | -0.548 | 1.110 |
| Non-Poaceae:Fluid-feeding arthropods-Poaceae:Fluid-feeding arthropods | 0.041 | -0.211 | 0.292 | 0.751 | -0.799 | 0.880 |
Change in SD: lnVR
# meta-regression: multiple intercepts
mr_Monocot_Dicot_guild_lnVR1 <- rma.mv(
yi = lnVR,
V = VCV_lnVR2,
mods = ~ Poaceae_guild - 1,
test = "t",
random = list(~1|Herbivore_Phylogeny, ~1|Herbivore_Latin_name,
~1|Study, ~1|Effect),
data = gdat
)
# getting marginal R2
r2_mr_Monocot_Dicot_guild_lnVR1 <- r2_ml(mr_Monocot_Dicot_guild_lnVR1)
# # meta-regression: contrasts x 7
# getting the level names out
level_names <- levels(gdat$Poaceae_guild)
# helper function to run metafor meta-regression
run_Monocot_Dicot_guild_lnVR <- function(name) {
rma.mv(yi = lnVR,
V = VCV_lnVR2,
mods = ~ relevel(Poaceae_guild, ref = name),
test = "t",
random = list(~1|Herbivore_Phylogeny, ~1|Herbivore_Latin_name,
~1|Study, ~1|Effect),
data = gdat)
}
# results of meta-regression including all contrast results; taking the last level out ([-length(level_names)])
mr_Monocot_Dicot_guild_lnVR <- map(level_names[-length(level_names)], run_Monocot_Dicot_guild_lnVR)Table E16
Table E16: Regression coefficients (estimate), 95%
confidence intervals (CIs), and variance explained,
R2[marginal] (R2) = 0.75% from the
meta-regression of lnVR with Poaceae_guild.
# getting estimates
res_mr_Monocot_Dicot_guild_lnVR1 <- get_pred1(mr_Monocot_Dicot_guild_lnVR1, mod = "Poaceae_guild")
#res_mr_annuality_lnVR2 <- map(mr_annuality_lnVR, ~ get_est2(.x, mod = "Annuality"))
res_mr_Monocot_Dicot_guild_lnVR <- map(mr_Monocot_Dicot_guild_lnVR, ~ get_pred2(.x, mod = "Poaceae_guild"))
# a list of the numbers to take out unnecessary contrasts
contra_list <- Map(seq, from=1, to=1:3)
# you need to flatten twice: first to make it a list and make it a vector
# I guess we can make it a loop (or vectorise)
res_mr_Monocot_Dicot_guild_lnVR2 <- map2_dfr(res_mr_Monocot_Dicot_guild_lnVR, contra_list, ~.x[-(.y), ])
# creating a table
mr_results(res_mr_Monocot_Dicot_guild_lnVR1, res_mr_Monocot_Dicot_guild_lnVR2) %>%
kable("html", digits = 3) %>%
kable_styling("striped", position = "left") %>%
scroll_box(width = "100%", height = "300px")| Fixed effect | Estimate | Lower CI [0.025] | Upper CI [0.975] | P value | Lower PI [0.025] | Upper PI [0.975] |
|---|---|---|---|---|---|---|
| Non-Poaceae:Chewing arthropods | -0.049 | -0.297 | 0.200 | 0.701 | -1.235 | 1.138 |
| Poaceae:Chewing arthropods | -0.097 | -0.256 | 0.061 | 0.229 | -1.268 | 1.073 |
| Non-Poaceae:Fluid-feeding arthropods | 0.058 | -0.180 | 0.296 | 0.632 | -1.126 | 1.242 |
| Poaceae:Fluid-feeding arthropods | -0.052 | -0.255 | 0.150 | 0.611 | -1.230 | 1.125 |
| Non-Poaceae:Chewing arthropods-Poaceae:Chewing arthropods | -0.049 | -0.309 | 0.211 | 0.712 | -1.238 | 1.140 |
| Non-Poaceae:Chewing arthropods-Non-Poaceae:Fluid-feeding arthropods | 0.107 | -0.232 | 0.445 | 0.537 | -1.102 | 1.315 |
| Non-Poaceae:Chewing arthropods-Poaceae:Fluid-feeding arthropods | -0.004 | -0.324 | 0.316 | 0.981 | -1.207 | 1.199 |
| Poaceae:Chewing arthropods-Non-Poaceae:Fluid-feeding arthropods | 0.155 | -0.129 | 0.440 | 0.284 | -1.039 | 1.350 |
| Poaceae:Chewing arthropods-Poaceae:Fluid-feeding arthropods | 0.045 | -0.210 | 0.300 | 0.730 | -1.143 | 1.233 |
| Non-Poaceae:Fluid-feeding arthropods-Poaceae:Fluid-feeding arthropods | -0.110 | -0.423 | 0.202 | 0.487 | -1.312 | 1.091 |
Figure 4A-B
## Figure 3A
p23 <- orchard_plot(mr_Monocot_Dicot_guild_lnRR1, data= gdat, group = 'Study',mod="Poaceae_guild", xlab = "log(Response Ratio) (lnRR)", alpha = 0.2, angle = 45)
## Figure 3B
p24 <- orchard_plot(mr_Monocot_Dicot_guild_lnVR1, data= gdat, group = 'Study',mod="Poaceae_guild", xlab = "log(Variability Ratio) (lnVR)", alpha = 0.2, angle = 45)
#ggsave(here("fig", "Monocot_Dicot_guild.png"))The combined effect of herbivore distinctions (specialists vs generalist) and guilds (Chewing arthropods / Fluid-feeding arthropods)
Mean change: lnRR
# reordering
gdat$Diet_guild <- factor(gdat$Diet_guild,
levels = c("Generalist:Chewing arthropods", "Specialist:Chewing arthropods", "Generalist:Fluid-feeding arthropods", "Specialist:Fluid-feeding arthropods"),
labels = c("Generalist:Chewing arthropods", "Specialist:Chewing arthropods", "Generalist:Fluid-feeding arthropods", "Specialist:Fluid-feeding arthropods"))
# meta-regression: multiple intercepts
mr_SpecGen_guild_lnRR1 <- rma.mv(
yi = lnRR,
V = VCV_lnRR2,
mods = ~ Diet_guild - 1,
test = "t",
random = list(~1|Herbivore_Phylogeny, ~1|Herbivore_Latin_name,
~1|Study, ~1|Effect),
data = gdat
)
# getting marginal R2
r2_mr_SpecGen_guild_lnRR1 <- r2_ml(mr_SpecGen_guild_lnRR1)
# # meta-regression: contrasts x 7
# getting the level names out
level_names <- levels(gdat$Diet_guild)
# helper function to run metafor meta-regression
run_SpecGen_guild_lnRR <- function(name) {
rma.mv(yi = lnRR,
V = VCV_lnRR2,
mods = ~ relevel(Diet_guild, ref = name),
test = "t",
random = list(~1|Herbivore_Phylogeny, ~1|Herbivore_Latin_name,
~1|Study, ~1|Effect),
data = gdat)
}
# results of meta-regression including all contrast results; taking the last level out ([-length(level_names)])
mr_SpecGen_guild_lnRR <- map(level_names[-length(level_names)], run_SpecGen_guild_lnRR)Table E17
Table E17: Regression coefficients (estimate), 95%
confidence intervals (CIs), and variance explained,
R2[marginal] (R2) = 6.15% from the
meta-regression of lnRR with Diet_guild.
# getting estimates
res_mr_SpecGen_guild_lnRR1 <- get_pred1(mr_SpecGen_guild_lnRR1, mod = "Diet_guild")
res_mr_SpecGen_guild_lnRR <- map(mr_SpecGen_guild_lnRR, ~ get_pred2(.x, mod = "Diet_guild"))
# a list of the numbers to take out unnecessary contrasts
contra_list <- Map(seq, from=1, to=1:3)
# you need to flatten twice: first to make it a list and make it a vector
# I guess we can make it a loop (or vectorise)
res_mr_SpecGen_guild_lnRR2 <- map2_dfr(res_mr_SpecGen_guild_lnRR, contra_list, ~.x[-(.y), ])
# creating a table
mr_results(res_mr_SpecGen_guild_lnRR1, res_mr_SpecGen_guild_lnRR2) %>%
kable("html", digits = 3) %>%
kable_styling("striped", position = "left") %>%
scroll_box(width = "100%", height = "300px")| Fixed effect | Estimate | Lower CI [0.025] | Upper CI [0.975] | P value | Lower PI [0.025] | Upper PI [0.975] |
|---|---|---|---|---|---|---|
| Generalist:Chewing arthropods | -0.339 | -0.513 | -0.165 | 0.000 | -1.170 | 0.492 |
| Specialist:Chewing arthropods | -0.382 | -0.571 | -0.194 | 0.000 | -1.217 | 0.452 |
| Generalist:Fluid-feeding arthropods | -0.141 | -0.387 | 0.105 | 0.261 | -0.990 | 0.708 |
| Specialist:Fluid-feeding arthropods | -0.149 | -0.302 | 0.004 | 0.056 | -0.976 | 0.678 |
| Generalist:Chewing arthropods-Specialist:Chewing arthropods | -0.044 | -0.297 | 0.210 | 0.737 | -0.895 | 0.808 |
| Generalist:Chewing arthropods-Generalist:Fluid-feeding arthropods | 0.198 | -0.103 | 0.499 | 0.197 | -0.669 | 1.065 |
| Generalist:Chewing arthropods-Specialist:Fluid-feeding arthropods | 0.190 | -0.040 | 0.420 | 0.105 | -0.655 | 1.035 |
| Specialist:Chewing arthropods-Generalist:Fluid-feeding arthropods | 0.242 | -0.068 | 0.551 | 0.126 | -0.629 | 1.112 |
| Specialist:Chewing arthropods-Specialist:Fluid-feeding arthropods | 0.234 | -0.006 | 0.473 | 0.056 | -0.614 | 1.081 |
| Generalist:Fluid-feeding arthropods-Specialist:Fluid-feeding arthropods | -0.008 | -0.298 | 0.282 | 0.957 | -0.871 | 0.855 |
Change in SD: lnVR
# meta-regression: multiple intercepts
mr_SpecGen_guild_lnVR1 <- rma.mv(
yi = lnVR,
V = VCV_lnVR2,
mods = ~ Diet_guild - 1,
test = "t",
random = list(~1|Herbivore_Phylogeny, ~1|Herbivore_Latin_name,
~1|Study, ~1|Effect),
data = gdat
)
# getting marginal R2
r2_mr_SpecGen_guild_lnVR1 <- r2_ml(mr_SpecGen_guild_lnVR1)
# # meta-regression: contrasts x 7
# getting the level names out
level_names <- levels(gdat$Diet_guild)
# helper function to run metafor meta-regression
run_SpecGen_guild_lnVR <- function(name) {
rma.mv(yi = lnVR,
V = VCV_lnVR2,
mods = ~ relevel(Diet_guild, ref = name),
test = "t",
random = list(~1|Herbivore_Phylogeny, ~1|Herbivore_Latin_name,
~1|Study, ~1|Effect),
data = gdat)
}
# results of meta-regression including all contrast results; taking the last level out ([-length(level_names)])
mr_SpecGen_guild_lnVR <- map(level_names[-length(level_names)], run_SpecGen_guild_lnVR)Table E18
Table E18: Regression coefficients (estimate), 95%
confidence intervals (CIs), and variance explained,
R2[marginal] (R2) = 1.2% from the
meta-regression of lnVR with Diet_guild.
# getting estimates
res_mr_SpecGen_guild_lnVR1 <- get_pred1(mr_SpecGen_guild_lnVR1, mod = "Diet_guild")
res_mr_SpecGen_guild_lnVR <- map(mr_SpecGen_guild_lnVR, ~ get_pred2(.x, mod = "Diet_guild"))
# a list of the numbers to take out unnecessary contrasts
contra_list <- Map(seq, from=1, to=1:3)
# you need to flatten twice: first to make it a list and make it a vector
# I guess we can make it a loop (or vectorise)
res_mr_SpecGen_guild_lnVR2 <- map2_dfr(res_mr_SpecGen_guild_lnVR, contra_list, ~.x[-(.y), ])
# creating a table
mr_results(res_mr_SpecGen_guild_lnVR1, res_mr_SpecGen_guild_lnVR2) %>%
kable("html", digits = 3) %>%
kable_styling("striped", position = "left") %>%
scroll_box(width = "100%", height = "300px")| Fixed effect | Estimate | Lower CI [0.025] | Upper CI [0.975] | P value | Lower PI [0.025] | Upper PI [0.975] |
|---|---|---|---|---|---|---|
| Generalist:Chewing arthropods | -0.068 | -0.266 | 0.129 | 0.497 | -1.249 | 1.112 |
| Specialist:Chewing arthropods | -0.107 | -0.328 | 0.115 | 0.344 | -1.291 | 1.078 |
| Generalist:Fluid-feeding arthropods | -0.132 | -0.438 | 0.174 | 0.399 | -1.335 | 1.071 |
| Specialist:Fluid-feeding arthropods | 0.039 | -0.144 | 0.222 | 0.675 | -1.139 | 1.217 |
| Generalist:Chewing arthropods-Specialist:Chewing arthropods | -0.038 | -0.331 | 0.254 | 0.798 | -1.238 | 1.161 |
| Generalist:Chewing arthropods-Generalist:Fluid-feeding arthropods | -0.063 | -0.428 | 0.301 | 0.733 | -1.282 | 1.156 |
| Generalist:Chewing arthropods-Specialist:Fluid-feeding arthropods | 0.107 | -0.160 | 0.375 | 0.431 | -1.086 | 1.301 |
| Specialist:Chewing arthropods-Generalist:Fluid-feeding arthropods | -0.025 | -0.403 | 0.353 | 0.896 | -1.248 | 1.198 |
| Specialist:Chewing arthropods-Specialist:Fluid-feeding arthropods | 0.146 | -0.137 | 0.428 | 0.312 | -1.052 | 1.343 |
| Generalist:Fluid-feeding arthropods-Specialist:Fluid-feeding arthropods | 0.171 | -0.186 | 0.527 | 0.347 | -1.046 | 1.387 |
Figure 4E-F
## Figure 3E
p26 <- orchard_plot(mr_SpecGen_guild_lnRR1, data= gdat, group = 'Study',mod="Diet_guild", xlab = "log(Response Ratio) (lnRR)", alpha = 0.2, angle = 45)
## Figure 3F
p27 <- orchard_plot(mr_SpecGen_guild_lnVR1, data= gdat, group = 'Study',mod="Diet_guild", xlab = "log(Response Ratio) (lnVR)", alpha = 0.2, angle = 45)
#ggsave(here("fig", "SpecGen_guild.png"))Model selection: multi-predictor model
Here we build the best model via an AICc based model selection method
implemented in the R package MuMin(Barton 2009). For the full model, we had
4 variables: Plant_lifespan, Poaceae_or_Non,
Herbivore_diet_breadth, and Feeding_guild. We
conducted with the full dataset (dat); therefore, we did
not include any interaction terms
(e.g. Lifespan_guild).
Model selection: lnRR
# making metafor talk to MuMIn
eval(metafor:::.MuMIn)
# use method = "ML" so that we can compare AIC
mr_full_lnRR <- rma.mv(
yi = lnRR,
V = VCV_lnRR,
mods = ~ Plant_lifespan +
Poaceae_or_Non +
Herbivore_diet_breadth +
Feeding_guild,
test = "t",
random = list(~1|Herbivore_Phylogeny, ~1|Herbivore_Latin_name,
~1|Study, ~1|Effect),
method = "ML", # for model selection
data = dat
)
# calling required functions
#getCall(mr_full_lnRR)
# dredge(full.model, evaluate=F) # show all candidate models
# n = 32 model exist
candidates_lnRR <- dredge(mr_full_lnRR, trace = 2)##
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|================================================================== | 94%saveRDS(candidates_lnRR, file = here("Rdata", "candidates_lnRR.rds"))# making metafor talk to MuMIn
eval(metafor:::.MuMIn)
candidates_lnRR <- readRDS(file = here("Rdata", "candidates_lnRR.rds"))
# displays delta AICc <2
candidates_aic2_lnRR <- subset(candidates_lnRR, delta < 2)
# model averaging
# it seems like models are using z values rather than t values (which will be OK)
mr_averaged_aic2_lnRR <- summary(model.avg(candidates_lnRR, delta < 2))
# relative importance of each predictor for all the models
importance_lnRR <- sw(candidates_lnRR)
# use REML if not for model comparison
model1_lnRR <- rma.mv(
yi = lnRR,
V = VCV_lnRR,
mods = ~ Feeding_guild,
test = "t",
random = list(~1|Herbivore_Phylogeny, ~1|Herbivore_Latin_name,
~1|Study, ~1|Effect),
method = "REML",
data = dat
)
model2_lnRR <- rma.mv(
yi = lnRR,
V = VCV_lnRR,
mods = ~ Feeding_guild + Poaceae_or_Non,
test = "t",
random = list(~1|Herbivore_Phylogeny, ~1|Herbivore_Latin_name,
~1|Study, ~1|Effect),
method = "REML",
data = dat
)Table E19
Table E19: The top 2 models (out of 16 possible
models) within the \(\Delta\)AIC
difference of 2, and which 4 variables: Plant_lifespan,
Poaceae_or_Non, Herbivore_diet_breadth, and
Feeding_guild were included (indicated by \(+\)); model weights (for the 4 models) and
the sum of weights for each of the variables (from the 16 models) are
included.
# creating a table
tibble(
`Model (variable weight)` = c("Model1", "Model2", "(Sum of weights)"),
Feeding_guild = c(if_else(candidates_aic2_lnRR$Feeding_guild == "+", "$+$", "NA"), round(importance_lnRR[1], 3)),
Poaceae_or_Non = c(if_else(candidates_aic2_lnRR$Poaceae_or_Non == "+", "$+$", "NA"), round(importance_lnRR[2], 3)),
Herbivore_diet_breadth = c(if_else(candidates_aic2_lnRR$Herbivore_diet_breadth == "+", "$+$", "NA"), round(importance_lnRR[3], 3)),
Plant_lifespan = c(if_else(candidates_aic2_lnRR$Plant_lifespan == "+", "$+$", "NA"), round(importance_lnRR[4], 3)),
delta_AICc = c(candidates_aic2_lnRR$delta, NA),
Weight = c(candidates_aic2_lnRR$weight, NA)
) %>%
kable("html", digits = 3) %>%
kable_styling("striped", position = "left") | Model (variable weight) | Feeding_guild | Poaceae_or_Non | Herbivore_diet_breadth | Plant_lifespan | delta_AICc | Weight |
|---|---|---|---|---|---|---|
| Model1 | \(+\) | NA | NA | NA | 0.000 | 0.629 |
| Model2 | \(+\) | \(+\) | NA | NA | 1.057 | 0.371 |
| (Sum of weights) | 0.78 | 0.462 | 0.326 | 0.265 | NA | NA |
Model averaging: lnRR
Table E20
Table E20: The average estimates for regression coefficients (Estimate), 95% confidence intervals (CIs), and variance explained, R2[marginal] (R2) from the 4 best meta-regression models.
# getting averaged R2 and variance components not provided by the MuMIn package
#average_sigma2 <- weighted.mean(x = c(model1_lnRR$sigma2, model2_lnRR$sigma2), w = candidates_aic2_lnRR$weight)
average_R2_lnRR <- weighted.mean(x = c(r2_ml(model1_lnRR)[1], r2_ml(model2_lnRR)[1]), w = candidates_aic2_lnRR$weight)
# creating a table
tibble(
`Fixed effect` = row.names(mr_averaged_aic2_lnRR$coefmat.full),
Estimate = mr_averaged_aic2_lnRR$coefmat.full[, 1],
`Lower CI [0.025]` = mr_averaged_aic2_lnRR$coefmat.full[, 1] - mr_averaged_aic2_lnRR$coefmat.full[, 2] * qnorm(0.975),
`Upper CI [0.975]` = mr_averaged_aic2_lnRR$coefmat.full[, 1] + mr_averaged_aic2_lnRR$coefmat.full[, 2] * qnorm(0.975),
`P value` = as.numeric(mr_averaged_aic2_lnRR$coefmat.full[,4]),
`R2` = c(average_R2_lnRR, rep(NA, 7))
) %>%
kable("html", digits = 3) %>%
kable_styling("striped", position = "left") %>% scroll_box(width = "100%",
height = "300px")| Fixed effect | Estimate | Lower CI [0.025] | Upper CI [0.975] | P value | R2 |
|---|---|---|---|---|---|
| intrcpt | -1.008 | -2.183 | 0.168 | 0.093 | 0.1 |
| Feeding_guildLeaf-mining arthropods | -0.148 | -1.668 | 1.372 | 0.849 | NA |
| Feeding_guildCell-feeding arthropods | 0.647 | -0.586 | 1.881 | 0.304 | NA |
| Feeding_guildMammalian chewers | 0.438 | -0.766 | 1.642 | 0.476 | NA |
| Feeding_guildBoring arthropods | 0.543 | -0.640 | 1.726 | 0.368 | NA |
| Feeding_guildChewing arthropods | 0.648 | -0.528 | 1.823 | 0.280 | NA |
| Feeding_guildFluid-feeding arthropods | 0.865 | -0.311 | 2.042 | 0.149 | NA |
| Poaceae_or_NonPoaceae | -0.031 | -0.155 | 0.094 | 0.632 | NA |
Model selection: lnVR
# making metafor talk to MuMIn
eval(metafor:::.MuMIn)
# use method = "ML" so that we can compare AIC
mr_full_lnVR <- rma.mv(
yi = lnVR,
V = VCV_lnVR,
mods = ~ Plant_lifespan +
Poaceae_or_Non +
Herbivore_diet_breadth +
Feeding_guild,
test = "t",
random = list(~1|Herbivore_Phylogeny, ~1|Herbivore_Latin_name,
~1|Study, ~1|Effect),
method = "ML", # for model selection
data = dat
)
# calling required functions
#getCall(mr_full_lnVR)
# dredge(full.model, evaluate=F) # show all candidate models
# n = 32 model exist
candidates_lnVR <- dredge(mr_full_lnVR, trace = 2)##
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|================================================================== | 94%saveRDS(candidates_lnVR, file = here("Rdata", "candidates_lnVR.rds"))# making metafor talk to MuMIn
eval(metafor:::.MuMIn)
candidates_lnVR <- readRDS(file = here("Rdata", "candidates_lnVR.rds"))
# displays delta AICc <2
candidates_aic2_lnVR <- subset(candidates_lnVR, delta < 2)
# model averaging
# it seems like models are using z values rather than t values (which will be OK)
mr_averaged_aic2_lnVR <- summary(model.avg(candidates_lnVR, delta < 2))
# relative importance of each predictor
importance_lnVR <- sw(candidates_lnVR)
# use REML if not for model comparison
model1_lnVR <- rma.mv(
yi = lnVR,
V = VCV_lnVR,
#mods = ~ 1,
test = "t",
random = list(~1|Herbivore_Phylogeny, ~1|Herbivore_Latin_name,
~1|Study, ~1|Effect),
method = "REML",
data = dat
)
model2_lnVR <- rma.mv(
yi = lnVR,
V = VCV_lnVR,
mods = ~ Poaceae_or_Non,
test = "t",
random = list(~1|Herbivore_Phylogeny, ~1|Herbivore_Latin_name,
~1|Study, ~1|Effect),
method = "REML",
data = dat
)
model3_lnVR <- rma.mv(
yi = lnVR,
V = VCV_lnVR,
mods = ~ Plant_lifespan,
test = "t",
random = list(~1|Herbivore_Phylogeny, ~1|Herbivore_Latin_name,
~1|Study, ~1|Effect),
method = "REML",
data = dat
)
model4_lnVR <- rma.mv(
yi = lnVR,
V = VCV_lnVR,
mods = ~ Herbivore_diet_breadth + Poaceae_or_Non,
test = "t",
random = list(~1|Herbivore_Phylogeny, ~1|Herbivore_Latin_name,
~1|Study, ~1|Effect),
method = "REML",
data = dat
)
model5_lnVR <- rma.mv(
yi = lnVR,
V = VCV_lnVR,
mods = ~ Plant_lifespan + Poaceae_or_Non,
test = "t",
random = list(~1|Herbivore_Phylogeny, ~1|Herbivore_Latin_name,
~1|Study, ~1|Effect),
method = "REML",
data = dat
)
model6_lnVR <- rma.mv(
yi = lnVR,
V = VCV_lnVR,
mods = ~ Herbivore_diet_breadth,
test = "t",
random = list(~1|Herbivore_Phylogeny, ~1|Herbivore_Latin_name,
~1|Study, ~1|Effect),
method = "REML",
data = dat
)Table E21
Table E21: The top 6 models (out of 16 possible
models) within the \(\Delta\)AIC
difference of 2, and which 4 variables: Plant_lifespan,
Poaceae_or_Non, Herbivore_diet_breadth, and
Feeding_guild were included (indicated by \(+\)); model weights (for the 5 models) and
the sum of weights for each of the variables (from the 16 models) are
included.
# creating a table
tibble(
`Model (variable weight)` = c("Model1", "Model2", "Model3", "Model4", "Model5", "Model6", "(Sum of weights)"),
Poaceae_or_Non = c(if_else(candidates_aic2_lnVR$Poaceae_or_Non == "+", "$+$", "NA"), round(importance_lnVR[1], 3)),
Plant_lifespan = c(if_else(candidates_aic2_lnVR$Plant_lifespan == "+", "$+$", "NA"), round(importance_lnVR[2], 3)),
Herbivore_diet_breadth = c(if_else(candidates_aic2_lnVR$Herbivore_diet_breadth == "+", "$+$", "NA"), round(importance_lnVR[3], 3)),
Feeding_guild = c(if_else(candidates_aic2_lnVR$Feeding_guild == "+", "$+$", "NA"), round(importance_lnVR[4], 3)),
delta_AICc = c(candidates_aic2_lnVR$delta, NA),
Weight = c(candidates_aic2_lnVR$weight, NA)
) %>%
kable("html", digits = 3) %>%
kable_styling("striped", position = "left") | Model (variable weight) | Poaceae_or_Non | Plant_lifespan | Herbivore_diet_breadth | Feeding_guild | delta_AICc | Weight |
|---|---|---|---|---|---|---|
| Model1 | NA | NA | NA | NA | 0.000 | 0.259 |
| Model2 | \(+\) | NA | NA | NA | 0.101 | 0.246 |
| Model3 | NA | \(+\) | NA | NA | 1.020 | 0.155 |
| Model4 | \(+\) | NA | \(+\) | NA | 1.509 | 0.122 |
| Model5 | \(+\) | \(+\) | NA | NA | 1.640 | 0.114 |
| Model6 | NA | NA | \(+\) | NA | 1.803 | 0.105 |
| (Sum of weights) | 0.477 | 0.35 | 0.312 | 0.015 | NA | NA |
Model averaging: lnVR
Table E22
Table E22: The average estimates for regression coefficients (Estimate), 95% confidence intervals (CIs), and variance explained, R2[marginal] (R2) from the 4 best meta-regression models.
# getting averaged R2 and variance components not provided by the MuMIn package
#average_sigma2 <- weighted.mean(x = c(model1_lnVR$sigma2, model2_lnVR$sigma2), w = candidates_aic2_lnVR$weight)
average_R2_lnVR <- weighted.mean(x = c(r2_ml(model1_lnVR)[1], r2_ml(model2_lnVR)[1], r2_ml(model3_lnVR)[1],
r2_ml(model4_lnVR)[1], r2_ml(model5_lnVR)[1], r2_ml(model6_lnVR)[1]),
w = candidates_aic2_lnVR$weight)
# creating a table
tibble(
`Fixed effect` = row.names(mr_averaged_aic2_lnVR$coefmat.full),
Estimate = mr_averaged_aic2_lnVR$coefmat.full[, 1],
`Lower CI [0.025]` = mr_averaged_aic2_lnVR$coefmat.full[, 1] - mr_averaged_aic2_lnVR$coefmat.full[, 2] * qnorm(0.975),
`Upper CI [0.975]` = mr_averaged_aic2_lnVR$coefmat.full[, 1] + mr_averaged_aic2_lnVR$coefmat.full[, 2] * qnorm(0.975),
`P value` = as.numeric(mr_averaged_aic2_lnVR$coefmat.full[,4]),
`R2` = c(average_R2_lnVR, rep(NA, 3))
) %>%
kable("html", digits = 3) %>%
kable_styling("striped", position = "left") %>% scroll_box(width = "100%")| Fixed effect | Estimate | Lower CI [0.025] | Upper CI [0.975] | P value | R2 |
|---|---|---|---|---|---|
| intrcpt | -0.049 | -0.220 | 0.122 | 0.574 | 0.005 |
| Poaceae_or_NonPoaceae | -0.062 | -0.242 | 0.117 | 0.496 | NA |
| Plant_lifespanPerennial | 0.018 | -0.079 | 0.115 | 0.717 | NA |
| Herbivore_diet_breadthSpecialist | 0.014 | -0.087 | 0.114 | 0.789 | NA |
Publication Bias Analysis
Funnel plot
Residual funnel plot: lnRR
Figure E7
#
res_funnel_plot_lnRR <- rma.mv(
yi = lnRR,
V = VCV_lnRR,
mods = ~ Poaceae_or_Non +
Feeding_guild,
test = "t",
random = list(~1|Herbivore_Phylogeny, ~1|Herbivore_Latin_name,
~1|Study, ~1|Effect),
method = "REML",
data = dat
)
funnel(res_funnel_plot_lnRR,
yaxis = "seinv",
level = c(90, 95, 99),
shade = c("white", "gray55", "gray75"),
refline = 0, legend = TRUE)
Figure E7: A residual funnel plot from the
meta-regression model with Plant_lifespan,
Poaceae_or_Non, & Feeding_guild; ‘residual
value’ is on lnRR and ‘inverse standard error’ is precision
1/sqrt(varlnRR).
Egger regression
Univariate Egger regression: lnRR
#
# the use of effective sampling size - more than varlnRR
# effective sample size
dat$Effective_N <- 1/dat$Nc + 1/dat$Ne
egger_regression_uni_lnRR <- rma.mv(
yi = lnRR,
V = VCV_lnRR,
mods = ~ sqrt(Effective_N),
test = "t",
random = list(~1|Herbivore_Phylogeny, ~1|Herbivore_Latin_name,
~1|Study, ~1|Effect),
method = "REML",
data = dat
)Table E23
Table E23: Regression coefficients (Estimate), 95%
confidence intervals (CIs), P values, variance explained,
R2[marginal] (R2) from the
meta-regression with sqrt(Effective_N).
# getting marginal R2
r2_egger_regression_uni_lnRR <- r2_ml(egger_regression_uni_lnRR)
# getting estimates: name does not work for slopes
res_egger_regression_uni_lnRR <- get_pred2(egger_regression_uni_lnRR, mod = "sqrt(varlnRR)")
# creating a table
tibble(
`Fixed effect` = row.names(egger_regression_uni_lnRR$beta),
Estimate = c(res_egger_regression_uni_lnRR$estimate),
`Lower CI [0.025]` = c(res_egger_regression_uni_lnRR$lowerCL),
`Upper CI [0.975]` = c(res_egger_regression_uni_lnRR$upperCL),
`P value` = c(egger_regression_uni_lnRR$pval),
`R2` = c(r2_egger_regression_uni_lnRR[1], NA)
) %>%
kable("html", digits = 3) %>%
kable_styling("striped", position = "left")| Fixed effect | Estimate | Lower CI [0.025] | Upper CI [0.975] | P value | R2 |
|---|---|---|---|---|---|
| intrcpt | 0.000 | 0.000 | 0.000 | 0.010 | 0.008 |
| sqrt(Effective_N) | -0.257 | -0.547 | 0.034 | 0.084 | NA |
Figure E8
pred_egger_regression_uni_lnRR <- predict.rma(egger_regression_uni_lnRR)
# plotting
fit_egger_regression_uni_lnRR <- dat %>%
mutate(
ymin = pred_egger_regression_uni_lnRR$ci.lb,
ymax = pred_egger_regression_uni_lnRR$ci.ub,
ymin2 = pred_egger_regression_uni_lnRR$cr.lb,
ymax2 = pred_egger_regression_uni_lnRR$cr.ub,
pred = pred_egger_regression_uni_lnRR$pred
) %>%
ggplot(aes(x = sqrt(varlnRR), y = lnRR, size = sqrt(1/varlnRR))) +
geom_point(shape = 21, fill = "grey90") +
# geom_ribbon(aes(ymin = ymin, ymax = ymax), fill = "#0072B2") + # not quite sure why this does not work
geom_smooth(aes(y = ymin2), method = "loess", se = FALSE, lty = "dotted", lwd = 0.25, colour = "#0072B2") +
geom_smooth(aes(y = ymax2), method = "loess", se = FALSE, lty = "dotted", lwd = 0.25, colour = "#0072B2") +
geom_smooth(aes(y = ymin), method = "loess", se = FALSE, lty = "dotted", lwd = 0.25, colour = "#D55E00") +
geom_smooth(aes(y = ymax), method = "loess", se = FALSE, lty = "dotted", lwd = 0.25, colour = "#D55E00") +
geom_smooth(aes(y = pred), method = "loess", se = FALSE, lty = "dashed", lwd = 0.5, colour = "black") +
ylim(-5, 3) + xlim(0.05, 7) +
labs(x = "sqrt(effective sample size)", y = "lnRR (effect size)", size = "Precision (1/SE)") +
guides(fill = "none", colour = "none") +
# themes
theme_bw() +
theme(legend.position = c(0, 1), legend.justification = c(0, 1)) +
theme(legend.direction = "horizontal") +
# theme(legend.background = element_rect(fill = "white", colour = "black")) +
theme(legend.background = element_blank()) +
theme(axis.text.y = element_text(size = 10, colour = "black", hjust = 0.5, angle = 90))
fit_egger_regression_uni_lnRR
Figure E8: A bubble plot showing a predicted
regression line for the contentious variable
sqrt(Effective_N), indicating 95% confidence regions
(orange dotted lines) and 95% prediction regions (blue dotted lines),
with observed effect sizes based on various sample sizes.
Multivariate Egger regression: lnRR
#
egger_regression_mul_lnRR <- rma.mv(
yi = lnRR,
V = VCV_lnRR,
mods = ~ sqrt(Effective_N) +
Poaceae_or_Non +
Feeding_guild,
test = "t",
random = list(~1|Herbivore_Phylogeny, ~1|Herbivore_Latin_name,
~1|Study, ~1|Effect),
method = "REML",
data = dat
)Table E24
Table E24: Regression coefficients (Estimate), 95%
confidence intervals (CIs), and variance explained,
R2[marginal] (R2) from the
meta-regression with sqrt(Effective_N),
Poaceae_or_Non, & Feeding_guild.
# getting marginal R2
r2_egger_regression_mul_lnRR <- r2_ml(egger_regression_mul_lnRR)
# creating a table
tibble(
`Fixed effect` = row.names(egger_regression_mul_lnRR$beta),
Estimate = c(egger_regression_mul_lnRR$b),
`Lower CI [0.025]` = c(egger_regression_mul_lnRR$ci.lb),
`Upper CI [0.975]` = c(egger_regression_mul_lnRR$ci.ub),
`P value` = c(egger_regression_mul_lnRR$pval),
`R2` = c(r2_egger_regression_mul_lnRR[1], rep(NA, 8))
) %>%
kable("html", digits = 3) %>%
kable_styling("striped", position = "left") %>% scroll_box(width = "100%",
height = "300px")| Fixed effect | Estimate | Lower CI [0.025] | Upper CI [0.975] | P value | R2 |
|---|---|---|---|---|---|
| intrcpt | -0.846 | -2.055 | 0.363 | 0.170 | 0.105 |
| sqrt(Effective_N) | -0.191 | -0.484 | 0.102 | 0.201 | NA |
| Poaceae_or_NonPoaceae | -0.089 | -0.256 | 0.077 | 0.292 | NA |
| Feeding_guildLeaf-mining arthropods | -0.180 | -1.729 | 1.370 | 0.820 | NA |
| Feeding_guildCell-feeding arthropods | 0.602 | -0.657 | 1.862 | 0.348 | NA |
| Feeding_guildMammalian chewers | 0.442 | -0.785 | 1.668 | 0.480 | NA |
| Feeding_guildBoring arthropods | 0.543 | -0.660 | 1.745 | 0.376 | NA |
| Feeding_guildChewing arthropods | 0.617 | -0.577 | 1.812 | 0.311 | NA |
| Feeding_guildFluid-feeding arthropods | 0.826 | -0.370 | 2.023 | 0.176 | NA |
Figure E9
pred_egger_regress_mul_lnRR <- predict.rma(egger_regression_mul_lnRR)
# plotting
fit_egger_regression_mul_lnRR <- dat %>% filter(!is.na(Plant_lifespan)) %>%
mutate(
ymin = pred_egger_regress_mul_lnRR$ci.lb,
ymax = pred_egger_regress_mul_lnRR$ci.ub,
ymin2 = pred_egger_regress_mul_lnRR$cr.lb,
ymax2 = pred_egger_regress_mul_lnRR$cr.ub,
pred = pred_egger_regress_mul_lnRR$pred
) %>%
ggplot(aes(x = sqrt(varlnRR), y = lnRR, size = sqrt(1/varlnRR))) +
geom_point(shape = 21, fill = "grey90") +
# geom_ribbon(aes(ymin = ymin, ymax = ymax), fill = "#0072B2") + # not quite sure why this does not work
geom_smooth(aes(y = ymin2), method = "loess", se = FALSE, lty = "dotted", lwd = 0.25, colour = "#0072B2") +
geom_smooth(aes(y = ymax2), method = "loess", se = FALSE, lty = "dotted", lwd = 0.25, colour = "#0072B2") +
geom_smooth(aes(y = ymin), method = "loess", se = FALSE, lty = "dotted", lwd = 0.25, colour = "#D55E00") +
geom_smooth(aes(y = ymax), method = "loess", se = FALSE, lty = "dotted", lwd = 0.25, colour = "#D55E00") +
geom_smooth(aes(y = pred), method = "loess", se = FALSE, lty = "dashed", lwd = 0.5, colour = "black") +
ylim(-5, 3) + xlim(0.05, 7) +
labs(x = "sqrt(effective sample size)", y = "lnRR (effect size)", size = "Precision (1/SE)") +
guides(fill = "none", colour = "none") +
# themes
theme_bw() +
theme(legend.position = c(0, 1), legend.justification = c(0, 1)) +
theme(legend.direction = "horizontal") +
# theme(legend.background = element_rect(fill = "white", colour = "black")) +
theme(legend.background = element_blank()) +
theme(axis.text.y = element_text(size = 10, colour = "black", hjust = 0.5, angle = 90))
fit_egger_regression_mul_lnRR
Figure E9: A bubble plot showing a predicted loess
line for the contentious variable sqrt(Effective_N) (given
the values of the other 3 variables in the model), with their 95%
confidence regions (orange dotted lines) and 95% prediction regions
(blue dotted lines) with observed effect sizes based on various sample
sizes. Note that the lines are not linear as these are based on
multivariate predictions of the data points.
PEESE (precision-effect estimation with standard error)
#
peese_lnRR <- rma.mv(
yi = lnRR,
V = VCV_lnRR,
mods = ~ Effective_N,
test = "t",
random = list(~1|Herbivore_Phylogeny, ~1|Herbivore_Latin_name,
~1|Study, ~1|Effect),
method = "REML",
data = dat
)Table E25
Table E25: Regression coefficients (Estimate), 95% confidence intervals (CIs), and variance explained, R2[marginal] (R2) from the meta-regression with .
# getting marginal R2
r2_peese_lnRR <- r2_ml(peese_lnRR)
# getting estimates: name does not work for slopes
res_epeese_lnRR <- get_pred2(peese_lnRR, mod = "varlnRR")
# creating a table
tibble(
`Fixed effect` = row.names(peese_lnRR$beta),
Estimate = c(res_epeese_lnRR$estimate),
`Lower CI [0.025]` = c(res_epeese_lnRR$lowerCL),
`Upper CI [0.975]` = c(res_epeese_lnRR$upperCL),
`P value` = c(peese_lnRR$pval),
`R2` = c(r2_peese_lnRR[1], NA)
) %>%
kable("html", digits = 3) %>%
kable_styling("striped", position = "left")| Fixed effect | Estimate | Lower CI [0.025] | Upper CI [0.975] | P value | R2 |
|---|---|---|---|---|---|
| intrcpt | 0.000 | 0.000 | 0.000 | 0.000 | 0.01 |
| Effective_N | -0.276 | -0.549 | -0.003 | 0.048 | NA |
Time-lag bias
Univariate time-lag bias: lnRR
#
time_lag_effect_uni_lnRR <- rma.mv(
yi = lnRR,
V = VCV_lnRR,
mods = ~ Year,
test = "t",
random = list(~1|Herbivore_Phylogeny, ~1|Herbivore_Latin_name,
~1|Study, ~1|Effect),
method = "REML",
data = dat
)Table26
Table E26: Regression coefficients (Estimate), 95%
confidence intervals (CIs), P value and variance explained,
R2[marginal] (R2) from the
meta-regression with Year.
# getting marginal R2
r2_time_lag_effect_uni_lnRR <- r2_ml(time_lag_effect_uni_lnRR)
# getting estimates: name does not work for slopes
res_time_lag_effect_uni_lnRR <- get_pred2(time_lag_effect_uni_lnRR, mod = "Year")
# creating a table
tibble(
`Fixed effect` = row.names(time_lag_effect_uni_lnRR$beta),
Estimate = c(res_time_lag_effect_uni_lnRR$estimate),
`Lower CI [0.025]` = c(res_time_lag_effect_uni_lnRR$lowerCL),
`Upper CI [0.975]` = c(res_time_lag_effect_uni_lnRR$upperCL),
`P value` = time_lag_effect_uni_lnRR$pval,
`R2` = c(r2_time_lag_effect_uni_lnRR[1], NA)
) %>%
kable("html", digits = 3) %>%
kable_styling("striped", position = "left")| Fixed effect | Estimate | Lower CI [0.025] | Upper CI [0.975] | P value | R2 |
|---|---|---|---|---|---|
| intrcpt | 0.000 | 0.000 | 0.000 | 0.004 | 0.048 |
| Year | -0.015 | -0.024 | -0.005 | 0.003 | NA |
Figure E10
pred_time_lag_effect_uni_lnRR <- predict.rma(time_lag_effect_uni_lnRR)
# plotting
fit_time_lag_effect_uni_lnRR <- dat %>%
mutate(
ymin = pred_time_lag_effect_uni_lnRR$ci.lb,
ymax = pred_time_lag_effect_uni_lnRR$ci.ub,
ymin2 = pred_time_lag_effect_uni_lnRR$cr.lb,
ymax2 = pred_time_lag_effect_uni_lnRR$cr.ub,
pred = pred_time_lag_effect_uni_lnRR$pred
) %>%
ggplot(aes(x = Year, y = lnRR, size = sqrt(1/varlnRR))) +
geom_point(shape = 21, fill = "grey90") +
# geom_ribbon(aes(ymin = ymin, ymax = ymax), fill = "#0072B2") + # not quite sure why this does not work
geom_smooth(aes(y = ymin2), method = "loess", se = FALSE, lty = "dotted", lwd = 0.25, colour = "#0072B2") +
geom_smooth(aes(y = ymax2), method = "loess", se = FALSE, lty = "dotted", lwd = 0.25, colour = "#0072B2") +
geom_smooth(aes(y = ymin), method = "loess", se = FALSE, lty = "dotted", lwd = 0.25, colour = "#D55E00") +
geom_smooth(aes(y = ymax), method = "loess", se = FALSE, lty = "dotted", lwd = 0.25, colour = "#D55E00") +
geom_smooth(aes(y = pred), method = "loess", se = FALSE, lty = "dashed", lwd = 0.5, colour = "black") +
ylim(-4, 3.5) + xlim(1989, 2020) +
#scale_x_continuous(breaks = c(1995, 2000, 2005, 2010, 2015, 2020)) +
labs(x = "Year", y = "lnRR (effect size)", size ="Precision (1/SE)") +
guides(fill = "none", colour = "none") +
# themes
theme_bw() +
theme(legend.position = c(0, 1), legend.justification = c(0, 1)) +
theme(legend.direction = "horizontal") +
# theme(legend.background = element_rect(fill = "white", colour = "black")) +
theme(legend.background = element_blank()) +
theme(axis.text.y = element_text(size = 10, colour = "black", hjust = 0.5, angle = 90))
fit_time_lag_effect_uni_lnRR
Figure E10: A bubble plot showing a predicted
regression line for the contentious variable Year,
indicating 95% confidence regions (orange dotted lines) and 95%
prediction regions (blue dotted lines), with observed effect sizes based
on various precisions (1/SE).
Multivariate time-lag bias: lnRR
#
time_lag_effect_mul_lnRR <- rma.mv(
yi = lnRR,
V = VCV_lnRR,
mods = ~ Year +
Poaceae_or_Non +
Feeding_guild,
test = "t",
random = list(~1|Herbivore_Phylogeny, ~1|Herbivore_Latin_name,
~1|Study, ~1|Effect),
method = "REML",
data = dat
)Table E27
Table E27: Regression coefficients (Estimate), 95%
confidence intervals (CIs), P values, and variance explained,
R2[marginal] (R2) from the
meta-regression with Year, `Plant_lifespan,
Poaceae_or_Non, & Feeding_guild.
# getting marginal R2
r2_time_lag_effect_mul_lnRR <- r2_ml(time_lag_effect_mul_lnRR)
# creating a table
tibble(
`Fixed effect` = row.names(time_lag_effect_mul_lnRR$beta),
Estimate = c(time_lag_effect_mul_lnRR$b),
`Lower CI [0.025]` = c(time_lag_effect_mul_lnRR$ci.lb),
`Upper CI [0.975]` = c(time_lag_effect_mul_lnRR$ci.ub),
`P value` = time_lag_effect_mul_lnRR$pval,
`R2` = c(r2_time_lag_effect_mul_lnRR[1], rep(NA, 8))
) %>%
kable("html", digits = 3) %>%
kable_styling("striped", position = "left") %>% scroll_box(width = "100%",
height = "300px")| Fixed effect | Estimate | Lower CI [0.025] | Upper CI [0.975] | P value | R2 |
|---|---|---|---|---|---|
| intrcpt | 27.866 | 8.785 | 46.947 | 0.004 | 0.139 |
| Year | -0.014 | -0.024 | -0.005 | 0.003 | NA |
| Poaceae_or_NonPoaceae | -0.086 | -0.254 | 0.082 | 0.317 | NA |
| Feeding_guildLeaf-mining arthropods | -0.263 | -1.820 | 1.294 | 0.740 | NA |
| Feeding_guildCell-feeding arthropods | 0.630 | -0.634 | 1.893 | 0.328 | NA |
| Feeding_guildMammalian chewers | 0.406 | -0.824 | 1.636 | 0.517 | NA |
| Feeding_guildBoring arthropods | 0.536 | -0.669 | 1.741 | 0.383 | NA |
| Feeding_guildChewing arthropods | 0.642 | -0.554 | 1.838 | 0.293 | NA |
| Feeding_guildFluid-feeding arthropods | 0.836 | -0.362 | 2.035 | 0.171 | NA |
Figure E11
pred_time_lag_effect_mul_lnRR <- predict.rma(time_lag_effect_mul_lnRR)
# plotting
fit_time_lag_effect_mul_lnRR <- dat %>% filter(!is.na(Plant_lifespan)) %>%
mutate(ymin = pred_time_lag_effect_mul_lnRR$ci.lb,
ymax = pred_time_lag_effect_mul_lnRR$ci.ub,
ymin2 = pred_time_lag_effect_mul_lnRR$cr.lb,
ymax2 = pred_time_lag_effect_mul_lnRR$cr.ub,
pred = pred_time_lag_effect_mul_lnRR$pred
) %>%
ggplot(aes(x = Year, y = lnRR, size = sqrt(1/varlnRR))) +
geom_point(shape = 21, fill = "grey90") +
# geom_ribbon(aes(ymin = ymin, ymax = ymax), fill = "#0072B2") + # not quite sure why this does not work
geom_smooth(aes(y = ymin2), method = "loess", se = FALSE, lty = "dotted", lwd = 0.25, colour = "#0072B2") +
geom_smooth(aes(y = ymax2), method = "loess", se = FALSE, lty = "dotted", lwd = 0.25, colour = "#0072B2") +
geom_smooth(aes(y = ymin), method = "loess", se = FALSE, lty = "dotted", lwd = 0.25, colour = "#D55E00") +
geom_smooth(aes(y = ymax), method = "loess", se = FALSE, lty = "dotted", lwd = 0.25, colour = "#D55E00") +
geom_smooth(aes(y = pred), method = "loess", se = FALSE, lty = "dashed", lwd = 0.5, colour = "black") +
ylim(-4, 3.5) + xlim(1989, 2020) +
#scale_x_continuous(breaks = c(1995, 2000, 2005, 2010, 2015, 2020)) +
labs(x = "Year", y = "lnRR (effect size)", size ="Precision (1/SE)") +
guides(fill = "none", colour = "none") +
# themes
theme_bw() +
theme(legend.position = c(0, 1), legend.justification = c(0, 1)) +
theme(legend.direction = "horizontal") +
# theme(legend.background = element_rect(fill = "white", colour = "black")) +
theme(legend.background = element_blank()) +
theme(axis.text.y = element_text(size = 10, colour = "black", hjust = 0.5, angle = 90))
fit_time_lag_effect_mul_lnRR
Figure E11: A bubble plot showing a predicted loess
line for the contentious variable Year (given the values of
the other 3 variables in the model), with their 95% confidence regions
(orange dotted lines) and 95% prediction regions (blue dotted lines)
with observed effect sizes based on various sample sizes. Note that the
lines are not linear as these are based on multivariate predictions of
the data points.
R Session Information
sessionInfo() %>% pander()R version 4.2.2 (2022-10-31)
Platform: aarch64-apple-darwin20 (64-bit)
locale: en_US.UTF-8||en_US.UTF-8||en_US.UTF-8||C||en_US.UTF-8||en_US.UTF-8
attached base packages: grid, stats, graphics, grDevices, utils, datasets, methods and base
other attached packages: DIZtools(v.0.0.7), MCMCglmm(v.2.34), coda(v.0.19-4), orchaRd(v.2.0), here(v.1.0.1), patchwork(v.1.1.2), performance(v.0.10.2), MuMIn(v.1.47.1), ape(v.5.7-1), metafor(v.3.8-1), metadat(v.1.2-0), Matrix(v.1.5-1), pander(v.0.6.5), magrittr(v.2.0.3), gridExtra(v.2.3), kableExtra(v.1.3.4), lubridate(v.1.9.2), forcats(v.1.0.0), stringr(v.1.5.0), dplyr(v.1.1.0), purrr(v.1.0.1), readr(v.2.1.4), tidyr(v.1.3.0), tibble(v.3.1.8), ggplot2(v.3.4.1) and tidyverse(v.2.0.0)
loaded via a namespace (and not attached): nlme(v.3.1-160), bit64(v.4.0.5), insight(v.0.19.0), webshot(v.0.5.4), httr(v.1.4.4), rprojroot(v.2.0.3), latex2exp(v.0.9.6), tensorA(v.0.36.2), tools(v.4.2.2), bslib(v.0.4.2), utf8(v.1.2.3), R6(v.2.5.1), vipor(v.0.4.5), mgcv(v.1.8-41), colorspace(v.2.1-0), withr(v.2.5.0), tidyselect(v.1.2.0), emmeans(v.1.8.4-1), bit(v.4.0.5), compiler(v.4.2.2), cli(v.3.6.0), rvest(v.1.0.3), pacman(v.0.5.1), xml2(v.1.3.3), labeling(v.0.4.2), sass(v.0.4.5), scales(v.1.2.1), mvtnorm(v.1.1-3), systemfonts(v.1.0.4), digest(v.0.6.31), rmarkdown(v.2.20), svglite(v.2.1.1), pkgconfig(v.2.0.3), htmltools(v.0.5.4), fastmap(v.1.1.0), highr(v.0.10), rlang(v.1.0.6), rstudioapi(v.0.14), jquerylib(v.0.1.4), generics(v.0.1.3), farver(v.2.1.1), jsonlite(v.1.8.4), vroom(v.1.6.1), ggbeeswarm(v.0.7.1), Rcpp(v.1.0.10), munsell(v.0.5.0), fansi(v.1.0.4), lifecycle(v.1.0.3), stringi(v.1.7.12), yaml(v.2.3.7), mathjaxr(v.1.6-0), parallel(v.4.2.2), crayon(v.1.5.2), lattice(v.0.20-45), splines(v.4.2.2), hms(v.1.1.2), knitr(v.1.42), pillar(v.1.8.1), cubature(v.2.0.4.6), estimability(v.1.4.1), corpcor(v.1.6.10), stats4(v.4.2.2), glue(v.1.6.2), evaluate(v.0.20), data.table(v.1.14.8), vctrs(v.0.5.2), tzdb(v.0.3.0), gtable(v.0.3.1), cachem(v.1.0.6), xfun(v.0.37), xtable(v.1.8-4), viridisLite(v.0.4.1), beeswarm(v.0.4.0), timechange(v.0.2.0) and ellipsis(v.0.3.2)
References
Barton, Kamil. 2009. “MuMIn: Multi-Model Inference.”
Http://r-Forge. R-Project. Org/Projects/Mumin/.
Hedges, Larry V, Jessica Gurevitch, and Peter S Curtis. 1999. “The
Meta-Analysis of Response Ratios in Experimental Ecology.”
Ecology 80 (4): 1150–56.
Higgins, Julian PT, Simon G Thompson, Jonathan J Deeks, and Douglas G
Altman. 2003. “Measuring Inconsistency in Meta-Analyses.”
Bmj 327 (7414): 557–60.
Nakagawa, Shinichi, Malgorzata Lagisz, Rose E O’Dea, Joanna Rutkowska,
Yefeng Yang, Daniel Noble, and Alistair M Senior. 2020. “The
Orchard Plot: Cultivating a Forest Plot for Use in Ecology, Evolution
and Beyond.” Research Synthesis Methods. https://protect-au.mimecast.com/s/lmCzC81VRgcNxxnvS6aDRG?domain=doi.org.
Nakagawa, Shinichi, and Eduardo SA Santos. 2012. “Methodological
Issues and Advances in Biological Meta-Analysis.”
Evolutionary Ecology 26 (5): 1253–74.
Senior, Alistair M, Catherine E Grueber, Tsukushi Kamiya, Malgorzata
Lagisz, Katie O’dwyer, Eduardo SA Santos, and Shinichi Nakagawa. 2016.
“Heterogeneity in Ecological and Evolutionary Meta-Analyses: Its
Magnitude and Implications.” Ecology 97 (12): 3293–99.
---
title: "Variable plant silicon defences suppresse herbivore performance globally but feeding strategy is key"
author: "Scott N. Johnson, Jamie M. Waterman, Susan E. Hartley, Julia Cooke, James M.W. Ryalls, Adam Frew, Malgorzata Lagisz and Shinichi Nakagawa"
date: "`r format(Sys.time(), '%d %B %Y')`"
output:
  html_document:
    code_download: true
    code_folding: hide
    depth: 4
    number_sections: no
    theme:  cosmo # “default”, “cerulean”, “journal”, “flatly”, “darkly”, “readable”, “spacelab”, “united”, “cosmo”, “lumen”, “paper”, “sandstone”, “simplex”, “yeti”
    toc: yes
    toc_float: yes
    toc_depth: 4
  pdf_document:
    toc: yes
subtitle: Electronic Supplementary Material
bibliography: references.bib
biblio-style: "apalike"
#csl: proceedings-of-the-royal-society-b.csl
link-citations: yes
---

```{r setup, include = FALSE}
# knitr setting
knitr::opts_chunk$set(
  message = FALSE,
  warning = FALSE, # no warnings
  tidy = TRUE
  ##cache = True
)

# clearing up
rm(list = ls())
```

## Setup

### Loading packages and custom functions

To run the script, some of packages need to be installed from `Github`.

```{r}
# loading packages
# devtools::install_github("thomasp85/patchwork")
# if (!requireNamespace("BiocManager", quietly = TRUE))
#     install.packages("BiocManager")
# 
# BiocManager::install("ggtree")
# install.packages("devtools")
# install.packages("tidyverse")
# install.packages("metafor")
# install.packages("patchwork")
# install.packages("R.rsp")

#install.packages("devtools")
#library(devtools)
#install_github("daniel1noble/metaAidR")

#devtools::install_github("daniel1noble/orchaRd", force = TRUE)

#install.packages("pacman")
#> Installing package into '/Users/danielnoble/Library/R/3.6/library'
#> (as 'lib' is unspecified)
#pacman::p_load(devtools, tidyverse, metafor, patchwork, R.rsp)
# Install orchaRd
#> Downloading GitHub repo itchyshin/orchard_plot@master
#> Installing package into '/Users/danielnoble/Library/R/3.6/library'
#> (as 'lib' is unspecified)
#library(orchaRd)

pacman::p_load(
  tidyverse, # tidy family and related packages below
  kableExtra,
  gridExtra, # may not use this
  purrr,
  magrittr, # extending piping
  pander, # nice tables
  metafor, # package for meta-analysis
  #ggbeeswarm, # making bee-swarm plots possible
  ape,     # pfor hylogenetic comparative methods
  MuMIn, # multi-model inference
  performance, # getting R2 from lmer + glmer objects
  #png, # reading png files
  grid, # graphic layout manipulation
  patchwork, # putting ggplots together - you need to install via devtool
  here, # gives a path
  #brms,  # Bayesian mixed model
  orchaRd, # drawing orchard and caterpillars plot
  MCMCglmm, # Bayesin mixed model
  DIZtools
  #metaAidR # some helper functions for meta-analysis
)
#options(mc.cores = parallel::detectCores())
#rstan_options(auto_write = TRUE)
```

### Custom functions

We have 5 custom functions named : `cont_gen()`, `get_pred1`, `get_pred2`, `mr_results`, and `make_VCV_matrix` all of which are used later (see below for their functionality) and the code are included here. 

```{r}
# custom functions

#' Title: Contrast name generator
#'
#' @param name: a vector of character strings
cont_gen <- function(name) {
  combination <- combn(name, 2)
  name_dat <- t(combination)
  names <- paste(name_dat[, 1], name_dat[, 2], sep = "-")
  return(names)
}

#' @title get_pred1: intercept-less model
#' @description Function to get CIs (confidence intervals) and PIs (prediction intervals) from rma objects (metafor)
#' @param model: rma.mv object 
#' @param mod: the name of a moderator 
get_pred1 <- function (model, mod = " ") {
  name <- name <- firstup(as.character(stringr::str_replace(row.names(model$beta), mod, "")))
  len <- length(name)
  
   if (len != 1) {
        newdata <- matrix(NA, ncol = len, nrow = len)
        for (i in 1:len) {
            pos <- which(model$X[, i] == 1)[[1]]
            newdata[, i] <- model$X[pos, ]
        }
        pred <- metafor::predict.rma(model, newmods = newdata)
    }
    else {
        pred <- metafor::predict.rma(model)
  }
  estimate <- pred$pred
  lowerCL <- pred$ci.lb
  upperCL <- pred$ci.ub 
  lowerPR <- pred$cr.lb
  upperPR <- pred$cr.ub 
  
  table <- tibble(name = factor(name, levels = name, labels = name), estimate = estimate,
                  lowerCL = lowerCL, upperCL = upperCL,
                  pval = model$pval,
                  lowerPR = lowerPR, upperPR = upperPR)
}

#' @title get_pred2: normal model
#' @description Function to get CIs (confidence intervals) and PIs (prediction intervals) from rma objects (metafor)
#' @param model: rma.mv object 
#' @param mod: the name of a moderator 
get_pred2 <- function (model, mod = " ") {
  name <- as.factor(str_replace(row.names(model$beta), 
                                paste0("relevel", "\\(", mod,", ref = name", "\\)"),
                                ""))
  len <- length(name)
  
  if(len != 1){
  newdata <- diag(len)
  pred <- predict.rma(model, intercept = FALSE, newmods = newdata[,-1])
  }
  else {
    pred <- predict.rma(model)
  }
  estimate <- pred$pred
  lowerCL <- pred$ci.lb
  upperCL <- pred$ci.ub 
  lowerPR <- pred$cr.lb
  upperPR <- pred$cr.ub 
  
  table <- tibble(name = factor(name, levels = name, labels = name), estimate = estimate,
                  lowerCL = lowerCL, upperCL = upperCL,
                  pval = model$pval,
                  lowerPR = lowerPR, upperPR = upperPR)
}

#' @title mr_results
#' @description Function to put results of meta-regression and its contrasts
#' @param res1: data frame 1
#' @param res1: data frame 2
mr_results <- function(res1, res2) {
  restuls <-tibble(
    `Fixed effect` = c(as.character(res1$name), cont_gen(res1$name)),
    Estimate = c(res1$estimate, res2$estimate),
    `Lower CI [0.025]` = c(res1$lowerCL, res2$lowerCL),
    `Upper CI  [0.975]` = c(res1$upperCL, res2$upperCL),
    `P value` = c(res1$pval, res2$pval),
    `Lower PI [0.025]` = c(res1$lowerPR, res2$lowerPR),
    `Upper PI  [0.975]` = c(res1$upperPR, res2$upperPR),
  )
}
# from metaAidR - https://github.com/daniel1noble/metaAidR/blob/master/R/make_VCV_matrix.R  
#' @title Covariance and correlation matrix function basing on shared level ID
#' @description Function for generating simple covariance and correlation matrices 
#' @param data Dataframe object containing effect sizes, their variance, unique IDs and clustering variable
#' @param V Name of the variable (as a string – e.g, "V1") containing effect size variances variances
#' @param cluster Name of the variable (as a string – e.g, "V1") indicating which effects belong to the same cluster. Same value of 'cluster' are assumed to be nonindependent (correlated).
#' @param obs Name of the variable (as a string – e.g, "V1") containing individual IDs for each value in the V (Vector of variances). If this parameter is missing, label will be labelled with consecutive integers starting from 1.
#' @param rho Known or assumed correlation value among effect sizes sharing same 'cluster' value. Default value is 0.5.
#' @param type Optional logical parameter indicating whether a full variance-covariance matrix (default or "vcv") is needed or a correlation matrix ("cor") for the non-independent blocks of variance values.
#' @export

make_VCV_matrix <- function(data, V, cluster, obs, type=c("vcv", "cor"), rho=0.5){
  type <- match.arg(type)
  if (missing(data)) {
    stop("Must specify dataframe via 'data' argument.")
  }
  if (missing(V)) {
    stop("Must specify name of the variance variable via 'V' argument.")
  }
  if (missing(cluster)) {
    stop("Must specify name of the clustering variable via 'cluster' argument.")
  }
  if (missing(obs)) {
    obs <- 1:length(V)   
  }
  if (missing(type)) {
    type <- "vcv" 
  }
  
  new_matrix <- matrix(0,nrow = dim(data)[1],ncol = dim(data)[1]) #make empty matrix of the same size as data length
  rownames(new_matrix) <- data[ ,obs]
  colnames(new_matrix) <- data[ ,obs]
  # find start and end coordinates for the subsets
  shared_coord <- which(data[ ,cluster] %in% data[duplicated(data[ ,cluster]), cluster]==TRUE)
  # matrix of combinations of coordinates for each experiment with shared control
  combinations <- do.call("rbind", tapply(shared_coord, data[shared_coord,cluster], function(x) t(utils::combn(x,2))))
  
  if(type == "vcv"){
    # calculate covariance values between  values at the positions in shared_list and place them on the matrix
    for (i in 1:dim(combinations)[1]){
      p1 <- combinations[i,1]
      p2 <- combinations[i,2]
      p1_p2_cov <- rho * sqrt(data[p1,V]) * sqrt(data[p2,V])
      new_matrix[p1,p2] <- p1_p2_cov
      new_matrix[p2,p1] <- p1_p2_cov
    }
    diag(new_matrix) <- data[ ,V]   #add the diagonal
  }
  
  if(type == "cor"){
    # calculate covariance values between  values at the positions in shared_list and place them on the matrix
    for (i in 1:dim(combinations)[1]){
      p1 <- combinations[i,1]
      p2 <- combinations[i,2]
      p1_p2_cov <- rho
      new_matrix[p1,p2] <- p1_p2_cov
      new_matrix[p2,p1] <- p1_p2_cov
    }
    diag(new_matrix) <- 1   #add the diagonal of 1
  }
  
  return(new_matrix)
}  

```


## The Silicon Herbivore Dataset

### Table of the dataset

The dataset used for our meta-analysis is below, followed by explanations of 10 variables extracted from the papers included (not all variables were used for our analyses; variables not directly used in our analyses are indicated by *).

**Extended Data Table 1:** 
The meta-analytic dataset of this study.

```{r}
# getting the data and formatting some variables (turning character vectors to factors)
full_data <- read_csv(here("data", "data_01_Oct_2023.csv")) %>%
  mutate_if(is.character, as.factor)

full_data<-subset(full_data, Xc > 0 & full_data$Xe > 0)

#full_data[full_data$Xc < 0, ][,'Xc']<-0.1
#full_data[full_data$Xe < 0, ][,'Xe']<-0.1

#determine whether value is negative
#full_data[full_data$Xe > full_data$Xc, ][,'Negative']<--1
#full_data[full_data$Xe < full_data$Xc, ][,'Negative']<-1
# making a scrollable table
kable(full_data, "html") %>%
  kable_styling("striped", position = "left") %>%
  scroll_box(width = "100%", height = "500px")
```

A. __Effect__: ID for effect sizes

B. __Study__: ID for publications (studies)

C. __Author__*: First author's family name

D. __Year__:	The year of publication of the study.

E. __Journal__*:	Journal name

F. __Plant_Species__:	Plant species name

G. __Plant_Phylogeny__: Plant Latin name for constructing phylogeny

H. __Plant_lifespan__: Whether the plant is annual or perennial

I. __Poaceae_or_Non__: Whether the plant is a Poaceae species or not 

J. __Herbivore_common_name__*: Common names for herbivore species

K. __Herbivore_Latin_name__: Herbivore species' Latin names

L. __Herbivore_Phylogeny__: Herbivore Latin name for constructing phylogeny

M. __Herbivore_diet_breadth__: : Whether a focal herbivore is a generalist or a specialist feeder

N. **Feeding_guild**: How the herbivore feeds on the plant 

O. **Performance_parameter***: Broad classification of herbivore performance parameter 

P. **Nc**: Sample size for the control group

Q. **Ne**: Sample size for the experimental group

R. **Xc**: Mean value for the control group 

S. **Xe**: Mean value for the experimental group

T. **Dev_c**: Standard deviation for the control group

U. **Dev_e**: Standard deviation for the experimental group

V. **Negative**: the vector of 1 or -1 which indicate if corresponding effect sizes should be reverse in its sign

W. **Si_Nc**: Sample size for the control group (for silicon uptake)

X. **Si_Ne**: Sample size for the experimental group (for silicon uptake)

Y. **Si_Xc**: Mean value for the control group (for silicon uptake)

Z. **Si_Xe**: Mean value for the experimental group (for silicon uptake)

AA. **Si_Dev_c**: Standard deviation for the control group (for silicon uptake)

AB. **Si_Dev_e**: Standard deviation for the experimental group (for silicon uptake)

### Table of sample sizes

```{r}
# selecting out variables, which we used for our analysis
dat <- full_data %>% select(-Author, -Journal, -Herbivore_common_name, -Performance_parameter)


# making a table of sample sizes for different variables
dat %>%
  summarise(
    `Effect sizes` = n_distinct(Effect),
    Studies = n_distinct(Study),
    `Plant species` = n_distinct(Plant_Species),
    `Herbivore species` = n_distinct(Herbivore_Latin_name),
    `Effect sizes (Silicon uptake)` = n_distinct(Effect[!is.na(Si_Nc)]),
    `Studies (Silicon uptake)` = n_distinct(Study[!is.na(Si_Nc)]),
    `Annual plants (Plant_lifespan)` = sum(Plant_lifespan == "Annual", na.rm = T), # na.rm is important when NA exists
    `Perennial plants (Plant_lifespan)` = sum(Plant_lifespan == "Perennial", na.rm = T),
    `Poaceae (Poaceae_or_Non)` = sum(Poaceae_or_Non == "Poaceae", na.rm = T),
    `Non-Poaceae (Poaceae_or_Non)` = sum(Poaceae_or_Non == "Non-Poaceae", na.rm = T),
    `Specialist (Herbivore_diet_breadth)` = sum(Herbivore_diet_breadth == "Specialist", na.rm = T),
    `Generalist (Herbivore_diet_breadth)` = sum(Herbivore_diet_breadth == "Generalist", na.rm = T),
    `Boring arthropods (Feeding_guild)` = sum(Feeding_guild == "Boring arthropods", na.rm = T),
    `Cell-feeding arthropods (Feeding_guild)` = sum(Feeding_guild == "Cell-feeding arthropods", na.rm = T),
    `Chewing arthropods (Feeding_guild)` = sum(Feeding_guild == "Chewing arthropods", na.rm = T),
    `Fluid-feeding arthropods (Feeding_guild)` = sum(Feeding_guild == "Fluid-feeding arthropods", na.rm = T),
    `Leaf-mining arthropods (Feeding_guild)` = sum(Feeding_guild == "Leaf-mining arthropods", na.rm = T),
    `Mammalian chewers (Feeding_guild)` = sum(Feeding_guild == "Mammalian chewers", na.rm = T),
    `Rasping/grazing invertebrates (Feeding_guild)` = sum(Feeding_guild == "Rasping / grazing invertebrates", na.rm = T)
  ) -> n_table1

# transposing the table and creating that table and adding a correct number of the papers for `Combined`
# n_authors <- n_distinct(dat$authors) # the total number of papers
n_table2 <- t(n_table1)
colnames(n_table2) <- "n (sample size)"
n_table2 %>%
  as_tibble(rownames = "Number") %>%
  rename("Number of" = "Number") %>%
  kable() %>% kable_styling("striped", position = "left") %>%
  scroll_box(width = "60%", height = "300px")
#pander(split.cell = 40, split.table = Inf) # not as nice as kable

```

### Missing data patterns 

The only missing values were studies that reported herbivore performance without quantifying plant silicon content.

```{r}
# summering missingness in our dataset
# funs(sum(is.na(.))) needs to be in funs as is.na has "." = each column
dat %>% summarise_all(~sum(is.na(.))) %>% # map(~sum(is.na(.)) # this is an alterantive way 
  t() %>% as_tibble(rownames = "Variable") %>% 
  rename("Number of missing data (n)" = "V1") %>% 
  #pander(split.cell = 40, split.table = Inf)
  kable() %>% kable_styling("striped", position = "left") %>%
  scroll_box(width = "60%", height = "300px")
```

## Meta-analysis: the Effect of Silicon on Herbivores

### Choosing effect size statistics: checking the mean-variance relationship

We checked the mean-variance relationship in our data. If there is such a relationship, it is preferable to use the logarithm of response ratio, lnRR [@hedges1999meta] rather than standardized mean difference, SMD (often known as Cohen’s *d* or Hedges’ *g*) because the latter assumes the homogeneity of variance (see below).

##### Figure E1
```{r fig.width=7, fig.height=6, }

# A)

cor_1 <- round(with(dat,cor(log(Xe), log(Dev_e))), 3)
mod_1 <- lm(log(Dev_e) ~ log(Xe), data = dat)

plot_exp <- ggplot(dat, aes(log(Xe), log(Dev_e))) + geom_point() +
  geom_smooth(method = "lm") + 
  labs(x = "ln(mean[experiment])", 
       y = "ln(SD[experiment])", 
       title = "Herbivore performance (experimental)") +
  xlim(-9, 9) + ylim(-9, 9) + 
  annotate('text',x = 7.5, y = -8, label = paste("r = ", cor_1)) + 
  annotate('text',x = 7.5, y = -6, label = paste("b = ", round(mod_1$coefficients[[2]],3)))

# B)
cor_2 <- round(with(dat,cor(log(Xc), log(Dev_c))), 3)
mod_2 <- lm(log(Dev_c) ~ log(Xc), data = dat)

plot_con <- ggplot(dat, aes(log(Xc), log(Dev_c))) + geom_point() +
  geom_smooth(method = "lm") + 
  labs(x = "ln(mean[control])", 
       y = "ln(SD[control])",
       title = "Herbivore performance (control)")+
  xlim(-9, 9) + ylim(-9, 9) + 
  annotate('text',x = 7.5, y = -8, label = paste("r = ", cor_2)) + 
  annotate('text',x = 7.5, y = -6, label = paste("b = ", round(mod_2$coefficients[[2]],3)))

# c)
cor_3 <- round(with(dat,cor.test(log(Si_Xe), log(Si_Dev_e)))$estimate[[1]], 3)
mod_3 <- lm(log(Si_Dev_e) ~ log(Si_Xe), data = dat)

Si_plot_exp <- ggplot(dat, aes(log(Si_Xe), log(Si_Dev_e))) + geom_point() +
  geom_smooth(method = "lm") + 
  labs(x = "ln(mean[experiment])", 
       y = "ln(SD[experiment])",
       title = "Silicon content (experimental)") +
  xlim(-9, 9) + ylim(-9, 9) + 
  annotate('text',x = 7.5, y = -8, label = paste("r = ", cor_3)) + 
  annotate('text',x = 7.5, y = -6, label = paste("b = ", round(mod_3$coefficients[[2]],3)))

# D)
cor_4 <- round(with(dat,cor.test(log(Si_Xc), log(Si_Dev_c)))$estimate[[1]], 3)
mod_4 <- lm(log(Si_Dev_c) ~ log(Si_Xc), data = dat)

Si_plot_con <- ggplot(dat, aes(log(Si_Xc), log(Si_Dev_c))) + geom_point() +
  geom_smooth(method = "lm") + 
  labs(x = "ln(mean[control])", 
       y = "ln(SD[control])",
       title = "Silicon content (control)")+
  xlim(-9, 9) + ylim(-9, 9) + 
  annotate('text',x = 7.5, y = -8, label = paste("r = ", cor_4)) +
  annotate('text',x = 7.5, y = -6, label = paste("b = ", round(mod_4$coefficients[[2]],3)))

Si_mean_SD <- Si_plot_exp + Si_plot_con +
  plot_annotation(tag_levels = "A", tag_suffix = ")")

mean_SD <- (plot_exp | plot_con) / (Si_plot_exp | Si_plot_con) +
  plot_annotation(tag_levels = "A", tag_suffix = ")")

mean_SD

# saving the figure for Scott
# ggsave(here("fig", "mean_SD.png"),
#        width = 20,
#        height = 20,
#        units = "cm")

# getting correlations

# Checking the slopes 


```

**Figure E1:** 
Mean-variance relationships for (A and B) herbivore performance and (C and D) silicon content in experimental (i.e. Si supplemented) and control plants. 


### Calculating effect sizes

```{r, }

# calculating effect size - think about turning into function
dat <- escalc(measure = "ROM", m1i = Xe, m2i = Xc, sd1i = Dev_e, sd2i = Dev_c, n1i = Nc, n2i = Ne, data = dat, var.names = c("lnRR", "varlnRR"))

dat <- escalc(measure = "VR", m1i = Xe, m2i = Xc, sd1i = Dev_e, sd2i = Dev_c, n1i = Nc, n2i = Ne, data = dat, var.names = c("lnVR", "varlnVR"))


# for Silicon accumulation
dat <- escalc(
  measure = "ROM", m1i = Si_Xe, m2i = Si_Xc, sd1i = Si_Dev_c, sd2i = Si_Dev_c, n1i = Si_Nc, n2i = Si_Ne,
  data = dat, var.names = c("Si_lnRR", "Si_varlnRR")
)

dat <- escalc(
  measure = "VR", m1i = Si_Xe, m2i = Si_Xc, sd1i = Si_Dev_e, sd2i = Si_Dev_c, n1i = Si_Nc, n2i = Si_Ne,
  data = dat, var.names = c("Si_lnVR", "Si_varlnVR")
)


# flipping the sign for effect sizes (lnRR not lnVR) when small means improvement (better)
dat %>%
  mutate(lnRR = Negative * lnRR) %>%
  as_tibble() %>% data.frame -> dat

```


### Phylogenetic tree and correlation matrix

##### Figure E2
```{r, fig.width=9, fig.height= 10}
herbiv_tree <- read.tree(file = here("phylo/plants_herbivtree_binary_JMW.tre"))
plant_tree <- read.tree(file = here("phylo/plants_planttree_binary.tre"))
#plot(herbiv_tree)
#plot(plant_tree)
htree <- compute.brlen(herbiv_tree)
ptree <- compute.brlen(plant_tree)
#is.ultrametric(htree) 
#is.ultrametric(ptree) 

par(mfrow = c(1,2))
plot(ptree)
mtext("Plant phylogeny", side = 1, cex = 1.5, line = 1)
plot(htree)
mtext("Herbivore phylogeny", side = 1,  cex = 1.5, line =1)

cor_htree <- vcv(htree,corr=T) # correlation matrix for Herbivores
cor_ptree <- vcv(ptree,corr=T) # correlation matrix for Plants
# saving for Scott 
# pdf(here("fig", "tree.pdf"), height=15, width=12)
# plot(tree)
# dev.off()  

```

**Figure E2:** 
Phylogenetic trees for  plants (left panel) and herbviores (right panel). 


## Meta-analytic models: lnRR and lnVR

```{r, }


levels(dat$Plant_Phylogeny)[levels(dat$Plant_Phylogeny) == "Saccharum_officinarum"] <- "Saccharum_spp."
levels(dat$Herbivore_Phylogeny)[levels(dat$Herbivore_Phylogeny) == "Sesamia_spp."] <- "Sesamia_inferens"
levels(dat$Herbivore_Phylogeny)[levels(dat$Herbivore_Phylogeny) == "Liriomyza_spp."] <- "Liriomyza_puella"
levels(dat$Herbivore_Phylogeny)[levels(dat$Herbivore_Phylogeny) == "Chlosyne_lacinia_saundersii"] <- "Chlosyne_lacinia"
levels(dat$Herbivore_Phylogeny)[levels(dat$Herbivore_Phylogeny) == "Deroceras_reticulatrum"] <- "Deroceras_reticulatum"
levels(dat$Herbivore_Phylogeny)[levels(dat$Herbivore_Phylogeny) == "Macrosiphoniellas_anborni"] <- "Macrosiphoniella_sanborni"
levels(dat$Herbivore_Phylogeny)[levels(dat$Herbivore_Phylogeny) == "Pseudaletia_unipuncta"] <- "Mythimna_unipuncta"

#lnRR
# matrix for controlling for correlated errors
VCV_lnRR <- make_VCV_matrix(dat, V = "varlnRR", cluster = "Study", obs = "Effect")

ma_lnRR <- rma.mv(
  yi = lnRR,
  V = VCV_lnRR,
  random = list(~1|Plant_Phylogeny, ~1|Plant_Species, 
                ~1|Herbivore_Phylogeny, ~1|Herbivore_Latin_name, 
                ~1|Study, ~1|Effect),
  R= list(Plant_Phylogeny = cor_ptree, Herbivore_Phylogeny = cor_htree),
  test = "t",
  data = dat)
 
# lnVR
# matrix for controlling for correlated errors
VCV_lnVR <- make_VCV_matrix(dat, V = "varlnVR", cluster = "Study", obs = "Effect")

ma_lnVR <- rma.mv(
  yi = lnVR,
  V = VCV_lnVR,
  random = list(~1|Plant_Phylogeny, ~1|Plant_Species, 
                ~1|Herbivore_Phylogeny, ~1|Herbivore_Latin_name, 
                ~1|Study, ~1|Effect),
  R= list(Plant_Phylogeny = cor_ptree, Herbivore_Phylogeny = cor_htree),
  test = "t",
  data = dat,
  control=list(optimizer="optim", optmethod="BFGS"))

# control=list(optimizer="optim", optmethod="Nelder-Mead")

```

##### Table E1
**Table E1:**
Overall effects (meta-analytic means), 95% confidence intervals (CIs) and 95% prediction intervals (95%) [@nakagawa2019orchard].


```{r, }
# getting a table of CI and PI
tma_lnRR <- get_pred1(ma_lnRR, mod = "Int")
tma_lnVR <- get_pred1(ma_lnVR, mod = "Int")

# Drawing a table for meta-analyses
tibble(`Effect size` = c("lnRR","lnVR"), 
       `Overall mean` = c(tma_lnRR$estimate, tma_lnVR$estimate), 
       `Lower CI [0.025]` = c(tma_lnRR$lowerCL, tma_lnVR$lowerCL), 
       `Upper CI [0.975]` = c(tma_lnRR$upperCL,tma_lnVR$upperCL),
       `P value`          = c(tma_lnRR$pval, tma_lnVR$pval),
       `Lower PI [0.025]` = c(tma_lnRR$lowerPR, tma_lnVR$lowerPR), 
       `Upper PI [0.975]` = c(tma_lnRR$upperPR, tma_lnVR$upperPR)) %>% 
  kable("html", digits = 3) %>% 
  kable_styling("striped", position = "left")
```
##### Table E2
**Table E2:**
Variance components (V) and heterogeneity, *I*^2^ (I2) [@higgins2003measuring] from the `metafor` model Note that in these models, *I*^2^~[total]~ is the sum of variance components of `Plant_Phylogeny`, `Plant_Species`, `Herbivore_Phylogeny`, `Study` and `Effect` -- [see [@nakagawa2012methodological;@senior2016heterogeneity]].

```{r, }
# getting I2
I2_lnRR <- i2_ml(ma_lnRR)
I2_lnVR <- i2_ml(ma_lnVR)

# getting sigma2 = V 
V_lnRR <- c(sum(ma_lnRR$sigma2), ma_lnRR$sigma2)
V_lnVR <-c(sum(ma_lnVR$sigma2), ma_lnVR$sigma2)

# Drawing a table for variance components V and I2
tibble(`Effect size` = c("lnRR (*I*^2^)","lnVR (*I*^2^)", "lnRR (*V*)", "lnVR (*V*)"), 
       Total     = c(I2_lnRR[1], I2_lnVR[1], V_lnRR[1], V_lnVR[1]), 
       `Plant phylogeny` = c(I2_lnRR[2], I2_lnVR[2],V_lnRR[2], V_lnVR[2]), 
       `Plant species`   = c(I2_lnRR[3], I2_lnVR[3], V_lnRR[3],V_lnVR[3]),
       `Herbivore phylogeny` = c(I2_lnRR[4], I2_lnVR[4], V_lnRR[4], V_lnVR[4]), 
       `Herbivore species`   = c(I2_lnRR[5], I2_lnVR[5], V_lnRR[5], V_lnVR[5]),
       Study     = c(I2_lnRR[6], I2_lnVR[6], V_lnRR[6],  V_lnVR[6]), 
       `Effect (within-study)` = c(I2_lnRR[7], I2_lnVR[7], V_lnRR[7],  V_lnVR[7]),
       ) %>% 
  kable("html", digits = 3) %>% 
  kable_styling("striped", position = "left")

```

##### Figure 2A-B

```{r,  fig.width=7, fig.height=6, }

## Figure 1A
p1 <- orchard_plot(ma_lnRR, data = dat, mod="1",group= 'Study', xlab = "log(Response Ratio) (lnRR)", alpha = 0.2, angle = 45) +
    scale_fill_manual(values="blue") +
  scale_colour_manual(values="blue") +
  annotate(geom="text", x=0.8, y=1.5, label="p < 0.001",
              color="black")


## Figure 1B
p2 <- orchard_plot(ma_lnVR, data = dat, mod="1",group= 'Study', xlab = "log(Variability Ratio) (lnVR)", alpha = 0.2, angle = 45) +
    scale_fill_manual(values="green") +
  scale_colour_manual(values="green") +
  annotate(geom="text", x=0.8, y=1.5, label="p = 0.106",
              color="black")


#ggsave(here("fig", "Overall.png"))
```

### Selecting random effects for meta-regression

```{r}
#lnRR

ma_lnRR2 <- rma.mv(
  yi = lnRR,
  V = VCV_lnRR,
  random = list( ~1|Plant_Species, 
                ~1|Herbivore_Phylogeny, ~1|Herbivore_Latin_name, 
                ~1|Study, ~1|Effect),
  R= list(Herbivore_Phylogeny = cor_htree),
  method = "ML",
  test = "t",
  data = dat)

# we take this model
ma_lnRR3 <- rma.mv(
  yi = lnRR,
  V = VCV_lnRR,
  random = list(~1|Herbivore_Phylogeny, ~1|Herbivore_Latin_name, 
                ~1|Study, ~1|Effect),
  R= list(Herbivore_Phylogeny = cor_htree),
  method = "ML",
  test = "t",
  data = dat)

ma_lnRR4 <- rma.mv(
  yi = lnRR,
  V = VCV_lnRR,
  random = list(~1|Herbivore_Phylogeny,
                ~1|Study, ~1|Effect),
  R= list(Herbivore_Phylogeny = cor_htree),
  method = "ML",
  test = "t",
  data = dat)

# TODO - these arent running due to estimation differences between models, do we need to fix?
#pval_lnRR1 <- round(anova(ma_lnRR, ma_lnRR2)$pval, 3)
#pval_lnRR2 <- round(anova(ma_lnRR2, ma_lnRR3)$pval, 3)
#pval_lnRR3 <- round(anova(ma_lnRR3, ma_lnRR4)$pval, 3)


#lnVR

ma_lnVR2 <- rma.mv(
  yi = lnVR,
  V = VCV_lnVR,
  random = list( ~1|Plant_Species, 
                ~1|Herbivore_Phylogeny, ~1|Herbivore_Latin_name, 
                ~1|Study, ~1|Effect),
  R= list(Herbivore_Phylogeny = cor_htree),
  method = "ML",
  test = "t",
  data = dat)

# we take this model
ma_lnVR3 <- rma.mv(
  yi = lnVR,
  V = VCV_lnVR,
  random = list(~1|Herbivore_Phylogeny, ~1|Herbivore_Latin_name, 
                ~1|Study, ~1|Effect),
  R= list(Herbivore_Phylogeny = cor_htree),
  method = "ML",
  test = "t",
  data = dat)

ma_lnVR4 <- rma.mv(
  yi = lnVR,
  V = VCV_lnVR,
  random = list(~1|Herbivore_Phylogeny,
                ~1|Study, ~1|Effect),
  R= list(Herbivore_Phylogeny = cor_htree),
  method = "ML",
  test = "t",
  data = dat)

#pval_lnVR1 <- round(anova(ma_lnVR, ma_lnVR2)$pval, 3)
#pval_lnVR2 <- round(anova(ma_lnVR2, ma_lnVR3)$pval, 3)
#pval_lnVR3 <- round(anova(ma_lnVR3, ma_lnVR4)$pval, 3)
```

Based on 3 criteria: 1) the amounts of variance each random effects explain, 2) the results of log-likelihood ratio tests, and 3) retaining some clustering factor(s) regardless of its effect, we decided to keep `Herbivore_Phylogeny`, `Herbivore_Latin_name`, `Study`, `Effect` for lnRR and lnVR.

## Meta-analysis of Silicon Content

```{r, }
# creating the smaller dataset - only one value per 
sdat <- dat %>% group_by(Study) %>% 
  summarise(Si_lnRR = first(Si_lnRR), 
            Si_varlnRR = first(Si_varlnRR), 
            Si_lnVR = first(Si_lnVR), 
            Si_varlnVR = first(Si_varlnVR),
            Plant_Phylogeny = first(Plant_Phylogeny),
            Plant_Species = first(Plant_Species)
  )

#lnRR
ma_lnRR_Si <- rma.mv(
  yi = Si_lnRR,
  V = Si_varlnRR,
  random = list(~1|Plant_Phylogeny, ~1|Plant_Species, ~1|Study),
  R= list(Phylogeny = cor_ptree),
  test = "t",
  data = sdat)
#summary(ma_lnRR_Si)

#lnVR 
ma_lnVR_Si <- rma.mv(
  yi = Si_lnVR,
  V = Si_varlnVR,
  random = list(~1|Plant_Phylogeny, ~1|Plant_Species, ~1|Study),
  R= list(Phylogeny = cor_ptree),
  est = "t",
  data = sdat)
#summary(ma_lnCVR_Si)

```

##### Table E3
**Table E3:**
Overall effects (meta-analytic means), 95% confidence intervals (CIs) and 95% prediction intervals (95%).

```{r, }
# getting a table of CI and PI
tma_lnRR_Si <- get_pred1(ma_lnRR_Si, mod = "Int")
tma_lnVR_Si <- get_pred1(ma_lnVR_Si, mod = "Int")

# Drawing a table for meta-analyses
tibble(`Effect size` = c("lnRR", "lnVR"), 
       `Overall mean` = c(tma_lnRR_Si$estimate, tma_lnVR_Si$estimate), 
       `Lower CI [0.025]` = c(tma_lnRR_Si$lowerCL, tma_lnVR_Si$lowerCL), 
       `Upper CI [0.975]` = c(tma_lnRR_Si$upperCL, tma_lnVR_Si$upperCL),
       `P value`         = c(tma_lnRR_Si$pval, tma_lnVR_Si$pval),
       `Lower PI [0.025]` = c(tma_lnRR_Si$lowerPR, tma_lnVR_Si$lowerPR), 
       `Upper PI [0.975]` = c(tma_lnRR_Si$upperPR, tma_lnVR_Si$upperPR)
       ) %>% 
  kable("html", digits = 3) %>% 
  kable_styling("striped", position = "left")

```
##### Table E4
**Table E4:**
Variance components (V) and heterogeneity, *I*^2^ (I2) [@higgins2003measuring] from the `metafor` model Note that in these models, *I*^2^~[total]~ is the sum of variance components of `Plant phylogeny`, `Plant species` and `Study`.

```{r, }
# getting I2
I2_lnRR_Si <- i2_ml(ma_lnRR_Si)
I2_lnVR_Si <- i2_ml(ma_lnVR_Si)

# getting sigma2 = V 
V_lnRR_Si <- c(sum(ma_lnRR_Si$sigma2), ma_lnRR_Si$sigma2)
V_lnVR_Si <-c(sum(ma_lnVR_Si$sigma2), ma_lnVR_Si$sigma2)
# Drawing a table for variance components V and I2
tibble(`Effect size` = c("lnRR (*I*^2^)", "lnVR (*I*^2^)", "lnRR (*V*)", "lnVR (*V*)"), 
       Total     = c(I2_lnRR_Si[1],I2_lnVR_Si[1],  V_lnRR_Si[1],  V_lnVR_Si[1]), 
       `Plant phylogeny` = c(I2_lnRR_Si[2],  I2_lnVR_Si[2], V_lnRR_Si[2], V_lnVR_Si[2]), 
       `Plant species`   = c(I2_lnRR_Si[3], I2_lnVR_Si[3], V_lnRR_Si[3],  V_lnVR_Si[3]), 
       Study     = c(I2_lnRR_Si[4], I2_lnVR_Si[4], V_lnRR_Si[4], V_lnVR_Si[4])
       ) %>% 
  kable("html", digits = 3) %>% 
  kable_styling("striped", position = "left")

```


##### Figure 2C-D

```{r,  fig.width=7, fig.height=6, }

## Remove NAs for orchardplot to work
sdat2<-na.omit(sdat)

## Figure 1C
p4 <- orchard_plot(ma_lnRR_Si, data = sdat2, mod="1",group= 'Study', xlab = "log(Response Ratio) (lnRR)", alpha = 0.2, angle = 45) +
    scale_fill_manual(values="blue") +
  scale_colour_manual(values="blue") +
  annotate(geom="text", x=0.8, y=1.5, label="p < 0.001",
              color="black")
## Figure 1D
p5 <- orchard_plot(ma_lnVR_Si, data = sdat2, mod="1",group= 'Study', xlab = "log(Variability Ratio) (lnVR)", alpha = 0.2, angle = 45) +
    scale_fill_manual(values="green") +
  scale_colour_manual(values="green") +
  annotate(geom="text", x=0.8, y=1.5, label="p < 0.001",
              color="black")

#ggsave(here("fig", "Overall_Silicon.png"))
```

## Bivariate Meta-analysis: Testing for Correlations Between Silicon Content and Herbivore Performance 

We conducted bivariate meta-analytic models for lnRR (mean effect size) and lnVR (absolute variance) to determine any relationship between silicon content and herbivore performance. We expected that these two effects will be negatively correlated (i.e. more plant silicon results in worse herbivore performance); we did not have any expectation for the corresponding lnVR pairings.

For this complex model, we only considered `Study` as a random effect, excluding `Species` and `Phylogeny`. 

```{r, , eval = FALSE}
# turning data into a long format to ran bivariate models
dat %>%
  select(Effect, Study, Year, Feeding_guild,
         lnRR, varlnRR,
         lnVR, varlnVR) -> dat2 # main effects
dat %>%
  select(Effect, Study, Year, Feeding_guild,
         lnRR = Si_lnRR, varlnRR = Si_varlnRR, 
         lnVR = Si_lnVR, varlnVR = Si_varlnVR) -> dat3 # silicon uptakes

# long format
ldat <- rbind(dat2, dat3) %>% 
  mutate(Number = 1:(2*nrow(dat)), 
         RR = rep(c("lnRR", "Si_lnRR"), each = nrow(dat)), 
         VR = rep(c("lnVR", "Si_lnVR"), each = nrow(dat)))

# lnRR
bi_ma_lnRR <- rma.mv(yi = lnRR, V = varlnRR, 
               mods = ~ RR - 1, 
               random =list(~ RR | Study), struct="UN", data=ldat) # only takes 2 random factor
#summary(bi_ma_lnRR)

#cor_lnRR <- round(bi_ma_lnRR$rho, 3)
#saveRDS(bi_ma_lnRR, file = here("Rdata", "bi_ma_lnRR.rds"))

# takes very long to run
ci_cor_lnRR <- confint(bi_ma_lnRR)
saveRDS(ci_cor_lnRR, file = here("Rdata", "ci_cor_lnRR.rds"))

# lnVR
bi_ma_lnVR <- rma.mv(yi = lnVR, V = varlnVR, 
               mods = ~ VR - 1, 
               random =list(~ VR | Study), struct="UN", data=ldat) # only takes 2 random factor
#summary(bi_ma_lnVR)
#cor_lnRR <- round(bi_ma_lnRR$rho, 3)
#saveRDS(bi_ma_lnVR, file = here("Rdata", "bi_ma_lnVR.rds"))

ci_cor_lnVR <- confint(bi_ma_lnVR)
saveRDS(ci_cor_lnVR, file = here("Rdata", "ci_cor_lnVR.rds"))

```


```{r}
# we probably rewrite here 

# bivariate correlations
ci_cor_lnRR <- readRDS(file = here("Rdata", "ci_cor_lnRR.rds"))
ci_cor_lnVR <- readRDS(file = here("Rdata", "ci_cor_lnVR.rds"))

```

We found a correlation of `r round(ci_cor_lnRR[[3]]$random[1],3)` (95% CI = [`r round(ci_cor_lnRR[[3]]$random[2],3)` , `r round(ci_cor_lnRR[[3]]$random[3],3)`]) between the two lnRRs, and a correlation of `r round(ci_cor_lnVR[[3]]$random[1],3)` (95% CI = [`r round(ci_cor_lnVR[[3]]$random[2],3)` , `r round(ci_cor_lnVR[[3]]$random[3],3)`]) between the two lnVRs.

## Run above correlations but only with chewing arthropods
```{r, , eval = FALSE}
chew_ldat<-subset(ldat, Feeding_guild == 'Chewing arthropods')

# lnRR
bi_ma_lnRR_chew <- rma.mv(yi = lnRR, V = varlnRR, 
               mods = ~ RR - 1, 
               random =list(~ RR | Study), struct="UN", data=chew_ldat) # only takes 2 random factor

# takes very long to run
ci_cor_lnRR_chew <- confint(bi_ma_lnRR_chew)
saveRDS(ci_cor_lnRR_chew, file = here("Rdata", "ci_cor_lnRR_chew.rds"))

#lnVR
bi_ma_lnVR_chew <- rma.mv(yi = lnVR, V = varlnVR, 
               mods = ~ VR - 1, 
               random =list(~ VR | Study), struct="UN", data=chew_ldat) # only takes 2 random factor
#summary(bi_ma_lnVR)
#cor_lnRR <- round(bi_ma_lnRR$rho, 3)
#saveRDS(bi_ma_lnVR, file = here("Rdata", "bi_ma_lnVR.rds"))

ci_cor_lnVR_chew <- confint(bi_ma_lnVR_chew)
saveRDS(ci_cor_lnVR_chew, file = here("Rdata", "ci_cor_lnVR_chew.rds"))

```

```{r}

# bivariate correlations chewing arthropods only
ci_cor_lnRR_chew <- readRDS(file = here("Rdata", "ci_cor_lnRR_chew.rds"))
ci_cor_lnVR_chew <- readRDS(file = here("Rdata", "ci_cor_lnVR_chew.rds"))

```

We found a correlation of `r round(ci_cor_lnRR_chew[[3]]$random[1],3)` (95% CI = [`r round(ci_cor_lnRR_chew[[3]]$random[2],3)` , `r round(ci_cor_lnRR_chew[[3]]$random[3],3)`]) between the two lnRRs, and a correlation of `r round(ci_cor_lnVR_chew[[3]]$random[1],3)` (95% CI = [`r round(ci_cor_lnVR_chew[[3]]$random[2],3)` , `r round(ci_cor_lnVR_chew[[3]]$random[3],3)`]) between the two lnVRs.


## Meta-regression: the Effect of Silicon and Herbivore Performance

### Univariate (uni-predictor) analyses

We ran a univariate meta-regression model for each of the following moderators: 1) `Poaceae_or_Non`, 2) `Plant_lifespan`, 3) `Herbivore_diet_breadth`, and 4) `Feeding_guild` (see the meta-data above). 


### Plant groups: Poaceae vs. Non-Poaceae

#### Mean change: lnRR

```{r, }
# reordering
dat$Poaceae_or_Non <- factor(dat$Poaceae_or_Non,
                            levels = rev(c("Poaceae", "Non-Poaceae")),
                            labels = rev(c("Poaceae", "Non-Poaceae")))

# meta-regression: multiple intercepts
mr_mdcot_lnRR1 <- rma.mv(
  yi = lnRR,
  V = VCV_lnRR,
  mods = ~ Poaceae_or_Non - 1,
  test = "t",
  random = list(~1|Herbivore_Phylogeny, ~1|Herbivore_Latin_name, 
                ~1|Study, ~1|Effect),
  data = dat
)

# getting marginal R2
r2_mr_mdcot_lnRR1 <- r2_ml(mr_mdcot_lnRR1)

# getting the level names out
level_names <- levels(dat$Poaceae_or_Non)

# helper function to run metafor meta-regression
run_rma <- function(name) {
  rma.mv(yi = lnRR, 
         V = VCV_lnRR,
         mods = ~ relevel(Poaceae_or_Non, ref = name), 
         test = "t",
         random = list(~1|Herbivore_Phylogeny, ~1|Herbivore_Latin_name, 
                       ~1|Study, ~1|Effect),
         data = dat)
}

# results of meta-regression including all contrast results; taking the last level out ([-length(level_names)])
mr_mdcot_lnRR <- map(level_names[-length(level_names)], run_rma)
```

##### Table E5
**Table E5:**
Regression coefficients (estimate), 95% confidence intervals (CIs), P values, and variance explained,  *R*^2^~[marginal]~ (R2) = `r as.numeric(round(r2_mr_mdcot_lnRR1[1], 4)*100)`% from the meta-regression of lnRR with `Poaceae_or_Non_Poaceae`. 
```{r, }
# getting estimates
res_mr_mdcot_lnRR1 <- get_pred1(mr_mdcot_lnRR1, mod = "Poaceae_or_Non")
#res_mr_annuality_lnRR2 <- map(mr_annuality_lnRR, ~ get_est2(.x, mod = "Annuality"))
res_mr_mdcot_lnRR <- map(mr_mdcot_lnRR, ~ get_pred2(.x, mod = "Poaceae_or_Non"))

# a list of the numbers to take out unnecessary contrasts
contra_list <- Map(seq, from=1, to=1:1)

# you need to flatten twice: first to make it a list and make it a vector
# I guess we can make it a loop (or vectorise)
res_mr_mdcot_lnRR2 <- map2_dfr(res_mr_mdcot_lnRR, contra_list, ~.x[-(.y), ]) 

# creating a table
mr_results(res_mr_mdcot_lnRR1, res_mr_mdcot_lnRR2) %>% 
  kable("html",  digits = 3) %>%
  kable_styling("striped", position = "left") %>%
  scroll_box(width = "100%")
```

#### Change in SD: lnVR

```{r, }

# meta-regression: multiple intercepts
mr_mdcot_lnVR1 <- rma.mv(
  yi = lnVR,
  V = VCV_lnVR,
  mods = ~ Poaceae_or_Non - 1,
  test = "t",
  random = list(~1|Herbivore_Phylogeny, ~1|Herbivore_Latin_name, 
                ~1|Study, ~1|Effect),
  data = dat
)

# getting marginal R2
r2_mr_mdcot_lnVR1 <- r2_ml(mr_mdcot_lnVR1)

# getting the level names out
level_names <- levels(dat$Poaceae_or_Non)

# helper function to run metafor meta-regression
run_rma <- function(name) {
  rma.mv(yi = lnVR, 
         V = VCV_lnVR,
         mods = ~ relevel(Poaceae_or_Non, ref = name), 
         test = "t",
         random = list(~1|Herbivore_Phylogeny, ~1|Herbivore_Latin_name, 
                       ~1|Study, ~1|Effect),
         data = dat)
}

# results of meta-regression including all contrast results; taking the last level out ([-length(level_names)])
mr_mdcot_lnVR <- map(level_names[-length(level_names)], run_rma)
```

##### Table E6
**Table E6:**
Regression coefficients (estimate), 95% confidence intervals (CIs), P values, and variance explained,  *R*^2^~[marginal]~ (R2) = `r as.numeric(round(r2_mr_mdcot_lnVR1[1], 4)*100)`% from the meta-regression of lnVR with `Poaceae_or_Non_Poaceae`. 
```{r, }
# getting estimates
res_mr_mdcot_lnVR1 <- get_pred1(mr_mdcot_lnVR1, mod = "Poaceae_or_Non")
#res_mr_annuality_lnRR2 <- map(mr_annuality_lnRR, ~ get_est2(.x, mod = "Annuality"))
res_mr_mdcot_lnVR <- map(mr_mdcot_lnVR, ~ get_pred2(.x, mod = "Poaceae_or_Non"))

# a list of the numbers to take out unnecessary contrasts
contra_list <- Map(seq, from=1, to=1:1)

# you need to flatten twice: first to make it a list and make it a vector
# I guess we can make it a loop (or vectorise)
res_mr_mdcot_lnVR2 <- map2_dfr(res_mr_mdcot_lnVR, contra_list, ~.x[-(.y), ]) 

# creating a table
mr_results(res_mr_mdcot_lnVR1, res_mr_mdcot_lnVR2) %>% 
  kable("html",  digits = 3) %>%
  kable_styling("striped", position = "left") %>%
  scroll_box(width = "100%")
```

##### Figure E3
```{r,  fig.width=7, fig.height=8, }

p10 <- orchard_plot(mr_mdcot_lnRR1, data = dat, mod="Poaceae_or_Non",group= 'Study', xlab = "log(Response Ratio) (lnRR)", alpha = 0.2, angle = 45) 


p11 <- orchard_plot(mr_mdcot_lnVR1, data = dat, mod="Poaceae_or_Non",group= 'Study', xlab = "log(Variabilty Ratio) (lnVR)", alpha = 0.2, angle = 45) 

p10/p11

#ggsave(here("fig", "Monocot_Dicot.png"))
```

**Figure E3:** 
Mean change (lnRR) and change in SD  (lnVR) in herbivore performance when feeding on Poaceae and non-Poaceae plants. The orchard plot shows the meta-analytic mean (mean effect size) with its 95% confidence interval (thick line) and 95% prediction interval (thin line), with observed effect sizes based on sample sizes.

### Plant lifespan: annual vs. perennial

#### Mean change: lnRR
```{r, }

# getting the level names out
level_names <- levels(dat$Plant_lifespan)

# lnRR
# meta-regression: multiple intercepts
mr_annuality_lnRR1 <- rma.mv(
  yi = lnRR,
  V = VCV_lnRR,
  mods = ~ Plant_lifespan - 1,
  test = "t",
  random = list(~1|Herbivore_Phylogeny, ~1|Herbivore_Latin_name, 
                ~1|Study, ~1|Effect),
  data = dat
)

# getting marginal R2
r2_mr_annuality_lnRR1 <- r2_ml(mr_annuality_lnRR1)

# meta-regression: contrasts 
# helper function to run metafor meta-regression
run_rma <- function(name) {
  rma.mv(yi = lnRR, 
         V = VCV_lnRR, 
         mods = ~ relevel(Plant_lifespan, ref = name), 
         test = "t",
        random = list(~1|Herbivore_Phylogeny, ~1|Herbivore_Latin_name, 
                      ~1|Study, ~1|Effect),
         data = dat)
}

# results of meta-regression including all contrast results; taking the last level out ([-length(level_names)])
mr_annuality_lnRR <- map(level_names[-length(level_names)], run_rma)
```
##### Table E7
**Table E7:**
Regression coefficients (estimate), 95% confidence intervals (CIs), and variance explained,  *R*^2^~[marginal]~ (R2) = `r as.numeric(round(r2_mr_annuality_lnRR1[1], 4)*100)`% from the meta-regression of lnRR with `Plant_lifespan`. 

```{r, }
# getting estimates
res_mr_annuality_lnRR1 <- get_pred1(mr_annuality_lnRR1, mod = "Plant_lifespan")
res_mr_annuality_lnRR <- map(mr_annuality_lnRR, ~ get_pred2(.x, mod = "Plant_lifespan"))

# a list of the numbers to take out unnecessary contrasts
contra_list <- Map(seq, from=1, to=1:1)

# you need to flatten twice: first to make it a list and make it a vector
# I guess we can make it a loop (or vectorise)
res_mr_annuality_lnRR2 <- map2_dfr(res_mr_annuality_lnRR, contra_list, ~.x[-(.y), ]) 

# creating a table
mr_results(res_mr_annuality_lnRR1, res_mr_annuality_lnRR2) %>% 
  kable("html",  digits = 3) %>%
  kable_styling("striped", position = "left") %>%
  scroll_box(width = "100%")
```

#### Change in SD: lnVR
```{r, }

# getting the level names out
level_names <- levels(dat$Plant_lifespan)

# lnRR
# meta-regression: multiple intercepts
mr_annuality_lnVR1 <- rma.mv(
  yi = lnVR,
  V = VCV_lnVR,
  mods = ~ Plant_lifespan - 1,
  test = "t",
  random = list(~1|Herbivore_Phylogeny, ~1|Herbivore_Latin_name, 
                ~1|Study, ~1|Effect),
  data = dat
)

# getting marginal R2
r2_mr_annuality_lnVR1 <- r2_ml(mr_annuality_lnVR1)

# meta-regression: contrasts 
# helper function to run metafor meta-regression
run_rma <- function(name) {
  rma.mv(yi = lnVR, 
         V = VCV_lnVR, 
         mods = ~ relevel(Plant_lifespan, ref = name), 
         test = "t",
        random = list(~1|Herbivore_Phylogeny, ~1|Herbivore_Latin_name, 
                      ~1|Study, ~1|Effect),
         data = dat)
}

# results of meta-regression including all contrast results; taking the last level out ([-length(level_names)])
mr_annuality_lnVR <- map(level_names[-length(level_names)], run_rma)
```

##### Table E8
**Table E8:**
Regression coefficients (estimate), 95% confidence intervals (CIs), and variance explained,  *R*^2^~[marginal]~ (R2) = `r as.numeric(round(r2_mr_annuality_lnVR1[1], 4)*100)`% from the meta-regression of lnVR with `Plant_lifespan`. 

```{r, }
# getting estimates
res_mr_annuality_lnVR1 <- get_pred1(mr_annuality_lnVR1, mod = "Plant_lifespan")
res_mr_annuality_lnVR <- map(mr_annuality_lnVR, ~ get_pred2(.x, mod = "Plant_lifespan"))

# a list of the numbers to take out unnecessary contrasts
contra_list <- Map(seq, from=1, to=1:1)

# you need to flatten twice: first to make it a list and make it a vector
# I guess we can make it a loop (or vectorise)
res_mr_annuality_lnVR2 <- map2_dfr(res_mr_annuality_lnVR, contra_list, ~.x[-(.y), ]) 

# creating a table
mr_results(res_mr_annuality_lnVR1, res_mr_annuality_lnVR2) %>% 
  kable("html",  digits = 3) %>%
  kable_styling("striped", position = "left") %>%
  scroll_box(width = "100%")
```

##### Figure E4
```{r,  fig.width=7, fig.height=8, }

p7 <- orchard_plot(mr_annuality_lnRR1, data = dat, mod="Plant_lifespan",group= 'Study', xlab = "log(Response Ratio) (lnRR)", alpha = 0.2, angle = 45) 
p8 <- orchard_plot(mr_annuality_lnVR1, data = dat, mod="Plant_lifespan",group= 'Study', xlab = "log(Variability Ratio) (lnVR)", alpha = 0.2, angle = 45) 
p7/p8

#ggsave(here("fig", "Annuality.png"))
```

**Figure E4:** Mean change (lnRR), and change in SD  (lnVR) in herbivore performance when feeding on annual or perennial plants. The orchard plot shows the meta-analytic mean (mean effect size) with its 95% confidence interval (thick line) and 95% prediction interval (thin line), with observed effect sizes based on sample sizes.



### Herbivore distinction: generalists vs. specialists

#### Mean change: lnRR

```{r, }
# meta-regression: multiple intercepts
mr_specgen_lnRR1 <- rma.mv(
  yi = lnRR,
  V = VCV_lnRR,
  mods = ~ Herbivore_diet_breadth - 1,
  test = "t",
  random = list(~1|Herbivore_Phylogeny, ~1|Herbivore_Latin_name, 
                ~1|Study, ~1|Effect),
  data = dat
)


# getting marginal R2
r2_mr_specgen_lnRR1 <- r2_ml(mr_specgen_lnRR1)

# getting the level names out
level_names <- levels(dat$Herbivore_diet_breadth)

# helper function to run metafor meta-regression
run_rma <- function(name) {
  rma.mv(yi = lnRR, 
         V = VCV_lnRR,
         mods = ~ relevel(Herbivore_diet_breadth, ref = name), 
         test = "t",
         random = list(~1|Herbivore_Phylogeny, ~1|Herbivore_Latin_name, 
                ~1|Study, ~1|Effect),
         data = dat)
}

# results of meta-regression including all contrast results; taking the last level out ([-length(level_names)])
mr_specgen_lnRR <- map(level_names[-length(level_names)], run_rma)
```

##### Table E9
**Table E9:**
Regression coefficients (estimate), 95% confidence intervals (CIs), P values and variance explained,  *R*^2^~[marginal]~ (R2) = `r as.numeric(round(r2_mr_specgen_lnRR1[1], 4)*100)`% from the meta-regression of lnRR with `Herbivore_diet_breadth`. 

```{r}
# getting estimates
res_mr_specgen_lnRR1 <- get_pred1(mr_specgen_lnRR1, mod = "Herbivore_diet_breadth")
#res_mr_annuality_lnRR2 <- map(mr_annuality_lnRR, ~ get_est2(.x, mod = "Annuality"))
res_mr_specgen_lnRR <- map(mr_specgen_lnRR, ~ get_pred2(.x, mod = "Herbivore_diet_breadth"))

# a list of the numbers to take out unnecessary contrasts
contra_list <- Map(seq, from=1, to=1:1)

# you need to flatten twice: first to make it a list and make it a vector
# I guess we can make it a loop (or vectorise)
res_mr_specgen_lnRR2 <- map2_dfr(res_mr_specgen_lnRR, contra_list, ~.x[-(.y), ]) 

# creating a table
mr_results(res_mr_specgen_lnRR1, res_mr_specgen_lnRR2) %>% 
  kable("html",  digits = 3) %>%
  kable_styling("striped", position = "left") %>%
  scroll_box(width = "100%")
```

####Change in SD: lnVR

```{r, }
# meta-regression: multiple intercepts
mr_specgen_lnVR1 <- rma.mv(
  yi = lnVR,
  V = VCV_lnVR,
  mods = ~ Herbivore_diet_breadth - 1,
  test = "t",
  random = list(~1|Herbivore_Phylogeny, ~1|Herbivore_Latin_name, 
                ~1|Study, ~1|Effect),
  data = dat
)


# getting marginal R2
r2_mr_specgen_lnVR1 <- r2_ml(mr_specgen_lnVR1)

# getting the level names out
level_names <- levels(dat$Herbivore_diet_breadth)

# helper function to run metafor meta-regression
run_rma <- function(name) {
  rma.mv(yi = lnVR, 
         V = VCV_lnVR,
         mods = ~ relevel(Herbivore_diet_breadth, ref = name), 
         test = "t",
         random = list(~1|Herbivore_Phylogeny, ~1|Herbivore_Latin_name, 
                ~1|Study, ~1|Effect),
         data = dat)
}

# results of meta-regression including all contrast results; taking the last level out ([-length(level_names)])
mr_specgen_lnVR <- map(level_names[-length(level_names)], run_rma)
```

##### Table E10
**Table E10:**
Regression coefficients (estimate), 95% confidence intervals (CIs), P values and variance explained,  *R*^2^~[marginal]~ (R2) = `r as.numeric(round(r2_mr_specgen_lnVR1[1], 4)*100)`% from the meta-regression of lnVR with `Herbivore_diet_breadth`. 

```{r}
# getting estimates
res_mr_specgen_lnVR1 <- get_pred1(mr_specgen_lnVR1, mod = "Herbivore_diet_breadth")
#res_mr_annuality_lnVR2 <- map(mr_annuality_lnVR, ~ get_est2(.x, mod = "Annuality"))
res_mr_specgen_lnVR <- map(mr_specgen_lnVR, ~ get_pred2(.x, mod = "Herbivore_diet_breadth"))

# a list of the numbers to take out unnecessary contrasts
contra_list <- Map(seq, from=1, to=1:1)

# you need to flatten twice: first to make it a list and make it a vector
# I guess we can make it a loop (or vectorise)
res_mr_specgen_lnVR2 <- map2_dfr(res_mr_specgen_lnVR, contra_list, ~.x[-(.y), ]) 

# creating a table
mr_results(res_mr_specgen_lnVR1, res_mr_specgen_lnVR2) %>% 
  kable("html",  digits = 3) %>%
  kable_styling("striped", position = "left") %>%
  scroll_box(width = "100%")
```

##### Figure E5
```{r,  fig.width=7, fig.height=8, }

p13 <- orchard_plot(mr_specgen_lnRR1, data = dat, mod="Herbivore_diet_breadth",group= 'Study', xlab = "log(Response Ratio) (lnRR)", alpha = 0.2, angle = 45) 

p14 <- orchard_plot(mr_specgen_lnVR1, data = dat, mod="Herbivore_diet_breadth",group= 'Study', xlab = "log(Variability Ratio) (lnVR)", alpha = 0.2, angle = 45) 

  
p13/p14

#ggsave(here("fig", "SpecGen.png"))
```
**Figure E5:** 
Mean change (lnRR) and change in SD  (lnVR) in herbivore performance comparing herbivore diet breadth (generalist or specialist). The orchard plot shows the meta-analytic mean (mean effect size) with its 95% confidence interval (thick line) and 95% prediction interval (thin line), with observed effect sizes based on sample sizes.


### Feeding guilds (7 groups)

#### Mean change: lnRR

```{r, }
# reordering
dat$Feeding_guild <- factor(dat$Feeding_guild,
                            levels = rev(c("Fluid-feeding arthropods",
                                       "Chewing arthropods", "Boring arthropods",
                                       "Mammalian chewers", "Cell-feeding arthropods",
                                       "Leaf-mining arthropods", "Rasping / grazing invertebrates")),
                            labels =  rev(c("Fluid-feeding arthropods",
                                       "Chewing arthropods", "Boring arthropods",
                                       "Mammalian chewers", "Cell-feeding arthropods",
                                       "Leaf-mining arthropods", "Rasping / grazing invertebrates")))

# meta-regression: multiple intercepts
mr_feeding_lnRR1 <- rma.mv(
  yi = lnRR,
  V = VCV_lnRR,
  mods = ~ Feeding_guild - 1,
  test = "t",
  random = list(~1|Herbivore_Phylogeny, ~1|Herbivore_Latin_name, 
                ~1|Study, ~1|Effect),
  data = dat
)

# getting marginal R2
r2_mr_feeding_lnRR1 <- r2_ml(mr_feeding_lnRR1)

# # meta-regression: contrasts x 7
# getting the level names out
level_names <- levels(dat$Feeding_guild)

# helper function to run metafor meta-regression
run_feeding_lnRR <- function(name) {
  rma.mv(yi = lnRR, 
         V = VCV_lnRR,
         mods = ~ relevel(Feeding_guild, ref = name), 
         test = "t",
         random = list(~1|Herbivore_Phylogeny, ~1|Herbivore_Latin_name, 
                ~1|Study, ~1|Effect),
         data = dat)
}

# results of meta-regression including all contrast results; taking the last level out ([-length(level_names)])
mr_feeding_lnRR <- map(level_names[-length(level_names)], run_feeding_lnRR)

```

##### Table E11
**Table E11:**
Regression coefficients (estimate), 95% confidence intervals (CIs), and variance explained,  *R*^2^~[marginal]~ (R2) = `r as.numeric(round(r2_mr_feeding_lnRR1[1], 4)*100)`% from the meta-regression of lnRR with `Feeding_guild`. 
```{r, }
# getting estimates
res_mr_feeding_lnRR1 <- get_pred1(mr_feeding_lnRR1, mod = "Feeding_guild")
res_mr_feeding_lnRR <- map(mr_feeding_lnRR, ~ get_pred2(.x, mod = "Feeding_guild"))

# a list of the numbers to take out unnecessary contrasts
contra_list <- Map(seq, from=1, to=1:6)

# I guess we can make it a loop (or vectorise)
res_mr_feeding_lnRR2 <- map2_dfr(res_mr_feeding_lnRR, contra_list, ~.x[-(.y), ]) 

# creating a table
mr_results(res_mr_feeding_lnRR1, res_mr_feeding_lnRR2) %>% 
  kable("html",  digits = 3) %>%
  kable_styling("striped", position = "left") %>%
  scroll_box(width = "100%", height = "300px")
```

#### Change in SD: lnVR

```{r, }
# meta-regression: multiple intercepts
mr_feeding_lnVR1 <- rma.mv(
  yi = lnVR,
  V = VCV_lnVR,
  mods = ~ Feeding_guild - 1,
  test = "t",
  random = list(~1|Herbivore_Phylogeny, ~1|Herbivore_Latin_name, 
                ~1|Study, ~1|Effect),
  data = dat
)

# getting marginal R2
r2_mr_feeding_lnVR1 <- r2_ml(mr_feeding_lnVR1)

# # meta-regression: contrasts x 7
# getting the level names out
level_names <- levels(dat$Feeding_guild)

# helper function to run metafor meta-regression
run_feeding_lnVR <- function(name) {
  rma.mv(yi = lnVR, 
         V = VCV_lnVR,
         mods = ~ relevel(Feeding_guild, ref = name), 
         test = "t",
         random = list(~1|Herbivore_Phylogeny, ~1|Herbivore_Latin_name, 
                ~1|Study, ~1|Effect),
         data = dat)
}

# results of meta-regression including all contrast results; taking the last level out ([-length(level_names)])
mr_feeding_lnVR <- map(level_names[-length(level_names)], run_feeding_lnVR)

```

##### Table E12
**Table E12:**
Regression coefficients (estimate), 95% confidence intervals (CIs), and variance explained,  *R*^2^~[marginal]~ (R2) = `r as.numeric(round(r2_mr_feeding_lnVR1[1], 4)*100)`% from the meta-regression of lnVR with `Feeding_guild`. 
```{r, }
# getting estimates
res_mr_feeding_lnVR1 <- get_pred1(mr_feeding_lnVR1, mod = "Feeding_guild")
res_mr_feeding_lnVR <- map(mr_feeding_lnVR, ~ get_pred2(.x, mod = "Feeding_guild"))

# a list of the numbers to take out unnecessary contrasts
contra_list <- Map(seq, from=1, to=1:6)

# I guess we can make it a loop (or vectorise)
res_mr_feeding_lnVR2 <- map2_dfr(res_mr_feeding_lnVR, contra_list, ~.x[-(.y), ]) 

# creating a table
mr_results(res_mr_feeding_lnVR1, res_mr_feeding_lnVR2) %>% 
  kable("html",  digits = 3) %>%
  kable_styling("striped", position = "left") %>%
  scroll_box(width = "100%", height = "300px")
```

##### Figure E6
```{r,  fig.width=7, fig.height=14, }

p17 <- orchard_plot(mr_feeding_lnRR1, data = dat, mod="Feeding_guild",group= 'Study', xlab = "log(Response Ratio) (lnRR)", alpha = 0.2, angle = 45) 

p18 <- orchard_plot(mr_feeding_lnVR1, data = dat, mod="Feeding_guild",group= 'Study', xlab = "log(Variability Ratio) (lnVR)", alpha = 0.2, angle = 45) 

# p11|p12 
# ggsave(here("fig", "Feeding_guild.png"))

p17/p18
#ggsave(here("fig", "Feeding_guild2.png"))
```
**Figure E6:** 
Mean change (lnRR), and change in SD  (lnVR) in herbivore performance comparing herbivore feeding guild. The orchard plot shows the meta-analytic mean (mean effect size) with its 95% confidence interval (thick line) and 95% prediction interval (thin line), with observed effect sizes based on sample sizes.



##### Figure 3A-B (N < 10 taken out)

```{r,  fig.width=7, fig.height=10, }
# TODO the esiest to jsut run models without these groups

# TODO - I cannot subset the data properly, I have tried a few alternative techniques, they work, but as the dataset from the model is has more 'Feeding_guild' levels than  the subset dataframe so there is an NA row in the figure.

datlowrm<-dat[!(dat$Feeding_guild=="Rasping / grazing invertebrates" | dat$Feeding_guild=="Leaf-mining arthropods" | dat$Feeding_guild=="Cell-feeding arthropods"),]


# matrix for controlling for correlated errors
VCV_lnRR_lrem <- make_VCV_matrix(datlowrm, V = "varlnRR", cluster = "Study", obs = "Effect")
#lnRR - run models removing groups with low effect sizes
# meta-regression: multiple intercepts
mr_feeding_lnRR1_1 <- rma.mv(
  yi = lnRR,
  V = VCV_lnRR_lrem,
  mods = ~ Feeding_guild - 1,
  test = "t",
  random = list(~1|Herbivore_Phylogeny, ~1|Herbivore_Latin_name, 
                ~1|Study, ~1|Effect),
  data = datlowrm
)

# getting marginal R2
r2_mr_feeding_lnRR1_1 <- r2_ml(mr_feeding_lnRR1_1)

# # meta-regression: contrasts x 7


# helper function to run metafor meta-regression
run_feeding_lnRR1 <- function(name) {
  rma.mv(yi = lnRR, 
         V = VCV_lnRR_lrem,
         mods = ~ relevel(Feeding_guild, ref = name), 
         test = "t",
         random = list(~1|Herbivore_Phylogeny, ~1|Herbivore_Latin_name, 
                ~1|Study, ~1|Effect),
         data = datlowrm)
}

# results of meta-regression including all contrast results; taking the last level out ([-length(level_names)])

#op_feeding_lnRR <- orchaRd::mod_results(mr_feeding_lnRR1, mod = "Feeding_guild", data = dat, group = "Study",
   # at = list(Feeding_guild = c("Fluid-feeding arthropods", 'Chewing arthropods','Boring arthropods','Mammalian chewers'), subset = TRUE))

#<- mod_results(mr_feeding_lnRR1, data = dat,mod = "Feeding_guild", group = 'Study')

# taking out some groups N < 10
#op_feeding_lnRR$mod_table <- op_feeding_lnRR$mod_table[-which(op_feeding_lnRR$mod_table$name == "Leaf-mining arthropods" | #op_feeding_lnRR$mod_table$name == "Rasping / grazing invertebrates" | op_feeding_lnRR$mod_table$name == "Cell-feeding arthropods"), ]

#op_feeding_lnRR$data <- op_feeding_lnRR$data[-which(op_feeding_lnRR$data$moderator == "Leaf-mining arthropods" | op_feeding_lnRR$data$moderator == "Rasping / grazing invertebrates" | op_feeding_lnRR$data$moderator ==  "Cell-feeding arthropods"), ]

m1 <- orchard_plot(mr_feeding_lnRR1_1 , data= datlowrm,mod="Feeding_guild", group= 'Study', xlab = "log(Response Ratio) (lnRR)", alpha = 0.2,  angle = 45) 





#lnVR
# matrix for controlling for correlated errors
VCV_lnVR_lrem <- make_VCV_matrix(datlowrm, V = "varlnVR", cluster = "Study", obs = "Effect")
#lnRR - run models removing groups with low effect sizes
# meta-regression: multiple intercepts
mr_feeding_lnVR1_1 <- rma.mv(
  yi = lnVR,
  V = VCV_lnVR_lrem,
  mods = ~ Feeding_guild - 1,
  test = "t",
  random = list(~1|Herbivore_Phylogeny, ~1|Herbivore_Latin_name, 
                ~1|Study, ~1|Effect),
  data = datlowrm
)

# getting marginal R2
r2_mr_feeding_lnVR1_1 <- r2_ml(mr_feeding_lnVR1_1)


# helper function to run metafor meta-regression
run_feeding_lnRR1 <- function(name) {
  rma.mv(yi = lnRR, 
         V = VCV_lnRR_lrem,
         mods = ~ relevel(Feeding_guild, ref = name), 
         test = "t",
         random = list(~1|Herbivore_Phylogeny, ~1|Herbivore_Latin_name, 
                ~1|Study, ~1|Effect),
         data = datlowrm)
}


#op_feeding_lnVR <- mod_results(mr_feeding_lnVR1, mod = "Feeding_guild")

# taking out some groups N < 10
#op_feeding_lnVR$mod_table <- op_feeding_lnVR$mod_table[-which(op_feeding_lnVR$mod_table$name == "Leaf-mining arthropods" | op_feeding_lnVR$mod_table$name == "Rasping / grazing invertebrates" | op_feeding_lnVR$mod_table$name == "Cell-feeding arthropods"), ]

#op_feeding_lnVR$data <- op_feeding_lnVR$data[-which(op_feeding_lnVR$data$moderator == "Leaf-mining arthropods" | op_feeding_lnVR$data$moderator == "Rasping / grazing invertebrates" | op_feeding_lnVR$data$moderator ==  "Cell-feeding arthropods"), ]

m2 <- orchard_plot(mr_feeding_lnVR1_1 , data= datlowrm,mod="Feeding_guild", group= 'Study', xlab = "log(Response Ratio) (lnVR)", alpha = 0.2,  angle = 45) 


#ggsave(here("fig", "Feeding_guild2m.png"))

```



### Interaction effects (subset of guilds with "Chewing arthropods" and "Fluid-feeding arthropods")

We ran a univariate meta-regression model for each of the following moderators:: 1) `Lifespan_guild`, 2) `Poaceae_guild`, and 3) `Diet_guild` (see the meta-data above). 

We with the 3 other categorical variables: 

```{r}
#
dat %>% filter(Feeding_guild == "Chewing arthropods" | Feeding_guild == "Fluid-feeding arthropods") %>%
  mutate(Lifespan_guild = factor(str_c(Plant_lifespan, Feeding_guild, sep = ":")),
         Poaceae_guild = factor(str_c(Poaceae_or_Non, Feeding_guild, sep = ":")),
         Diet_guild = factor(str_c(Herbivore_diet_breadth, Feeding_guild, sep = ":")) ) -> gdat
                                        
                                        
```

### The combined effect of plant lifespan and guilds (Chewing arthropods/ Fluid-feeding arthropods)

#### Mean change: lnRR

```{r, }
# reordering
gdat$Lifespan_guild <- factor(gdat$Lifespan_guild,
                            levels = c("Annual:Chewing arthropods","Perennial:Chewing arthropods", "Annual:Fluid-feeding arthropods", "Perennial:Fluid-feeding arthropods"),
                            labels = c("Annual:Chewing arthropods","Perennial:Chewing arthropods", "Annual:Fluid-feeding arthropods", "Perennial:Fluid-feeding arthropods"))

# vcv matrix 
VCV_lnRR2 <- make_VCV_matrix(gdat, V = "varlnRR", cluster = "Study", obs = "Effect")

# meta-regression: multiple intercepts
mr_Annuality_guild_lnRR1 <- rma.mv(
  yi = lnRR,
  V = VCV_lnRR2,
  mods = ~ Lifespan_guild - 1,
  test = "t",
  random = list(~1|Herbivore_Phylogeny, ~1|Herbivore_Latin_name, 
                ~1|Study, ~1|Effect),
  data = gdat
)

# getting marginal R2
r2_mr_Annuality_guild_lnRR1 <- r2_ml(mr_Annuality_guild_lnRR1)

# # meta-regression: contrasts x 7
# getting the level names out
level_names <- levels(gdat$Lifespan_guild)

# helper function to run metafor meta-regression
run_Annuality_guild_lnRR <- function(name) {
  rma.mv(yi = lnRR, 
         V = VCV_lnRR2,
         mods = ~ relevel(Lifespan_guild, ref = name), 
         test = "t",
         random = list(~1|Herbivore_Phylogeny, ~1|Herbivore_Latin_name, 
                       ~1|Study, ~1|Effect),
         data = gdat)
}

# results of meta-regression including all contrast results; taking the last level out ([-length(level_names)])
mr_Annuality_guild_lnRR <- map(level_names[-length(level_names)], run_Annuality_guild_lnRR)

```

##### Table E13
**Table E13:**
Regression coefficients (estimate), 95% confidence intervals (CIs), and variance explained,  *R*^2^~[marginal]~ (R2) = `r as.numeric(round(r2_mr_Annuality_guild_lnRR1[1], 4)*100)`% from the meta-regression of lnRR with `Lifespan_guild`. 
```{r, }
# getting estimates
res_mr_Annuality_guild_lnRR1 <- get_pred1(mr_Annuality_guild_lnRR1, mod = "Lifespan_guild")
res_mr_Annuality_guild_lnRR <- map(mr_Annuality_guild_lnRR, ~ get_pred2(.x, mod = "Lifespan_guild"))

# a list of the numbers to take out unnecessary contrasts
contra_list <- Map(seq, from=1, to=1:3)

res_mr_Annuality_guild_lnRR2 <- map2_dfr(res_mr_Annuality_guild_lnRR, contra_list, ~.x[-(.y), ]) 

# creating a table
mr_results(res_mr_Annuality_guild_lnRR1, res_mr_Annuality_guild_lnRR2) %>% 
  kable("html",  digits = 3) %>%
  kable_styling("striped", position = "left") %>%
  scroll_box(width = "100%", height = "300px")
```

#### Change in SD: lnVR

```{r, }

# vcv matrix 
VCV_lnVR2 <- make_VCV_matrix(gdat, V = "varlnVR", cluster = "Study", obs = "Effect")

# meta-regression: multiple intercepts
mr_Annuality_guild_lnVR1 <- rma.mv(
  yi = lnVR,
  V = VCV_lnVR2,
  mods = ~ Lifespan_guild - 1,
  test = "t",
  random = list(~1|Herbivore_Phylogeny, ~1|Herbivore_Latin_name, 
                ~1|Study, ~1|Effect),
  data = gdat
)

# getting marginal R2
r2_mr_Annuality_guild_lnVR1 <- r2_ml(mr_Annuality_guild_lnVR1)

# # meta-regression: contrasts x 7
# getting the level names out
level_names <- levels(gdat$Lifespan_guild)

# helper function to run metafor meta-regression
run_Annuality_guild_lnVR <- function(name) {
  rma.mv(yi = lnVR, 
         V = VCV_lnVR2,
         mods = ~ relevel(Lifespan_guild, ref = name), 
         test = "t",
         random = list(~1|Herbivore_Phylogeny, ~1|Herbivore_Latin_name, 
                       ~1|Study, ~1|Effect),
         data = gdat)
}

# results of meta-regression including all contrast results; taking the last level out ([-length(level_names)])
mr_Annuality_guild_lnVR <- map(level_names[-length(level_names)], run_Annuality_guild_lnVR)

```

##### Table E14
**Table E14:**
Regression coefficients (estimate), 95% confidence intervals (CIs), and variance explained,  *R*^2^~[marginal]~ (R2) = `r as.numeric(round(r2_mr_Annuality_guild_lnVR1[1], 4)*100)`% from the meta-regression of lnVR with `Lifespan_guild`. 
```{r, }
# getting estimates
res_mr_Annuality_guild_lnVR1 <- get_pred1(mr_Annuality_guild_lnVR1, mod = "Lifespan_guild")
res_mr_Annuality_guild_lnVR <- map(mr_Annuality_guild_lnVR, ~ get_pred2(.x, mod = "Lifespan_guild"))

# a list of the numbers to take out unnecessary contrasts
contra_list <- Map(seq, from=1, to=1:3)

res_mr_Annuality_guild_lnVR2 <- map2_dfr(res_mr_Annuality_guild_lnVR, contra_list, ~.x[-(.y), ]) 

# creating a table
mr_results(res_mr_Annuality_guild_lnVR1, res_mr_Annuality_guild_lnVR2) %>% 
  kable("html",  digits = 3) %>%
  kable_styling("striped", position = "left") %>%
  scroll_box(width = "100%", height = "300px")
```

##### Figure 4C-D
```{r,  fig.width=7, fig.height=10, }

## Figure 3C
p20 <- orchard_plot(mr_Annuality_guild_lnRR1, data= gdat, group = 'Study',mod="Lifespan_guild", xlab = "log(Response Ratio) (lnRR)", alpha = 0.2,  angle = 45) 
## Figure 3D
p21 <- orchard_plot(mr_Annuality_guild_lnVR1, data= gdat, group = 'Study',mod="Lifespan_guild", xlab = "log(Variability Ratio) (lnVR)", alpha = 0.2,  angle = 45) 
## Figure 3D

#ggsave(here("fig", "Annuality_guild.png"))
```


### The combined effect of flowering divisions (Poaceae vs Non-Poaceae) and guilds (Chewing arthropods / Fluid-feeding arthropods)

#### Mean change: lnRR

```{r, }
# reordering
gdat$Poaceae_guild <- factor(gdat$Poaceae_guild,
                            levels = c("Non-Poaceae:Chewing arthropods","Poaceae:Chewing arthropods", "Non-Poaceae:Fluid-feeding arthropods", "Poaceae:Fluid-feeding arthropods"),
                            labels = c("Non-Poaceae:Chewing arthropods","Poaceae:Chewing arthropods", "Non-Poaceae:Fluid-feeding arthropods", "Poaceae:Fluid-feeding arthropods"))

# meta-regression: multiple intercepts
mr_Monocot_Dicot_guild_lnRR1 <- rma.mv(
  yi = lnRR,
  V = VCV_lnRR2,
  mods = ~ Poaceae_guild - 1,
  test = "t",
  random = list(~1|Herbivore_Phylogeny, ~1|Herbivore_Latin_name, 
                       ~1|Study, ~1|Effect),
  data = gdat
)

# getting marginal R2
r2_mr_Monocot_Dicot_guild_lnRR1 <- r2_ml(mr_Monocot_Dicot_guild_lnRR1)

# # meta-regression: contrasts x 7
# getting the level names out
level_names <- levels(gdat$Poaceae_guild)

# helper function to run metafor meta-regression
run_Monocot_Dicot_guild_lnRR <- function(name) {
  rma.mv(yi = lnRR, 
         V = VCV_lnRR2,
         mods = ~ relevel(Poaceae_guild, ref = name), 
         test = "t",
         random = list(~1|Herbivore_Phylogeny, ~1|Herbivore_Latin_name, 
                       ~1|Study, ~1|Effect),
         data = gdat)
}

# results of meta-regression including all contrast results; taking the last level out ([-length(level_names)])
mr_Monocot_Dicot_guild_lnRR <- map(level_names[-length(level_names)], run_Monocot_Dicot_guild_lnRR)

```

##### Table E15
**Table E15:**
Regression coefficients (estimate), 95% confidence intervals (CIs), and variance explained,  *R*^2^~[marginal]~ (R2) = `r as.numeric(round(r2_mr_Monocot_Dicot_guild_lnRR1[1], 4)*100)`% from the meta-regression of lnRR with `Poaceae_guild`. 
```{r, }
# getting estimates
res_mr_Monocot_Dicot_guild_lnRR1 <- get_pred1(mr_Monocot_Dicot_guild_lnRR1, mod = "Poaceae_guild")
#res_mr_annuality_lnRR2 <- map(mr_annuality_lnRR, ~ get_est2(.x, mod = "Annuality"))
res_mr_Monocot_Dicot_guild_lnRR <- map(mr_Monocot_Dicot_guild_lnRR, ~ get_pred2(.x, mod = "Poaceae_guild"))

# a list of the numbers to take out unnecessary contrasts
contra_list <- Map(seq, from=1, to=1:3)

# you need to flatten twice: first to make it a list and make it a vector
# I guess we can make it a loop (or vectorise)
res_mr_Monocot_Dicot_guild_lnRR2 <- map2_dfr(res_mr_Monocot_Dicot_guild_lnRR, contra_list, ~.x[-(.y), ]) 

# creating a table
mr_results(res_mr_Monocot_Dicot_guild_lnRR1, res_mr_Monocot_Dicot_guild_lnRR2) %>% 
  kable("html",  digits = 3) %>%
  kable_styling("striped", position = "left") %>%
  scroll_box(width = "100%", height = "300px")
```

#### Change in SD: lnVR

```{r, }
# meta-regression: multiple intercepts
mr_Monocot_Dicot_guild_lnVR1 <- rma.mv(
  yi = lnVR,
  V = VCV_lnVR2,
  mods = ~ Poaceae_guild - 1,
  test = "t",
  random = list(~1|Herbivore_Phylogeny, ~1|Herbivore_Latin_name, 
                       ~1|Study, ~1|Effect),
  data = gdat
)

# getting marginal R2
r2_mr_Monocot_Dicot_guild_lnVR1 <- r2_ml(mr_Monocot_Dicot_guild_lnVR1)

# # meta-regression: contrasts x 7
# getting the level names out
level_names <- levels(gdat$Poaceae_guild)

# helper function to run metafor meta-regression
run_Monocot_Dicot_guild_lnVR <- function(name) {
  rma.mv(yi = lnVR, 
         V = VCV_lnVR2,
         mods = ~ relevel(Poaceae_guild, ref = name), 
         test = "t",
         random = list(~1|Herbivore_Phylogeny, ~1|Herbivore_Latin_name, 
                       ~1|Study, ~1|Effect),
         data = gdat)
}

# results of meta-regression including all contrast results; taking the last level out ([-length(level_names)])
mr_Monocot_Dicot_guild_lnVR <- map(level_names[-length(level_names)], run_Monocot_Dicot_guild_lnVR)

```

##### Table E16
**Table E16:**
Regression coefficients (estimate), 95% confidence intervals (CIs), and variance explained,  *R*^2^~[marginal]~ (R2) = `r as.numeric(round(r2_mr_Monocot_Dicot_guild_lnVR1[1], 4)*100)`% from the meta-regression of lnVR with `Poaceae_guild`. 
```{r, }
# getting estimates
res_mr_Monocot_Dicot_guild_lnVR1 <- get_pred1(mr_Monocot_Dicot_guild_lnVR1, mod = "Poaceae_guild")
#res_mr_annuality_lnVR2 <- map(mr_annuality_lnVR, ~ get_est2(.x, mod = "Annuality"))
res_mr_Monocot_Dicot_guild_lnVR <- map(mr_Monocot_Dicot_guild_lnVR, ~ get_pred2(.x, mod = "Poaceae_guild"))

# a list of the numbers to take out unnecessary contrasts
contra_list <- Map(seq, from=1, to=1:3)

# you need to flatten twice: first to make it a list and make it a vector
# I guess we can make it a loop (or vectorise)
res_mr_Monocot_Dicot_guild_lnVR2 <- map2_dfr(res_mr_Monocot_Dicot_guild_lnVR, contra_list, ~.x[-(.y), ]) 

# creating a table
mr_results(res_mr_Monocot_Dicot_guild_lnVR1, res_mr_Monocot_Dicot_guild_lnVR2) %>% 
  kable("html",  digits = 3) %>%
  kable_styling("striped", position = "left") %>%
  scroll_box(width = "100%", height = "300px")
```

##### Figure 4A-B
```{r,  fig.width=7, fig.height=10, }

## Figure 3A
p23 <- orchard_plot(mr_Monocot_Dicot_guild_lnRR1, data= gdat, group = 'Study',mod="Poaceae_guild", xlab = "log(Response Ratio) (lnRR)", alpha = 0.2,  angle = 45) 

## Figure 3B
p24 <- orchard_plot(mr_Monocot_Dicot_guild_lnVR1, data= gdat, group = 'Study',mod="Poaceae_guild", xlab = "log(Variability Ratio) (lnVR)", alpha = 0.2,  angle = 45) 

#ggsave(here("fig", "Monocot_Dicot_guild.png"))
```


### The combined effect of herbivore distinctions (specialists vs generalist) and guilds (Chewing arthropods / Fluid-feeding arthropods)

#### Mean change: lnRR

```{r, }
# reordering
gdat$Diet_guild <- factor(gdat$Diet_guild,
                            levels = c("Generalist:Chewing arthropods", "Specialist:Chewing arthropods", "Generalist:Fluid-feeding arthropods", "Specialist:Fluid-feeding arthropods"),
                            labels = c("Generalist:Chewing arthropods", "Specialist:Chewing arthropods", "Generalist:Fluid-feeding arthropods", "Specialist:Fluid-feeding arthropods"))

# meta-regression: multiple intercepts
mr_SpecGen_guild_lnRR1 <- rma.mv(
  yi = lnRR,
  V = VCV_lnRR2,
  mods = ~ Diet_guild - 1,
  test = "t",
  random = list(~1|Herbivore_Phylogeny, ~1|Herbivore_Latin_name, 
                       ~1|Study, ~1|Effect),
  data = gdat
)

# getting marginal R2
r2_mr_SpecGen_guild_lnRR1 <- r2_ml(mr_SpecGen_guild_lnRR1)

# # meta-regression: contrasts x 7
# getting the level names out
level_names <- levels(gdat$Diet_guild)

# helper function to run metafor meta-regression
run_SpecGen_guild_lnRR <- function(name) {
  rma.mv(yi = lnRR, 
         V = VCV_lnRR2,
         mods = ~ relevel(Diet_guild, ref = name), 
         test = "t",
         random = list(~1|Herbivore_Phylogeny, ~1|Herbivore_Latin_name, 
                       ~1|Study, ~1|Effect),
         data = gdat)
}

# results of meta-regression including all contrast results; taking the last level out ([-length(level_names)])
mr_SpecGen_guild_lnRR <- map(level_names[-length(level_names)], run_SpecGen_guild_lnRR)

```

##### Table E17
**Table E17:**
Regression coefficients (estimate), 95% confidence intervals (CIs), and variance explained,  *R*^2^~[marginal]~ (R2) = `r as.numeric(round(r2_mr_SpecGen_guild_lnRR1[1], 4)*100)`% from the meta-regression of lnRR with `Diet_guild`. 
```{r, }
# getting estimates
res_mr_SpecGen_guild_lnRR1 <- get_pred1(mr_SpecGen_guild_lnRR1, mod = "Diet_guild")
res_mr_SpecGen_guild_lnRR <- map(mr_SpecGen_guild_lnRR, ~ get_pred2(.x, mod = "Diet_guild"))

# a list of the numbers to take out unnecessary contrasts
contra_list <- Map(seq, from=1, to=1:3)

# you need to flatten twice: first to make it a list and make it a vector
# I guess we can make it a loop (or vectorise)
res_mr_SpecGen_guild_lnRR2 <- map2_dfr(res_mr_SpecGen_guild_lnRR, contra_list, ~.x[-(.y), ]) 

# creating a table
mr_results(res_mr_SpecGen_guild_lnRR1, res_mr_SpecGen_guild_lnRR2) %>% 
  kable("html",  digits = 3) %>%
  kable_styling("striped", position = "left") %>%
  scroll_box(width = "100%", height = "300px")
```

#### Change in SD: lnVR

```{r, }

# meta-regression: multiple intercepts
mr_SpecGen_guild_lnVR1 <- rma.mv(
  yi = lnVR,
  V = VCV_lnVR2,
  mods = ~ Diet_guild - 1,
  test = "t",
  random = list(~1|Herbivore_Phylogeny, ~1|Herbivore_Latin_name, 
                       ~1|Study, ~1|Effect),
  data = gdat
)

# getting marginal R2
r2_mr_SpecGen_guild_lnVR1 <- r2_ml(mr_SpecGen_guild_lnVR1)

# # meta-regression: contrasts x 7
# getting the level names out
level_names <- levels(gdat$Diet_guild)

# helper function to run metafor meta-regression
run_SpecGen_guild_lnVR <- function(name) {
  rma.mv(yi = lnVR, 
         V = VCV_lnVR2,
         mods = ~ relevel(Diet_guild, ref = name), 
         test = "t",
         random = list(~1|Herbivore_Phylogeny, ~1|Herbivore_Latin_name, 
                       ~1|Study, ~1|Effect),
         data = gdat)
}

# results of meta-regression including all contrast results; taking the last level out ([-length(level_names)])
mr_SpecGen_guild_lnVR <- map(level_names[-length(level_names)], run_SpecGen_guild_lnVR)

```

##### Table E18
**Table E18:**
Regression coefficients (estimate), 95% confidence intervals (CIs), and variance explained,  *R*^2^~[marginal]~ (R2) = `r as.numeric(round(r2_mr_SpecGen_guild_lnVR1[1], 4)*100)`% from the meta-regression of lnVR with `Diet_guild`. 
```{r, }
# getting estimates
res_mr_SpecGen_guild_lnVR1 <- get_pred1(mr_SpecGen_guild_lnVR1, mod = "Diet_guild")
res_mr_SpecGen_guild_lnVR <- map(mr_SpecGen_guild_lnVR, ~ get_pred2(.x, mod = "Diet_guild"))

# a list of the numbers to take out unnecessary contrasts
contra_list <- Map(seq, from=1, to=1:3)

# you need to flatten twice: first to make it a list and make it a vector
# I guess we can make it a loop (or vectorise)
res_mr_SpecGen_guild_lnVR2 <- map2_dfr(res_mr_SpecGen_guild_lnVR, contra_list, ~.x[-(.y), ]) 

# creating a table
mr_results(res_mr_SpecGen_guild_lnVR1, res_mr_SpecGen_guild_lnVR2) %>% 
  kable("html",  digits = 3) %>%
  kable_styling("striped", position = "left") %>%
  scroll_box(width = "100%", height = "300px")
```

##### Figure 4E-F
```{r,  fig.width=7, fig.height=10, }

## Figure 3E
p26 <- orchard_plot(mr_SpecGen_guild_lnRR1, data= gdat, group = 'Study',mod="Diet_guild", xlab = "log(Response Ratio) (lnRR)", alpha = 0.2,  angle = 45) 

## Figure 3F
p27 <- orchard_plot(mr_SpecGen_guild_lnVR1, data= gdat, group = 'Study',mod="Diet_guild", xlab = "log(Response Ratio) (lnVR)", alpha = 0.2,  angle = 45) 

#ggsave(here("fig", "SpecGen_guild.png"))
```




### Model selection: multi-predictor model

Here we build the best model via an AICc based model selection method implemented in the R package `MuMin`[@barton2009mumin]. For the full model, we had 4 variables: `Plant_lifespan`, `Poaceae_or_Non`, `Herbivore_diet_breadth`, and `Feeding_guild`. We conducted with the full dataset (`dat`); therefore, we did not include any interaction terms (e.g. `Lifespan_guild`). 

#### Model selection: lnRR

```{r}
# making metafor talk to MuMIn
eval(metafor:::.MuMIn)
# use method = "ML" so that we can compare AIC
mr_full_lnRR <- rma.mv(
  yi = lnRR,
  V = VCV_lnRR,
  mods = ~ Plant_lifespan +
    Poaceae_or_Non +
    Herbivore_diet_breadth +
    Feeding_guild,
  test = "t",
  random = list(~1|Herbivore_Phylogeny, ~1|Herbivore_Latin_name, 
                       ~1|Study, ~1|Effect),
  method = "ML", # for model selection
  data = dat
)
# calling required functions
#getCall(mr_full_lnRR)
# dredge(full.model, evaluate=F) # show all candidate models
# n = 32 model exist
candidates_lnRR <- dredge(mr_full_lnRR, trace = 2)
saveRDS(candidates_lnRR, file = here("Rdata", "candidates_lnRR.rds"))
```

```{r}
# making metafor talk to MuMIn
eval(metafor:::.MuMIn)
candidates_lnRR <- readRDS(file = here("Rdata", "candidates_lnRR.rds"))
# displays delta AICc <2
candidates_aic2_lnRR <- subset(candidates_lnRR, delta < 2) 
# model averaging
# it seems like models are using z values rather than t values (which will be OK)
mr_averaged_aic2_lnRR <- summary(model.avg(candidates_lnRR, delta < 2)) 
# relative importance of each predictor for all the models
importance_lnRR <- sw(candidates_lnRR)

# use REML if not for model comparison
model1_lnRR <- rma.mv(
  yi = lnRR,
  V = VCV_lnRR, 
  mods = ~ Feeding_guild,
  test = "t",
  random = list(~1|Herbivore_Phylogeny, ~1|Herbivore_Latin_name, 
                       ~1|Study, ~1|Effect),
  method = "REML", 
  data = dat
)

model2_lnRR <- rma.mv(
  yi = lnRR,
  V = VCV_lnRR, 
  mods = ~ Feeding_guild + Poaceae_or_Non,
  test = "t",
  random = list(~1|Herbivore_Phylogeny, ~1|Herbivore_Latin_name, 
                       ~1|Study, ~1|Effect),
  method = "REML", 
  data = dat
)


```
##### Table E19
**Table E19:**
The top 2 models (out of 16 possible models) within the $\Delta$AIC difference of 2, and which 4 variables: `Plant_lifespan`, `Poaceae_or_Non`, `Herbivore_diet_breadth`, and `Feeding_guild` were included (indicated by $+$); model weights (for the 4 models) and the sum of weights for each of the variables (from the 16 models) are included. 

```{r}
# creating a table

tibble(
  `Model (variable weight)` = c("Model1", "Model2", "(Sum of weights)"),
  Feeding_guild = c(if_else(candidates_aic2_lnRR$Feeding_guild == "+", "$+$", "NA"), round(importance_lnRR[1], 3)),
  Poaceae_or_Non = c(if_else(candidates_aic2_lnRR$Poaceae_or_Non == "+", "$+$", "NA"), round(importance_lnRR[2], 3)),
  Herbivore_diet_breadth = c(if_else(candidates_aic2_lnRR$Herbivore_diet_breadth == "+", "$+$", "NA"), round(importance_lnRR[3], 3)),
  Plant_lifespan = c(if_else(candidates_aic2_lnRR$Plant_lifespan == "+", "$+$", "NA"), round(importance_lnRR[4], 3)),
  delta_AICc = c(candidates_aic2_lnRR$delta, NA),
  Weight = c(candidates_aic2_lnRR$weight, NA)
) %>%
  kable("html", digits = 3) %>%
  kable_styling("striped", position = "left") 
```

#### Model averaging: lnRR
##### Table E20
**Table E20:**
The average estimates for regression coefficients (Estimate), 95% confidence intervals (CIs), and variance explained, *R*^2^~[marginal]~ (R2) from the 4 best meta-regression models.
```{r}
# getting averaged R2 and variance components not provided by the MuMIn package
#average_sigma2 <- weighted.mean(x = c(model1_lnRR$sigma2, model2_lnRR$sigma2), w = candidates_aic2_lnRR$weight)
average_R2_lnRR <- weighted.mean(x = c(r2_ml(model1_lnRR)[1], r2_ml(model2_lnRR)[1]), w = candidates_aic2_lnRR$weight)
# creating a table
tibble(
  `Fixed effect` = row.names(mr_averaged_aic2_lnRR$coefmat.full),
  Estimate = mr_averaged_aic2_lnRR$coefmat.full[, 1],
  `Lower CI [0.025]` = mr_averaged_aic2_lnRR$coefmat.full[, 1] - mr_averaged_aic2_lnRR$coefmat.full[, 2] * qnorm(0.975),
  `Upper CI  [0.975]` = mr_averaged_aic2_lnRR$coefmat.full[, 1] + mr_averaged_aic2_lnRR$coefmat.full[, 2] * qnorm(0.975),
  `P value` = as.numeric(mr_averaged_aic2_lnRR$coefmat.full[,4]),
  `R2` = c(average_R2_lnRR, rep(NA, 7))
) %>%
  kable("html", digits = 3) %>%
  kable_styling("striped", position = "left") %>% scroll_box(width = "100%", 
    height = "300px")
```


#### Model selection: lnVR

```{r}
# making metafor talk to MuMIn
eval(metafor:::.MuMIn)
# use method = "ML" so that we can compare AIC
mr_full_lnVR <- rma.mv(
  yi = lnVR,
  V = VCV_lnVR,
  mods = ~ Plant_lifespan +
    Poaceae_or_Non +
    Herbivore_diet_breadth +
    Feeding_guild,
  test = "t",
  random = list(~1|Herbivore_Phylogeny, ~1|Herbivore_Latin_name, 
                       ~1|Study, ~1|Effect),
  method = "ML", # for model selection
  data = dat
)
# calling required functions
#getCall(mr_full_lnVR)
# dredge(full.model, evaluate=F) # show all candidate models
# n = 32 model exist
candidates_lnVR <- dredge(mr_full_lnVR, trace = 2)
saveRDS(candidates_lnVR, file = here("Rdata", "candidates_lnVR.rds"))
```

```{r}
# making metafor talk to MuMIn
eval(metafor:::.MuMIn)
candidates_lnVR <- readRDS(file = here("Rdata", "candidates_lnVR.rds"))
# displays delta AICc <2
candidates_aic2_lnVR <- subset(candidates_lnVR, delta < 2) 
# model averaging
# it seems like models are using z values rather than t values (which will be OK)
mr_averaged_aic2_lnVR <- summary(model.avg(candidates_lnVR, delta < 2)) 

# relative importance of each predictor
importance_lnVR <- sw(candidates_lnVR)
# use REML if not for model comparison
model1_lnVR <- rma.mv(
  yi = lnVR,
  V = VCV_lnVR, 
  #mods = ~ 1,
  test = "t",
  random = list(~1|Herbivore_Phylogeny, ~1|Herbivore_Latin_name, 
                       ~1|Study, ~1|Effect),
  method = "REML", 
  data = dat
)
model2_lnVR <- rma.mv(
  yi = lnVR,
  V = VCV_lnVR, 
  mods = ~ Poaceae_or_Non, 
  test = "t",
  random = list(~1|Herbivore_Phylogeny, ~1|Herbivore_Latin_name, 
                       ~1|Study, ~1|Effect),
  method = "REML", 
  data = dat
)
model3_lnVR <- rma.mv(
  yi = lnVR,
  V = VCV_lnVR, 
  mods = ~ Plant_lifespan,
  test = "t",
  random = list(~1|Herbivore_Phylogeny, ~1|Herbivore_Latin_name, 
                       ~1|Study, ~1|Effect),
  method = "REML", 
  data = dat
)
model4_lnVR <- rma.mv(
  yi = lnVR,
  V = VCV_lnVR, 
  mods = ~ Herbivore_diet_breadth + Poaceae_or_Non,
  test = "t",
  random = list(~1|Herbivore_Phylogeny, ~1|Herbivore_Latin_name, 
                       ~1|Study, ~1|Effect),
  method = "REML", 
  data = dat
)
model5_lnVR <- rma.mv(
  yi = lnVR,
  V = VCV_lnVR, 
  mods = ~ Plant_lifespan + Poaceae_or_Non,
  test = "t",
  random = list(~1|Herbivore_Phylogeny, ~1|Herbivore_Latin_name, 
                       ~1|Study, ~1|Effect),
  method = "REML", 
  data = dat
)
model6_lnVR <- rma.mv(
  yi = lnVR,
  V = VCV_lnVR, 
  mods = ~ Herbivore_diet_breadth,
  test = "t",
  random = list(~1|Herbivore_Phylogeny, ~1|Herbivore_Latin_name, 
                       ~1|Study, ~1|Effect),
  method = "REML", 
  data = dat
)
```
##### Table E21
**Table E21:**
The top 6 models (out of 16 possible models) within the $\Delta$AIC difference of 2, and which 4 variables: `Plant_lifespan`, `Poaceae_or_Non`, `Herbivore_diet_breadth`, and `Feeding_guild` were included (indicated by $+$); model weights (for the 5 models) and the sum of weights for each of the variables (from the 16 models) are included. 

```{r}
# creating a table
tibble(
  `Model (variable weight)` = c("Model1", "Model2", "Model3", "Model4", "Model5", "Model6", "(Sum of weights)"),
  Poaceae_or_Non = c(if_else(candidates_aic2_lnVR$Poaceae_or_Non == "+", "$+$", "NA"), round(importance_lnVR[1], 3)),
  Plant_lifespan = c(if_else(candidates_aic2_lnVR$Plant_lifespan == "+", "$+$", "NA"), round(importance_lnVR[2], 3)),
  Herbivore_diet_breadth = c(if_else(candidates_aic2_lnVR$Herbivore_diet_breadth == "+", "$+$", "NA"), round(importance_lnVR[3], 3)),
  Feeding_guild = c(if_else(candidates_aic2_lnVR$Feeding_guild == "+", "$+$", "NA"), round(importance_lnVR[4], 3)),
  delta_AICc = c(candidates_aic2_lnVR$delta, NA),
  Weight = c(candidates_aic2_lnVR$weight, NA)
) %>%
  kable("html", digits = 3) %>%
  kable_styling("striped", position = "left") 
```

#### Model averaging: lnVR
##### Table E22
**Table E22:**
The average estimates for regression coefficients (Estimate), 95% confidence intervals (CIs), and variance explained, *R*^2^~[marginal]~ (R2) from the 4 best meta-regression models.
```{r}
# getting averaged R2 and variance components not provided by the MuMIn package
#average_sigma2 <- weighted.mean(x = c(model1_lnVR$sigma2, model2_lnVR$sigma2), w = candidates_aic2_lnVR$weight)
average_R2_lnVR <- weighted.mean(x = c(r2_ml(model1_lnVR)[1], r2_ml(model2_lnVR)[1], r2_ml(model3_lnVR)[1], 
                                       r2_ml(model4_lnVR)[1],  r2_ml(model5_lnVR)[1],  r2_ml(model6_lnVR)[1]), 
                                 w = candidates_aic2_lnVR$weight)
# creating a table
tibble(
  `Fixed effect` = row.names(mr_averaged_aic2_lnVR$coefmat.full),
  Estimate = mr_averaged_aic2_lnVR$coefmat.full[, 1],
  `Lower CI [0.025]` = mr_averaged_aic2_lnVR$coefmat.full[, 1] - mr_averaged_aic2_lnVR$coefmat.full[, 2] * qnorm(0.975),
  `Upper CI  [0.975]` = mr_averaged_aic2_lnVR$coefmat.full[, 1] + mr_averaged_aic2_lnVR$coefmat.full[, 2] * qnorm(0.975),
  `P value` = as.numeric(mr_averaged_aic2_lnVR$coefmat.full[,4]),
  `R2` = c(average_R2_lnVR, rep(NA, 3))
) %>%
  kable("html", digits = 3) %>%
  kable_styling("striped", position = "left") %>% scroll_box(width = "100%")
```


## Publication Bias Analysis
 
### Funnel plot

#### Residual funnel plot: lnRR
 
##### Figure E7

```{r,}
#
res_funnel_plot_lnRR <- rma.mv(
  yi = lnRR,
  V = VCV_lnRR, 
  mods = ~ Poaceae_or_Non +
    Feeding_guild,
  test = "t",
  random = list(~1|Herbivore_Phylogeny, ~1|Herbivore_Latin_name, 
                       ~1|Study, ~1|Effect),
  method = "REML", 
  data = dat
)
funnel(res_funnel_plot_lnRR, 
       yaxis = "seinv", 
       level = c(90, 95, 99), 
       shade = c("white", "gray55", "gray75"), 
       refline = 0, legend = TRUE)
```

**Figure E7:** A residual funnel plot from the meta-regression model with `Plant_lifespan`, `Poaceae_or_Non`, & `Feeding_guild`; 'residual value' is on lnRR and 'inverse standard error' is precision `1/sqrt(varlnRR)`.

### Egger regression


#### Univariate Egger regression: lnRR


```{r}
#
# the use of effective sampling size - more than varlnRR
#  effective sample size
dat$Effective_N <- 1/dat$Nc + 1/dat$Ne


egger_regression_uni_lnRR <- rma.mv(
  yi = lnRR,
  V = VCV_lnRR, 
  mods = ~ sqrt(Effective_N),
  test = "t",
  random = list(~1|Herbivore_Phylogeny, ~1|Herbivore_Latin_name, 
                       ~1|Study, ~1|Effect),
  method = "REML", 
  data = dat
)
```
##### Table E23
**Table E23:**
Regression coefficients (Estimate), 95% confidence intervals (CIs), P values, variance explained, *R*^2^~[marginal]~ (R2) from the meta-regression with `sqrt(Effective_N)`. 

```{r}
# getting marginal R2
r2_egger_regression_uni_lnRR <- r2_ml(egger_regression_uni_lnRR)
# getting estimates: name does not work for slopes

res_egger_regression_uni_lnRR <- get_pred2(egger_regression_uni_lnRR, mod = "sqrt(varlnRR)")
# creating a table
tibble(
  `Fixed effect` = row.names(egger_regression_uni_lnRR$beta),
  Estimate = c(res_egger_regression_uni_lnRR$estimate),
  `Lower CI [0.025]` = c(res_egger_regression_uni_lnRR$lowerCL),
  `Upper CI  [0.975]` = c(res_egger_regression_uni_lnRR$upperCL),
  `P value` = c(egger_regression_uni_lnRR$pval),
  `R2` = c(r2_egger_regression_uni_lnRR[1], NA)
) %>%
  kable("html", digits = 3) %>%
  kable_styling("striped", position = "left")
```

##### Figure E8

```{r, fig.width=7, fig.height= 4}
pred_egger_regression_uni_lnRR <- predict.rma(egger_regression_uni_lnRR)
# plotting
fit_egger_regression_uni_lnRR <- dat %>%
  mutate(
    ymin = pred_egger_regression_uni_lnRR$ci.lb,
    ymax = pred_egger_regression_uni_lnRR$ci.ub,
    ymin2 = pred_egger_regression_uni_lnRR$cr.lb,
    ymax2 = pred_egger_regression_uni_lnRR$cr.ub,
    pred = pred_egger_regression_uni_lnRR$pred
  ) %>%
  ggplot(aes(x = sqrt(varlnRR), y = lnRR, size = sqrt(1/varlnRR))) +
  geom_point(shape = 21, fill = "grey90") +
  # geom_ribbon(aes(ymin = ymin, ymax = ymax), fill = "#0072B2")  + # not quite sure why this does not work
  geom_smooth(aes(y = ymin2), method = "loess", se = FALSE, lty = "dotted", lwd = 0.25, colour = "#0072B2") +
  geom_smooth(aes(y = ymax2), method = "loess", se = FALSE, lty = "dotted", lwd = 0.25, colour = "#0072B2") +
  geom_smooth(aes(y = ymin), method = "loess", se = FALSE, lty = "dotted", lwd = 0.25, colour = "#D55E00") +
  geom_smooth(aes(y = ymax), method = "loess", se = FALSE, lty = "dotted", lwd = 0.25, colour = "#D55E00") +
  geom_smooth(aes(y = pred), method = "loess", se = FALSE, lty = "dashed", lwd = 0.5, colour = "black") +
  ylim(-5, 3) + xlim(0.05, 7) +
  labs(x = "sqrt(effective sample size)", y = "lnRR (effect size)", size = "Precision (1/SE)") +
  guides(fill = "none", colour = "none") +
  # themes
  theme_bw() +
  theme(legend.position = c(0, 1), legend.justification = c(0, 1)) +
  theme(legend.direction = "horizontal") +
  # theme(legend.background = element_rect(fill = "white", colour = "black")) +
  theme(legend.background = element_blank()) +
  theme(axis.text.y = element_text(size = 10, colour = "black", hjust = 0.5, angle = 90))
fit_egger_regression_uni_lnRR
```

**Figure E8:**
A bubble plot showing a predicted regression line for the contentious variable `sqrt(Effective_N)`, indicating 95% confidence regions (orange dotted lines) and 95% prediction regions (blue dotted lines), with observed effect sizes based on various sample sizes. 


#### Multivariate Egger regression: lnRR


```{r}
#
egger_regression_mul_lnRR <- rma.mv(
  yi = lnRR,
  V = VCV_lnRR, 
  mods = ~ sqrt(Effective_N) +
    Poaceae_or_Non +
    Feeding_guild,
  test = "t",
  random = list(~1|Herbivore_Phylogeny, ~1|Herbivore_Latin_name, 
                       ~1|Study, ~1|Effect),
  method = "REML", 
  data = dat
)
```

##### Table E24
**Table E24:**
Regression coefficients (Estimate), 95% confidence intervals (CIs), and variance explained, *R*^2^~[marginal]~ (R2) from the meta-regression with `sqrt(Effective_N)`, `Poaceae_or_Non`, & `Feeding_guild`. 

```{r}
# getting marginal R2
r2_egger_regression_mul_lnRR <- r2_ml(egger_regression_mul_lnRR)
# creating a table
tibble(
  `Fixed effect` = row.names(egger_regression_mul_lnRR$beta),
  Estimate = c(egger_regression_mul_lnRR$b),
  `Lower CI [0.025]` = c(egger_regression_mul_lnRR$ci.lb),
  `Upper CI  [0.975]` = c(egger_regression_mul_lnRR$ci.ub),
  `P value` = c(egger_regression_mul_lnRR$pval),
  `R2` = c(r2_egger_regression_mul_lnRR[1], rep(NA, 8))
) %>%
  kable("html", digits = 3) %>%
  kable_styling("striped", position = "left") %>% scroll_box(width = "100%", 
    height = "300px")
```


##### Figure E9
```{r, fig.width=7, fig.height= 4}
pred_egger_regress_mul_lnRR <- predict.rma(egger_regression_mul_lnRR)
# plotting
fit_egger_regression_mul_lnRR <- dat %>% filter(!is.na(Plant_lifespan)) %>% 
  mutate(
    ymin = pred_egger_regress_mul_lnRR$ci.lb,
    ymax = pred_egger_regress_mul_lnRR$ci.ub,
    ymin2 = pred_egger_regress_mul_lnRR$cr.lb,
    ymax2 = pred_egger_regress_mul_lnRR$cr.ub,
    pred = pred_egger_regress_mul_lnRR$pred
  ) %>%
  ggplot(aes(x = sqrt(varlnRR), y = lnRR, size = sqrt(1/varlnRR))) +
  geom_point(shape = 21, fill = "grey90") +
  # geom_ribbon(aes(ymin = ymin, ymax = ymax), fill = "#0072B2")  + # not quite sure why this does not work
  geom_smooth(aes(y = ymin2), method = "loess", se = FALSE, lty = "dotted", lwd = 0.25, colour = "#0072B2") +
  geom_smooth(aes(y = ymax2), method = "loess", se = FALSE, lty = "dotted", lwd = 0.25, colour = "#0072B2") +
  geom_smooth(aes(y = ymin), method = "loess", se = FALSE, lty = "dotted", lwd = 0.25, colour = "#D55E00") +
  geom_smooth(aes(y = ymax), method = "loess", se = FALSE, lty = "dotted", lwd = 0.25, colour = "#D55E00") +
  geom_smooth(aes(y = pred), method = "loess", se = FALSE, lty = "dashed", lwd = 0.5, colour = "black") +
  ylim(-5, 3) + xlim(0.05, 7) +
  labs(x = "sqrt(effective sample size)", y = "lnRR (effect size)", size = "Precision (1/SE)") +
  guides(fill = "none", colour = "none") +
  # themes
  theme_bw() +
  theme(legend.position = c(0, 1), legend.justification = c(0, 1)) +
  theme(legend.direction = "horizontal") +
  # theme(legend.background = element_rect(fill = "white", colour = "black")) +
  theme(legend.background = element_blank()) +
  theme(axis.text.y = element_text(size = 10, colour = "black", hjust = 0.5, angle = 90))
fit_egger_regression_mul_lnRR
```

**Figure E9:**
A bubble plot showing a predicted loess line for the contentious variable `sqrt(Effective_N)` (given the values of the other 3 variables in the model), with their 95% confidence regions (orange dotted lines) and 95% prediction regions (blue dotted lines) with observed effect sizes based on various sample sizes. Note that the lines are not linear as these are based on multivariate predictions of the data points. 

#### PEESE (precision-effect estimation with standard error)

```{r, }
#
peese_lnRR <- rma.mv(
  yi = lnRR,
  V = VCV_lnRR, 
  mods = ~ Effective_N,
  test = "t",
  random = list(~1|Herbivore_Phylogeny, ~1|Herbivore_Latin_name, 
                       ~1|Study, ~1|Effect),
  method = "REML", 
  data = dat
)
```

##### Table E25
**Table E25:**
Regression coefficients (Estimate), 95% confidence intervals (CIs), and variance explained, *R*^2^~[marginal]~ (R2) from the meta-regression with . 
```{r}
# getting marginal R2
r2_peese_lnRR <- r2_ml(peese_lnRR)
# getting estimates: name does not work for slopes
res_epeese_lnRR <- get_pred2(peese_lnRR, mod = "varlnRR")
# creating a table
tibble(
  `Fixed effect` = row.names(peese_lnRR$beta),
  Estimate = c(res_epeese_lnRR$estimate),
  `Lower CI [0.025]` = c(res_epeese_lnRR$lowerCL),
  `Upper CI  [0.975]` = c(res_epeese_lnRR$upperCL),
  `P value` = c(peese_lnRR$pval),
  `R2` = c(r2_peese_lnRR[1], NA)
) %>%
  kable("html", digits = 3) %>%
    kable_styling("striped", position = "left")
```


### Time-lag bias

#### Univariate time-lag bias: lnRR
```{r, }
#
time_lag_effect_uni_lnRR <- rma.mv(
  yi = lnRR,
  V = VCV_lnRR, 
  mods = ~ Year,
  test = "t",
  random = list(~1|Herbivore_Phylogeny, ~1|Herbivore_Latin_name, 
                       ~1|Study, ~1|Effect),
  method = "REML", 
  data = dat
)
```
##### Table26
**Table E26:**
Regression coefficients (Estimate), 95% confidence intervals (CIs), P value and variance explained, *R*^2^~[marginal]~ (R2) from the meta-regression with `Year`. 

```{r}
# getting marginal R2
r2_time_lag_effect_uni_lnRR <- r2_ml(time_lag_effect_uni_lnRR)
# getting estimates: name does not work for slopes

res_time_lag_effect_uni_lnRR <- get_pred2(time_lag_effect_uni_lnRR, mod = "Year")
# creating a table
tibble(
  `Fixed effect` = row.names(time_lag_effect_uni_lnRR$beta),
  Estimate = c(res_time_lag_effect_uni_lnRR$estimate),
  `Lower CI [0.025]` = c(res_time_lag_effect_uni_lnRR$lowerCL),
  `Upper CI  [0.975]` = c(res_time_lag_effect_uni_lnRR$upperCL),
  `P value` = time_lag_effect_uni_lnRR$pval,
  `R2` = c(r2_time_lag_effect_uni_lnRR[1], NA)
) %>%
  kable("html", digits = 3) %>%
  kable_styling("striped", position = "left")
```

##### Figure E10

```{r, fig.width=7, fig.height= 4}
pred_time_lag_effect_uni_lnRR <- predict.rma(time_lag_effect_uni_lnRR)
# plotting
fit_time_lag_effect_uni_lnRR <- dat %>%
  mutate(
    ymin = pred_time_lag_effect_uni_lnRR$ci.lb,
    ymax = pred_time_lag_effect_uni_lnRR$ci.ub,
    ymin2 = pred_time_lag_effect_uni_lnRR$cr.lb,
    ymax2 = pred_time_lag_effect_uni_lnRR$cr.ub,
    pred = pred_time_lag_effect_uni_lnRR$pred
  ) %>%
  ggplot(aes(x = Year, y = lnRR, size = sqrt(1/varlnRR))) +
  geom_point(shape = 21, fill = "grey90") +
  # geom_ribbon(aes(ymin = ymin, ymax = ymax), fill = "#0072B2")  + # not quite sure why this does not work
  geom_smooth(aes(y = ymin2), method = "loess", se = FALSE, lty = "dotted", lwd = 0.25, colour = "#0072B2") +
  geom_smooth(aes(y = ymax2), method = "loess", se = FALSE, lty = "dotted", lwd = 0.25, colour = "#0072B2") +
  geom_smooth(aes(y = ymin), method = "loess", se = FALSE, lty = "dotted", lwd = 0.25, colour = "#D55E00") +
  geom_smooth(aes(y = ymax), method = "loess", se = FALSE, lty = "dotted", lwd = 0.25, colour = "#D55E00") +
  geom_smooth(aes(y = pred), method = "loess", se = FALSE, lty = "dashed", lwd = 0.5, colour = "black") +
  ylim(-4, 3.5) + xlim(1989, 2020) +
  #scale_x_continuous(breaks = c(1995, 2000, 2005, 2010, 2015, 2020)) +
  labs(x = "Year", y = "lnRR (effect size)", size ="Precision (1/SE)") +
  guides(fill = "none", colour = "none") +
  # themes
  theme_bw() +
  theme(legend.position = c(0, 1), legend.justification = c(0, 1)) +
  theme(legend.direction = "horizontal") +
  # theme(legend.background = element_rect(fill = "white", colour = "black")) +
  theme(legend.background = element_blank()) +
  theme(axis.text.y = element_text(size = 10, colour = "black", hjust = 0.5, angle = 90))

fit_time_lag_effect_uni_lnRR
```

**Figure E10:** A bubble plot showing a predicted regression line for the contentious variable `Year`, indicating 95% confidence regions (orange dotted lines) and 95% prediction regions (blue dotted lines), with observed effect sizes based on various precisions (1/SE). 

#### Multivariate time-lag bias: lnRR
```{r}
# 
time_lag_effect_mul_lnRR <- rma.mv(
  yi = lnRR,
  V = VCV_lnRR, 
  mods = ~ Year +
      Poaceae_or_Non +
    Feeding_guild,
  test = "t",
  random = list(~1|Herbivore_Phylogeny, ~1|Herbivore_Latin_name, 
                       ~1|Study, ~1|Effect),
  method = "REML", 
  data = dat
)
```
##### Table E27
**Table E27:**
Regression coefficients (Estimate), 95% confidence intervals (CIs), P values, and variance explained, *R*^2^~[marginal]~ (R2) from the meta-regression with `Year`, ``Plant_lifespan`, `Poaceae_or_Non`, & `Feeding_guild`.  

```{r}
# getting marginal R2
r2_time_lag_effect_mul_lnRR <- r2_ml(time_lag_effect_mul_lnRR)
# creating a table
tibble(
  `Fixed effect` = row.names(time_lag_effect_mul_lnRR$beta),
  Estimate = c(time_lag_effect_mul_lnRR$b),
  `Lower CI [0.025]` = c(time_lag_effect_mul_lnRR$ci.lb),
  `Upper CI  [0.975]` = c(time_lag_effect_mul_lnRR$ci.ub),
  `P value` = time_lag_effect_mul_lnRR$pval,
  `R2` = c(r2_time_lag_effect_mul_lnRR[1], rep(NA, 8))
) %>%
  kable("html", digits = 3) %>%
  kable_styling("striped", position = "left") %>% scroll_box(width = "100%", 
    height = "300px")
```

##### Figure E11

```{r, fig.width=7, fig.height= 4}
pred_time_lag_effect_mul_lnRR <- predict.rma(time_lag_effect_mul_lnRR)
# plotting
fit_time_lag_effect_mul_lnRR <- dat %>% filter(!is.na(Plant_lifespan)) %>%
  mutate(ymin = pred_time_lag_effect_mul_lnRR$ci.lb,
    ymax = pred_time_lag_effect_mul_lnRR$ci.ub,
    ymin2 = pred_time_lag_effect_mul_lnRR$cr.lb,
    ymax2 = pred_time_lag_effect_mul_lnRR$cr.ub,
    pred = pred_time_lag_effect_mul_lnRR$pred
  ) %>%
  ggplot(aes(x = Year, y = lnRR, size = sqrt(1/varlnRR))) +
  geom_point(shape = 21, fill = "grey90") +
  # geom_ribbon(aes(ymin = ymin, ymax = ymax), fill = "#0072B2")  + # not quite sure why this does not work
  geom_smooth(aes(y = ymin2), method = "loess", se = FALSE, lty = "dotted", lwd = 0.25, colour = "#0072B2") +
  geom_smooth(aes(y = ymax2), method = "loess", se = FALSE, lty = "dotted", lwd = 0.25, colour = "#0072B2") +
  geom_smooth(aes(y = ymin), method = "loess", se = FALSE, lty = "dotted", lwd = 0.25, colour = "#D55E00") +
  geom_smooth(aes(y = ymax), method = "loess", se = FALSE, lty = "dotted", lwd = 0.25, colour = "#D55E00") +
  geom_smooth(aes(y = pred), method = "loess", se = FALSE, lty = "dashed", lwd = 0.5, colour = "black") +
  ylim(-4, 3.5) + xlim(1989, 2020) +
  #scale_x_continuous(breaks = c(1995, 2000, 2005, 2010, 2015, 2020)) +
  labs(x = "Year", y = "lnRR (effect size)", size ="Precision (1/SE)") +
  guides(fill = "none", colour = "none") +
  # themes
  theme_bw() +
  theme(legend.position = c(0, 1), legend.justification = c(0, 1)) +
  theme(legend.direction = "horizontal") +
  # theme(legend.background = element_rect(fill = "white", colour = "black")) +
  theme(legend.background = element_blank()) +
  theme(axis.text.y = element_text(size = 10, colour = "black", hjust = 0.5, angle = 90))

fit_time_lag_effect_mul_lnRR
```

**Figure E11:**
A bubble plot showing a predicted loess line for the contentious variable `Year` (given the values of the other 3 variables in the model), with their 95% confidence regions (orange dotted lines) and 95% prediction regions (blue dotted lines) with observed effect sizes based on various sample sizes. Note that the lines are not linear as these are based on multivariate predictions of the data points. 

## R Session Information

```{r}
sessionInfo() %>% pander()
```

## References