######Figure 2 and Table S1 setwd("D:/20211011/20211010_submission") library(readxl) phe <- read_excel("D:/20211011/20211010_submission/belowground_phenology_20211031.xlsx") library(metafor) library(readxl) data_overall_a_as <- phe[!apply(phe[,c("Study_ID", "Obs", "As_sensitivity", "Vi")], 1, anyNA),] a_overall_as<- rma(As_sensitivity, Vi, data = data_overall_a_as, method="REML", random=~1|Study_ID/Obs) a_overall_as data_overall_a_ae <- phe[!apply(phe[,c("Study_ID", "Obs", "Ae_sensitivity", "Vi")], 1, anyNA),] a_overall_ae<- rma(Ae_sensitivity, Vi, data = data_overall_a_ae, method="REML", random=~1|Study_ID/Obs) a_overall_ae data_overall_a_al <- phe[!apply(phe[,c("Study_ID", "Obs", "Al_sensitivity", "Vi")], 1, anyNA),] a_overall_al<- rma(Al_sensitivity, Vi, data = data_overall_a_al, method="REML", random=~1|Study_ID/Obs) a_overall_al data_overall_b_bs <- phe[!apply(phe[,c("Study_ID", "Obs", "Bs_sensitivity", "Vi")], 1, anyNA),] b_overall_bs<- rma(Bs_sensitivity, Vi, data = data_overall_b_bs, method="REML", random=~1|Study_ID/Obs) b_overall_bs data_overall_b_be <- phe[!apply(phe[,c("Study_ID", "Obs", "Be_sensitivity", "Vi")], 1, anyNA),] b_overall_be<- rma(Be_sensitivity, Vi, data = data_overall_b_be, method="REML", random=~1|Study_ID/Obs) b_overall_be data_overall_b_bl <- phe[!apply(phe[,c("Study_ID", "Obs", "Bl_sensitivity", "Vi")], 1, anyNA),] b_overall_bl<- rma(Bl_sensitivity, Vi, data = data_overall_b_bl, method="REML", random=~1|Study_ID/Obs) b_overall_bl ####### Woody phe <- subset(phe, Class=="Woody") data_Woody_a_as <- phe[!apply(phe[,c("Study_ID", "Obs", "As_sensitivity", "Vi")], 1, anyNA),] a_Woody_as<- rma(As_sensitivity, Vi, data = data_Woody_a_as, method="REML", random=~1|Study_ID/Obs) a_Woody_as data_Woody_a_ae <- phe[!apply(phe[,c("Study_ID", "Obs", "Ae_sensitivity", "Vi")], 1, anyNA),] a_Woody_ae<- rma(Ae_sensitivity, Vi, data = data_Woody_a_ae, method="REML", random=~1|Study_ID/Obs) a_Woody_ae data_Woody_a_al <- phe[!apply(phe[,c("Study_ID", "Obs", "Al_sensitivity", "Vi")], 1, anyNA),] a_Woody_al<- rma(Al_sensitivity, Vi, data = data_Woody_a_al, method="REML", random=~1|Study_ID/Obs) a_Woody_al data_Woody_b_bs <- phe[!apply(phe[,c("Study_ID", "Obs", "Bs_sensitivity", "Vi")], 1, anyNA),] b_Woody_bs<- rma(Bs_sensitivity, Vi, data = data_Woody_b_bs, method="REML", random=~1|Study_ID/Obs) b_Woody_bs data_Woody_b_be <- phe[!apply(phe[,c("Study_ID", "Obs", "Be_sensitivity", "Vi")], 1, anyNA),] b_Woody_be<- rma(Be_sensitivity, Vi, data = data_Woody_b_be, method="REML", random=~1|Study_ID/Obs) b_Woody_be data_Woody_b_bl <- phe[!apply(phe[,c("Study_ID", "Obs", "Bl_sensitivity", "Vi")], 1, anyNA),] b_Woody_bl<- rma(Bl_sensitivity, Vi, data = data_Woody_b_bl, method="REML", random=~1|Study_ID/Obs) b_Woody_bl ####### herbaceous phe <- read_excel("C:/Users/Dell/Desktop/belowground_phenology_20211031.xlsx") library(metafor) library(readxl) library(rJava) library(glmulti) phe <- subset(phe, Class=="herbaceous") data_herbaceous_a_as <- phe[!apply(phe[,c("Study_ID", "Obs", "As_sensitivity", "Vi")], 1, anyNA),] a_herbaceous_as<- rma(As_sensitivity, Vi, data = data_herbaceous_a_as, method="REML", random=~1|Study_ID/Obs) a_herbaceous_as data_herbaceous_a_ae <- phe[!apply(phe[,c("Study_ID", "Obs", "Ae_sensitivity", "Vi")], 1, anyNA),] a_herbaceous_ae<- rma(Ae_sensitivity, Vi, data = data_herbaceous_a_ae, method="REML", random=~1|Study_ID/Obs) a_herbaceous_ae data_herbaceous_a_al <- phe[!apply(phe[,c("Study_ID", "Obs", "Al_sensitivity", "Vi")], 1, anyNA),] a_herbaceous_al<- rma(Al_sensitivity, Vi, data = data_herbaceous_a_al, method="REML", random=~1|Study_ID/Obs) a_herbaceous_al data_herbaceous_b_bs <- phe[!apply(phe[,c("Study_ID", "Obs", "Bs_sensitivity", "Vi")], 1, anyNA),] b_herbaceous_bs<- rma(Bs_sensitivity, Vi, data = data_herbaceous_b_bs, method="REML", random=~1|Study_ID/Obs) b_herbaceous_bs data_herbaceous_b_be <- phe[!apply(phe[,c("Study_ID", "Obs", "Be_sensitivity", "Vi")], 1, anyNA),] b_herbaceous_be<- rma(Be_sensitivity, Vi, data = data_herbaceous_b_be, method="REML", random=~1|Study_ID/Obs) b_herbaceous_be data_herbaceous_b_bl <- phe[!apply(phe[,c("Study_ID", "Obs", "Bl_sensitivity", "Vi")], 1, anyNA),] b_herbaceous_bl<- rma(Bl_sensitivity, Vi, data = data_herbaceous_b_bl, method="REML", random=~1|Study_ID/Obs) b_herbaceous_bl ######Figure 3 phe <- read_excel("D:/20211011/20211010_submission/belowground_phenology_20211031.xlsx") library(randomForest) library(readxl) data1 <- subset(phe, Class=="herbaceous") d1_as<- data1[c("MAT","MAP","Wetness","Warm_magnitude","Warm_duration","NPP","H","SLA","LN","As_sensitivity")] d_as <- d1_as[!apply(d1_as[,c("MAT","MAP","Wetness","Warm_magnitude","Warm_duration","NPP","H","SLA","LN","As_sensitivity")], 1, anyNA),] names(d_as) rfModel <- randomForest(As_sensitivity~MAT+MAP+Wetness+Warm_magnitude+Warm_duration+NPP+H+SLA+LN,data = d_as, mtry=4,ntree =1000,proximity = TRUE, importance=TRUE) importance(rfModel) varImpPlot(rfModel) plot(rfModel) start_LN<- rma(As_sensitivity~LN, Vi, data = data1, method="REML") start_LN start_Warm_magnitude<- rma(As_sensitivity~Warm_magnitude, Vi, data = data1, method="REML") start_Warm_magnitude start_SLA<- rma(As_sensitivity~SLA, Vi, data = data1, method="REML") start_SLA d1_ae<- data1[c("MAT","MAP","Wetness","Warm_magnitude","Warm_duration","NPP","H","SLA","LN","Ae_sensitivity","As_sensitivity")] d_ae <- d1_ae[!apply(d1_ae[,c("MAT","MAP","Wetness","Warm_magnitude","Warm_duration","NPP","H","SLA","LN","Ae_sensitivity","As_sensitivity")], 1, anyNA),] names(d_ae) rfModel <- randomForest(Ae_sensitivity~MAT+MAP+Wetness+Warm_magnitude+Warm_duration+NPP+H+SLA+LN+As_sensitivity,data = d_ae, mtry=4,ntree =1000,proximity = TRUE, importance=TRUE) importance(rfModel) varImpPlot(rfModel) plot(rfModel) end_Warm_magnitude<- rma(Ae_sensitivity~Warm_magnitude, Vi, data = data1, method="REML") end_Warm_magnitude end_start<- rma(Ae_sensitivity~As_sensitivity, Vi, data = data1, method="REML") end_start data_Field <- subset(data1, Field_lab=="Field") end_MAP<- rma(Ae_sensitivity~MAP, Vi, data = data_Field, method="REML") end_MAP d1_bs<- data1[c("MAT","MAP","Wetness","Warm_magnitude","Warm_duration","NPP","H","SLA","LN","Bs_sensitivity")] d_bs <- d1_bs[!apply(d1_as[,c("MAT","MAP","Wetness","Warm_magnitude","Warm_duration","NPP","H","SLA","LN","Bs_sensitivity")], 1, anyNA),] names(d_bs) rfModel <- randomForest(Bs_sensitivity~MAT+MAP+Wetness+Warm_magnitude+Warm_duration+NPP+H+SLA+LN,data = d_bs, mtry=4,ntree =1000,proximity = TRUE, importance=TRUE) importance(rfModel) varImpPlot(rfModel) plot(rfModel) data_Field <- subset(data1, Field_lab=="Field") start_Wetness<- rma(Bs_sensitivity~Wetness, Vi, data = data_Field, method="REML") start_Wetness d1_be<- data1[c("MAT","MAP","Wetness","Warm_magnitude","Warm_duration","NPP","H","SLA","LN","Be_sensitivity","Bs_sensitivity")] d_be <- d1_be[!apply(d1_ae[,c("MAT","MAP","Wetness","Warm_magnitude","Warm_duration","NPP","H","SLA","LN","Be_sensitivity","Bs_sensitivity")], 1, anyNA),] names(d_be) rfModel <- randomForest(Be_sensitivity~MAT+MAP+Wetness+Warm_magnitude+Warm_duration+NPP+H+SLA+LN+Bs_sensitivity,data = d_be, mtry=4,ntree =1000,proximity = TRUE, importance=TRUE) importance(rfModel) varImpPlot(rfModel) plot(rfModel) end_start<- rma(Be_sensitivity~Bs_sensitivity, Vi, data = data1, method="REML") end_start end_H<- rma(Be_sensitivity~H, Vi, data = data1, method="REML") end_H end_Warm_magnitude<- rma(Be_sensitivity~Warm_magnitude, Vi, data = data1, method="REML") end_Warm_magnitude ######Figure 4 library(randomForest) library(readxl) data2 <- subset(phe, Class=="Woody") d2_as<- data2[c("MAT","MAP","Wetness","Warm_magnitude","Warm_duration","NPP","H","SLA","LN","As_sensitivity")] d_as <- d2_as[!apply(d2_as[,c("MAT","MAP","Wetness","Warm_magnitude","Warm_duration","NPP","H","SLA","LN","As_sensitivity")], 1, anyNA),] names(d_as) rfModel <- randomForest(As_sensitivity~MAT+MAP+Wetness+Warm_magnitude+Warm_duration+NPP+H+SLA+LN,data = d_as, mtry=4,ntree =1000,proximity = TRUE, importance=TRUE) importance(rfModel) varImpPlot(rfModel) plot(rfModel) start_Warm_duration<- rma(As_sensitivity~Warm_duration, Vi, data = data2, method="REML") start_Warm_duration data_Field <- subset(data2, Field_lab=="Field") start_MAT<- rma(As_sensitivity~MAT, Vi, data = data_Field, method="REML") start_MAT d2_ae<- data2[c("MAT","MAP","Wetness","Warm_magnitude","Warm_duration","NPP","H","SLA","LN","Ae_sensitivity","As_sensitivity")] d_ae <- d2_ae[!apply(d2_ae[,c("MAT","MAP","Wetness","Warm_magnitude","Warm_duration","NPP","H","SLA","LN","Ae_sensitivity","As_sensitivity")], 1, anyNA),] names(d_ae) rfModel <- randomForest(Ae_sensitivity~MAT+MAP+Wetness+Warm_magnitude+Warm_duration+NPP+H+SLA+LN+As_sensitivity,data = d_ae, mtry=4,ntree =1000,proximity = TRUE, importance=TRUE) importance(rfModel) varImpPlot(rfModel) plot(rfModel) end_Warm_duration<- rma(Ae_sensitivity~Warm_duration, Vi, data = data2, method="REML") end_Warm_duration end_start<- rma(Ae_sensitivity~As_sensitivity, Vi, data = data2, method="REML") end_start data_Field <- subset(data2, Field_lab=="Field") end_MAT<- rma(As_sensitivity~MAT, Vi, data = data_Field, method="REML") end_MAT d2_bs<- data2[c("MAT","MAP","Wetness","Warm_magnitude","Warm_duration","NPP","H","SLA","LN","Bs_sensitivity")] d_bs <- d2_bs[!apply(d2_bs[,c("MAT","MAP","Wetness","Warm_magnitude","Warm_duration","NPP","H","SLA","LN","Bs_sensitivity")], 1, anyNA),] names(d_bs) rfModel <- randomForest(Bs_sensitivity~MAT+MAP+Wetness+Warm_magnitude+Warm_duration+NPP+H+SLA+LN,data = d_bs, mtry=4,ntree =1000,proximity = TRUE, importance=TRUE) importance(rfModel) varImpPlot(rfModel) plot(rfModel) data_Field <- subset(data2, Field_lab=="Field") start_MAT<- rma(Bs_sensitivity~MAT, Vi, data = data_Field, method="REML") start_MAT start_MAP<- rma(Bs_sensitivity~MAP, Vi, data = data_Field, method="REML") start_MAP start_H<- rma(Bs_sensitivity~H, Vi, data = data2, method="REML") start_H d2_be<- data1[c("MAT","MAP","Wetness","Warm_magnitude","Warm_duration","NPP","H","SLA","LN","Be_sensitivity","Bs_sensitivity")] d_be <- d1_be[!apply(d2_ae[,c("MAT","MAP","Wetness","Warm_magnitude","Warm_duration","NPP","H","SLA","LN","Be_sensitivity","Bs_sensitivity")], 1, anyNA),] names(d_be) rfModel <- randomForest(Be_sensitivity~MAT+MAP+Wetness+Warm_magnitude+Warm_duration+NPP+H+SLA+LN+Bs_sensitivity,data = d_be, mtry=4,ntree =1000,proximity = TRUE, importance=TRUE) importance(rfModel) varImpPlot(rfModel) plot(rfModel) data_Field <- subset(data2, Field_lab=="Field") end_NPP<- rma(Be_sensitivity~NPP, Vi, data = data_Field, method="REML") end_NPP end_magnitude<- rma(Be_sensitivity~Warm_magnitude, Vi, data = data2, method="REML") end_magnitude ############Figure S1 and Table S4 data_overall_a <- phe[!apply(phe[,c("Study_ID", "Obs", "As_sensitivity", "Vi")], 1, anyNA),] a_overall<- rma(As_sensitivity, Vi, data = data_overall_a, method="REML", random=~1|Study_ID/Obs) a_overall summary(a_overall) a_overall funnel(a_overall) res=rma(As_sensitivity,Vi,data=data_overall_a) ranktest(res) regtest(data_overall_a$As_sensitivity,data_overall_a$vi)######P >0.05意味着没有偏倚 fsn(data_overall_a$As_sensitivity,data_overall_a$Vi, type="Rosenberg") #################敏感性检验 leave1out(res) leave1out(res, transf=exp) data_overall_b <- phe[!apply(phe[,c("Study_ID", "Obs", "Bs_sensitivity", "Vi")], 1, anyNA),] b_overall<- rma(Bs_sensitivity, Vi, data = data_overall_b, method="REML", random=~1|Study_ID/Obs) b_overall funnel(b_overall) res=rma(Bs_sensitivity,Vi,data=data_overall_b) ranktest(res) regtest(data_overall_b$Bs_sensitivity,data_overall_b$vi)######P >0.05意味着没有偏倚 fsn(data_overall_b$Bs_sensitivity,data_overall_b$Vi, type="Rosenberg") #################敏感性检验 leave1out(res) leave1out(res, transf=exp) data_overall_a <- phe[!apply(phe[,c("Study_ID", "Obs", "Ae_sensitivity", "Vi")], 1, anyNA),] a_overall<- rma(Ae_sensitivity, Vi, data = data_overall_a, method="REML", random=~1|Study_ID/Obs) a_overall summary(a_overall) a_overall funnel(a_overall) res=rma(Ae_sensitivity,Vi,data=data_overall_a) ranktest(res) regtest(data_overall_a$Ae_sensitivity,data_overall_a$vi)######P >0.05意味着没有偏倚 fsn(data_overall_a$Ae_sensitivity,data_overall_a$Vi, type="Rosenberg") #################敏感性检验 leave1out(res) leave1out(res, transf=exp) data_overall_b <- phe[!apply(phe[,c("Study_ID", "Obs", "Be_sensitivity", "Vi")], 1, anyNA),] b_overall<- rma(Be_sensitivity, Vi, data = data_overall_b, method="REML", random=~1|Study_ID/Obs) b_overall funnel(b_overall) res=rma(Be_sensitivity,Vi,data=data_overall_b) ranktest(res) regtest(data_overall_b$Be_sensitivity,data_overall_b$vi)######P >0.05意味着没有偏倚 fsn(data_overall_b$Be_sensitivity,data_overall_b$Vi, type="Rosenberg") #################敏感性检验 leave1out(res) leave1out(res, transf=exp) data_overall_a <- phe[!apply(phe[,c("Study_ID", "Obs", "Al_sensitivity", "Vi")], 1, anyNA),] a_overall<- rma(Al_sensitivity, Vi, data = data_overall_a, method="REML", random=~1|Study_ID/Obs) a_overall summary(a_overall) a_overall funnel(a_overall) res=rma(Al_sensitivity,Vi,data=data_overall_a) ranktest(res) regtest(data_overall_a$Al_sensitivity,data_overall_a$vi)######P >0.05意味着没有偏倚 fsn(data_overall_a$Al_sensitivity,data_overall_a$Vi, type="Rosenberg") #################敏感性检验 leave1out(res) leave1out(res, transf=exp) data_overall_b <- phe[!apply(phe[,c("Study_ID", "Obs", "Bl_sensitivity", "Vi")], 1, anyNA),] b_overall<- rma(Bl_sensitivity, Vi, data = data_overall_b, method="REML", random=~1|Study_ID/Obs) b_overall funnel(b_overall) res=rma(Bl_sensitivity,Vi,data=data_overall_b) ranktest(res) regtest(data_overall_b$Bl_sensitivity,data_overall_b$vi)######P >0.05意味着没有偏倚 fsn(data_overall_b$Bl_sensitivity,data_overall_b$Vi, type="Rosenberg") #################敏感性检验 leave1out(res) leave1out(res, transf=exp) ####### Woody data_Woody <- subset(phe, Class=="Woody") data_Woody_a <- data_Woody[!apply(data_Woody[,c("Study_ID", "Obs", "As_sensitivity", "Vi")], 1, anyNA),] a_Woody<- rma(As_sensitivity, Vi, data = data_Woody_a, method="REML", random=~1|Study_ID/Obs) a_Woody summary(a_Woody) funnel(a_Woody) res=rma(As_sensitivity,Vi,data=data_Woody_a) ranktest(res) regtest(data_Woody_a$As_sensitivity,data_Woody_a$vi)######P >0.05意味着没有偏倚 fsn(data_Woody_a$As_sensitivity,data_Woody_a$Vi, type="Rosenberg") #################敏感性检验 leave1out(res) leave1out(res, transf=exp) data_Woody_b <- data_Woody[!apply(data_Woody[,c("Study_ID", "Obs", "Bs_sensitivity", "Vi")], 1, anyNA),] b_Woody<- rma(Bs_sensitivity, Vi, data = data_Woody_b, method="REML", random=~1|Study_ID/Obs) b_Woody summary(b_Woody) funnel(b_Woody) res=rma(Bs_sensitivity,Vi,data=data_Woody_b) ranktest(res) regtest(data_Woody_b$Bs_sensitivity,data_Woody_b$vi)######P >0.05意味着没有偏倚 fsn(data_Woody_b$Bs_sensitivity,data_Woody_b$Vi, type="Rosenberg") #################敏感性检验 leave1out(res) leave1out(res, transf=exp) ####### Woody data_Woody <- subset(phe, Class=="Woody") data_Woody_a <- data_Woody[!apply(data_Woody[,c("Study_ID", "Obs", "Ae_sensitivity", "Vi")], 1, anyNA),] a_Woody<- rma(Ae_sensitivity, Vi, data = data_Woody_a, method="REML", random=~1|Study_ID/Obs) a_Woody summary(a_Woody) funnel(a_Woody) res=rma(Ae_sensitivity,Vi,data=data_Woody_a) ranktest(res) regtest(data_Woody_a$Ae_sensitivity,data_Woody_a$vi)######P >0.05意味着没有偏倚 fsn(data_Woody_a$Ae_sensitivity,data_Woody_a$Vi, type="Rosenberg") #################敏感性检验 leave1out(res) leave1out(res, transf=exp) data_Woody_b <- data_Woody[!apply(data_Woody[,c("Study_ID", "Obs", "Be_sensitivity", "Vi")], 1, anyNA),] b_Woody<- rma(Be_sensitivity, Vi, data = data_Woody_b, method="REML", random=~1|Study_ID/Obs) b_Woody summary(b_Woody) funnel(b_Woody) res=rma(Be_sensitivity,Vi,data=data_Woody_b) ranktest(res) regtest(data_Woody_b$Be_sensitivity,data_Woody_b$vi)######P >0.05意味着没有偏倚 fsn(data_Woody_b$Be_sensitivity,data_Woody_b$Vi, type="Rosenberg") #################敏感性检验 leave1out(res) leave1out(res, transf=exp) data_Woody <- subset(phe, Class=="Woody") data_Woody_a <- data_Woody[!apply(data_Woody[,c("Study_ID", "Obs", "Al_sensitivity", "Vi")], 1, anyNA),] a_Woody<- rma(Al_sensitivity, Vi, data = data_Woody_a, method="REML", random=~1|Study_ID/Obs) a_Woody summary(a_Woody) funnel(a_Woody) res=rma(Al_sensitivity,Vi,data=data_Woody_a) ranktest(res) regtest(data_Woody_a$Al_sensitivity,data_Woody_a$vi)######P >0.05意味着没有偏倚 fsn(data_Woody_a$Al_sensitivity,data_Woody_a$Vi, type="Rosenberg") #################敏感性检验 leave1out(res) leave1out(res, transf=exp) data_Woody_b <- data_Woody[!apply(data_Woody[,c("Study_ID", "Obs", "Bl_sensitivity", "Vi")], 1, anyNA),] b_Woody<- rma(Bl_sensitivity, Vi, data = data_Woody_b, method="REML", random=~1|Study_ID/Obs) b_Woody summary(b_Woody) funnel(b_Woody) res=rma(Bl_sensitivity,Vi,data=data_Woody_b) ranktest(res) regtest(data_Woody_b$Bl_sensitivity,data_Woody_b$vi)######P >0.05意味着没有偏倚 fsn(data_Woody_b$Bl_sensitivity,data_Woody_b$Vi, type="Rosenberg") #################敏感性检验 leave1out(res) leave1out(res, transf=exp) ####### herbaceous data_herbaceous <- subset(phe, Class=="herbaceous") data_herbaceous_a <- data_herbaceous[!apply(data_herbaceous[,c("Study_ID", "Obs", "As_sensitivity", "Vi")], 1, anyNA),] a_herbaceous<- rma(As_sensitivity, Vi, data = data_herbaceous_a, method="REML", random=~1|Study_ID/Obs) a_herbaceous summary(a_herbaceous) funnel(a_herbaceous) res=rma(As_sensitivity,Vi,data=data_herbaceous_a) ranktest(res) regtest(data_herbaceous_a$As_sensitivity,data_herbaceous_a$vi)######P >0.05意味着没有偏倚 fsn(data_herbaceous_a$As_sensitivity,data_herbaceous_a$Vi, type="Rosenberg") data_herbaceous_b <- data_herbaceous[!apply(data_herbaceous[,c("Study_ID", "Obs", "Bs_sensitivity", "Vi")], 1, anyNA),] b_herbaceous<- rma(Bs_sensitivity, Vi, data = data_herbaceous_b, method="REML", random=~1|Study_ID/Obs) b_herbaceous summary(b_herbaceous) funnel(b_herbaceous) res=rma(Bs_sensitivity,Vi,data=data_herbaceous_b) ranktest(res) regtest(data_herbaceous_b$Bs_sensitivity,data_herbaceous_b$vi)######P >0.05意味着没有偏倚 fsn(data_herbaceous_b$Bs_sensitivity,data_herbaceous_b$Vi, type="Rosenberg") ####### herbaceous data_herbaceous <- subset(phe, Class=="herbaceous") data_herbaceous_a <- data_herbaceous[!apply(data_herbaceous[,c("Study_ID", "Obs", "Ae_sensitivity", "Vi")], 1, anyNA),] a_herbaceous<- rma(Ae_sensitivity, Vi, data = data_herbaceous_a, method="REML", random=~1|Study_ID/Obs) a_herbaceous summary(a_herbaceous) funnel(a_herbaceous) res=rma(Ae_sensitivity,Vi,data=data_herbaceous_a) ranktest(res) regtest(data_herbaceous_a$Ae_sensitivity,data_herbaceous_a$vi)######P >0.05意味着没有偏倚 fsn(data_herbaceous_a$Ae_sensitivity,data_herbaceous_a$Vi, type="Rosenberg") data_herbaceous_b <- data_herbaceous[!apply(data_herbaceous[,c("Study_ID", "Obs", "Be_sensitivity", "Vi")], 1, anyNA),] b_herbaceous<- rma(Be_sensitivity, Vi, data = data_herbaceous_b, method="REML", random=~1|Study_ID/Obs) b_herbaceous summary(b_herbaceous) funnel(b_herbaceous) res=rma(Be_sensitivity,Vi,data=data_herbaceous_b) ranktest(res) regtest(data_herbaceous_b$Be_sensitivity,data_herbaceous_b$vi)######P >0.05意味着没有偏倚 fsn(data_herbaceous_b$Be_sensitivity,data_herbaceous_b$Vi, type="Rosenberg") ####### herbaceous data_herbaceous <- subset(phe, Class=="herbaceous") data_herbaceous_a <- data_herbaceous[!apply(data_herbaceous[,c("Study_ID", "Obs", "Al_sensitivity", "Vi")], 1, anyNA),] a_herbaceous<- rma(Al_sensitivity, Vi, data = data_herbaceous_a, method="REML", random=~1|Study_ID/Obs) a_herbaceous summary(a_herbaceous) funnel(a_herbaceous) res=rma(Al_sensitivity,Vi,data=data_herbaceous_a) ranktest(res) regtest(data_herbaceous_a$Al_sensitivity,data_herbaceous_a$vi)######P >0.05意味着没有偏倚 fsn(data_herbaceous_a$Al_sensitivity,data_herbaceous_a$Vi, type="Rosenberg") data_herbaceous_b <- data_herbaceous[!apply(data_herbaceous[,c("Study_ID", "Obs", "Bl_sensitivity", "Vi")], 1, anyNA),] b_herbaceous<- rma(Bl_sensitivity, Vi, data = data_herbaceous_b, method="REML", random=~1|Study_ID/Obs) b_herbaceous summary(b_herbaceous) funnel(b_herbaceous) res=rma(Bl_sensitivity,Vi,data=data_herbaceous_b) ranktest(res) regtest(data_herbaceous_b$Bl_sensitivity,data_herbaceous_b$vi)######P >0.05意味着没有偏倚 fsn(data_herbaceous_b$Bl_sensitivity,data_herbaceous_b$Vi, type="Rosenberg") ####### Tree data_Tree <- subset(phe, Growth_form=="Tree") data_Tree_a <- data_Tree[!apply(data_Tree[,c("Study_ID", "Obs", "As_sensitivity", "Vi")], 1, anyNA),] a_Tree<- rma(As_sensitivity, Vi, data = data_Tree_a, method="REML", random=~1|Study_ID/Obs) a_Tree summary(a_Tree) data_Tree_b <- data_Tree[!apply(data_Tree[,c("Study_ID", "Obs", "Bs_sensitivity", "Vi")], 1, anyNA),] b_Tree<- rma(Bs_sensitivity, Vi, data = data_Tree_b, method="REML", random=~1|Study_ID/Obs) b_Tree summary(b_Tree) funnel(a_Tree) res=rma(As_sensitivity,Vi,data=data_Tree_a) ranktest(res) regtest(data_Tree_a$As_sensitivity,data_Tree_a$vi)######P >0.05意味着没有偏倚 fsn(data_Tree_a$As_sensitivity,data_Tree_a$Vi, type="Rosenberg") funnel(b_Tree) res=rma(Bs_sensitivity,Vi,data=data_Tree_b) ranktest(res) regtest(data_Tree_b$Bs_sensitivity,data_Tree_b$vi)######P >0.05意味着没有偏倚 fsn(data_Tree_b$Bs_sensitivity,data_Tree_b$Vi, type="Rosenberg") ####### Tree data_Tree <- subset(phe, Growth_form=="Tree") data_Tree_a <- data_Tree[!apply(data_Tree[,c("Study_ID", "Obs", "Ae_sensitivity", "Vi")], 1, anyNA),] a_Tree<- rma(Ae_sensitivity, Vi, data = data_Tree_a, method="REML", random=~1|Study_ID/Obs) a_Tree summary(a_Tree) data_Tree_b <- data_Tree[!apply(data_Tree[,c("Study_ID", "Obs", "Be_sensitivity", "Vi")], 1, anyNA),] b_Tree<- rma(Be_sensitivity, Vi, data = data_Tree_b, method="REML", random=~1|Study_ID/Obs) b_Tree summary(b_Tree) funnel(a_Tree) res=rma(Ae_sensitivity,Vi,data=data_Tree_a) ranktest(res) regtest(data_Tree_a$Ae_sensitivity,data_Tree_a$vi)######P >0.05意味着没有偏倚 fsn(data_Tree_a$Ae_sensitivity,data_Tree_a$Vi, type="Rosenberg") funnel(b_Tree) res=rma(Be_sensitivity,Vi,data=data_Tree_b) ranktest(res) regtest(data_Tree_b$Be_sensitivity,data_Tree_b$vi)######P >0.05意味着没有偏倚 fsn(data_Tree_b$Be_sensitivity,data_Tree_b$Vi, type="Rosenberg") ####### Tree data_Tree <- subset(phe, Growth_form=="Tree") data_Tree_a <- data_Tree[!apply(data_Tree[,c("Study_ID", "Obs", "Al_sensitivity", "Vi")], 1, anyNA),] a_Tree<- rma(Al_sensitivity, Vi, data = data_Tree_a, method="REML", random=~1|Study_ID/Obs) a_Tree summary(a_Tree) data_Tree_b <- data_Tree[!apply(data_Tree[,c("Study_ID", "Obs", "Bl_sensitivity", "Vi")], 1, anyNA),] b_Tree<- rma(Bl_sensitivity, Vi, data = data_Tree_b, method="REML", random=~1|Study_ID/Obs) b_Tree summary(b_Tree) funnel(a_Tree) res=rma(Al_sensitivity,Vi,data=data_Tree_a) ranktest(res) regtest(data_Tree_a$Al_sensitivity,data_Tree_a$vi)######P >0.05意味着没有偏倚 fsn(data_Tree_a$Al_sensitivity,data_Tree_a$Vi, type="Rosenberg") funnel(b_Tree) res=rma(Bl_sensitivity,Vi,data=data_Tree_b) ranktest(res) regtest(data_Tree_b$Bl_sensitivity,data_Tree_b$vi)######P >0.05意味着没有偏倚 fsn(data_Tree_b$Bl_sensitivity,data_Tree_b$Vi, type="Rosenberg") ####### Shrub data_Shrub <- subset(phe, Growth_form=="Shrub") data_Shrub_a <- data_Shrub[!apply(data_Shrub[,c("Study_ID", "Obs", "As_sensitivity", "Vi")], 1, anyNA),] a_Shrub<- rma(As_sensitivity, Vi, data = data_Shrub_a, method="REML", random=~1|Study_ID/Obs) a_Shrub summary(a_Shrub) data_Shrub_b <- data_Shrub[!apply(data_Shrub[,c("Study_ID", "Obs", "Bs_sensitivity", "Vi")], 1, anyNA),] b_Shrub<- rma(Bs_sensitivity, Vi, data = data_Shrub_b, method="REML", random=~1|Study_ID/Obs) b_Shrub summary(b_Shrub) funnel(a_Shrub) res=rma(As_sensitivity,Vi,data=data_Shrub_a) ranktest(res) regtest(data_Shrub_a$As_sensitivity,data_Shrub_a$vi)######P >0.05意味着没有偏倚 fsn(data_Shrub_a$As_sensitivity,data_Shrub_a$Vi, type="Rosenberg") funnel(b_Shrub) res=rma(Bs_sensitivity,Vi,data=data_Shrub_b) ranktest(res) regtest(data_Shrub_b$Bs_sensitivity,data_Shrub_b$vi)######P >0.05意味着没有偏倚 fsn(data_Shrub_b$Bs_sensitivity,data_Shrub_b$Vi, type="Rosenberg") ####### Shrub data_Shrub <- subset(phe, Growth_form=="Shrub") data_Shrub_a <- data_Shrub[!apply(data_Shrub[,c("Study_ID", "Obs", "Ae_sensitivity", "Vi")], 1, anyNA),] a_Shrub<- rma(Ae_sensitivity, Vi, data = data_Shrub_a, method="REML", random=~1|Study_ID/Obs) a_Shrub summary(a_Shrub) data_Shrub_b <- data_Shrub[!apply(data_Shrub[,c("Study_ID", "Obs", "Be_sensitivity", "Vi")], 1, anyNA),] b_Shrub<- rma(Be_sensitivity, Vi, data = data_Shrub_b, method="REML", random=~1|Study_ID/Obs) b_Shrub summary(b_Shrub) funnel(a_Shrub) res=rma(Ae_sensitivity,Vi,data=data_Shrub_a) ranktest(res) regtest(data_Shrub_a$Ae_sensitivity,data_Shrub_a$vi)######P >0.05意味着没有偏倚 fsn(data_Shrub_a$Ae_sensitivity,data_Shrub_a$Vi, type="Rosenberg") funnel(b_Shrub) res=rma(Be_sensitivity,Vi,data=data_Shrub_b) ranktest(res) regtest(data_Shrub_b$Be_sensitivity,data_Shrub_b$vi)######P >0.05意味着没有偏倚 fsn(data_Shrub_b$Be_sensitivity,data_Shrub_b$Vi, type="Rosenberg") ####### Shrub data_Shrub <- subset(phe, Growth_form=="Shrub") data_Shrub_a <- data_Shrub[!apply(data_Shrub[,c("Study_ID", "Obs", "Al_sensitivity", "Vi")], 1, anyNA),] a_Shrub<- rma(Al_sensitivity, Vi, data = data_Shrub_a, method="REML", random=~1|Study_ID/Obs) a_Shrub summary(a_Shrub) data_Shrub_b <- data_Shrub[!apply(data_Shrub[,c("Study_ID", "Obs", "Bl_sensitivity", "Vi")], 1, anyNA),] b_Shrub<- rma(Bl_sensitivity, Vi, data = data_Shrub_b, method="REML", random=~1|Study_ID/Obs) b_Shrub summary(b_Shrub) funnel(a_Shrub) res=rma(Al_sensitivity,Vi,data=data_Shrub_a) ranktest(res) regtest(data_Shrub_a$Al_sensitivity,data_Shrub_a$vi)######P >0.05意味着没有偏倚 fsn(data_Shrub_a$Al_sensitivity,data_Shrub_a$Vi, type="Rosenberg") funnel(b_Shrub) res=rma(Bl_sensitivity,Vi,data=data_Shrub_b) ranktest(res) regtest(data_Shrub_b$Bl_sensitivity,data_Shrub_b$vi)######P >0.05意味着没有偏倚 fsn(data_Shrub_b$Bl_sensitivity,data_Shrub_b$Vi, type="Rosenberg") ####### Grass data_Grass <- subset(phe, Growth_form=="Grass") data_Grass_a <- data_Grass[!apply(data_Grass[,c("Study_ID", "Obs", "As_sensitivity", "Vi")], 1, anyNA),] a_Grass<- rma(As_sensitivity, Vi, data = data_Grass_a, method="REML", random=~1|Study_ID/Obs) a_Grass summary(a_Grass) data_Grass_b <- data_Grass[!apply(data_Grass[,c("Study_ID", "Obs", "Bs_sensitivity", "Vi")], 1, anyNA),] b_Grass<- rma(Bs_sensitivity, Vi, data = data_Grass_b, method="REML", random=~1|Study_ID/Obs) b_Grass summary(b_Grass) funnel(a_Grass) res=rma(As_sensitivity,Vi,data=data_Grass_a) ranktest(res) regtest(data_Grass_a$As_sensitivity,data_Grass_a$vi)######P >0.05意味着没有偏倚 fsn(data_Grass_a$As_sensitivity,data_Grass_a$Vi, type="Rosenberg") funnel(b_Grass) res=rma(Bs_sensitivity,Vi,data=data_Grass_b) ranktest(res) regtest(data_Grass_b$Bs_sensitivity,data_Grass_b$vi)######P >0.05意味着没有偏倚 fsn(data_Grass_b$Bs_sensitivity,data_Grass_b$Vi, type="Rosenberg") ####### Grass data_Grass <- subset(phe, Growth_form=="Grass") data_Grass_a <- data_Grass[!apply(data_Grass[,c("Study_ID", "Obs", "Ae_sensitivity", "Vi")], 1, anyNA),] a_Grass<- rma(Ae_sensitivity, Vi, data = data_Grass_a, method="REML", random=~1|Study_ID/Obs) a_Grass summary(a_Grass) data_Grass_b <- data_Grass[!apply(data_Grass[,c("Study_ID", "Obs", "Be_sensitivity", "Vi")], 1, anyNA),] b_Grass<- rma(Be_sensitivity, Vi, data = data_Grass_b, method="REML", random=~1|Study_ID/Obs) b_Grass summary(b_Grass) funnel(a_Grass) res=rma(Ae_sensitivity,Vi,data=data_Grass_a) ranktest(res) regtest(data_Grass_a$Ae_sensitivity,data_Grass_a$vi)######P >0.05意味着没有偏倚 fsn(data_Grass_a$Ae_sensitivity,data_Grass_a$Vi, type="Rosenberg") funnel(b_Grass) res=rma(Be_sensitivity,Vi,data=data_Grass_b) ranktest(res) regtest(data_Grass_b$Be_sensitivity,data_Grass_b$vi)######P >0.05意味着没有偏倚 fsn(data_Grass_b$Be_sensitivity,data_Grass_b$Vi, type="Rosenberg") ####### Grass data_Grass <- subset(phe, Growth_form=="Grass") data_Grass_a <- data_Grass[!apply(data_Grass[,c("Study_ID", "Obs", "Al_sensitivity", "Vi")], 1, anyNA),] a_Grass<- rma(Al_sensitivity, Vi, data = data_Grass_a, method="REML", random=~1|Study_ID/Obs) a_Grass summary(a_Grass) data_Grass_b <- data_Grass[!apply(data_Grass[,c("Study_ID", "Obs", "Bl_sensitivity", "Vi")], 1, anyNA),] b_Grass<- rma(Bl_sensitivity, Vi, data = data_Grass_b, method="REML", random=~1|Study_ID/Obs) b_Grass summary(b_Grass) funnel(a_Grass) res=rma(Al_sensitivity,Vi,data=data_Grass_a) ranktest(res) regtest(data_Grass_a$Al_sensitivity,data_Grass_a$vi)######P >0.05意味着没有偏倚 fsn(data_Grass_a$Al_sensitivity,data_Grass_a$Vi, type="Rosenberg") funnel(b_Grass) res=rma(Bl_sensitivity,Vi,data=data_Grass_b) ranktest(res) regtest(data_Grass_b$Bl_sensitivity,data_Grass_b$vi)######P >0.05意味着没有偏倚 fsn(data_Grass_b$Bl_sensitivity,data_Grass_b$Vi, type="Rosenberg") ####### Forb data_Forb <- subset(phe, Growth_form=="Forb") data_Forb_a <- data_Forb[!apply(data_Forb[,c("Study_ID", "Obs", "As_sensitivity", "Vi")], 1, anyNA),] a_Forb<- rma(As_sensitivity, Vi, data = data_Forb_a, method="REML", random=~1|Study_ID/Obs) a_Forb summary(a_Forb) data_Forb_b <- data_Forb[!apply(data_Forb[,c("Study_ID", "Obs", "Bs_sensitivity", "Vi")], 1, anyNA),] b_Forb<- rma(Bs_sensitivity, Vi, data = data_Forb_b, method="REML", random=~1|Study_ID/Obs) b_Forb summary(b_Forb) funnel(a_Forb) res=rma(As_sensitivity,Vi,data=data_Forb_a) ranktest(res) regtest(data_Forb_a$As_sensitivity,data_Forb_a$vi)######P >0.05意味着没有偏倚 fsn(data_Forb_a$As_sensitivity,data_Forb_a$Vi, type="Rosenberg") funnel(b_Forb) res=rma(Bs_sensitivity,Vi,data=data_Forb_b) ranktest(res) regtest(data_Forb_b$Bs_sensitivity,data_Forb_b$vi)######P >0.05意味着没有偏倚 fsn(data_Forb_b$Bs_sensitivity,data_Forb_b$Vi, type="Rosenberg") ####### Forb data_Forb <- subset(phe, Growth_form=="Forb") data_Forb_a <- data_Forb[!apply(data_Forb[,c("Study_ID", "Obs", "Ae_sensitivity", "Vi")], 1, anyNA),] a_Forb<- rma(Ae_sensitivity, Vi, data = data_Forb_a, method="REML", random=~1|Study_ID/Obs) a_Forb summary(a_Forb) data_Forb_b <- data_Forb[!apply(data_Forb[,c("Study_ID", "Obs", "Be_sensitivity", "Vi")], 1, anyNA),] b_Forb<- rma(Be_sensitivity, Vi, data = data_Forb_b, method="REML", random=~1|Study_ID/Obs) b_Forb summary(b_Forb) funnel(a_Forb) res=rma(Ae_sensitivity,Vi,data=data_Forb_a) ranktest(res) regtest(data_Forb_a$Ae_sensitivity,data_Forb_a$vi)######P >0.05意味着没有偏倚 fsn(data_Forb_a$Ae_sensitivity,data_Forb_a$Vi, type="Rosenberg") funnel(b_Forb) res=rma(Be_sensitivity,Vi,data=data_Forb_b) ranktest(res) regtest(data_Forb_b$Be_sensitivity,data_Forb_b$vi)######P >0.05意味着没有偏倚 fsn(data_Forb_b$Be_sensitivity,data_Forb_b$Vi, type="Rosenberg") ####### Forb data_Forb <- subset(phe, Growth_form=="Forb") data_Forb_a <- data_Forb[!apply(data_Forb[,c("Study_ID", "Obs", "Al_sensitivity", "Vi")], 1, anyNA),] a_Forb<- rma(Al_sensitivity, Vi, data = data_Forb_a, method="REML", random=~1|Study_ID/Obs) a_Forb summary(a_Forb) data_Forb_b <- data_Forb[!apply(data_Forb[,c("Study_ID", "Obs", "Bl_sensitivity", "Vi")], 1, anyNA),] b_Forb<- rma(Bl_sensitivity, Vi, data = data_Forb_b, method="REML", random=~1|Study_ID/Obs) b_Forb summary(b_Forb) funnel(a_Forb) res=rma(Al_sensitivity,Vi,data=data_Forb_a) ranktest(res) regtest(data_Forb_a$Al_sensitivity,data_Forb_a$vi)######P >0.05意味着没有偏倚 fsn(data_Forb_a$Al_sensitivity,data_Forb_a$Vi, type="Rosenberg") funnel(b_Forb) res=rma(Bl_sensitivity,Vi,data=data_Forb_b) ranktest(res) regtest(data_Forb_b$Bl_sensitivity,data_Forb_b$vi)######P >0.05意味着没有偏倚 fsn(data_Forb_b$Bl_sensitivity,data_Forb_b$Vi, type="Rosenberg") ####### Evergreen data_Evergreen <- subset(phe, Woody_category=="Evergreen") data_Evergreen_a <- data_Evergreen[!apply(data_Evergreen[,c("Study_ID", "Obs", "As_sensitivity", "Vi")], 1, anyNA),] a_Evergreen<- rma(As_sensitivity, Vi, data = data_Evergreen_a, method="REML", random=~1|Study_ID/Obs) a_Evergreen summary(a_Evergreen) data_Evergreen_b <- data_Evergreen[!apply(data_Evergreen[,c("Study_ID", "Obs", "Bs_sensitivity", "Vi")], 1, anyNA),] b_Evergreen<- rma(Bs_sensitivity, Vi, data = data_Evergreen_b, method="REML", random=~1|Study_ID/Obs) b_Evergreen summary(b_Evergreen) funnel(a_Evergreen) res=rma(As_sensitivity,Vi,data=data_Evergreen_a) ranktest(res) regtest(data_Evergreen_a$As_sensitivity,data_Evergreen_a$vi)######P >0.05意味着没有偏倚 fsn(data_Evergreen_a$As_sensitivity,data_Evergreen_a$Vi, type="Rosenberg") funnel(b_Evergreen) res=rma(Bs_sensitivity,Vi,data=data_Evergreen_b) ranktest(res) regtest(data_Evergreen_b$Bs_sensitivity,data_Evergreen_b$vi)######P >0.05意味着没有偏倚 fsn(data_Evergreen_b$Bs_sensitivity,data_Evergreen_b$Vi, type="Rosenberg") ####### Evergreen data_Evergreen <- subset(phe, Woody_category=="Evergreen") data_Evergreen_a <- data_Evergreen[!apply(data_Evergreen[,c("Study_ID", "Obs", "Ae_sensitivity", "Vi")], 1, anyNA),] a_Evergreen<- rma(Ae_sensitivity, Vi, data = data_Evergreen_a, method="REML", random=~1|Study_ID/Obs) a_Evergreen summary(a_Evergreen) data_Evergreen_b <- data_Evergreen[!apply(data_Evergreen[,c("Study_ID", "Obs", "Be_sensitivity", "Vi")], 1, anyNA),] b_Evergreen<- rma(Be_sensitivity, Vi, data = data_Evergreen_b, method="REML", random=~1|Study_ID/Obs) b_Evergreen summary(b_Evergreen) funnel(a_Evergreen) res=rma(Ae_sensitivity,Vi,data=data_Evergreen_a) ranktest(res) regtest(data_Evergreen_a$Ae_sensitivity,data_Evergreen_a$vi)######P >0.05意味着没有偏倚 fsn(data_Evergreen_a$Ae_sensitivity,data_Evergreen_a$Vi, type="Rosenberg") funnel(b_Evergreen) res=rma(Be_sensitivity,Vi,data=data_Evergreen_b) ranktest(res) regtest(data_Evergreen_b$Be_sensitivity,data_Evergreen_b$vi)######P >0.05意味着没有偏倚 fsn(data_Evergreen_b$Be_sensitivity,data_Evergreen_b$Vi, type="Rosenberg") ####### Evergreen data_Evergreen <- subset(phe, Woody_category=="Evergreen") data_Evergreen_a <- data_Evergreen[!apply(data_Evergreen[,c("Study_ID", "Obs", "Al_sensitivity", "Vi")], 1, anyNA),] a_Evergreen<- rma(Al_sensitivity, Vi, data = data_Evergreen_a, method="REML", random=~1|Study_ID/Obs) a_Evergreen summary(a_Evergreen) data_Evergreen_b <- data_Evergreen[!apply(data_Evergreen[,c("Study_ID", "Obs", "Bl_sensitivity", "Vi")], 1, anyNA),] b_Evergreen<- rma(Bl_sensitivity, Vi, data = data_Evergreen_b, method="REML", random=~1|Study_ID/Obs) b_Evergreen summary(b_Evergreen) funnel(a_Evergreen) res=rma(Al_sensitivity,Vi,data=data_Evergreen_a) ranktest(res) regtest(data_Evergreen_a$Al_sensitivity,data_Evergreen_a$vi)######P >0.05意味着没有偏倚 fsn(data_Evergreen_a$Al_sensitivity,data_Evergreen_a$Vi, type="Rosenberg") funnel(b_Evergreen) res=rma(Bl_sensitivity,Vi,data=data_Evergreen_b) ranktest(res) regtest(data_Evergreen_b$Bl_sensitivity,data_Evergreen_b$vi)######P >0.05意味着没有偏倚 fsn(data_Evergreen_b$Bl_sensitivity,data_Evergreen_b$Vi, type="Rosenberg") ####### Deciduous data_Deciduous <- subset(phe, Woody_category=="Deciduous") data_Deciduous_a <- data_Deciduous[!apply(data_Deciduous[,c("Study_ID", "Obs", "As_sensitivity", "Vi")], 1, anyNA),] a_Deciduous<- rma(As_sensitivity, Vi, data = data_Deciduous_a, method="REML", random=~1|Study_ID/Obs) a_Deciduous summary(a_Deciduous) data_Deciduous_b <- data_Deciduous[!apply(data_Deciduous[,c("Study_ID", "Obs", "Bs_sensitivity", "Vi")], 1, anyNA),] b_Deciduous<- rma(Bs_sensitivity, Vi, data = data_Deciduous_b, method="REML", random=~1|Study_ID/Obs) b_Deciduous summary(b_Deciduous) funnel(a_Deciduous) res=rma(As_sensitivity,Vi,data=data_Deciduous_a) ranktest(res) regtest(data_Deciduous_a$As_sensitivity,data_Deciduous_a$vi)######P >0.05意味着没有偏倚 fsn(data_Deciduous_a$As_sensitivity,data_Deciduous_a$Vi, type="Rosenberg") funnel(b_Deciduous) res=rma(Bs_sensitivity,Vi,data=data_Deciduous_b) ranktest(res) regtest(data_Deciduous_b$Bs_sensitivity,data_Deciduous_b$vi)######P >0.05意味着没有偏倚 fsn(data_Deciduous_b$Bs_sensitivity,data_Deciduous_b$Vi, type="Rosenberg") ####### Deciduous data_Deciduous <- subset(phe, Woody_category=="Deciduous") data_Deciduous_a <- data_Deciduous[!apply(data_Deciduous[,c("Study_ID", "Obs", "Ae_sensitivity", "Vi")], 1, anyNA),] a_Deciduous<- rma(Ae_sensitivity, Vi, data = data_Deciduous_a, method="REML", random=~1|Study_ID/Obs) a_Deciduous summary(a_Deciduous) data_Deciduous_b <- data_Deciduous[!apply(data_Deciduous[,c("Study_ID", "Obs", "Be_sensitivity", "Vi")], 1, anyNA),] b_Deciduous<- rma(Be_sensitivity, Vi, data = data_Deciduous_b, method="REML", random=~1|Study_ID/Obs) b_Deciduous summary(b_Deciduous) funnel(a_Deciduous) res=rma(Ae_sensitivity,Vi,data=data_Deciduous_a) ranktest(res) regtest(data_Deciduous_a$Ae_sensitivity,data_Deciduous_a$vi)######P >0.05意味着没有偏倚 fsn(data_Deciduous_a$Ae_sensitivity,data_Deciduous_a$Vi, type="Rosenberg") funnel(b_Deciduous) res=rma(Be_sensitivity,Vi,data=data_Deciduous_b) ranktest(res) regtest(data_Deciduous_b$Be_sensitivity,data_Deciduous_b$vi)######P >0.05意味着没有偏倚 fsn(data_Deciduous_b$Be_sensitivity,data_Deciduous_b$Vi, type="Rosenberg") ####### Deciduous data_Deciduous <- subset(phe, Woody_category=="Deciduous") data_Deciduous_a <- data_Deciduous[!apply(data_Deciduous[,c("Study_ID", "Obs", "Al_sensitivity", "Vi")], 1, anyNA),] a_Deciduous<- rma(Al_sensitivity, Vi, data = data_Deciduous_a, method="REML", random=~1|Study_ID/Obs) a_Deciduous summary(a_Deciduous) data_Deciduous_b <- data_Deciduous[!apply(data_Deciduous[,c("Study_ID", "Obs", "Bl_sensitivity", "Vi")], 1, anyNA),] b_Deciduous<- rma(Bl_sensitivity, Vi, data = data_Deciduous_b, method="REML", random=~1|Study_ID/Obs) b_Deciduous summary(b_Deciduous) funnel(a_Deciduous) res=rma(Al_sensitivity,Vi,data=data_Deciduous_a) ranktest(res) regtest(data_Deciduous_a$Al_sensitivity,data_Deciduous_a$vi)######P >0.05意味着没有偏倚 fsn(data_Deciduous_a$Al_sensitivity,data_Deciduous_a$Vi, type="Rosenberg") funnel(b_Deciduous) res=rma(Bl_sensitivity,Vi,data=data_Deciduous_b) ranktest(res) regtest(data_Deciduous_b$Bl_sensitivity,data_Deciduous_b$vi)######P >0.05意味着没有偏倚 fsn(data_Deciduous_b$Bl_sensitivity,data_Deciduous_b$Vi, type="Rosenberg") ####### Grassland data_Grassland <- subset(phe, Ecosystem_type=="Grassland") data_Grassland_a <- data_Grassland[!apply(data_Grassland[,c("Study_ID", "Obs", "As_sensitivity", "Vi")], 1, anyNA),] a_Grassland<- rma(As_sensitivity, Vi, data = data_Grassland_a, method="REML", random=~1|Study_ID/Obs) a_Grassland summary(a_Grassland) data_Grassland_b <- data_Grassland[!apply(data_Grassland[,c("Study_ID", "Obs", "Bs_sensitivity", "Vi")], 1, anyNA),] b_Grassland<- rma(Bs_sensitivity, Vi, data = data_Grassland_b, method="REML", random=~1|Study_ID/Obs) b_Grassland summary(b_Grassland) funnel(a_Grassland) res=rma(As_sensitivity,Vi,data=data_Grassland_a) ranktest(res) regtest(data_Grassland_a$As_sensitivity,data_Grassland_a$vi)######P >0.05意味着没有偏倚 fsn(data_Grassland_a$As_sensitivity,data_Grassland_a$Vi, type="Rosenberg") funnel(b_Grassland) res=rma(Bs_sensitivity,Vi,data=data_Grassland_b) ranktest(res) regtest(data_Grassland_b$Bs_sensitivity,data_Grassland_b$vi)######P >0.05意味着没有偏倚 fsn(data_Grassland_b$Bs_sensitivity,data_Grassland_b$Vi, type="Rosenberg") ####### Grassland data_Grassland <- subset(phe, Ecosystem_type=="Grassland") data_Grassland_a <- data_Grassland[!apply(data_Grassland[,c("Study_ID", "Obs", "Ae_sensitivity", "Vi")], 1, anyNA),] a_Grassland<- rma(Ae_sensitivity, Vi, data = data_Grassland_a, method="REML", random=~1|Study_ID/Obs) a_Grassland summary(a_Grassland) data_Grassland_b <- data_Grassland[!apply(data_Grassland[,c("Study_ID", "Obs", "Be_sensitivity", "Vi")], 1, anyNA),] b_Grassland<- rma(Be_sensitivity, Vi, data = data_Grassland_b, method="REML", random=~1|Study_ID/Obs) b_Grassland summary(b_Grassland) funnel(a_Grassland) res=rma(Ae_sensitivity,Vi,data=data_Grassland_a) ranktest(res) regtest(data_Grassland_a$Ae_sensitivity,data_Grassland_a$vi)######P >0.05意味着没有偏倚 fsn(data_Grassland_a$Ae_sensitivity,data_Grassland_a$Vi, type="Rosenberg") funnel(b_Grassland) res=rma(Be_sensitivity,Vi,data=data_Grassland_b) ranktest(res) regtest(data_Grassland_b$Be_sensitivity,data_Grassland_b$vi)######P >0.05意味着没有偏倚 fsn(data_Grassland_b$Be_sensitivity,data_Grassland_b$Vi, type="Rosenberg") ####### Grassland data_Grassland <- subset(phe, Ecosystem_type=="Grassland") data_Grassland_a <- data_Grassland[!apply(data_Grassland[,c("Study_ID", "Obs", "Al_sensitivity", "Vi")], 1, anyNA),] a_Grassland<- rma(Al_sensitivity, Vi, data = data_Grassland_a, method="REML", random=~1|Study_ID/Obs) a_Grassland summary(a_Grassland) data_Grassland_b <- data_Grassland[!apply(data_Grassland[,c("Study_ID", "Obs", "Bl_sensitivity", "Vi")], 1, anyNA),] b_Grassland<- rma(Bl_sensitivity, Vi, data = data_Grassland_b, method="REML", random=~1|Study_ID/Obs) b_Grassland summary(b_Grassland) funnel(a_Grassland) res=rma(Al_sensitivity,Vi,data=data_Grassland_a) ranktest(res) regtest(data_Grassland_a$Al_sensitivity,data_Grassland_a$vi)######P >0.05意味着没有偏倚 fsn(data_Grassland_a$Al_sensitivity,data_Grassland_a$Vi, type="Rosenberg") funnel(b_Grassland) res=rma(Bl_sensitivity,Vi,data=data_Grassland_b) ranktest(res) regtest(data_Grassland_b$Bl_sensitivity,data_Grassland_b$vi)######P >0.05意味着没有偏倚 fsn(data_Grassland_b$Bl_sensitivity,data_Grassland_b$Vi, type="Rosenberg") ####### Forest data_Forest <- subset(phe, Ecosystem_type=="Forest") data_Forest_a <- data_Forest[!apply(data_Forest[,c("Study_ID", "Obs", "As_sensitivity", "Vi")], 1, anyNA),] a_Forest<- rma(As_sensitivity, Vi, data = data_Forest_a, method="REML", random=~1|Study_ID/Obs) a_Forest summary(a_Forest) data_Forest_b <- data_Forest[!apply(data_Forest[,c("Study_ID", "Obs", "Bs_sensitivity", "Vi")], 1, anyNA),] b_Forest<- rma(Bs_sensitivity, Vi, data = data_Forest_b, method="REML", random=~1|Study_ID/Obs) b_Forest summary(b_Forest) funnel(a_Forest) res=rma(As_sensitivity,Vi,data=data_Forest_a) ranktest(res) regtest(data_Forest_a$As_sensitivity,data_Forest_a$vi)######P >0.05意味着没有偏倚 fsn(data_Forest_a$As_sensitivity,data_Forest_a$Vi, type="Rosenberg") funnel(b_Forest) res=rma(Bs_sensitivity,Vi,data=data_Forest_b) ranktest(res) regtest(data_Forest_b$Bs_sensitivity,data_Forest_b$vi)######P >0.05意味着没有偏倚 fsn(data_Forest_b$Bs_sensitivity,data_Forest_b$Vi, type="Rosenberg") ####### Forest data_Forest <- subset(phe, Ecosystem_type=="Forest") data_Forest_a <- data_Forest[!apply(data_Forest[,c("Study_ID", "Obs", "Ae_sensitivity", "Vi")], 1, anyNA),] a_Forest<- rma(Ae_sensitivity, Vi, data = data_Forest_a, method="REML", random=~1|Study_ID/Obs) a_Forest summary(a_Forest) data_Forest_b <- data_Forest[!apply(data_Forest[,c("Study_ID", "Obs", "Be_sensitivity", "Vi")], 1, anyNA),] b_Forest<- rma(Be_sensitivity, Vi, data = data_Forest_b, method="REML", random=~1|Study_ID/Obs) b_Forest summary(b_Forest) funnel(a_Forest) res=rma(Ae_sensitivity,Vi,data=data_Forest_a) ranktest(res) regtest(data_Forest_a$Ae_sensitivity,data_Forest_a$vi)######P >0.05意味着没有偏倚 fsn(data_Forest_a$Ae_sensitivity,data_Forest_a$Vi, type="Rosenberg") funnel(b_Forest) res=rma(Be_sensitivity,Vi,data=data_Forest_b) ranktest(res) regtest(data_Forest_b$Be_sensitivity,data_Forest_b$vi)######P >0.05意味着没有偏倚 fsn(data_Forest_b$Be_sensitivity,data_Forest_b$Vi, type="Rosenberg") ####### Forest data_Forest <- subset(phe, Ecosystem_type=="Forest") data_Forest_a <- data_Forest[!apply(data_Forest[,c("Study_ID", "Obs", "Al_sensitivity", "Vi")], 1, anyNA),] a_Forest<- rma(Al_sensitivity, Vi, data = data_Forest_a, method="REML", random=~1|Study_ID/Obs) a_Forest summary(a_Forest) data_Forest_b <- data_Forest[!apply(data_Forest[,c("Study_ID", "Obs", "Bl_sensitivity", "Vi")], 1, anyNA),] b_Forest<- rma(Bl_sensitivity, Vi, data = data_Forest_b, method="REML", random=~1|Study_ID/Obs) b_Forest summary(b_Forest) funnel(a_Forest) res=rma(Al_sensitivity,Vi,data=data_Forest_a) ranktest(res) regtest(data_Forest_a$Al_sensitivity,data_Forest_a$vi)######P >0.05意味着没有偏倚 fsn(data_Forest_a$Al_sensitivity,data_Forest_a$Vi, type="Rosenberg") funnel(b_Forest) res=rma(Bl_sensitivity,Vi,data=data_Forest_b) ranktest(res) regtest(data_Forest_b$Bl_sensitivity,data_Forest_b$vi)######P >0.05意味着没有偏倚 fsn(data_Forest_b$Bl_sensitivity,data_Forest_b$Vi, type="Rosenberg") ####### Farmland data_Farmland <- subset(phe, Ecosystem_type=="Farmland") data_Farmland_a <- data_Farmland[!apply(data_Farmland[,c("Study_ID", "Obs", "As_sensitivity", "Vi")], 1, anyNA),] a_Farmland<- rma(As_sensitivity, Vi, data = data_Farmland_a, method="REML", random=~1|Study_ID/Obs) a_Farmland summary(a_Farmland) data_Farmland_b <- data_Farmland[!apply(data_Farmland[,c("Study_ID", "Obs", "Bs_sensitivity", "Vi")], 1, anyNA),] b_Farmland<- rma(Bs_sensitivity, Vi, data = data_Farmland_b, method="REML", random=~1|Study_ID/Obs) b_Farmland summary(b_Farmland) funnel(a_Farmland) res=rma(As_sensitivity,Vi,data=data_Farmland_a) ranktest(res) regtest(data_Farmland_a$As_sensitivity,data_Farmland_a$vi)######P >0.05意味着没有偏倚 fsn(data_Farmland_a$As_sensitivity,data_Farmland_a$Vi, type="Rosenberg") funnel(b_Farmland) res=rma(Bs_sensitivity,Vi,data=data_Farmland_b) ranktest(res) regtest(data_Farmland_b$Bs_sensitivity,data_Farmland_b$vi)######P >0.05意味着没有偏倚 fsn(data_Farmland_b$Bs_sensitivity,data_Farmland_b$Vi, type="Rosenberg") ####### Farmland data_Farmland <- subset(phe, Ecosystem_type=="Farmland") data_Farmland_a <- data_Farmland[!apply(data_Farmland[,c("Study_ID", "Obs", "Ae_sensitivity", "Vi")], 1, anyNA),] a_Farmland<- rma(Ae_sensitivity, Vi, data = data_Farmland_a, method="REML", random=~1|Study_ID/Obs) a_Farmland summary(a_Farmland) data_Farmland_b <- data_Farmland[!apply(data_Farmland[,c("Study_ID", "Obs", "Be_sensitivity", "Vi")], 1, anyNA),] b_Farmland<- rma(Be_sensitivity, Vi, data = data_Farmland_b, method="REML", random=~1|Study_ID/Obs) b_Farmland summary(b_Farmland) funnel(a_Farmland) res=rma(Ae_sensitivity,Vi,data=data_Farmland_a) ranktest(res) regtest(data_Farmland_a$Ae_sensitivity,data_Farmland_a$vi)######P >0.05意味着没有偏倚 fsn(data_Farmland_a$Ae_sensitivity,data_Farmland_a$Vi, type="Rosenberg") funnel(b_Farmland) res=rma(Be_sensitivity,Vi,data=data_Farmland_b) ranktest(res) regtest(data_Farmland_b$Be_sensitivity,data_Farmland_b$vi)######P >0.05意味着没有偏倚 fsn(data_Farmland_b$Be_sensitivity,data_Farmland_b$Vi, type="Rosenberg") ####### Farmland data_Farmland <- subset(phe, Ecosystem_type=="Farmland") data_Farmland_a <- data_Farmland[!apply(data_Farmland[,c("Study_ID", "Obs", "Al_sensitivity", "Vi")], 1, anyNA),] a_Farmland<- rma(Al_sensitivity, Vi, data = data_Farmland_a, method="REML", random=~1|Study_ID/Obs) a_Farmland summary(a_Farmland) data_Farmland_b <- data_Farmland[!apply(data_Farmland[,c("Study_ID", "Obs", "Bl_sensitivity", "Vi")], 1, anyNA),] b_Farmland<- rma(Bl_sensitivity, Vi, data = data_Farmland_b, method="REML", random=~1|Study_ID/Obs) b_Farmland summary(b_Farmland) funnel(a_Farmland) res=rma(Al_sensitivity,Vi,data=data_Farmland_a) ranktest(res) regtest(data_Farmland_a$Al_sensitivity,data_Farmland_a$vi)######P >0.05意味着没有偏倚 fsn(data_Farmland_a$Al_sensitivity,data_Farmland_a$Vi, type="Rosenberg") funnel(b_Farmland) res=rma(Bl_sensitivity,Vi,data=data_Farmland_b) ranktest(res) regtest(data_Farmland_b$Bl_sensitivity,data_Farmland_b$vi)######P >0.05意味着没有偏倚 fsn(data_Farmland_b$Bl_sensitivity,data_Farmland_b$Vi, type="Rosenberg") ####### Shrubland data_Shrubland <- subset(phe, Ecosystem_type=="Shrubland") data_Shrubland_a <- data_Shrubland[!apply(data_Shrubland[,c("Study_ID", "Obs", "As_sensitivity", "Vi")], 1, anyNA),] a_Shrubland<- rma(As_sensitivity, Vi, data = data_Shrubland_a, method="REML", random=~1|Study_ID/Obs) a_Shrubland summary(a_Shrubland) data_Shrubland_b <- data_Shrubland[!apply(data_Shrubland[,c("Study_ID", "Obs", "Bs_sensitivity", "Vi")], 1, anyNA),] b_Shrubland<- rma(Bs_sensitivity, Vi, data = data_Shrubland_b, method="REML", random=~1|Study_ID/Obs) b_Shrubland summary(b_Shrubland) funnel(a_Shrubland) res=rma(As_sensitivity,Vi,data=data_Shrubland_a) ranktest(res) regtest(data_Shrubland_a$As_sensitivity,data_Shrubland_a$vi)######P >0.05意味着没有偏倚 fsn(data_Shrubland_a$As_sensitivity,data_Shrubland_a$Vi, type="Rosenberg") funnel(b_Shrubland) res=rma(Bs_sensitivity,Vi,data=data_Shrubland_b) ranktest(res) regtest(data_Shrubland_b$Bs_sensitivity,data_Shrubland_b$vi)######P >0.05意味着没有偏倚 fsn(data_Shrubland_b$Bs_sensitivity,data_Shrubland_b$Vi, type="Rosenberg") ####### Shrubland data_Shrubland <- subset(phe, Ecosystem_type=="Shrubland") data_Shrubland_a <- data_Shrubland[!apply(data_Shrubland[,c("Study_ID", "Obs", "Ae_sensitivity", "Vi")], 1, anyNA),] a_Shrubland<- rma(Ae_sensitivity, Vi, data = data_Shrubland_a, method="REML", random=~1|Study_ID/Obs) a_Shrubland summary(a_Shrubland) data_Shrubland_b <- data_Shrubland[!apply(data_Shrubland[,c("Study_ID", "Obs", "Be_sensitivity", "Vi")], 1, anyNA),] b_Shrubland<- rma(Be_sensitivity, Vi, data = data_Shrubland_b, method="REML", random=~1|Study_ID/Obs) b_Shrubland summary(b_Shrubland) funnel(a_Shrubland) res=rma(Ae_sensitivity,Vi,data=data_Shrubland_a) ranktest(res) regtest(data_Shrubland_a$Ae_sensitivity,data_Shrubland_a$vi)######P >0.05意味着没有偏倚 fsn(data_Shrubland_a$Ae_sensitivity,data_Shrubland_a$Vi, type="Rosenberg") funnel(b_Shrubland) res=rma(Be_sensitivity,Vi,data=data_Shrubland_b) ranktest(res) regtest(data_Shrubland_b$Be_sensitivity,data_Shrubland_b$vi)######P >0.05意味着没有偏倚 fsn(data_Shrubland_b$Be_sensitivity,data_Shrubland_b$Vi, type="Rosenberg") ####### Shrubland data_Shrubland <- subset(phe, Ecosystem_type=="Shrubland") data_Shrubland_a <- data_Shrubland[!apply(data_Shrubland[,c("Study_ID", "Obs", "Al_sensitivity", "Vi")], 1, anyNA),] a_Shrubland<- rma(Al_sensitivity, Vi, data = data_Shrubland_a, method="REML", random=~1|Study_ID/Obs) a_Shrubland summary(a_Shrubland) data_Shrubland_b <- data_Shrubland[!apply(data_Shrubland[,c("Study_ID", "Obs", "Bl_sensitivity", "Vi")], 1, anyNA),] b_Shrubland<- rma(Bl_sensitivity, Vi, data = data_Shrubland_b, method="REML", random=~1|Study_ID/Obs) b_Shrubland summary(b_Shrubland) funnel(a_Shrubland) res=rma(Al_sensitivity,Vi,data=data_Shrubland_a) ranktest(res) regtest(data_Shrubland_a$Al_sensitivity,data_Shrubland_a$vi)######P >0.05意味着没有偏倚 fsn(data_Shrubland_a$Al_sensitivity,data_Shrubland_a$Vi, type="Rosenberg") funnel(b_Shrubland) res=rma(Bl_sensitivity,Vi,data=data_Shrubland_b) ranktest(res) regtest(data_Shrubland_b$Bl_sensitivity,data_Shrubland_b$vi)######P >0.05意味着没有偏倚 fsn(data_Shrubland_b$Bl_sensitivity,data_Shrubland_b$Vi, type="Rosenberg") ##############Figure S5 phe_Woody <- subset(phe, Class=="Woody") phe_Woody_1 <- subset(phe_Woody, Field_lab=="Field") res=rma(As_sensitivity,Vi,data=phe_Woody_1) res res1=rma(Ae_sensitivity,Vi,data=phe_Woody_1) res1 res2=rma(Bs_sensitivity,Vi,data=phe_Woody_1) res2 res3=rma(Be_sensitivity,Vi,data=phe_Woody_1) res3 phe_Woody_1 <- subset(phe_Woody, Field_lab=="Lab") res4=rma(As_sensitivity,Vi,data=phe_Woody_1) res4 res5=rma(Ae_sensitivity,Vi,data=phe_Woody_1) res5 res6=rma(Bs_sensitivity,Vi,data=phe_Woody_1) res6 res7=rma(Be_sensitivity,Vi,data=phe_Woody_1) res7 res_a_start <- rma.mv(yi=As_sensitivity, V=Vi, mods=~Field_lab, random=~1|Study_ID/Obs, data=phe_Woody) res_a_start res_a_end <- rma.mv(yi=Ae_sensitivity, V=Vi, mods=~Field_lab, random=~1|Study_ID/Obs, data=phe_Woody) res_a_end res_b_start <- rma.mv(yi=Bs_sensitivity, V=Vi, mods=~Field_lab, random=~1|Study_ID/Obs, data=phe_Woody) res_b_start res_b_end <- rma.mv(yi=Be_sensitivity, V=Vi, mods=~Field_lab, random=~1|Study_ID/Obs, data=phe_Woody) res_b_end phe_herbaceous <- subset(phe, Class=="herbaceous") phe_herbaceous_1 <- subset(phe_herbaceous, Field_lab=="Field") res8=rma(As_sensitivity,Vi,data=phe_herbaceous_1) res8 res9=rma(Ae_sensitivity,Vi,data=phe_herbaceous_1) res9 res10=rma(Bs_sensitivity,Vi,data=phe_herbaceous_1) res10 res11=rma(Be_sensitivity,Vi,data=phe_herbaceous_1) res11 phe_herbaceous_1 <- subset(phe_herbaceous, Field_lab=="Lab") res12=rma(As_sensitivity,Vi,data=phe_herbaceous_1) res12 res13=rma(Ae_sensitivity,Vi,data=phe_herbaceous_1) res13 res14=rma(Bs_sensitivity,Vi,data=phe_herbaceous_1) res14 res15=rma(Be_sensitivity,Vi,data=phe_herbaceous_1) res15 res_a_start <- rma.mv(yi=As_sensitivity, V=Vi, mods=~Field_lab, random=~1|Study_ID/Obs, data=phe_herbaceous) res_a_start res_a_end <- rma.mv(yi=Ae_sensitivity, V=Vi, mods=~Field_lab, random=~1|Study_ID/Obs, data=phe_herbaceous) res_a_end res_b_start <- rma.mv(yi=Bs_sensitivity, V=Vi, mods=~Field_lab, random=~1|Study_ID/Obs, data=phe_herbaceous) res_b_start res_b_end <- rma.mv(yi=Be_sensitivity, V=Vi, mods=~Field_lab, random=~1|Study_ID/Obs, data=phe_herbaceous) res_b_end