load(".RData")In this document, we present data, code and some results not provided in the main text of the manuscript “Within- and among-population variation in vital traits, life histories, and population dynamics of a species living in a variable environment” by Simone Vincenzi, Marc Mangel, Dusan Jesensek, J Carlos Garza, Alain J Crivelli.
The tag-recapture data is stored in access files that can be exported as .csv and then read into R. For instance, to read into R the Huda datafile:
huda.df = read.csv("huda_2014_complete.csv",header=T,
stringsAsFactors = FALSE,na.strings ="")
colnames(huda.df)## [1] "Num" "Mark" "Date" "Year" "Month" "Run"
## [7] "Sector" "Length" "Weight" "Age" "Cohort" "Sex"
## [13] "adc" "Tag_loss" "Fin"Columns of the access (then .csv) datasets are the following: Num = consecutive number of sampled fish Mark = fish ID with Carlin tag. Tag was assigned if fish > 110 mm at the time of sampling Date = date of sampling (M/D/YY) Year = year of sampling Month = month of sampling Run = in which run (first, second, or third) the fish was sampled Sector = every stream is divided in sector, it is the sector in which the fish was sampled Length = fish total length (in mm) Weight = fish weight (in g) Age = age of fish at the time of sampling (0 in the first year of life, 1 in the second etc.) Cohort = year of birth, e.g. COO = born in year 2000 Sex = sex if indentified from morphology (rarely happened) adc = if the adipose fin was cut (thus whether the fish has been already sampled) Tag_loss = 1 for tag lost, NA otherwise Fin = fish ID if the fish was smaller that 110 mm at the time of sampling
head(huda.df)## Num Mark Date Year Month Run Sector Length Weight Age Cohort Sex adc
## 1 3 6824 6/24/02 2002 6 1 1 164 38.8 2 C00 <NA> NA
## 2 4 6806 6/24/02 2002 6 1 1 263 148.1 3 C99 f 1
## 3 5 6807 6/24/02 2002 6 1 1 186 54.1 2 C00 <NA> 1
## 4 6 6827 6/24/02 2002 6 1 1 183 56.2 2 C00 <NA> 1
## 5 7 6800 6/24/02 2002 6 1 1 187 58.6 2 C00 <NA> NA
## 6 8 6096 6/24/02 2002 6 1 1 139 25.6 1 C01 <NA> NA
## Tag_loss Fin
## 1 NA <NA>
## 2 NA <NA>
## 3 NA <NA>
## 4 NA <NA>
## 5 NA <NA>
## 6 NA <NA>library(dplyr, quietly = T)##
## Attaching package: 'dplyr'
##
## The following objects are masked from 'package:stats':
##
## filter, lag
##
## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, unionlibrary(ggplot2, quietly = T)
library(grid, quietly = T)
library(marked, quietly = T)## This is marked 1.1.8library(mgcv, quietly = T)##
## Attaching package: 'nlme'
##
## The following object is masked from 'package:lme4':
##
## lmList
##
## The following object is masked from 'package:dplyr':
##
## collapse
##
## This is mgcv 1.8-7. For overview type 'help("mgcv-package")'.library(MuMIn, quietly = T)Parts about code and data would be completed before manuscript revision/manuscript publication.
“Sex-specific growth trajectories were different only in Trebu, although predicted length-at-age started to differ only after age 2+. In Zadla, Sve, and Zak (using dataset DataS for the latter), sex-specific 95% CI of k and largely overlapped and pointed to no difference in the mean growth trajectories of males and females see (see Online Code and Results).”
Here are these results. In trebu_Linf_sex_k_sex_s.synth$coh.par there are the model fitting results for males and females when Sex is used as predictor of k and L_inf, along with their 95% confidence intervals, number of Samples in the dataset, expected and observed length at age 1, 2, and 3 in September.
trebu_Linf_sex_k_sex_s.synth$coh.par## Cohort Sex linf linf_lcl linf_ucl k k_lcl k_ucl t0
## 1 0 M 326.40 309.4342 343.3658 0.23903 0.2110255 0.2670345 -1.1448
## 2 0 F 279.79 267.4928 292.0872 0.29213 0.2595842 0.3246758 -1.1448
## t0_lcl t0_ucl Samples expL1 expL2 expL3 obsL1
## 1 -1.282171 -1.007429 296 130.9212 172.4817 205.2061 133.5326
## 2 -1.282171 -1.007429 279 130.2627 168.1422 196.4258 130.5506
## obsL1_lcl obsL1_ucl obsL2 obsL2_lcl obsL2_ucl obsL3 obsL3_lcl
## 1 115 154.725 167.7564 139.550 200.225 207.1957 163.25
## 2 116 153.600 166.6389 140.325 200.450 197.5294 168.25
## obsL3_ucl
## 1 251.25
## 2 230.00It can be seen that both expected and observed mean size of males and females start to diverge after age 2.
In Zadla, Sve, and Zak, we found no differences in vBGF’s parameters, and expected and observed mean size of males and females
zadla_Linf_sex_k_sex_s.synth$coh.par## Cohort Sex linf linf_lcl linf_ucl k k_lcl k_ucl t0
## 1 0 M 362.49 297.9452 427.0348 0.28433 0.1762595 0.3924005 -0.93602
## 2 0 F 356.71 306.7418 406.6782 0.29766 0.1979822 0.3973378 -0.93602
## t0_lcl t0_ucl Samples expL1 expL2 expL3 obsL1
## 1 -1.269553 -0.6024868 133 153.4494 205.1832 244.1137 158.9200
## 2 -1.269553 -0.6024868 153 156.2434 207.8528 246.1755 158.9589
## obsL1_lcl obsL1_ucl obsL2 obsL2_lcl obsL2_ucl obsL3 obsL3_lcl
## 1 132.0 195.65 203.0882 148.475 256.1 239.00 167.875
## 2 131.8 197.20 205.6585 156.000 248.0 258.55 209.900
## obsL3_ucl
## 1 337.000
## 2 334.075sve_Linf_sex_k_sex_s.synth$coh.par## Cohort Sex linf linf_lcl linf_ucl k k_lcl k_ucl t0
## 1 0 M 332.10 305.2206 358.9794 0.31528 0.2498787 0.3806813 -0.669
## 2 0 F 311.14 275.0780 347.2020 0.31582 0.2342487 0.3973913 -0.669
## t0_lcl t0_ucl Samples expL1 expL2 expL3 obsL1
## 1 -0.8801312 -0.4578688 105 135.8805 188.9413 227.6536 134.9762
## 2 -0.8801312 -0.4578688 100 127.4702 177.2097 213.4793 129.9545
## obsL1_lcl obsL1_ucl obsL2 obsL2_lcl obsL2_ucl obsL3 obsL3_lcl
## 1 115.025 159.00 189.0357 154.025 236.00 223.1765 196.800
## 2 115.000 159.85 174.3824 150.125 206.35 222.1667 197.375
## obsL3_ucl
## 1 253.600
## 2 256.475zak_Linf_sex_k_sex_s_sex.synth$coh.par## Cohort Sex linf linf_lcl linf_ucl k k_lcl k_ucl t0
## 1 0 M 304.78 293.2917 316.2683 0.37983 0.3455143 0.4141457 -0.38944
## 2 0 F 295.92 285.1014 306.7386 0.39858 0.3635783 0.4335817 -0.38944
## t0_lcl t0_ucl Samples expL1 expL2 expL3 obsL1
## 1 -0.467889 -0.310991 833 124.9811 181.8015 220.6655 127.4091
## 2 -0.467889 -0.310991 730 125.8371 181.7480 219.2795 127.9727
## obsL1_lcl obsL1_ucl obsL2 obsL2_lcl obsL2_ucl obsL3 obsL3_lcl
## 1 107.95 154 182.4141 138.800 249.6 220.6667 174
## 2 113.00 159 180.9680 142.225 243.1 222.3333 179
## obsL3_ucl
## 1 286.25
## 2 301.35Huda
data.models.huda.a.recmod## model npar AIC DeltaAIC
## 8 Phi(~Coh_n + time)p(~time) 65 2826.404 0.00000
## 4 Phi(~Coh_n + Age)p(~time) 42 2849.536 23.13216
## 1 Phi(~Age)p(~time) 27 2852.972 26.56788
## 14 Phi(~Season + Age)p(~time) 28 2854.283 27.87867
## 2 Phi(~bs(Age))p(~time) 29 2855.320 28.91540
## 15 Phi(~Season * Age)p(~time) 29 2856.214 29.81012
## 21 Phi(~time)p(~time) 50 2861.661 35.25670
## 5 Phi(~Coh_n * Age)p(~time) 57 2863.270 36.86571
## 3 Phi(~Coh_n)p(~time) 41 2873.629 47.22463
## 6 Phi(~Coh_n + Season)p(~time) 42 2874.672 48.26819
## 18 Phi(~bs(Temperature) * Season)p(~time) 33 2885.391 58.98639
## 10 Phi(~Density)p(~time) 27 2888.315 61.91083
## 11 Phi(~Density + Season)p(~time) 28 2890.018 63.61375
## 12 Phi(~Density * Season)p(~time) 29 2891.713 65.30859
## 19 Phi(~bs(Temperature))p(~time) 29 2892.330 65.92588
## 7 Phi(~Coh_n * Season)p(~time) 57 2893.480 67.07615
## 20 Phi(~1)p(~time) 26 2895.969 69.56502
## 17 Phi(~Temperature * Season)p(~time) 29 2896.514 70.11022
## 16 Phi(~Temperature)p(~time) 27 2896.635 70.23042
## 13 Phi(~Season)p(~time) 27 2897.584 71.17953
## 9 Phi(~Coh_n * time)p(~time) 425 3447.466 621.06211
## weight neg2lnl convergence
## 8 9.999870e-01 2696.404 0
## 4 9.482233e-06 2765.536 0
## 1 1.701577e-06 2798.972 0
## 14 8.835216e-07 2798.283 0
## 2 5.261319e-07 2797.320 0
## 15 3.363636e-07 2798.214 0
## 21 2.208506e-08 2761.661 0
## 5 9.878870e-09 2749.270 0
## 3 5.562835e-11 2791.629 0
## 6 3.301340e-11 2790.672 0
## 18 1.553323e-13 2819.391 0
## 10 3.599393e-14 2834.315 0
## 11 1.536189e-14 2834.018 0
## 12 6.582856e-15 2833.713 0
## 19 4.834733e-15 2834.330 0
## 7 2.720151e-15 2779.480 0
## 20 7.836872e-16 2843.969 0
## 17 5.966976e-16 2838.514 0
## 16 5.618907e-16 2842.635 0
## 13 3.495871e-16 2843.584 0
## 9 1.374266e-135 2597.466 0Lipovesck
data.models.lipo.a.recmod## model npar AIC DeltaAIC
## 29 Phi(~time)p(~1) 17 1351.489 0.00000
## 10 Phi(~Coh_n + time)p(~1) 30 1361.404 9.91577
## 26 Phi(~bs(Temperature) * Season)p(~1) 9 1379.757 28.26833
## 24 Phi(~Temperature * Season * Flood)p(~1) 7 1386.432 34.94282
## 18 Phi(~Flood + Season)p(~1) 4 1399.588 48.09961
## 19 Phi(~Flood * Season)p(~1) 4 1399.588 48.09961
## 14 Phi(~Density * Flood)p(~1) 5 1402.633 51.14421
## 13 Phi(~Density + Flood)p(~1) 4 1408.691 57.20240
## 9 Phi(~Coh.pflood + Flood)p(~1) 4 1412.439 60.95039
## 15 Phi(~Flood)p(~1) 3 1412.593 61.10434
## 16 Phi(~Flood + Age)p(~1) 4 1414.248 62.75923
## 17 Phi(~Flood * Age)p(~1) 5 1416.155 64.66583
## 7 Phi(~Coh_n * Season)p(~1) 29 1435.049 83.56046
## 6 Phi(~Coh_n + Season)p(~1) 16 1454.423 102.93453
## 8 Phi(~Coh.pflood)p(~1) 3 1456.820 105.33096
## 5 Phi(~Coh_n * Age)p(~1) 29 1458.743 107.25397
## 4 Phi(~Coh_n + Age)p(~1) 16 1460.608 109.11916
## 25 Phi(~Temperature * Season)p(~1) 5 1461.630 110.14144
## 3 Phi(~Coh_n)p(~1) 15 1463.653 112.16478
## 27 Phi(~bs(Temperature))p(~1) 5 1464.149 112.66055
## 22 Phi(~Season * Age)p(~1) 5 1471.784 120.29543
## 21 Phi(~Season + Age)p(~1) 4 1471.828 120.33924
## 20 Phi(~Season)p(~1) 3 1480.199 128.71010
## 23 Phi(~Temperature)p(~1) 3 1482.952 131.46333
## 12 Phi(~Density)p(~1) 3 1482.964 131.47537
## 2 Phi(~bs(Age))p(~1) 5 1486.803 135.31385
## 1 Phi(~Age)p(~1) 3 1487.291 135.80190
## 28 Phi(~1)p(~1) 2 1495.270 143.78121
## 11 Phi(~Coh_n * time)p(~1) 225 1718.038 366.54932
## weight neg2lnl convergence
## 29 9.930205e-01 1317.489 0
## 10 6.978727e-03 1301.404 0
## 26 7.220523e-07 1361.757 0
## 24 2.565796e-08 1372.432 0
## 18 3.566646e-11 1391.588 0
## 19 3.566646e-11 1391.588 0
## 14 7.782768e-12 1392.633 0
## 13 3.763700e-13 1400.691 0
## 9 5.777618e-14 1404.439 0
## 15 5.349576e-14 1406.593 0
## 16 2.338644e-14 1406.248 0
## 17 9.014705e-15 1406.155 0
## 7 7.112704e-19 1377.049 0
## 6 4.415789e-23 1422.423 0
## 8 1.332391e-23 1450.820 0
## 5 5.093951e-24 1400.743 0
## 4 2.004630e-24 1428.608 0
## 25 1.202399e-24 1451.630 0
## 3 4.372057e-25 1433.653 0
## 27 3.412165e-25 1454.149 0
## 22 7.501319e-27 1461.784 0
## 21 7.338784e-27 1463.828 0
## 20 1.116643e-28 1474.199 0
## 23 2.818768e-29 1476.952 0
## 12 2.801840e-29 1476.964 0
## 2 4.110819e-30 1476.803 0
## 1 3.220697e-30 1481.291 0
## 28 5.960269e-32 1491.270 0
## 11 2.522232e-80 1268.038 0Lower Idrijca
data.models.loidri.a.recmod## model npar AIC DeltaAIC
## 13 Phi(~Season + Age)p(~1) 4 2820.883 0.0000000
## 2 Phi(~bs(Age))p(~1) 5 2821.036 0.1534059
## 14 Phi(~Season * Age)p(~1) 5 2821.426 0.5434386
## 1 Phi(~Age)p(~1) 3 2821.631 0.7481001
## 4 Phi(~Coh_n + Age)p(~1) 20 2821.775 0.8928380
## 3 Phi(~Coh_n)p(~1) 19 2822.170 1.2876336
## 6 Phi(~Coh_n + Season)p(~1) 20 2823.290 2.4075540
## 19 Phi(~1)p(~1) 2 2825.358 4.4754259
## 12 Phi(~Season)p(~1) 3 2825.436 4.5529274
## 16 Phi(~Temperature * Season)p(~1) 5 2826.417 5.5341842
## 15 Phi(~Temperature)p(~1) 3 2826.621 5.7382682
## 10 Phi(~Density)p(~1) 3 2826.710 5.8278226
## 11 Phi(~Density + Season)p(~1) 4 2826.823 5.9403215
## 18 Phi(~bs(Temperature))p(~1) 5 2827.599 6.7161956
## 17 Phi(~bs(Temperature) * Season)p(~1) 9 2830.204 9.3211913
## 5 Phi(~Coh_n * Age)p(~1) 37 2836.954 16.0714034
## 20 Phi(~time)p(~1) 22 2840.893 20.0104239
## 7 Phi(~Coh_n * Season)p(~1) 37 2844.318 23.4356740
## 8 Phi(~Coh_n + time)p(~1) 39 2846.290 25.4074371
## 9 Phi(~Coh_n * time)p(~1) 379 3356.746 535.8631512
## weight neg2lnl convergence
## 13 1.879496e-01 2812.883 0
## 2 1.740723e-01 2811.036 0
## 14 1.432304e-01 2811.426 0
## 1 1.292985e-01 2815.631 0
## 4 1.202719e-01 2781.775 0
## 3 9.872686e-02 2784.170 0
## 6 5.639592e-02 2783.290 0
## 19 2.005465e-02 2821.358 0
## 12 1.929238e-02 2819.436 0
## 16 1.181160e-02 2816.417 0
## 15 1.066577e-02 2820.621 0
## 10 1.019872e-02 2820.710 0
## 11 9.640887e-03 2818.823 0
## 18 6.540908e-03 2817.599 0
## 17 1.778158e-03 2812.204 0
## 5 6.083878e-05 2762.954 0
## 20 8.488541e-06 2796.893 0
## 7 1.531262e-06 2770.318 0
## 8 5.713294e-07 2768.290 0
## 9 8.181569e-118 2598.746 0Upper Idrijca
data.models.uppidri.a.recmod## model npar AIC DeltaAIC
## 17 Phi(~bs(Temperature) * Season)p(~Age) 10 2487.688 0.000000
## 16 Phi(~Temperature * Season)p(~Age) 6 2492.640 4.952231
## 18 Phi(~bs(Temperature))p(~Age) 6 2495.553 7.865235
## 1 Phi(~Age)p(~Age) 4 2495.560 7.872389
## 2 Phi(~bs(Age))p(~Age) 6 2496.365 8.677348
## 13 Phi(~Season + Age)p(~Age) 5 2496.759 9.070927
## 19 Phi(~1)p(~Age) 3 2497.849 10.160979
## 14 Phi(~Season * Age)p(~Age) 6 2497.905 10.216727
## 20 Phi(~time)p(~Age) 23 2497.969 10.281507
## 10 Phi(~Density)p(~Age) 4 2498.770 11.082162
## 12 Phi(~Season)p(~Age) 4 2499.546 11.857730
## 15 Phi(~Temperature)p(~Age) 4 2499.594 11.906229
## 11 Phi(~Density + Season)p(~Age) 5 2500.459 12.770869
## 4 Phi(~Coh_n + Age)p(~Age) 20 2513.570 25.881887
## 8 Phi(~Coh_n + time)p(~Age) 39 2514.926 27.238516
## 3 Phi(~Coh_n)p(~Age) 19 2518.020 30.332348
## 6 Phi(~Coh_n + Season)p(~Age) 20 2519.781 32.092936
## 5 Phi(~Coh_n * Age)p(~Age) 36 2520.721 33.032734
## 7 Phi(~Coh_n * Season)p(~Age) 36 2527.197 39.508722
## 9 Phi(~Coh_n * time)p(~Age) 359 3013.199 525.511367
## weight neg2lnl convergence
## 17 8.503783e-01 2467.688 0
## 16 7.149058e-02 2480.640 0
## 18 1.666089e-02 2483.553 0
## 1 1.660140e-02 2487.560 0
## 2 1.110069e-02 2484.365 0
## 13 9.117702e-03 2486.759 0
## 19 5.286686e-03 2491.849 0
## 14 5.141362e-03 2485.905 0
## 20 4.977500e-03 2451.969 0
## 10 3.335426e-03 2490.770 0
## 12 2.263283e-03 2491.546 0
## 15 2.209059e-03 2491.594 0
## 11 1.433683e-03 2490.459 0
## 4 2.039069e-06 2473.570 0
## 8 1.034770e-06 2436.926 0
## 3 2.203061e-07 2480.020 0
## 6 9.135233e-08 2479.781 0
## 5 5.710118e-08 2448.721 0
## 7 2.240796e-09 2455.197 0
## 9 6.550414e-115 2295.199 0Gacnik
data.models.gat.a.recmod## model npar AIC DeltaAIC weight
## 9 Phi(~Coh_n + time)p(~time) 45 18691.53 0.000000 9.883131e-01
## 16 Phi(~time)p(~time) 32 18700.40 8.875062 1.168690e-02
## 5 Phi(~Coh_n * Age)p(~time) 44 18828.41 136.886334 1.863823e-30
## 7 Phi(~Coh_n * Temperature)p(~time) 44 18867.89 176.365363 4.984742e-39
## 4 Phi(~Coh_n + Age)p(~time) 31 18886.26 194.729018 5.128917e-43
## 6 Phi(~Coh_n + Temperature)p(~time) 31 18888.00 196.472970 2.144526e-43
## 12 Phi(~Density + Age)p(~time) 19 18906.09 214.560441 2.533301e-47
## 14 Phi(~bs(Temperature))p(~time) 20 18914.33 222.799947 4.116241e-49
## 3 Phi(~Coh_n)p(~time) 30 18937.61 246.083136 3.619267e-54
## 13 Phi(~Temperature)p(~time) 18 18948.10 256.577822 1.904270e-56
## 11 Phi(~Density)p(~time) 18 18952.56 261.028907 2.056779e-57
## 8 Phi(~Coh.p)p(~time) 18 18976.48 284.952949 1.312647e-62
## 10 Phi(~Coh_n * time)p(~time) 240 18976.80 285.268508 1.121050e-62
## 2 Phi(~bs(Age))p(~time) 20 18978.12 286.589565 5.791102e-63
## 1 Phi(~Age)p(~time) 18 18980.73 289.202869 1.567795e-63
## 15 Phi(~1)p(~time) 17 19024.46 332.937564 4.993685e-73
## neg2lnl convergence
## 9 18601.53 0
## 16 18636.40 0
## 5 18740.41 0
## 7 18779.89 0
## 4 18824.26 0
## 6 18826.00 0
## 12 18868.09 0
## 14 18874.33 0
## 3 18877.61 0
## 13 18912.10 0
## 11 18916.56 0
## 8 18940.48 0
## 10 18496.80 0
## 2 18938.12 0
## 1 18944.73 0
## 15 18990.46 0Zakojska
data.models.zak.a.recmod## model npar AIC DeltaAIC
## 11 Phi(~Coh_n + time)p(~Age) 33 4695.403 0.000000
## 23 Phi(~time)p(~Age) 20 4698.754 3.350781
## 20 Phi(~bs(Temperature) * Flood)p(~Age) 10 4784.002 88.599217
## 6 Phi(~Coh_n + Flood)p(~Age) 17 4786.112 90.708677
## 10 Phi(~Coh.pflood + Flood)p(~Age) 6 4791.825 96.421553
## 4 Phi(~Coh_n + Age)p(~Age) 17 4797.331 101.927971
## 5 Phi(~Coh_n * Age)p(~Age) 30 4797.365 101.962186
## 7 Phi(~Coh_n * Flood)p(~Age) 30 4802.581 107.177931
## 17 Phi(~Flood * Age)p(~Age) 6 4806.678 111.274620
## 16 Phi(~Flood + Age)p(~Age) 5 4811.301 115.897918
## 14 Phi(~Density + Flood)p(~Age) 5 4846.285 150.882422
## 15 Phi(~Flood)p(~Age) 4 4847.078 151.674992
## 3 Phi(~Coh_n)p(~Age) 16 4850.046 154.642658
## 19 Phi(~Temperature * Flood)p(~Age) 6 4850.126 154.723351
## 9 Phi(~Coh.pflood)p(~Age) 5 4966.077 270.673526
## 1 Phi(~Age)p(~Age) 4 5024.150 328.746997
## 2 Phi(~bs(Age))p(~Age) 6 5025.453 330.049678
## 21 Phi(~bs(Temperature))p(~Age) 6 5040.190 344.787432
## 8 Phi(~Coh.p)p(~Age) 4 5040.938 345.534786
## 18 Phi(~Temperature)p(~Age) 4 5044.503 349.100169
## 22 Phi(~1)p(~Age) 3 5061.126 365.722862
## 13 Phi(~Density)p(~Age) 4 5063.105 367.701640
## 12 Phi(~Coh_n * time)p(~Age) 254 5067.162 371.758783
## weight neg2lnl convergence
## 11 8.422932e-01 4629.403 0
## 23 1.577068e-01 4658.754 0
## 20 4.857220e-20 4764.002 0
## 6 1.691704e-20 4752.112 0
## 10 9.722751e-22 4779.825 0
## 4 6.195632e-23 4763.331 0
## 5 6.090541e-23 4737.365 0
## 7 4.488190e-24 4742.581 0
## 17 5.787440e-25 4794.678 0
## 16 5.735220e-26 4801.301 0
## 14 1.451315e-33 4836.285 0
## 15 9.764665e-34 4839.078 0
## 3 2.214302e-34 4818.046 0
## 19 2.126741e-34 4838.126 0
## 9 1.410762e-59 4956.077 0
## 1 3.459075e-72 5016.150 0
## 2 1.803376e-72 5013.453 0
## 21 1.137166e-75 5028.190 0
## 8 7.825966e-76 5032.938 0
## 18 1.316208e-76 5036.503 0
## 22 3.234096e-80 5055.126 0
## 13 1.202449e-80 5055.105 0
## 12 1.581500e-81 4559.162 0Zadla
data.models.zadla.a.recmod## model npar AIC DeltaAIC
## 13 Phi(~Flood + Age)p(~1) 4 313.6529 0.000000
## 12 Phi(~Flood)p(~1) 3 314.7111 1.058162
## 14 Phi(~Flood * Age)p(~1) 5 315.1574 1.504465
## 18 Phi(~Flood + Sex)p(~1) 4 315.9233 2.270350
## 11 Phi(~Density + Flood)p(~1) 4 316.1743 2.521354
## 19 Phi(~Flood * Sex)p(~1) 5 317.1742 3.521270
## 21 Phi(~Temperature * Flood)p(~1) 5 317.4471 3.794156
## 20 Phi(~Temperature)p(~1) 3 318.6557 5.002820
## 1 Phi(~Age)p(~1) 3 320.9758 7.322895
## 23 Phi(~bs(Temperature))p(~1) 5 321.9438 8.290874
## 24 Phi(~1)p(~1) 2 321.9743 8.321401
## 16 Phi(~Sex + Age)p(~1) 4 322.2095 8.556582
## 15 Phi(~Sex)p(~1) 3 323.4962 9.843286
## 17 Phi(~Sex * Age)p(~1) 5 323.8645 10.211575
## 10 Phi(~Density)p(~1) 3 323.9493 10.296338
## 25 Phi(~time)p(~1) 9 324.7156 11.062643
## 2 Phi(~bs(Age))p(~1) 5 324.7344 11.081454
## 22 Phi(~bs(Temperature) * Flood)p(~1) 9 325.2793 11.626416
## 6 Phi(~Coh_n + Flood)p(~1) 17 336.1160 22.463063
## 4 Phi(~Coh_n + Age)p(~1) 17 339.4685 25.815619
## 8 Phi(~Coh_n + time)p(~1) 23 342.4291 28.776187
## 3 Phi(~Coh_n)p(~1) 16 343.8174 30.164461
## 7 Phi(~Coh_n * Flood)p(~1) 31 351.1939 37.541001
## 5 Phi(~Coh_n * Age)p(~1) 31 353.4777 39.824829
## 9 Phi(~Coh_n * time)p(~1) 121 520.9746 207.321647
## weight neg2lnl convergence
## 13 3.154334e-01 305.6529 0
## 12 1.858364e-01 308.7111 0
## 14 1.486680e-01 305.1574 0
## 18 1.013695e-01 307.9233 0
## 11 8.941340e-02 308.1743 0
## 19 5.423426e-02 307.1742 0
## 21 4.731699e-02 307.4471 0
## 20 2.585587e-02 312.6557 0
## 1 8.105154e-03 314.9758 0
## 23 4.995367e-03 311.9438 0
## 24 4.919698e-03 317.9743 0
## 16 4.373908e-03 314.2095 0
## 15 2.298611e-03 317.4962 0
## 17 1.912019e-03 313.8645 0
## 10 1.832678e-03 317.9493 0
## 25 1.249353e-03 306.7156 0
## 2 1.237658e-03 314.7344 0
## 22 9.424613e-04 307.2793 0
## 6 4.179415e-06 302.1160 0
## 4 7.818389e-07 305.4685 0
## 8 1.779255e-07 296.4291 0
## 3 8.887473e-08 311.8174 0
## 7 2.223222e-09 289.1939 0
## 5 7.096691e-10 291.4777 0
## 9 3.017061e-46 278.9746 0Trebu
data.models.trebu.a.recmod## model npar AIC DeltaAIC weight
## 15 Phi(~bs(Temperature))p(~1) 5 830.5885 0.000000 6.002411e-01
## 5 Phi(~Coh_n * Age)p(~1) 27 833.3705 2.782016 1.493546e-01
## 17 Phi(~time)p(~1) 9 833.5520 2.963444 1.364024e-01
## 9 Phi(~Density + Temperature)p(~1) 4 836.4494 5.860878 3.203702e-02
## 6 Phi(~Coh_n + time)p(~1) 21 836.6954 6.106824 2.832994e-02
## 8 Phi(~Density)p(~1) 3 837.3551 6.766611 2.036924e-02
## 10 Phi(~Density * Temperature)p(~1) 5 837.6262 7.037715 1.778710e-02
## 4 Phi(~Coh_n + Age)p(~1) 15 839.2944 8.705869 7.724497e-03
## 2 Phi(~bs(Age))p(~1) 5 841.2988 10.710306 2.835386e-03
## 3 Phi(~Coh_n)p(~1) 14 842.1336 11.545091 1.867846e-03
## 16 Phi(~1)p(~1) 2 843.2127 12.624218 1.088959e-03
## 11 Phi(~Sex)p(~1) 3 844.1358 13.547303 6.863824e-04
## 1 Phi(~Age)p(~1) 3 845.0831 14.494619 4.274243e-04
## 14 Phi(~Temperature)p(~1) 3 845.1562 14.567686 4.120907e-04
## 12 Phi(~Sex + Age)p(~1) 4 845.9592 15.370637 2.758255e-04
## 13 Phi(~Sex * Age)p(~1) 5 847.0450 16.456471 1.602686e-04
## 7 Phi(~Coh_n * time)p(~1) 105 961.7446 131.156046 1.986791e-29
## neg2lnl convergence
## 15 820.5885 0
## 5 779.3705 0
## 17 815.5520 0
## 9 828.4494 0
## 6 794.6954 0
## 8 831.3551 0
## 10 827.6262 0
## 4 809.2944 0
## 2 831.2988 0
## 3 814.1336 0
## 16 839.2127 0
## 11 838.1358 0
## 1 839.0831 0
## 14 839.1562 0
## 12 837.9592 0
## 13 837.0450 0
## 7 751.7446 0Sve
data.models.sve.a.recmod## model npar AIC DeltaAIC
## 12 Phi(~Flood)p(~Coh_n) 13 240.8952 0.000000
## 13 Phi(~Flood + Age)p(~Coh_n) 14 242.5012 1.606051
## 18 Phi(~Flood + Sex)p(~Coh_n) 14 242.8822 1.987037
## 23 Phi(~bs(Temperature))p(~Coh_n) 15 244.3637 3.468524
## 14 Phi(~Flood * Age)p(~Coh_n) 15 244.4514 3.556239
## 25 Phi(~time)p(~Coh_n) 17 244.8045 3.909368
## 11 Phi(~Density * Flood)p(~Coh_n) 15 244.8268 3.931616
## 19 Phi(~Flood * Sex)p(~Coh_n) 15 244.8811 3.985907
## 21 Phi(~Temperature * Flood)p(~Coh_n) 15 244.8825 3.987308
## 10 Phi(~Density)p(~Coh_n) 13 247.0925 6.197341
## 22 Phi(~bs(Temperature) * Flood)p(~Coh_n) 17 247.5332 6.637985
## 20 Phi(~Temperature)p(~Coh_n) 13 247.9440 7.048776
## 6 Phi(~Coh_n + Flood)p(~Coh_n) 23 253.3308 12.435666
## 8 Phi(~Coh_n + time)p(~Coh_n) 27 255.5629 14.667697
## 24 Phi(~1)p(~Coh_n) 12 259.6769 18.781759
## 5 Phi(~Coh_n * Age)p(~Coh_n) 33 260.4693 19.574138
## 15 Phi(~Sex)p(~Coh_n) 13 261.5530 20.657805
## 1 Phi(~Age)p(~Coh_n) 13 261.6748 20.779612
## 2 Phi(~bs(Age))p(~Coh_n) 15 262.7549 21.859747
## 16 Phi(~Sex + Age)p(~Coh_n) 14 263.5515 22.656296
## 17 Phi(~Sex * Age)p(~Coh_n) 15 264.8562 23.961009
## 3 Phi(~Coh_n)p(~Coh_n) 22 269.4253 28.530078
## 4 Phi(~Coh_n + Age)p(~Coh_n) 23 270.3095 29.414273
## 7 Phi(~Coh_n * Flood)p(~Coh_n) 33 272.3909 31.495712
## 9 Phi(~Coh_n * time)p(~Coh_n) 77 338.6952 97.800032
## weight neg2lnl convergence
## 12 3.531665e-01 214.8952 0
## 13 1.582085e-01 214.5012 0
## 18 1.307675e-01 214.8822 0
## 23 6.234464e-02 214.3637 0
## 14 5.966946e-02 214.4514 0
## 25 5.001163e-02 210.8045 0
## 11 4.945839e-02 214.8268 0
## 19 4.813387e-02 214.8811 0
## 21 4.810017e-02 214.8825 0
## 10 1.593104e-02 221.0925 0
## 22 1.278084e-02 213.5332 0
## 20 1.040776e-02 221.9440 0
## 6 7.040589e-04 207.3308 0
## 8 2.306374e-04 201.5629 0
## 24 2.948304e-05 235.6769 0
## 5 1.983853e-05 194.4693 0
## 15 1.153969e-05 235.5530 0
## 1 1.085786e-05 235.6748 0
## 2 6.326970e-06 232.7549 0
## 16 4.248418e-06 235.5515 0
## 17 2.212649e-06 234.8562 0
## 3 2.252951e-07 225.4253 0
## 4 1.447943e-07 224.3095 0
## 7 5.114140e-08 206.3909 0
## 9 2.046315e-22 184.6952 0L_inf and survival (estimate is mean survival)
##
## Call:
## lm(formula = linf ~ estimate)
##
## Residuals:
## Min 1Q Median 3Q Max
## -43.286 -16.805 -9.121 22.112 47.392
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 561.66 52.69 10.66 5.26e-06 ***
## estimate -468.23 112.83 -4.15 0.00321 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 30.54 on 8 degrees of freedom
## Multiple R-squared: 0.6828, Adjusted R-squared: 0.6432
## F-statistic: 17.22 on 1 and 8 DF, p-value: 0.003209k and survival (estimate is mean survival)
##
## Call:
## lm(formula = k ~ estimate)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.06380 -0.04402 -0.01101 0.02906 0.12545
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.07038 0.10601 -0.664 0.5254
## estimate 0.71926 0.22700 3.169 0.0132 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.06145 on 8 degrees of freedom
## Multiple R-squared: 0.5565, Adjusted R-squared: 0.5011
## F-statistic: 10.04 on 1 and 8 DF, p-value: 0.01322L_inf and k
##
## Call:
## lm(formula = linf ~ k)
##
## Residuals:
## Min 1Q Median 3Q Max
## -43.390 -4.061 1.788 15.325 21.636
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 487.14 22.24 21.906 1.99e-08 ***
## k -540.53 81.57 -6.627 0.000165 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 21.29 on 8 degrees of freedom
## Multiple R-squared: 0.8459, Adjusted R-squared: 0.8266
## F-statistic: 43.91 on 1 and 8 DF, p-value: 0.0001647