Stronger net selection on males across animals
Supplementary material reporting R code
Lennart Winkler1, Maria Moiron2, Edward H. Morrow3 and Tim Janicke1,2
1Applied Zoology, Technical University Dresden
2Centre d’Écologie Fonctionnelle et Évolutive, UMR 5175, CNRS, Université de Montpellier 3Department for Environmental and Life Sciences, Karlstad University
Last updated: 2021-06-13
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Knit directory: Net_Selection_eLife_code/
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Supplementary material reporting R code for the manuscript ‘Stronger net selection on males across animals’.
Phenotypic gambit
Statistical analyses were carried out in two steps. First, we examined the key assumption of the ‘phenotypic gambit’ by testing whether estimates of phenotypic variance predict the estimated genetic variance. For this we computed the Pearson correlation coefficient r, testing the relationship between CVP and CVG for both sexes and the two fitness components separately. In addition, we tested whether the sex bias in CVP translates into a sex bias in CVG by correlating the coefficient of variation ratio lnCVR (Nakagawa et al. 2015), which refers to the ln-transformed ratio of male CV to female CV, with positive values indicating a male bias. The analyses on the phenotypic gambit were motivated from a methodological perspective and we did not expect that inter-specific variation in the difference between CVP and CVG can be explained by a shared phylogenetic history. However, for completeness, we also ran correlations on phylogenetic independent contrasts (PICs; computed using the ape R-package (version 5.4.1) in R (Paradis & Schliep 2019)) to test whether our findings were robust when accounting for potential phylogenetic non-independence. We report Pearson’s correlation coefficients r for normally distributed data and Spearman’s rho if assumptions of normality were violated.
Load and prepare data
# load packages
rm(list = ls())
library(ape);library(metafor); library(Matrix); library(MASS); library(pwr);library(multcomp);library(psych);library(outliers)
library(matrixcalc)
library(PerformanceAnalytics)
library(tidyr)
library(MCMCglmm)
library(matrixcalc)
library(dplyr)
library(stargazer)
library(data.table)
library(ggplot2)# load and organize data
Data <- read.csv("./data/META_SexSpecGenVar_Data_v23.csv", sep=",", header=TRUE,fileEncoding="UTF-8-BOM")
theTree <- read.tree("./data/META_SexSpecGenVar_Pylogeny_v05_NEWICK.txt")
stacked_gen_Data <- gather(Data, key = "Sex",value = "genCV", genCV_male, genCV_female)
stacked_phen_Data <- gather(Data, key = "Sex",value = "phenCV", phenCV_male, phenCV_female)# Subsetting dataset
RS_gen_metaData<-subset(stacked_gen_Data, FitnessCat == "RS")
LS_gen_metaData<-subset(stacked_gen_Data, FitnessCat == "LS")
RS_phen_metaData<-subset(stacked_phen_Data, FitnessCat == "RS")
LS_phen_metaData<-subset(stacked_phen_Data, FitnessCat == "LS")MCMC APPROACH - Reproductive Success
# Prune phylogenetic tree to data subset
RS_gen_metaData$animal <- factor(RS_gen_metaData$animal)
RS_Species_Data <- unique(RS_gen_metaData$animal)
summary(RS_Species_Data)
RS_theTree<-drop.tip(theTree, theTree$tip.label[-na.omit(match(RS_Species_Data, theTree$tip.label))])
plot(RS_theTree)
#Check phylogenetic tree
sort(RS_theTree$tip.label) == sort(unique(RS_gen_metaData$animal)) # check if tip names correspond to data names
is.ultrametric(RS_theTree) # check if BL are aligned contemporaneously
isSymmetric(vcv(RS_theTree, corr=TRUE)) # check symmetry of phylogenetic correlation matrix
rawC <- vcv(RS_theTree, corr=TRUE)
is.positive.definite(rawC) # if FALSE will have to force symmetry
forcedC <- as.matrix(forceSymmetric(vcv(RS_theTree, corr=TRUE)))
is.positive.definite(forcedC)
comparedC <- rawC == forcedC
rawC[cbind(which(comparedC!=TRUE, arr.ind = T))] - forcedC[cbind(which(comparedC!=TRUE, arr.ind = T))] < 1e-5# Run MCMC models for reproductive success
pr<-list(R=list(V=1,nu=0.002), G=list(G1=list(V=1,nu=0.002),
G2=list(V=1,nu=0.002),
G3=list(V=1,nu=0.002)))
BURNIN = 1000000
NITT = 11000000
THIN = 500
BURNIN = 100000
NITT = 1100000
THIN = 500
names(RS_phen_metaData)
RS_phen_metaData$Index <- as.factor(RS_phen_metaData$Index)
RS_phen_metaData$Study_ID <- as.factor(RS_phen_metaData$Study_ID)
RS_phen_MCMC_model <- MCMCglmm(phenCV~factor(Sex),random=~animal + Index + Study_ID,
pedigree=RS_theTree,
prior=pr,
data=RS_phen_metaData,
pr = TRUE,
burnin = BURNIN,
nitt=NITT,
thin=THIN)# Model summaries for MCMC models on reproductive success
# Model output: phenotypic variance in reproductive success
summary(RS_phen_MCMC_model)
Iterations = 100001:1099501
Thinning interval = 500
Sample size = 2000
DIC: 50.043
G-structure: ~animal
post.mean l-95% CI u-95% CI eff.samp
animal 0.02255 0.0002015 0.07153 2000
~Index
post.mean l-95% CI u-95% CI eff.samp
Index 0.06268 0.002205 0.1299 2000
~Study_ID
post.mean l-95% CI u-95% CI eff.samp
Study_ID 0.05648 0.0003111 0.144 2000
R-structure: ~units
post.mean l-95% CI u-95% CI eff.samp
units 0.0598 0.03933 0.08289 2241
Location effects: phenCV ~ factor(Sex)
post.mean l-95% CI u-95% CI eff.samp pMCMC
(Intercept) 0.6941 0.4875 0.8888 2000 <5e-04 ***
factor(Sex)phenCV_male 0.2329 0.1500 0.3183 2000 <5e-04 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1#summary(RS_phen_MatSyst_MCMC_model)
#summary(RS_phen_StudyType_MCMC_model)
#summary(RS_phen_SSD_MCMC_model)
#summary(RS_phen_SexualDimorphism_MCMC_model)
#summary(RS_phen_BreedingDesign_MCMC_model)
#summary(RS_gen_MCMC_model)
#summary(RS_gen_MatSyst_MCMC_model)
#summary(RS_gen_StudyType_MCMC_model)
#summary(RS_gen_SSD_MCMC_model)
#summary(RS_gen_SexualDimorphism_MCMC_model)
#summary(RS_gen_BreedingDesign_MCMC_model)Including Plots
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sessionInfo()R version 4.0.0 (2020-04-24)
Platform: x86_64-w64-mingw32/x64 (64-bit)
Running under: Windows 10 x64 (build 19041)
Matrix products: default
locale:
[1] LC_COLLATE=German_Germany.1252 LC_CTYPE=German_Germany.1252
[3] LC_MONETARY=German_Germany.1252 LC_NUMERIC=C
[5] LC_TIME=German_Germany.1252
attached base packages:
[1] stats graphics grDevices utils datasets methods base
other attached packages:
[1] ggplot2_3.3.3 data.table_1.14.0
[3] stargazer_5.2.2 dplyr_1.0.5
[5] MCMCglmm_2.32 coda_0.19-4
[7] tidyr_1.1.3 PerformanceAnalytics_2.0.4
[9] xts_0.12.1 zoo_1.8-9
[11] matrixcalc_1.0-3 outliers_0.14
[13] psych_2.1.3 multcomp_1.4-16
[15] TH.data_1.0-10 survival_3.1-12
[17] mvtnorm_1.1-1 pwr_1.3-0
[19] MASS_7.3-51.5 metafor_2.4-0
[21] Matrix_1.2-18 ape_5.4-1
[23] workflowr_1.6.2
loaded via a namespace (and not attached):
[1] Rcpp_1.0.6 lattice_0.20-41 corpcor_1.6.9 assertthat_0.2.1
[5] rprojroot_2.0.2 digest_0.6.27 utf8_1.2.1 R6_2.5.0
[9] evaluate_0.14 highr_0.8 pillar_1.5.1 rlang_0.4.10
[13] cubature_2.0.4.1 whisker_0.4 rmarkdown_2.7 splines_4.0.0
[17] stringr_1.4.0 munsell_0.5.0 compiler_4.0.0 httpuv_1.6.1
[21] xfun_0.22 pkgconfig_2.0.3 mnormt_2.0.2 tmvnsim_1.0-2
[25] htmltools_0.5.1.1 tidyselect_1.1.0 tibble_3.1.0 tensorA_0.36.2
[29] quadprog_1.5-8 codetools_0.2-16 fansi_0.4.2 withr_2.4.1
[33] crayon_1.4.1 later_1.2.0 grid_4.0.0 gtable_0.3.0
[37] nlme_3.1-147 lifecycle_1.0.0 DBI_1.1.1 git2r_0.28.0
[41] magrittr_2.0.1 scales_1.1.1 stringi_1.5.3 fs_1.5.0
[45] promises_1.2.0.1 ellipsis_0.3.1 vctrs_0.3.6 generics_0.1.0
[49] sandwich_3.0-0 tools_4.0.0 glue_1.4.2 purrr_0.3.4
[53] parallel_4.0.0 yaml_2.2.1 colorspace_2.0-0 knitr_1.31