Population density affects sexual selection in the red flour beetle
Analyses excluding individuals without mating success
Lennart Winkler1, Ronja
Eilhardt1 & Tim Janicke1,2
1Applied Zoology, Technical University Dresden
2Centre d’Écologie Fonctionnelle et Évolutive, UMR 5175,
CNRS, Université de Montpellier
Last updated: 2022-07-24
Checks: 6 1
Knit directory:
Density_and_sexual_selection_2022/
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Supplementary material reporting R code for the manuscript ‘Population density affects sexual selection in the red flour beetle’.
Load and prepare data
Before we started the analyses, we loaded all necessary packages and data.
#load packages
rm(list = ls())
library(ggeffects)
library(ggplot2)
library(gridExtra)
library(lme4)
library(lmerTest)
library(readr)
library(dplyr)
library(EnvStats)
library(cowplot)
library(gridGraphics)
library(car)
library(RColorBrewer)
library(boot)
library(data.table)
library(base)
library(tidyr)
library(ICC)
#load data
DB_data=read_delim("./data/DB_AllData_V04.CSV",";", escape_double = FALSE, trim_ws = TRUE)
#Set factors and level factors
DB_data$Week=as.factor(DB_data$Week)
DB_data$Date=as.factor(DB_data$Date)
DB_data$Sex=as.factor(DB_data$Sex)
DB_data$Gr_size=as.factor(DB_data$Gr_size)
DB_data$Gr_size <- factor(DB_data$Gr_size, levels=c("SG","LG"))
DB_data$Area=as.factor(DB_data$Area)
#Load Body mass data
DB_BM_female <- read_delim("./data/DB_mass_focals_female.CSV",
";", escape_double = FALSE, trim_ws = TRUE)
DB_BM_male <- read_delim("./data/DB_mass_focals_males.CSV",
";", escape_double = FALSE, trim_ws = TRUE)
DB_data_m=merge(DB_data,DB_BM_male,by.x = 'Well_ID',by.y = 'ID_male_focals')
DB_data_f=merge(DB_data,DB_BM_female,by.x = 'F1_ID',by.y = 'ID_female_focals')
DB_data=rbind(DB_data_m,DB_data_f)
###Exclude incomplete data
DB_data=DB_data[DB_data$excluded!=1,]
#Exclude zero MS (all data)####
DB_data=DB_data[DB_data$MatingPartners_number!=0,]
#Calculate total offspring number ####
DB_data$Total_N_MTP1=colSums(rbind(DB_data$N_MTP1_1,DB_data$N_MTP1_2,DB_data$N_MTP1_3,DB_data$N_MTP1_4,DB_data$N_MTP1_5,DB_data$N_MTP1_6), na.rm = T)
DB_data$Total_N_Rd=colSums(rbind(DB_data$N_RD_1,DB_data$N_RD_2,DB_data$N_RD_3,DB_data$N_RD_4,DB_data$N_RD_5,DB_data$N_RD_6), na.rm = T)/DB_data$N_comp
#Calculate proportional RS ####
#Percentage focal offspring
DB_data$m_prop_RS=NA
DB_data$m_prop_RS=(DB_data$Total_N_MTP1/(DB_data$Total_N_MTP1+DB_data$Total_N_Rd))*100
DB_data$m_prop_RS[DB_data$Sex=='F']=NA
DB_data$f_prop_RS=NA
DB_data$f_prop_RS=(DB_data$Total_N_MTP1/(DB_data$Total_N_MTP1+DB_data$Total_N_Rd))*100
DB_data$f_prop_RS[DB_data$Sex=='M']=NA
#Calculate proportion of successful matings ####
DB_data$Prop_MS=NA
DB_data$Prop_MS=DB_data$Matings_number/(DB_data$Attempts_number+DB_data$Matings_number)
DB_data$Prop_MS[DB_data$Prop_MS==0]=NA
#Calculate total encounters ####
DB_data$Total_Encounters=NA
DB_data$Total_Encounters=DB_data$Attempts_number+DB_data$Matings_number
# Treatment identifier for each density ####
n=1
DB_data$Treatment=NA
for(n in 1:length(DB_data$Sex)){if(DB_data$Gr_size[n]=='SG' && DB_data$Area[n]=='Large'){DB_data$Treatment[n]='D = 0.26'
}else if(DB_data$Gr_size[n]=='LG' && DB_data$Area[n]=='Large'){DB_data$Treatment[n]='D = 0.52'
}else if(DB_data$Gr_size[n]=='SG' && DB_data$Area[n]=='Small'){DB_data$Treatment[n]='D = 0.67'
}else if(DB_data$Gr_size[n]=='LG' && DB_data$Area[n]=='Small'){DB_data$Treatment[n]='D = 1.33'
}else{DB_data$Treatment[n]=NA}}
DB_data$Treatment=as.factor(DB_data$Treatment)
# Exclude Incubator 3 data #### -> poor performance
DB_data_clean=DB_data[DB_data$Incu3!=1,]
# Calculate genetic MS ####
# Only clean data
DB_data_clean$gMS=NA
for(i in 1:length(DB_data_clean$Sex)) {if (DB_data_clean$N_MTP1_1[i]>=1 & !is.na (DB_data_clean$N_MTP1_1[i])){
DB_data_clean$gMS[i]=1
}else{DB_data_clean$gMS[i]=0}}
for(i in 1:length(DB_data_clean$Sex)) {if (DB_data_clean$N_MTP1_2[i]>=1 & !is.na (DB_data_clean$N_MTP1_2[i])){
DB_data_clean$gMS[i]=DB_data_clean$gMS[i]+1
}else{}}
for(i in 1:length(DB_data_clean$Sex)) {if (DB_data_clean$N_MTP1_3[i]>=1 & !is.na (DB_data_clean$N_MTP1_3[i])){
DB_data_clean$gMS[i]=DB_data_clean$gMS[i]+1}else{}}
for(i in 1:length(DB_data_clean$Sex)) {if (DB_data_clean$N_MTP1_4[i]>=1 & !is.na (DB_data_clean$N_MTP1_4[i])){
DB_data_clean$gMS[i]=DB_data_clean$gMS[i]+1}else{}}
for(i in 1:length(DB_data_clean$Sex)) {if (DB_data_clean$N_MTP1_5[i]>=1 & !is.na (DB_data_clean$N_MTP1_5[i])){
DB_data_clean$gMS[i]=DB_data_clean$gMS[i]+1}else{}}
for(i in 1:length(DB_data_clean$Sex)) {if (DB_data_clean$N_MTP1_6[i]>=1 & !is.na (DB_data_clean$N_MTP1_6[i])){
DB_data_clean$gMS[i]=DB_data_clean$gMS[i]+1}else{}}
# All data
DB_data$gMS=NA
for(i in 1:length(DB_data$Sex)) {if (DB_data$N_MTP1_1[i]>=1 & !is.na (DB_data$N_MTP1_1[i])){
DB_data$gMS[i]=1
}else{DB_data$gMS[i]=0}}
for(i in 1:length(DB_data$Sex)) {if (DB_data$N_MTP1_2[i]>=1 & !is.na (DB_data$N_MTP1_2[i])){
DB_data$gMS[i]=DB_data$gMS[i]+1
}else{}}
for(i in 1:length(DB_data$Sex)) {if (DB_data$N_MTP1_3[i]>=1 & !is.na (DB_data$N_MTP1_3[i])){
DB_data$gMS[i]=DB_data$gMS[i]+1}else{}}
for(i in 1:length(DB_data$Sex)) {if (DB_data$N_MTP1_4[i]>=1 & !is.na (DB_data$N_MTP1_4[i])){
DB_data$gMS[i]=DB_data$gMS[i]+1}else{}}
for(i in 1:length(DB_data$Sex)) {if (DB_data$N_MTP1_5[i]>=1 & !is.na (DB_data$N_MTP1_5[i])){
DB_data$gMS[i]=DB_data$gMS[i]+1}else{}}
for(i in 1:length(DB_data$Sex)) {if (DB_data$N_MTP1_6[i]>=1 & !is.na (DB_data$N_MTP1_6[i])){
DB_data$gMS[i]=DB_data$gMS[i]+1}else{}}
#Calculate Rd competition RS ####
DB_data_clean$m_RS_Rd_comp=NA
for(i in 1:length(DB_data_clean$Sex)) {if (DB_data_clean$N_MTP1_1[i]>=1 & !is.na (DB_data_clean$N_MTP1_1[i])){
DB_data_clean$m_RS_Rd_comp[i]=DB_data_clean$N_RD_1[i]
}else{DB_data_clean$m_RS_Rd_comp[i]=0}}
for(i in 1:length(DB_data_clean$Sex)) {if (DB_data_clean$N_MTP1_2[i]>=1 & !is.na (DB_data_clean$N_MTP1_2[i])){
DB_data_clean$m_RS_Rd_comp[i]=DB_data_clean$m_RS_Rd_comp[i]+DB_data_clean$N_RD_2[i]
}else{}}
for(i in 1:length(DB_data_clean$Sex)) {if (DB_data_clean$N_MTP1_3[i]>=1 & !is.na (DB_data_clean$N_MTP1_3[i])){
DB_data_clean$m_RS_Rd_comp[i]=DB_data_clean$m_RS_Rd_comp[i]+DB_data_clean$N_RD_3[i]
}else{}}
for(i in 1:length(DB_data_clean$Sex)) {if (DB_data_clean$N_MTP1_4[i]>=1 & !is.na (DB_data_clean$N_MTP1_4[i])){
DB_data_clean$m_RS_Rd_comp[i]=DB_data_clean$m_RS_Rd_comp[i]+DB_data_clean$N_RD_4[i]
}else{}}
for(i in 1:length(DB_data_clean$Sex)) {if (DB_data_clean$N_MTP1_5[i]>=1 & !is.na (DB_data_clean$N_MTP1_5[i])){
DB_data_clean$m_RS_Rd_comp[i]=DB_data_clean$m_RS_Rd_comp[i]+DB_data_clean$N_RD_5[i]
}else{}}
for(i in 1:length(DB_data_clean$Sex)) {if (DB_data_clean$N_MTP1_6[i]>=1 & !is.na (DB_data_clean$N_MTP1_6[i])){
DB_data_clean$m_RS_Rd_comp[i]=DB_data_clean$m_RS_Rd_comp[i]+DB_data_clean$N_RD_6[i]
}else{}}
# Check matings of males #### -> add copulations where offspring found but no copulation registered
for(i in 1:length(DB_data_clean$Sex)) {if (DB_data_clean$N_MTP1_1[i]>=1 && DB_data_clean$Cop_Fe_1[i]==0 & !is.na (DB_data_clean$Cop_Fe_1[i])& !is.na (DB_data_clean$N_MTP1_1[i])){
DB_data_clean$Cop_Fe_1[i]=1}else{}}
for(i in 1:length(DB_data_clean$Sex)) {if (DB_data_clean$N_MTP1_2[i]>=1 && DB_data_clean$Cop_Fe_2[i]==0 & !is.na (DB_data_clean$Cop_Fe_2[i])& !is.na (DB_data_clean$N_MTP1_2[i])){
DB_data_clean$Cop_Fe_2[i]=1}else{}}
for(i in 1:length(DB_data_clean$Sex)) {if (DB_data_clean$N_MTP1_3[i]>=1 && DB_data_clean$Cop_Fe_3[i]==0 & !is.na (DB_data_clean$Cop_Fe_3[i])& !is.na (DB_data_clean$N_MTP1_3[i])){
DB_data_clean$Cop_Fe_3[i]=1}else{}}
for(i in 1:length(DB_data_clean$Sex)) {if (DB_data_clean$N_MTP1_4[i]>=1 && DB_data_clean$Cop_Fe_4[i]==0 & !is.na (DB_data_clean$Cop_Fe_4[i])& !is.na (DB_data_clean$N_MTP1_4[i])){
DB_data_clean$Cop_Fe_4[i]=1}else{}}
for(i in 1:length(DB_data_clean$Sex)) {if (DB_data_clean$N_MTP1_5[i]>=1 && DB_data_clean$Cop_Fe_5[i]==0 & !is.na (DB_data_clean$Cop_Fe_5[i])& !is.na (DB_data_clean$N_MTP1_5[i])){
DB_data_clean$Cop_Fe_5[i]=1}else{}}
for(i in 1:length(DB_data_clean$Sex)) {if (DB_data_clean$N_MTP1_6[i]>=1 && DB_data_clean$Cop_Fe_6[i]==0 & !is.na (DB_data_clean$Cop_Fe_6[i])& !is.na (DB_data_clean$N_MTP1_6[i])){
DB_data_clean$Cop_Fe_6[i]=1}else{}}
# Calculate Rd competition RS of all copulations with potential sperm competition with the focal ####
DB_data_clean$m_RS_Rd_comp_full=NA
for(i in 1:length(DB_data_clean$Sex)) {if (DB_data_clean$Cop_Fe_1[i]>=1 & !is.na (DB_data_clean$Cop_Fe_1[i])){
DB_data_clean$m_RS_Rd_comp_full[i]=DB_data_clean$N_RD_1[i]
}else{DB_data_clean$m_RS_Rd_comp_full[i]=0}}
for(i in 1:length(DB_data_clean$Sex)) {if (DB_data_clean$Cop_Fe_2[i]>=1 & !is.na (DB_data_clean$Cop_Fe_2[i])){
DB_data_clean$m_RS_Rd_comp_full[i]=DB_data_clean$m_RS_Rd_comp_full[i]+DB_data_clean$N_RD_2[i]
}else{}}
for(i in 1:length(DB_data_clean$Sex)) {if (DB_data_clean$Cop_Fe_3[i]>=1 & !is.na (DB_data_clean$Cop_Fe_3[i])){
DB_data_clean$m_RS_Rd_comp_full[i]=DB_data_clean$m_RS_Rd_comp_full[i]+DB_data_clean$N_RD_3[i]
}else{}}
for(i in 1:length(DB_data_clean$Sex)) {if (DB_data_clean$Cop_Fe_4[i]>=1 & !is.na (DB_data_clean$Cop_Fe_4[i])){
DB_data_clean$m_RS_Rd_comp_full[i]=DB_data_clean$m_RS_Rd_comp_full[i]+DB_data_clean$N_RD_4[i]
}else{}}
for(i in 1:length(DB_data_clean$Sex)) {if (DB_data_clean$Cop_Fe_5[i]>=1 & !is.na (DB_data_clean$Cop_Fe_5[i])){
DB_data_clean$m_RS_Rd_comp_full[i]=DB_data_clean$m_RS_Rd_comp_full[i]+DB_data_clean$N_RD_5[i]
}else{}}
for(i in 1:length(DB_data_clean$Sex)) {if (DB_data_clean$Cop_Fe_6[i]>=1 & !is.na (DB_data_clean$Cop_Fe_6[i])){
DB_data_clean$m_RS_Rd_comp_full[i]=DB_data_clean$m_RS_Rd_comp_full[i]+DB_data_clean$N_RD_6[i]
}else{}}
# Calculate trait values ####
# Males ####
# Total number of matings (all data)
DB_data$m_TotMatings=NA
DB_data$m_TotMatings=DB_data$Matings_number
DB_data$m_TotMatings[DB_data$Sex=='F']=NA
# Avarage mating duration (all data)
DB_data$MatingDuration_av[DB_data$MatingDuration_av==0]=NA
DB_data$m_MatingDuration_av=NA
DB_data$m_MatingDuration_av=DB_data$MatingDuration_av
DB_data$m_MatingDuration_av[DB_data$Sex=='F']=NA
DB_data$MatingDuration_av[DB_data$MatingDuration_av==0]=NA
# Total number of mating attempts (all data)
DB_data$m_Attempts_number=NA
DB_data$m_Attempts_number=DB_data$Attempts_number
DB_data$m_Attempts_number[DB_data$Sex=='F']=NA
# Proportional mating success (all data)
DB_data$m_Prop_MS=NA
DB_data$m_Prop_MS=DB_data$Prop_MS
DB_data$m_Prop_MS[DB_data$Sex=='F']=NA
#Total encounters (all data)
DB_data$m_Total_Encounters=NA
DB_data$m_Total_Encounters=DB_data$Total_Encounters
DB_data$m_Total_Encounters[DB_data$Sex=='F']=NA
# Reproductive success
DB_data_clean$m_RS=NA
DB_data_clean$m_RS=DB_data_clean$Total_N_MTP1
DB_data_clean$m_RS[DB_data_clean$Sex=='F']=NA
# Mating success (number of different partners)
# Clean data
DB_data_clean$m_cMS=NA
DB_data_clean$m_cMS=DB_data_clean$MatingPartners_number
DB_data_clean$m_cMS[DB_data_clean$Sex=='F']=NA
for(i in 1:length(DB_data_clean$m_cMS)) {if (DB_data_clean$gMS[i]>DB_data_clean$m_cMS[i] & !is.na (DB_data_clean$m_cMS[i])){
DB_data_clean$m_cMS[i]=DB_data_clean$gMS[i]}else{}}
# All data
DB_data$m_cMS=NA
DB_data$m_cMS=DB_data$MatingPartners_number
DB_data$m_cMS[DB_data$Sex=='F']=NA
for(i in 1:length(DB_data$m_cMS)) {if (DB_data$gMS[i]>DB_data$m_cMS[i] & !is.na (DB_data$m_cMS[i])){
DB_data$m_cMS[i]=DB_data$gMS[i]}else{}}
# Insemination success
DB_data_clean$m_InSuc=NA
DB_data_clean$m_InSuc=DB_data_clean$gMS/DB_data_clean$m_cMS
for(i in 1:length(DB_data_clean$m_InSuc)) {if (DB_data_clean$m_cMS[i]==0 & !is.na (DB_data_clean$m_cMS[i])){
DB_data_clean$m_InSuc[i]=NA}else{}}
# Fertilization success
DB_data_clean$m_feSuc=NA
DB_data_clean$m_feSuc=DB_data_clean$m_RS/(DB_data_clean$m_RS+DB_data_clean$m_RS_Rd_comp)
for(i in 1:length(DB_data_clean$m_feSuc)) {if (DB_data_clean$m_InSuc[i]==0 | is.na (DB_data_clean$m_InSuc[i])){
DB_data_clean$m_feSuc[i]=NA}else{}}
# Fecundicty of partners
DB_data_clean$m_pFec=NA
DB_data_clean$m_pFec=(DB_data_clean$m_RS+DB_data_clean$m_RS_Rd_comp)/DB_data_clean$gMS
for(i in 1:length(DB_data_clean$m_pFec)) {if (DB_data_clean$gMS[i]==0){
DB_data_clean$m_pFec[i]=NA}else{}}
# Paternity success
DB_data_clean$m_PS=NA
DB_data_clean$m_PS=DB_data_clean$m_RS/(DB_data_clean$m_RS+DB_data_clean$m_RS_Rd_comp_full)
for(i in 1:length(DB_data_clean$m_PS)) {if (DB_data_clean$m_RS[i]==0 & !is.na (DB_data_clean$m_RS[i])){
DB_data_clean$m_PS[i]=NA}else{}}
# Fecundity of partners in all females the focal copulated with
DB_data_clean$m_pFec_compl=NA
DB_data_clean$m_pFec_compl=(DB_data_clean$m_RS+DB_data_clean$m_RS_Rd_comp_full)/DB_data_clean$m_cMS
for(i in 1:length(DB_data_clean$m_pFec)) {if (DB_data_clean$m_cMS[i]==0 & !is.na (DB_data_clean$m_cMS[i])){
DB_data_clean$m_pFec[i]=NA}else{}}
# Females ####
# Total number of matings (all data)
DB_data$f_TotMatings=NA
DB_data$f_TotMatings=DB_data$Matings_number
DB_data$f_TotMatings[DB_data$Sex=='M']=NA
# Avarage mating duration (all data)
DB_data$f_MatingDuration_av=NA
DB_data$f_MatingDuration_av=DB_data$MatingDuration_av
DB_data$f_MatingDuration_av[DB_data$Sex=='M']=NA
DB_data$MatingDuration_av[DB_data$MatingDuration_av==0]=NA
# Total number of mating attempts (all data)
DB_data$f_Attempts_number=NA
DB_data$f_Attempts_number=DB_data$Attempts_number
DB_data$f_Attempts_number[DB_data$Sex=='M']=NA
# Proportional mating success (all data)
DB_data$f_Prop_MS=NA
DB_data$f_Prop_MS=DB_data$Prop_MS
DB_data_clean$f_Prop_MS[DB_data_clean$Sex=='M']=NA
#Total encounters (all data)
DB_data$f_Total_Encounters=NA
DB_data$f_Total_Encounters=DB_data$Total_Encounters
DB_data$f_Total_Encounters[DB_data$Sex=='M']=NA
# Reproductive success
DB_data_clean$f_RS=NA
DB_data_clean$f_RS=DB_data_clean$Total_N_MTP1
DB_data_clean$f_RS[DB_data_clean$Sex=='M']=NA
# Mating success (number of different partners)
# Clean data
DB_data_clean$f_cMS=NA
DB_data_clean$f_cMS=DB_data_clean$MatingPartners_number
DB_data_clean$f_cMS[DB_data_clean$Sex=='M']=NA
for(i in 1:length(DB_data_clean$f_cMS)) {if (DB_data_clean$gMS[i]>DB_data_clean$f_cMS[i] & !is.na (DB_data_clean$f_cMS[i])){
DB_data_clean$f_cMS[i]=DB_data_clean$gMS[i]}else{}}
# All data
DB_data$f_cMS=NA
DB_data$f_cMS=DB_data$MatingPartners_number
DB_data$f_cMS[DB_data$Sex=='M']=NA
for(i in 1:length(DB_data$f_cMS)) {if (DB_data$gMS[i]>DB_data$f_cMS[i] & !is.na (DB_data$f_cMS[i])){
DB_data$f_cMS[i]=DB_data$gMS[i]}else{}}
# Fecundity per mating partner
DB_data_clean$f_fec_pMate=NA
DB_data_clean$f_fec_pMate=DB_data_clean$f_RS/DB_data_clean$f_cMS
for(i in 1:length(DB_data_clean$f_fec_pMate)) {if (DB_data_clean$f_RS[i]==0 & !is.na (DB_data_clean$f_RS[i])){
DB_data_clean$f_fec_pMate[i]=0}else{}}
for(i in 1:length(DB_data_clean$f_fec_pMate)) {if (DB_data_clean$f_cMS[i]==0 & !is.na (DB_data_clean$f_cMS[i])){
DB_data_clean$f_fec_pMate[i]=NA}else{}}
# Relativize data per treatment and sex ####
# Small group + large Area
DB_data_clean_0.26=DB_data_clean[DB_data_clean$Treatment=='D = 0.26',]
DB_data_clean_0.26$rel_m_RS=NA
DB_data_clean_0.26$rel_m_prop_RS=NA
DB_data_clean_0.26$rel_m_cMS=NA
DB_data_clean_0.26$rel_m_InSuc=NA
DB_data_clean_0.26$rel_m_feSuc=NA
DB_data_clean_0.26$rel_m_pFec=NA
DB_data_clean_0.26$rel_m_PS=NA
DB_data_clean_0.26$rel_m_pFec_compl=NA
DB_data_clean_0.26$rel_f_RS=NA
DB_data_clean_0.26$rel_f_prop_RS=NA
DB_data_clean_0.26$rel_f_cMS=NA
DB_data_clean_0.26$rel_f_fec_pMate=NA
DB_data_clean_0.26$rel_m_RS=DB_data_clean_0.26$m_RS/mean(DB_data_clean_0.26$m_RS,na.rm=T)
DB_data_clean_0.26$rel_m_prop_RS=DB_data_clean_0.26$m_prop_RS/mean(DB_data_clean_0.26$m_prop_RS,na.rm=T)
DB_data_clean_0.26$rel_m_cMS=DB_data_clean_0.26$m_cMS/mean(DB_data_clean_0.26$m_cMS,na.rm=T)
DB_data_clean_0.26$rel_m_InSuc=DB_data_clean_0.26$m_InSuc/mean(DB_data_clean_0.26$m_InSuc,na.rm=T)
DB_data_clean_0.26$rel_m_feSuc=DB_data_clean_0.26$m_feSuc/mean(DB_data_clean_0.26$m_feSuc,na.rm=T)
DB_data_clean_0.26$rel_m_pFec=DB_data_clean_0.26$m_pFec/mean(DB_data_clean_0.26$m_pFec,na.rm=T)
DB_data_clean_0.26$rel_m_PS=DB_data_clean_0.26$m_PS/mean(DB_data_clean_0.26$m_PS,na.rm=T)
DB_data_clean_0.26$rel_m_pFec_compl=DB_data_clean_0.26$m_pFec_compl/mean(DB_data_clean_0.26$m_pFec_compl,na.rm=T)
DB_data_clean_0.26$rel_f_RS=DB_data_clean_0.26$f_RS/mean(DB_data_clean_0.26$f_RS,na.rm=T)
DB_data_clean_0.26$rel_f_prop_RS=DB_data_clean_0.26$f_prop_RS/mean(DB_data_clean_0.26$f_prop_RS,na.rm=T)
DB_data_clean_0.26$rel_f_cMS=DB_data_clean_0.26$f_cMS/mean(DB_data_clean_0.26$f_cMS,na.rm=T)
DB_data_clean_0.26$rel_f_fec_pMate=DB_data_clean_0.26$f_fec_pMate/mean(DB_data_clean_0.26$f_fec_pMate,na.rm=T)
# Large group + large Area
DB_data_clean_0.52=DB_data_clean[DB_data_clean$Treatment=='D = 0.52',]
#Relativize data
DB_data_clean_0.52$rel_m_RS=NA
DB_data_clean_0.52$rel_m_prop_RS=NA
DB_data_clean_0.52$rel_m_cMS=NA
DB_data_clean_0.52$rel_m_InSuc=NA
DB_data_clean_0.52$rel_m_feSuc=NA
DB_data_clean_0.52$rel_m_pFec=NA
DB_data_clean_0.52$rel_m_PS=NA
DB_data_clean_0.52$rel_m_pFec_compl=NA
DB_data_clean_0.52$rel_f_RS=NA
DB_data_clean_0.52$rel_f_prop_RS=NA
DB_data_clean_0.52$rel_f_cMS=NA
DB_data_clean_0.52$rel_f_fec_pMate=NA
DB_data_clean_0.52$rel_m_RS=DB_data_clean_0.52$m_RS/mean(DB_data_clean_0.52$m_RS,na.rm=T)
DB_data_clean_0.52$rel_m_prop_RS=DB_data_clean_0.52$m_prop_RS/mean(DB_data_clean_0.52$m_prop_RS,na.rm=T)
DB_data_clean_0.52$rel_m_cMS=DB_data_clean_0.52$m_cMS/mean(DB_data_clean_0.52$m_cMS,na.rm=T)
DB_data_clean_0.52$rel_m_InSuc=DB_data_clean_0.52$m_InSuc/mean(DB_data_clean_0.52$m_InSuc,na.rm=T)
DB_data_clean_0.52$rel_m_feSuc=DB_data_clean_0.52$m_feSuc/mean(DB_data_clean_0.52$m_feSuc,na.rm=T)
DB_data_clean_0.52$rel_m_pFec=DB_data_clean_0.52$m_pFec/mean(DB_data_clean_0.52$m_pFec,na.rm=T)
DB_data_clean_0.52$rel_m_PS=DB_data_clean_0.52$m_PS/mean(DB_data_clean_0.52$m_PS,na.rm=T)
DB_data_clean_0.52$rel_m_pFec_compl=DB_data_clean_0.52$m_pFec_compl/mean(DB_data_clean_0.52$m_pFec_compl,na.rm=T)
DB_data_clean_0.52$rel_f_RS=DB_data_clean_0.52$f_RS/mean(DB_data_clean_0.52$f_RS,na.rm=T)
DB_data_clean_0.52$rel_f_prop_RS=DB_data_clean_0.52$f_prop_RS/mean(DB_data_clean_0.52$f_prop_RS,na.rm=T)
DB_data_clean_0.52$rel_f_cMS=DB_data_clean_0.52$f_cMS/mean(DB_data_clean_0.52$f_cMS,na.rm=T)
DB_data_clean_0.52$rel_f_fec_pMate=DB_data_clean_0.52$f_fec_pMate/mean(DB_data_clean_0.52$f_fec_pMate,na.rm=T)
# Small group + small Area
DB_data_clean_0.67=DB_data_clean[DB_data_clean$Treatment=='D = 0.67',]
#Relativize data
DB_data_clean_0.67$rel_m_RS=NA
DB_data_clean_0.67$rel_m_prop_RS=NA
DB_data_clean_0.67$rel_m_cMS=NA
DB_data_clean_0.67$rel_m_InSuc=NA
DB_data_clean_0.67$rel_m_feSuc=NA
DB_data_clean_0.67$rel_m_pFec=NA
DB_data_clean_0.67$rel_m_PS=NA
DB_data_clean_0.67$rel_m_pFec_compl=NA
DB_data_clean_0.67$rel_f_RS=NA
DB_data_clean_0.67$rel_f_prop_RS=NA
DB_data_clean_0.67$rel_f_cMS=NA
DB_data_clean_0.67$rel_f_fec_pMate=NA
DB_data_clean_0.67$rel_m_RS=DB_data_clean_0.67$m_RS/mean(DB_data_clean_0.67$m_RS,na.rm=T)
DB_data_clean_0.67$rel_m_prop_RS=DB_data_clean_0.67$m_prop_RS/mean(DB_data_clean_0.67$m_prop_RS,na.rm=T)
DB_data_clean_0.67$rel_m_cMS=DB_data_clean_0.67$m_cMS/mean(DB_data_clean_0.67$m_cMS,na.rm=T)
DB_data_clean_0.67$rel_m_InSuc=DB_data_clean_0.67$m_InSuc/mean(DB_data_clean_0.67$m_InSuc,na.rm=T)
DB_data_clean_0.67$rel_m_feSuc=DB_data_clean_0.67$m_feSuc/mean(DB_data_clean_0.67$m_feSuc,na.rm=T)
DB_data_clean_0.67$rel_m_pFec=DB_data_clean_0.67$m_pFec/mean(DB_data_clean_0.67$m_pFec,na.rm=T)
DB_data_clean_0.67$rel_m_PS=DB_data_clean_0.67$m_PS/mean(DB_data_clean_0.67$m_PS,na.rm=T)
DB_data_clean_0.67$rel_m_pFec_compl=DB_data_clean_0.67$m_pFec_compl/mean(DB_data_clean_0.67$m_pFec_compl,na.rm=T)
DB_data_clean_0.67$rel_f_RS=DB_data_clean_0.67$f_RS/mean(DB_data_clean_0.67$f_RS,na.rm=T)
DB_data_clean_0.67$rel_f_prop_RS=DB_data_clean_0.67$f_prop_RS/mean(DB_data_clean_0.67$f_prop_RS,na.rm=T)
DB_data_clean_0.67$rel_f_cMS=DB_data_clean_0.67$f_cMS/mean(DB_data_clean_0.67$f_cMS,na.rm=T)
DB_data_clean_0.67$rel_f_fec_pMate=DB_data_clean_0.67$f_fec_pMate/mean(DB_data_clean_0.67$f_fec_pMate,na.rm=T)
# Large group + small Area
DB_data_clean_1.33=DB_data_clean[DB_data_clean$Treatment=='D = 1.33',]
#Relativize data
DB_data_clean_1.33$rel_m_RS=NA
DB_data_clean_1.33$rel_m_prop_RS=NA
DB_data_clean_1.33$rel_m_cMS=NA
DB_data_clean_1.33$rel_m_InSuc=NA
DB_data_clean_1.33$rel_m_feSuc=NA
DB_data_clean_1.33$rel_m_pFec=NA
DB_data_clean_1.33$rel_m_PS=NA
DB_data_clean_1.33$rel_m_pFec_compl=NA
DB_data_clean_1.33$rel_f_RS=NA
DB_data_clean_1.33$rel_f_prop_RS=NA
DB_data_clean_1.33$rel_f_cMS=NA
DB_data_clean_1.33$rel_f_fec_pMate=NA
DB_data_clean_1.33$rel_m_RS=DB_data_clean_1.33$m_RS/mean(DB_data_clean_1.33$m_RS,na.rm=T)
DB_data_clean_1.33$rel_m_prop_RS=DB_data_clean_1.33$m_prop_RS/mean(DB_data_clean_1.33$m_prop_RS,na.rm=T)
DB_data_clean_1.33$rel_m_cMS=DB_data_clean_1.33$m_cMS/mean(DB_data_clean_1.33$m_cMS,na.rm=T)
DB_data_clean_1.33$rel_m_InSuc=DB_data_clean_1.33$m_InSuc/mean(DB_data_clean_1.33$m_InSuc,na.rm=T)
DB_data_clean_1.33$rel_m_feSuc=DB_data_clean_1.33$m_feSuc/mean(DB_data_clean_1.33$m_feSuc,na.rm=T)
DB_data_clean_1.33$rel_m_pFec=DB_data_clean_1.33$m_pFec/mean(DB_data_clean_1.33$m_pFec,na.rm=T)
DB_data_clean_1.33$rel_m_PS=DB_data_clean_1.33$m_PS/mean(DB_data_clean_1.33$m_PS,na.rm=T)
DB_data_clean_1.33$rel_m_pFec_compl=DB_data_clean_1.33$m_pFec_compl/mean(DB_data_clean_1.33$m_pFec_compl,na.rm=T)
DB_data_clean_1.33$rel_f_RS=DB_data_clean_1.33$f_RS/mean(DB_data_clean_1.33$f_RS,na.rm=T)
DB_data_clean_1.33$rel_f_prop_RS=DB_data_clean_1.33$f_prop_RS/mean(DB_data_clean_1.33$f_prop_RS,na.rm=T)
DB_data_clean_1.33$rel_f_cMS=DB_data_clean_1.33$f_cMS/mean(DB_data_clean_1.33$f_cMS,na.rm=T)
DB_data_clean_1.33$rel_f_fec_pMate=DB_data_clean_1.33$f_fec_pMate/mean(DB_data_clean_1.33$f_fec_pMate,na.rm=T)
# Set colors for figures
colpal=brewer.pal(4, 'Dark2')
colpal2=brewer.pal(3, 'Set1')
colpal3=brewer.pal(4, 'Paired')
slava_ukrajini=(c('#0057B8','#FFD700'))
colorESEB=c('#01519c','#ffdf33')
colorESEB2=c('#1DA1F2','#ffec69')
# Merge data according to treatment #### -> Reduce treatments to area and population size
#Area
DB_data_clean_Large_area=rbind(DB_data_clean_0.26,DB_data_clean_0.52)
DB_data_clean_Small_area=rbind(DB_data_clean_0.67,DB_data_clean_1.33)
#Population size
DB_data_clean_Small_pop=rbind(DB_data_clean_0.26,DB_data_clean_0.67)
DB_data_clean_Large_pop=rbind(DB_data_clean_0.52,DB_data_clean_1.33)
# Merge data according to treatment full data set #### -> Reduce treatments to area and population size
DB_data_0.26=DB_data[DB_data$Treatment=='D = 0.26',]
DB_data_0.52=DB_data[DB_data$Treatment=='D = 0.52',]
DB_data_0.67=DB_data[DB_data$Treatment=='D = 0.67',]
DB_data_1.33=DB_data[DB_data$Treatment=='D = 1.33',]
#Area
DB_data_Large_area_full=rbind(DB_data_0.26,DB_data_0.52)
DB_data_Small_area_full=rbind(DB_data_0.67,DB_data_1.33)
#Population size
DB_data_Small_pop_full=rbind(DB_data_0.26,DB_data_0.67)
DB_data_Large_pop_full=rbind(DB_data_0.52,DB_data_1.33)Treatment effects
Mating behaviour
We first tested the effect that the treatments (group size and area)
had on the mating behaviour of focal beetles.
Behavioural
variables:
- Number of matings
- Number of different mating
partners (mating success)
- Mating duration in seconds
- Mating
encounters (mating number + mating attempts)
- Proportion of
successful matings (mating number/mating number + mating attempts)
### Number of matings Figure: Number of matings
# Figure: Number of matings
# Treatment: Group size
p1<-ggplot(DB_data, aes(x=Sex, y=as.numeric(Matings_number),fill=Gr_size, col=Gr_size)) +
geom_point(position=position_jitterdodge(jitter.width=0.6,jitter.height = 0,dodge.width=0.9),shape=19, alpha=0.75, size = 2)+
stat_summary(fun.min = function(z) { quantile(z,0.25) },
fun.max = function(z) { quantile(z,0.75) },
fun = mean,position=position_dodge(.9), size = 0.9,col='black',show.legend = F)+
scale_color_manual(values=c(colorESEB[1],colorESEB[2]),name = "Treatment", labels = c('Small group','Large group'))+
scale_fill_manual(values=c(colorESEB[1],colorESEB[2]),name = "Treatment", labels = c('Small group','Large group'))+
xlab('Sex')+ylab("Number of matings")+ggtitle('')+ theme(plot.title = element_text(hjust = 0.5))+
scale_x_discrete(labels = c('Female','Male'),drop=FALSE)+ ylim(0,12)+labs(tag = "A")+
annotate("text",label='n =',x=0.55,y=12,size=4)+
annotate("text",label='91',x=0.78,y=12,size=4)+
annotate("text",label='74',x=1.23,y=12,size=4)+
annotate("text",label='85',x=1.78,y=12,size=4)+
annotate("text",label='85',x=2.23,y=12,size=4)+
theme(panel.border = element_blank(),
plot.margin = margin(0.15,2.2,0,0.2,"cm"),
plot.title = element_text(hjust = 0.5),
panel.background = element_blank(),
legend.key=element_blank(),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
legend.position = c(1.08, 0.8),
plot.tag.position=c(0.01,0.98),
legend.title = element_blank(),
legend.text = element_text(colour="black", size=10),
axis.line.x = element_line(colour = "black", size = 1),
axis.line.y = element_line(colour = "black", size = 1),
axis.text.x = element_text(face="plain", color="black", size=16, angle=0),
axis.text.y = element_text(face="plain", color="black", size=16, angle=0),
axis.title.x = element_text(size=16,face="plain", margin = margin(r=0,10,0,0)),
axis.title.y = element_text(size=16,face="plain", margin = margin(r=10,0,0,0)),
axis.ticks = element_line(size = 1),
axis.ticks.length = unit(.3, "cm"))+
guides(colour = guide_legend(override.aes = list(size=4)))
# Treatment: Area
p1.2<-ggplot(DB_data, aes(x=Sex, y=as.numeric(Matings_number),fill=Area, col=Area)) +
geom_point(position=position_jitterdodge(jitter.width=0.6,jitter.height = 0,dodge.width=0.9),shape=19, alpha=0.75, size = 2)+
stat_summary(fun.min = function(z) { quantile(z,0.25) },
fun.max = function(z) { quantile(z,0.75) },
fun = mean,position=position_dodge(.9), size = 0.9,col='black',show.legend = F)+
scale_color_manual(values=c(colorESEB2[1],colorESEB2[2]),name = "Treatment", labels = c('Large area','Small area'))+
scale_fill_manual(values=c(colorESEB2[1],colorESEB2[2]),name = "Treatment", labels = c('Large area','Small area'))+
xlab('Sex')+ylab("")+ggtitle('')+ theme(plot.title = element_text(hjust = 0.5))+labs(tag = "B")+
scale_x_discrete(labels = c('Female','Male'),drop=FALSE)+ ylim(0,12)+
annotate("text",label='n =',x=0.55,y=12,size=4)+
annotate("text",label='86',x=0.78,y=12,size=4)+
annotate("text",label='79',x=1.23,y=12,size=4)+
annotate("text",label='88',x=1.78,y=12,size=4)+
annotate("text",label='82',x=2.23,y=12,size=4)+
theme(panel.border = element_blank(),
plot.margin = margin(0.15,2.2,0,0,"cm"),
plot.title = element_text(hjust = 0.5),
panel.background = element_blank(),
legend.key=element_blank(),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
legend.position = c(1.08, 0.8),
plot.tag.position=c(0.01,0.98),
legend.title = element_blank(),
legend.text = element_text(colour="black", size=10),
axis.line.x = element_line(colour = "black", size = 1),
axis.line.y = element_line(colour = "black", size = 1),
axis.text.x = element_text(face="plain", color="black", size=16, angle=0),
axis.text.y = element_text(face="plain", color="black", size=16, angle=0),
axis.title.x = element_text(size=16,face="plain", margin = margin(r=0,10,0,0)),
axis.title.y = element_text(size=16,face="plain", margin = margin(r=10,0,0,0)),
axis.ticks = element_line(size = 1),
axis.ticks.length = unit(.3, "cm"))+
guides(colour = guide_legend(override.aes = list(size=4)))
grid.arrange(grobs = list(p1,p1.2), nrow = 1,ncol=2, widths=c(2.3, 2.3))
Figure 1: Effects of group size (A) and area treatment (B) on the number
of matings of female and male focals. Black bars indicate means and
quartile borders.
Statistical models: Number of matings (quasi-Poisson
GLM)
Effect of group size on number of matings in females.
mod1.1=glm(f_TotMatings~Gr_size,data=DB_data,family = quasipoisson)
summary(mod1.1)
Call:
glm(formula = f_TotMatings ~ Gr_size, family = quasipoisson,
data = DB_data)
Deviance Residuals:
Min 1Q Median 3Q Max
-1.4425 -1.0911 -0.3395 0.3940 3.3970
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.16475 0.07523 15.483 <2e-16 ***
Gr_sizeLG -0.24080 0.12830 -1.877 0.0628 .
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
(Dispersion parameter for quasipoisson family taken to be 1.41487)
Null deviance: 164.51 on 129 degrees of freedom
Residual deviance: 159.41 on 128 degrees of freedom
(148 observations deleted due to missingness)
AIC: NA
Number of Fisher Scoring iterations: 5
Effect of group size on number of matings in males.
# Sex: Male
# Treatment: Group size
mod1.2=glm(m_TotMatings~Gr_size,data=DB_data,family = quasipoisson)
summary(mod1.2)
Call:
glm(formula = m_TotMatings ~ Gr_size, family = quasipoisson,
data = DB_data)
Deviance Residuals:
Min 1Q Median 3Q Max
-1.4492 -0.9859 -0.2238 0.4178 3.0179
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.16913 0.06702 17.445 < 2e-16 ***
Gr_sizeLG -0.32183 0.10258 -3.137 0.00206 **
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
(Dispersion parameter for quasipoisson family taken to be 1.055494)
Null deviance: 150.57 on 147 degrees of freedom
Residual deviance: 140.06 on 146 degrees of freedom
(130 observations deleted due to missingness)
AIC: NA
Number of Fisher Scoring iterations: 5
Effect of area on number of matings in females.
# Sex: Female
# Treatment: Area
mod1.3=glm(f_TotMatings~Area,data=DB_data,family = quasipoisson)
summary(mod1.3)
Call:
glm(formula = f_TotMatings ~ Area, family = quasipoisson, data = DB_data)
Deviance Residuals:
Min 1Q Median 3Q Max
-1.3980 -1.1952 -0.4536 0.4041 3.7722
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.13548 0.08198 13.85 <2e-16 ***
AreaSmall -0.13785 0.12650 -1.09 0.278
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
(Dispersion parameter for quasipoisson family taken to be 1.485185)
Null deviance: 164.51 on 129 degrees of freedom
Residual deviance: 162.74 on 128 degrees of freedom
(148 observations deleted due to missingness)
AIC: NA
Number of Fisher Scoring iterations: 5
Effect of area on number of matings in males.
# Sex: Male
# Treatment: Area
mod1.4=glm(m_TotMatings~Area,data=DB_data,family = quasipoisson)
summary(mod1.4)
Call:
glm(formula = m_TotMatings ~ Area, family = quasipoisson, data = DB_data)
Deviance Residuals:
Min 1Q Median 3Q Max
-1.2727 -1.1805 -0.4376 0.1891 3.4165
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.98739 0.07560 13.061 <2e-16 ***
AreaSmall 0.06382 0.10665 0.598 0.55
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
(Dispersion parameter for quasipoisson family taken to be 1.165813)
Null deviance: 150.57 on 147 degrees of freedom
Residual deviance: 150.15 on 146 degrees of freedom
(130 observations deleted due to missingness)
AIC: NA
Number of Fisher Scoring iterations: 5Number of mating partners
Figure: Number of mating partners (mating success)
# Figure: Number of mating partners (mating success)
# Treatment: Group size
p2<-ggplot(DB_data, aes(x=Sex, y=as.numeric(MatingPartners_number),fill=Gr_size, col=Gr_size)) +
geom_point(position=position_jitterdodge(jitter.width=0.6,jitter.height = 0,dodge.width=0.9),shape=19, alpha=0.75, size = 2)+
stat_summary(fun.min = function(z) { quantile(z,0.25) },
fun.max = function(z) { quantile(z,0.75) },
fun = mean,position=position_dodge(.9), size = 0.9,col='black',show.legend = F)+
scale_color_manual(values=c(colorESEB[1],colorESEB[2]),name = "Treatment", labels = c('Small group','Large group'))+
scale_fill_manual(values=c(colorESEB[1],colorESEB[2]),name = "Treatment", labels = c('Small group','Large group'))+
xlab('Sex')+ylab("Number of partners")+ggtitle('')+ theme(plot.title = element_text(hjust = 0.5))+
scale_x_discrete(labels = c('Female','Male'),drop=FALSE)+ ylim(0,5.4)+labs(tag = "A")+
annotate("text",label='n =',x=0.55,y=5.4,size=4)+
annotate("text",label='91',x=0.78,y=5.4,size=4)+
annotate("text",label='74',x=1.23,y=5.4,size=4)+
annotate("text",label='85',x=1.78,y=5.4,size=4)+
annotate("text",label='85',x=2.23,y=5.4,size=4)+
theme(panel.border = element_blank(),
plot.margin = margin(0,2.2,0,0.2,"cm"),
plot.title = element_text(hjust = 0.5),
panel.background = element_blank(),
legend.key=element_blank(),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
legend.position = c(1.08, 0.8),
plot.tag.position=c(0.01,0.98),
legend.title = element_blank(),
legend.text = element_text(colour="black", size=10),
axis.line.x = element_line(colour = "black", size = 1),
axis.line.y = element_line(colour = "black", size = 1),
axis.text.x = element_text(face="plain", color="black", size=16, angle=0),
axis.text.y = element_text(face="plain", color="black", size=16, angle=0),
axis.title.x = element_text(size=16,face="plain", margin = margin(r=0,10,0,0)),
axis.title.y = element_text(size=16,face="plain", margin = margin(r=10,0,0,0)),
axis.ticks = element_line(size = 1),
axis.ticks.length = unit(.3, "cm"))+
guides(colour = guide_legend(override.aes = list(size=4)))
# Treatment: Area
p2.2<-ggplot(DB_data, aes(x=Sex, y=as.numeric(MatingPartners_number),fill=Area, col=Area)) +
geom_point(position=position_jitterdodge(jitter.width=0.6,jitter.height = 0,dodge.width=0.9),shape=19, alpha=0.75, size = 2)+
stat_summary(fun.min = function(z) { quantile(z,0.25) },
fun.max = function(z) { quantile(z,0.75) },
fun = mean,position=position_dodge(.9), size = 0.9,col='black',show.legend = F)+
scale_color_manual(values=c(colorESEB2[1],colorESEB2[2]),name = "Treatment", labels = c('Large area','Small area'))+
scale_fill_manual(values=c(colorESEB2[1],colorESEB2[2]),name = "Treatment", labels = c('Large area','Small area'))+
xlab('Sex')+ylab("")+ggtitle('')+ theme(plot.title = element_text(hjust = 0.5))+
scale_x_discrete(labels = c('Female','Male'),drop=FALSE)+ ylim(0,5.4)+labs(tag = "B")+
annotate("text",label='n =',x=0.55,y=5.4,size=4)+
annotate("text",label='86',x=0.78,y=5.4,size=4)+
annotate("text",label='79',x=1.23,y=5.4,size=4)+
annotate("text",label='88',x=1.78,y=5.4,size=4)+
annotate("text",label='82',x=2.23,y=5.4,size=4)+
theme(panel.border = element_blank(),
plot.margin = margin(0,2.2,0,0,"cm"),
plot.title = element_text(hjust = 0.5),
panel.background = element_blank(),
legend.key=element_blank(),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
legend.position = c(1.08, 0.8),
plot.tag.position=c(0.01,0.98),
legend.title = element_blank(),
legend.text = element_text(colour="black", size=10),
axis.line.x = element_line(colour = "black", size = 1),
axis.line.y = element_line(colour = "black", size = 1),
axis.text.x = element_text(face="plain", color="black", size=16, angle=0),
axis.text.y = element_text(face="plain", color="black", size=16, angle=0),
axis.title.x = element_text(size=16,face="plain", margin = margin(r=0,10,0,0)),
axis.title.y = element_text(size=16,face="plain", margin = margin(r=10,0,0,0)),
axis.ticks = element_line(size = 1),
axis.ticks.length = unit(.3, "cm"))+
guides(colour = guide_legend(override.aes = list(size=4)))
plot2<-grid.arrange(grobs = list(p2,p2.2), nrow = 1,ncol=2, widths=c(2.3, 2.3))
Figure 2: Effects of group size (A) and area treatment (B) on the number
of mating partners of female and male focals. Black bars indicate means
and quartile borders.
Statistical models: Number of mating partners (quasi-Poisson
GLM)
Effect of group size on number of mating partners in
females.
# Statistical models: Number of mating partners (quasi-Poisson GLM)
# Sex: Female
# Treatment: Group size
mod2.1=glm(f_cMS~Gr_size,data=DB_data,family = quasipoisson)
summary(mod2.1)
Call:
glm(formula = f_cMS ~ Gr_size, family = quasipoisson, data = DB_data)
Deviance Residuals:
Min 1Q Median 3Q Max
-0.80779 -0.67409 0.04713 0.12129 1.74624
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.60614 0.05211 11.631 <2e-16 ***
Gr_sizeLG 0.10606 0.07987 1.328 0.187
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
(Dispersion parameter for quasipoisson family taken to be 0.3883379)
Null deviance: 49.041 on 129 degrees of freedom
Residual deviance: 48.360 on 128 degrees of freedom
(148 observations deleted due to missingness)
AIC: NA
Number of Fisher Scoring iterations: 4
Effect of group size on number of mating partners in males.
# Sex: Male
# Treatment: Group size
mod2.2=glm(m_cMS~Gr_size,data=DB_data,family = quasipoisson)
summary(mod2.2)
Call:
glm(formula = m_cMS ~ Gr_size, family = quasipoisson, data = DB_data)
Deviance Residuals:
Min 1Q Median 3Q Max
-0.69644 -0.61944 0.09646 0.18208 1.89372
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.56157 0.05425 10.352 <2e-16 ***
Gr_sizeLG 0.06258 0.07505 0.834 0.406
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
(Dispersion parameter for quasipoisson family taken to be 0.3766698)
Null deviance: 52.151 on 147 degrees of freedom
Residual deviance: 51.889 on 146 degrees of freedom
(130 observations deleted due to missingness)
AIC: NA
Number of Fisher Scoring iterations: 4
Effect of area on number of mating partners in females.
# Sex: Female
# Treatment: Area
mod2.3=glm(f_cMS~Area,data=DB_data,family = quasipoisson)
summary(mod2.3)
Call:
glm(formula = f_cMS ~ Area, family = quasipoisson, data = DB_data)
Deviance Residuals:
Min 1Q Median 3Q Max
-0.75624 -0.69493 0.03009 0.09814 1.89572
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.67179 0.05356 12.542 <2e-16 ***
AreaSmall -0.04885 0.08059 -0.606 0.545
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
(Dispersion parameter for quasipoisson family taken to be 0.3988074)
Null deviance: 49.041 on 129 degrees of freedom
Residual deviance: 48.894 on 128 degrees of freedom
(148 observations deleted due to missingness)
AIC: NA
Number of Fisher Scoring iterations: 4
Effect of area on number of mating partners in males.
# Sex: Male
# Treatment: Area
mod2.4=glm(m_cMS~Area,data=DB_data,family = quasipoisson)
summary(mod2.4)
Call:
glm(formula = m_cMS ~ Area, family = quasipoisson, data = DB_data)
Deviance Residuals:
Min 1Q Median 3Q Max
-0.6711 -0.6458 0.1246 0.1528 1.9274
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.60374 0.05228 11.549 <2e-16 ***
AreaSmall -0.02059 0.07535 -0.273 0.785
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
(Dispersion parameter for quasipoisson family taken to be 0.379889)
Null deviance: 52.151 on 147 degrees of freedom
Residual deviance: 52.123 on 146 degrees of freedom
(130 observations deleted due to missingness)
AIC: NA
Number of Fisher Scoring iterations: 4Mating duration
Figure: Mating duration in seconds
#Figure: Mating duration in seconds
# Treatment: Group size
p3<-ggplot(DB_data, aes(x=Sex, y=as.numeric(MatingDuration_av),fill=Gr_size, col=Gr_size)) +
geom_point(position=position_jitterdodge(jitter.width=0.6,jitter.height = 0,dodge.width=0.9),shape=19, alpha=0.75, size = 2)+
stat_summary(fun.min = function(z) { quantile(z,0.25) },
fun.max = function(z) { quantile(z,0.75) },
fun = mean,position=position_dodge(.9), size = 0.9,col='black',show.legend = F)+
scale_color_manual(values=c(colorESEB[1],colorESEB[2]),name = "Treatment", labels = c('Small group','Large group'))+
scale_fill_manual(values=c(colorESEB[1],colorESEB[2]),name = "Treatment", labels = c('Small group','Large group'))+
xlab('Sex')+ylab("Mean mating duration")+ggtitle('')+ theme(plot.title = element_text(hjust = 0.5))+
scale_x_discrete(labels = c('Female','Male'),drop=FALSE)+ ylim(0,390)+labs(tag = "A")+
annotate("text",label='n =',x=0.55,y=390,size=4)+
annotate("text",label='91',x=0.78,y=390,size=4)+
annotate("text",label='74',x=1.23,y=390,size=4)+
annotate("text",label='85',x=1.78,y=390,size=4)+
annotate("text",label='85',x=2.23,y=390,size=4)+
theme(panel.border = element_blank(),
plot.margin = margin(0,2.2,0.15,0.2,"cm"),
plot.title = element_text(hjust = 0.5),
panel.background = element_blank(),
legend.key=element_blank(),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
legend.position = c(1.08, 0.8),
plot.tag.position=c(0.01,0.98),
legend.title = element_blank(),
legend.text = element_text(colour="black", size=10),
axis.line.x = element_line(colour = "black", size = 1),
axis.line.y = element_line(colour = "black", size = 1),
axis.text.x = element_text(face="plain", color="black", size=16, angle=0),
axis.text.y = element_text(face="plain", color="black", size=16, angle=0),
axis.title.x = element_text(size=16,face="plain", margin = margin(r=0,10,0,0)),
axis.title.y = element_text(size=16,face="plain", margin = margin(r=10,0,0,0)),
axis.ticks = element_line(size = 1),
axis.ticks.length = unit(.3, "cm"))+
guides(colour = guide_legend(override.aes = list(size=4)))
# Treatment: Area
p3.2<-ggplot(DB_data, aes(x=Sex, y=as.numeric(MatingDuration_av),fill=Area, col=Area)) +
geom_point(position=position_jitterdodge(jitter.width=0.6,jitter.height = 0,dodge.width=0.9),shape=19, alpha=0.75, size = 2)+
stat_summary(fun.min = function(z) { quantile(z,0.25) },
fun.max = function(z) { quantile(z,0.75) },
fun = mean,position=position_dodge(.9), size = 0.9,col='black',show.legend = F)+
scale_color_manual(values=c(colorESEB2[1],colorESEB2[2]),name = "Treatment", labels = c('Large area','Small area'))+
scale_fill_manual(values=c(colorESEB2[1],colorESEB2[2]),name = "Treatment", labels = c('Large area','Small area'))+
xlab('Sex')+ylab("")+ggtitle('')+ theme(plot.title = element_text(hjust = 0.5))+
scale_x_discrete(labels = c('Female','Male'),drop=FALSE)+ ylim(0,390)+labs(tag = "B")+
annotate("text",label='n =',x=0.55,y=390,size=4)+
annotate("text",label='86',x=0.78,y=390,size=4)+
annotate("text",label='79',x=1.23,y=390,size=4)+
annotate("text",label='88',x=1.78,y=390,size=4)+
annotate("text",label='82',x=2.23,y=390,size=4)+
theme(panel.border = element_blank(),
plot.margin = margin(0,2.2,0.15,0,"cm"),
plot.title = element_text(hjust = 0.5),
panel.background = element_blank(),
legend.key=element_blank(),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
legend.position = c(1.08, 0.8),
plot.tag.position=c(0.01,0.98),
legend.title = element_blank(),
legend.text = element_text(colour="black", size=10),
axis.line.x = element_line(colour = "black", size = 1),
axis.line.y = element_line(colour = "black", size = 1),
axis.text.x = element_text(face="plain", color="black", size=16, angle=0),
axis.text.y = element_text(face="plain", color="black", size=16, angle=0),
axis.title.x = element_text(size=16,face="plain", margin = margin(r=0,10,0,0)),
axis.title.y = element_text(size=16,face="plain", margin = margin(r=10,0,0,0)),
axis.ticks = element_line(size = 1),
axis.ticks.length = unit(.3, "cm"))+
guides(colour = guide_legend(override.aes = list(size=4)))
plot3<-grid.arrange(grobs = list(p3,p3.2), nrow = 1,ncol=2, widths=c(2.3, 2.3))
Figure 3: Effects of group size (A) and area treatment (B) on the Mating
duration (in seconds) of female and male focals. Black bars indicate
means and quartile borders.
Statistical models: Mating duration (Gaussian GLM)
Effect
of group size on mating duration in females.
# Statistical models: Mating duration (Gaussian GLM)
# Sex: Female
# Treatment: Group size
mod3.1=glm(f_MatingDuration_av~Gr_size,data=DB_data,family = gaussian)
summary(mod3.1)
Call:
glm(formula = f_MatingDuration_av ~ Gr_size, family = gaussian,
data = DB_data)
Deviance Residuals:
Min 1Q Median 3Q Max
-41.63 -20.35 -6.36 13.62 260.37
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 77.626 3.963 19.589 <2e-16 ***
Gr_sizeLG -8.203 6.266 -1.309 0.193
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
(Dispersion parameter for gaussian family taken to be 1224.805)
Null deviance: 158874 on 129 degrees of freedom
Residual deviance: 156775 on 128 degrees of freedom
(148 observations deleted due to missingness)
AIC: 1297.3
Number of Fisher Scoring iterations: 2
Effect of group size on mating duration in males.
# Sex: Male
# Treatment: Group size
mod3.2=glm(m_MatingDuration_av~Gr_size,data=DB_data,family = gaussian)
summary(mod3.2)
Call:
glm(formula = m_MatingDuration_av ~ Gr_size, family = gaussian,
data = DB_data)
Deviance Residuals:
Min 1Q Median 3Q Max
-65.622 -21.472 -10.327 9.798 296.048
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 79.202 4.594 17.240 <2e-16 ***
Gr_sizeLG -4.250 6.453 -0.659 0.511
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
(Dispersion parameter for gaussian family taken to be 1540.677)
Null deviance: 225607 on 147 degrees of freedom
Residual deviance: 224939 on 146 degrees of freedom
(130 observations deleted due to missingness)
AIC: 1510.3
Number of Fisher Scoring iterations: 2
Effect of area on mating duration in females.
# Sex: Female
# Treatment: Area
mod3.3=glm(f_MatingDuration_av~Area,data=DB_data,family = gaussian)
summary(mod3.3)
Call:
glm(formula = f_MatingDuration_av ~ Area, family = gaussian,
data = DB_data)
Deviance Residuals:
Min 1Q Median 3Q Max
-38.502 -19.592 -7.618 14.121 262.498
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 75.502 4.178 18.070 <2e-16 ***
AreaSmall -2.549 6.202 -0.411 0.682
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
(Dispersion parameter for gaussian family taken to be 1239.57)
Null deviance: 158874 on 129 degrees of freedom
Residual deviance: 158665 on 128 degrees of freedom
(148 observations deleted due to missingness)
AIC: 1298.8
Number of Fisher Scoring iterations: 2
Effect of area on mating duration in males.
# Sex: Male
# Treatment: Area
mod3.4=glm(m_MatingDuration_av~Area,data=DB_data,family = gaussian)
summary(mod3.4)
Call:
glm(formula = m_MatingDuration_av ~ Area, family = gaussian,
data = DB_data)
Deviance Residuals:
Min 1Q Median 3Q Max
-71.872 -21.242 -10.322 8.445 289.798
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 73.112 4.485 16.302 <2e-16 ***
AreaSmall 8.090 6.430 1.258 0.21
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
(Dispersion parameter for gaussian family taken to be 1528.679)
Null deviance: 225607 on 147 degrees of freedom
Residual deviance: 223187 on 146 degrees of freedom
(130 observations deleted due to missingness)
AIC: 1509.2
Number of Fisher Scoring iterations: 2Mating encounters
Figure: Mating encounters (mating number + mating attempts)
# Figure: Mating encounters (mating number + mating attempts)
# Treatment: Group size
p4<-ggplot(DB_data, aes(x=Sex, y=as.numeric(Total_Encounters),fill=Gr_size, col=Gr_size)) +
geom_point(position=position_jitterdodge(jitter.width=0.6,jitter.height = 0,dodge.width=0.9),shape=19, alpha=0.75, size = 2)+
stat_summary(fun.min = function(z) { quantile(z,0.25) },
fun.max = function(z) { quantile(z,0.75) },
fun = mean,position=position_dodge(.9), size = 0.9,col='black',show.legend = F)+
scale_color_manual(values=c(colorESEB[1],colorESEB[2]),name = "Treatment", labels = c('Small group','Large group'))+
scale_fill_manual(values=c(colorESEB[1],colorESEB[2]),name = "Treatment", labels = c('Small group','Large group'))+
xlab('Sex')+ylab("Mating encounters")+ggtitle('')+ theme(plot.title = element_text(hjust = 0.5))+
scale_x_discrete(labels = c('Female','Male'),drop=FALSE)+ ylim(0,33)+labs(tag = "A")+
annotate("text",label='n =',x=0.55,y=33,size=4)+
annotate("text",label='91',x=0.78,y=33,size=4)+
annotate("text",label='74',x=1.23,y=33,size=4)+
annotate("text",label='85',x=1.78,y=33,size=4)+
annotate("text",label='85',x=2.23,y=33,size=4)+
theme(panel.border = element_blank(),
plot.margin = margin(0,2.2,0.15,0.2,"cm"),
plot.title = element_text(hjust = 0.5),
panel.background = element_blank(),
legend.key=element_blank(),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
legend.position = c(1.08, 0.8),
plot.tag.position=c(0.01,0.98),
legend.title = element_blank(),
legend.text = element_text(colour="black", size=10),
axis.line.x = element_line(colour = "black", size = 1),
axis.line.y = element_line(colour = "black", size = 1),
axis.text.x = element_text(face="plain", color="black", size=16, angle=0),
axis.text.y = element_text(face="plain", color="black", size=16, angle=0),
axis.title.x = element_text(size=16,face="plain", margin = margin(r=0,10,0,0)),
axis.title.y = element_text(size=16,face="plain", margin = margin(r=10,0,0,0)),
axis.ticks = element_line(size = 1),
axis.ticks.length = unit(.3, "cm"))+
guides(colour = guide_legend(override.aes = list(size=4)))
# Treatment: Area
p4.2<-ggplot(DB_data, aes(x=Sex, y=as.numeric(Total_Encounters),fill=Area, col=Area)) +
geom_point(position=position_jitterdodge(jitter.width=0.6,jitter.height = 0,dodge.width=0.9),shape=19, alpha=0.75, size = 2)+
stat_summary(fun.min = function(z) { quantile(z,0.25) },
fun.max = function(z) { quantile(z,0.75) },
fun = mean,position=position_dodge(.9), size = 0.9,col='black',show.legend = F)+
scale_color_manual(values=c(colorESEB2[1],colorESEB2[2]),name = "Treatment", labels = c('Large area','Small area'))+
scale_fill_manual(values=c(colorESEB2[1],colorESEB2[2]),name = "Treatment", labels = c('Large area','Small area'))+
xlab('Sex')+ylab("")+ggtitle('')+ theme(plot.title = element_text(hjust = 0.5))+
scale_x_discrete(labels = c('Female','Male'),drop=FALSE)+ ylim(0,33)+labs(tag = "B")+
annotate("text",label='n =',x=0.55,y=33,size=4)+
annotate("text",label='86',x=0.78,y=33,size=4)+
annotate("text",label='79',x=1.23,y=33,size=4)+
annotate("text",label='88',x=1.78,y=33,size=4)+
annotate("text",label='82',x=2.23,y=33,size=4)+
theme(panel.border = element_blank(),
plot.margin = margin(0,2.2,0.15,0,"cm"),
plot.title = element_text(hjust = 0.5),
panel.background = element_blank(),
legend.key=element_blank(),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
legend.position = c(1.08, 0.8),
plot.tag.position=c(0.01,0.98),
legend.title = element_blank(),
legend.text = element_text(colour="black", size=10),
axis.line.x = element_line(colour = "black", size = 1),
axis.line.y = element_line(colour = "black", size = 1),
axis.text.x = element_text(face="plain", color="black", size=16, angle=0),
axis.text.y = element_text(face="plain", color="black", size=16, angle=0),
axis.title.x = element_text(size=16,face="plain", margin = margin(r=0,10,0,0)),
axis.title.y = element_text(size=16,face="plain", margin = margin(r=10,0,0,0)),
axis.ticks = element_line(size = 1),
axis.ticks.length = unit(.3, "cm"))+
guides(colour = guide_legend(override.aes = list(size=4)))
plot4<-grid.arrange(grobs = list(p4,p4.2), nrow = 1,ncol=2, widths=c(2.3, 2.3))
Figure 4: Effects of group size (A) and area treatment (B) on the number
of mating encounters (mating number + mating attempts) of female and
male focals. Black bars indicate means and quartile borders.
Statistical models: Mating encounters (Gaussian GLM)
Effect of group size on mating encounters in females.
# Statistical models: Mating encounters (Gaussian GLM)
# Sex: Female
# Treatment: Group size
mod4.1=glm(f_Total_Encounters~Gr_size,data=DB_data,family = gaussian)
summary(mod4.1)
Call:
glm(formula = f_Total_Encounters ~ Gr_size, family = gaussian,
data = DB_data)
Deviance Residuals:
Min 1Q Median 3Q Max
-8.3718 -2.9038 -0.9038 2.6282 15.6282
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 9.3718 0.4727 19.827 < 2e-16 ***
Gr_sizeLG -2.4679 0.7474 -3.302 0.00124 **
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
(Dispersion parameter for gaussian family taken to be 17.42763)
Null deviance: 2420.8 on 129 degrees of freedom
Residual deviance: 2230.7 on 128 degrees of freedom
(148 observations deleted due to missingness)
AIC: 744.46
Number of Fisher Scoring iterations: 2
Effect of group size on mating encounters in males.
# Sex: Male
# Treatment: Group size
mod4.2=glm(m_Total_Encounters~Gr_size,data=DB_data,family = gaussian)
summary(mod4.2)
Call:
glm(formula = m_Total_Encounters ~ Gr_size, family = gaussian,
data = DB_data)
Deviance Residuals:
Min 1Q Median 3Q Max
-7.726 -3.453 -0.726 1.728 21.274
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 10.7260 0.5699 18.822 < 2e-16 ***
Gr_sizeLG -3.2727 0.8005 -4.088 7.15e-05 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
(Dispersion parameter for gaussian family taken to be 23.70621)
Null deviance: 3857.3 on 147 degrees of freedom
Residual deviance: 3461.1 on 146 degrees of freedom
(130 observations deleted due to missingness)
AIC: 892.52
Number of Fisher Scoring iterations: 2
Effect of area on mating encounters in females.
# Sex: Female
# Treatment: Area
mod4.3=glm(f_Total_Encounters~Area,data=DB_data,family = gaussian)
summary(mod4.3)
Call:
glm(formula = f_Total_Encounters ~ Area, family = gaussian, data = DB_data)
Deviance Residuals:
Min 1Q Median 3Q Max
-7.4366 -3.3220 -0.4366 2.5634 16.6780
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 8.4366 0.5161 16.35 <2e-16 ***
AreaSmall -0.1146 0.7660 -0.15 0.881
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
(Dispersion parameter for gaussian family taken to be 18.90895)
Null deviance: 2420.8 on 129 degrees of freedom
Residual deviance: 2420.3 on 128 degrees of freedom
(148 observations deleted due to missingness)
AIC: 755.06
Number of Fisher Scoring iterations: 2
Effect of area on mating encounters in males.
# Sex: Male
# Treatment: Area
mod4.4=glm(m_Total_Encounters~Area,data=DB_data,family = gaussian)
summary(mod4.4)
Call:
glm(formula = m_Total_Encounters ~ Area, family = gaussian, data = DB_data)
Deviance Residuals:
Min 1Q Median 3Q Max
-7.556 -3.556 -1.080 2.395 22.444
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 8.6053 0.5870 14.659 <2e-16 ***
AreaSmall 0.9503 0.8417 1.129 0.261
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
(Dispersion parameter for gaussian family taken to be 26.19134)
Null deviance: 3857.3 on 147 degrees of freedom
Residual deviance: 3823.9 on 146 degrees of freedom
(130 observations deleted due to missingness)
AIC: 907.28
Number of Fisher Scoring iterations: 2Proportion of successful matings
Figure: Proportion of successful matings (mating number/mating number + mating attempts)
# Figure: Proportion of successful matings (mating number/mating number + mating attempts)
# Treatment: Group size
p5<-ggplot(DB_data, aes(x=Sex, y=as.numeric(Prop_MS),fill=Gr_size, col=Gr_size)) +
geom_point(position=position_jitterdodge(jitter.width=0.6,jitter.height = 0,dodge.width=0.9),shape=19, alpha=0.75, size = 2)+
stat_summary(fun.min = function(z) { quantile(z,0.25) },
fun.max = function(z) { quantile(z,0.75) },
fun = mean,position=position_dodge(.9), size = 0.9,col='black',show.legend = F)+
scale_color_manual(values=c(colorESEB[1],colorESEB[2]),name = "Treatment", labels = c('Small group','Large group'))+
scale_fill_manual(values=c(colorESEB[1],colorESEB[2]),name = "Treatment", labels = c('Small group','Large group'))+
xlab('Sex')+ylab("Proportion successful matings")+ggtitle('')+ theme(plot.title = element_text(hjust = 0.5))+
scale_x_discrete(labels = c('Female','Male'),drop=FALSE)+ ylim(0,1.1)+labs(tag = "A")+
annotate("text",label='n =',x=0.55,y=1.1,size=4)+
annotate("text",label='91',x=0.78,y=1.1,size=4)+
annotate("text",label='74',x=1.23,y=1.1,size=4)+
annotate("text",label='85',x=1.78,y=1.1,size=4)+
annotate("text",label='85',x=2.23,y=1.1,size=4)+
theme(panel.border = element_blank(),
plot.margin = margin(0.15,2.2,0,0.2,"cm"),
plot.title = element_text(hjust = 0.5),
panel.background = element_blank(),
legend.key=element_blank(),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
legend.position = c(1.08, 0.8),
plot.tag.position=c(0.01,0.98),
legend.title = element_blank(),
legend.text = element_text(colour="black", size=10),
axis.line.x = element_line(colour = "black", size = 1),
axis.line.y = element_line(colour = "black", size = 1),
axis.text.x = element_text(face="plain", color="black", size=16, angle=0),
axis.text.y = element_text(face="plain", color="black", size=16, angle=0),
axis.title.x = element_text(size=16,face="plain", margin = margin(r=0,10,0,0)),
axis.title.y = element_text(size=16,face="plain", margin = margin(r=10,0,0,0)),
axis.ticks = element_line(size = 1),
axis.ticks.length = unit(.3, "cm"))+
guides(colour = guide_legend(override.aes = list(size=4)))
# Treatment: Area
p5.2<-ggplot(DB_data, aes(x=Sex, y=as.numeric(Prop_MS),fill=Area, col=Area)) +
geom_point(position=position_jitterdodge(jitter.width=0.6,jitter.height = 0,dodge.width=0.9),shape=19, alpha=0.75, size = 2)+
stat_summary(fun.min = function(z) { quantile(z,0.25) },
fun.max = function(z) { quantile(z,0.75) },
fun = mean,position=position_dodge(.9), size = 0.9,col='black',show.legend = F)+
scale_color_manual(values=c(colorESEB2[1],colorESEB2[2]),name = "Treatment", labels = c('Large area','Small area'))+
scale_fill_manual(values=c(colorESEB2[1],colorESEB2[2]),name = "Treatment", labels = c('Large area','Small area'))+
xlab('Sex')+ylab("")+ggtitle('')+ theme(plot.title = element_text(hjust = 0.5))+
scale_x_discrete(labels = c('Female','Male'),drop=FALSE)+ ylim(0,1.1)+labs(tag = "B")+
annotate("text",label='n =',x=0.55,y=1.1,size=4)+
annotate("text",label='86',x=0.78,y=1.1,size=4)+
annotate("text",label='79',x=1.23,y=1.1,size=4)+
annotate("text",label='88',x=1.78,y=1.1,size=4)+
annotate("text",label='82',x=2.23,y=1.1,size=4)+
theme(panel.border = element_blank(),
plot.margin = margin(0.15,2.2,0,0,"cm"),
plot.title = element_text(hjust = 0.5),
panel.background = element_blank(),
legend.key=element_blank(),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
legend.position = c(1.08, 0.8),
plot.tag.position=c(0.01,0.98),
legend.title = element_blank(),
legend.text = element_text(colour="black", size=10),
axis.line.x = element_line(colour = "black", size = 1),
axis.line.y = element_line(colour = "black", size = 1),
axis.text.x = element_text(face="plain", color="black", size=16, angle=0),
axis.text.y = element_text(face="plain", color="black", size=16, angle=0),
axis.title.x = element_text(size=16,face="plain", margin = margin(r=0,10,0,0)),
axis.title.y = element_text(size=16,face="plain", margin = margin(r=10,0,0,0)),
axis.ticks = element_line(size = 1),
axis.ticks.length = unit(.3, "cm"))+
guides(colour = guide_legend(override.aes = list(size=4)))
plot5<-grid.arrange(grobs = list(p5,p5.2), nrow = 1,ncol=2, widths=c(2.3, 2.3))
Figure 5: Effects of group size (A) and area treatment (B) on the
proportion of successful matings of female and male focals. Black bars
indicate means and quartile borders.
Statistical models: Proportion of successful matings
(quasi-binomial GLM)
Effect of group size on proportion of
successful matings in females.
# Statistical models: Proportion of successful matings (quasi-binomial GLM)
# Sex: Female
# Treatment: Group size
mod5.1=glm(cbind(f_TotMatings,f_Attempts_number)~Gr_size,data=DB_data,family = quasibinomial)
summary(mod5.1)
Call:
glm(formula = cbind(f_TotMatings, f_Attempts_number) ~ Gr_size,
family = quasibinomial, data = DB_data)
Deviance Residuals:
Min 1Q Median 3Q Max
-2.8166 -0.8025 -0.0549 0.7731 3.4012
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -0.6544 0.0926 -7.067 9.09e-11 ***
Gr_sizeLG 0.1003 0.1598 0.627 0.531
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
(Dispersion parameter for quasibinomial family taken to be 1.410505)
Null deviance: 190.01 on 129 degrees of freedom
Residual deviance: 189.45 on 128 degrees of freedom
(148 observations deleted due to missingness)
AIC: NA
Number of Fisher Scoring iterations: 4
Effect of group size on proportion of successful matings in
males.
# Sex: Male
# Treatment: Group size
mod5.2=glm(cbind(m_TotMatings,m_Attempts_number)~Gr_size,data=DB_data,family = quasibinomial)
summary(mod5.2)
Call:
glm(formula = cbind(m_TotMatings, m_Attempts_number) ~ Gr_size,
family = quasibinomial, data = DB_data)
Deviance Residuals:
Min 1Q Median 3Q Max
-2.4940 -0.6358 0.1065 0.7179 4.2524
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -0.84669 0.08881 -9.533 <2e-16 ***
Gr_sizeLG 0.06083 0.13667 0.445 0.657
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
(Dispersion parameter for quasibinomial family taken to be 1.297307)
Null deviance: 194.77 on 147 degrees of freedom
Residual deviance: 194.51 on 146 degrees of freedom
(130 observations deleted due to missingness)
AIC: NA
Number of Fisher Scoring iterations: 4
Effect of area on proportion of successful matings in
females.
# Sex: Female
# Treatment: Area
mod5.3=glm(cbind(f_TotMatings,f_Attempts_number)~Area,data=DB_data,family = quasibinomial)
summary(mod5.3)
Call:
glm(formula = cbind(f_TotMatings, f_Attempts_number) ~ Area,
family = quasibinomial, data = DB_data)
Deviance Residuals:
Min 1Q Median 3Q Max
-2.6934 -0.8646 -0.0321 0.8745 3.1859
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -0.53673 0.09988 -5.374 3.53e-07 ***
AreaSmall -0.19021 0.15125 -1.258 0.211
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
(Dispersion parameter for quasibinomial family taken to be 1.391415)
Null deviance: 190.01 on 129 degrees of freedom
Residual deviance: 187.80 on 128 degrees of freedom
(148 observations deleted due to missingness)
AIC: NA
Number of Fisher Scoring iterations: 4
Effect of area on proportion of successful matings in males.
# Sex: Male
# Treatment: Area
mod5.4=glm(cbind(m_TotMatings,m_Attempts_number)~Area,data=DB_data,family = quasibinomial)
summary(mod5.4)
Call:
glm(formula = cbind(m_TotMatings, m_Attempts_number) ~ Area,
family = quasibinomial, data = DB_data)
Deviance Residuals:
Min 1Q Median 3Q Max
-2.5859 -0.6372 0.1125 0.7222 4.1648
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -0.79113 0.09600 -8.241 8.97e-14 ***
AreaSmall -0.05894 0.13483 -0.437 0.663
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
(Dispersion parameter for quasibinomial family taken to be 1.293625)
Null deviance: 194.77 on 147 degrees of freedom
Residual deviance: 194.52 on 146 degrees of freedom
(130 observations deleted due to missingness)
AIC: NA
Number of Fisher Scoring iterations: 4Reproductive success
Secondly, we tested the effect that the treatments (group size and
area) had on the reproductive success of focal beetles.
Figure:
Reproductive success
# Figure: Reproductive success
# Treatment: Group size
p6<-ggplot(DB_data_clean, aes(x=Sex, y=as.numeric(Total_N_MTP1),fill=Gr_size, col=Gr_size)) +
geom_point(position=position_jitterdodge(jitter.width=0.6,jitter.height = 0,dodge.width=0.9),shape=19, alpha=0.75, size = 2)+
stat_summary(fun.min = function(z) { quantile(z,0.25) },
fun.max = function(z) { quantile(z,0.75) },
fun = mean,position=position_dodge(.9), size = 0.9,col='black',show.legend = F)+
scale_color_manual(values=c(colorESEB[1],colorESEB[2]),name = "Treatment", labels = c('Small group','Large group'))+
scale_fill_manual(values=c(colorESEB[1],colorESEB[2]),name = "Treatment", labels = c('Small group','Large group'))+
xlab('Sex')+ylab("Number of offspring")+ggtitle('')+ theme(plot.title = element_text(hjust = 0.5))+
scale_x_discrete(labels = c('Female','Male'),drop=FALSE)+ ylim(0,320)+labs(tag = "A")+
annotate("text",label='n =',x=0.55,y=320,size=4)+
annotate("text",label='59',x=0.78,y=320,size=4)+
annotate("text",label='48',x=1.23,y=320,size=4)+
annotate("text",label='51',x=1.78,y=320,size=4)+
annotate("text",label='59',x=2.23,y=320,size=4)+
theme(panel.border = element_blank(),
plot.margin = margin(0,2.2,0,0.2,"cm"),
plot.title = element_text(hjust = 0.5),
panel.background = element_blank(),
legend.key=element_blank(),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
legend.position = c(1.1, 0.8),
plot.tag.position=c(0.01,0.98),
legend.title = element_blank(),
legend.text = element_text(colour="black", size=10),
axis.line.x = element_line(colour = "black", size = 1),
axis.line.y = element_line(colour = "black", size = 1),
axis.text.x = element_text(face="plain", color="black", size=16, angle=0),
axis.text.y = element_text(face="plain", color="black", size=16, angle=0),
axis.title.x = element_text(size=16,face="plain", margin = margin(r=0,10,0,0)),
axis.title.y = element_text(size=16,face="plain", margin = margin(r=10,0,0,0)),
axis.ticks = element_line(size = 1),
axis.ticks.length = unit(.3, "cm"))+
guides(colour = guide_legend(override.aes = list(size=4)))
# Treatment: Area
p6.2<-ggplot(DB_data_clean, aes(x=Sex, y=as.numeric(Total_N_MTP1),fill=Area, col=Area)) +
geom_point(position=position_jitterdodge(jitter.width=0.6,jitter.height = 0,dodge.width=0.9),shape=19, alpha=0.75, size = 2)+
stat_summary(fun.min = function(z) { quantile(z,0.25) },
fun.max = function(z) { quantile(z,0.75) },
fun = mean,position=position_dodge(.9), size = 0.9,col='black',show.legend = F)+
scale_color_manual(values=c(colorESEB2[1],colorESEB2[2]),name = "Treatment", labels = c('Large area','Small area'))+
scale_fill_manual(values=c(colorESEB2[1],colorESEB2[2]),name = "Treatment", labels = c('Large area','Small area'))+
xlab('Sex')+ylab("")+ggtitle('')+ theme(plot.title = element_text(hjust = 0.5))+
scale_x_discrete(labels = c('Female','Male'),drop=FALSE)+ ylim(0,320)+labs(tag = "B")+
annotate("text",label='n =',x=0.55,y=320,size=4)+
annotate("text",label='56',x=0.78,y=320,size=4)+
annotate("text",label='51',x=1.23,y=320,size=4)+
annotate("text",label='57',x=1.78,y=320,size=4)+
annotate("text",label='53',x=2.23,y=320,size=4)+
theme(panel.border = element_blank(),
plot.margin = margin(0,2.2,0,0,"cm"),
plot.title = element_text(hjust = 0.5),
panel.background = element_blank(),
legend.key=element_blank(),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
legend.position = c(1.1, 0.8),
plot.tag.position=c(0.01,0.98),
legend.title = element_blank(),
legend.text = element_text(colour="black", size=10),
axis.line.x = element_line(colour = "black", size = 1),
axis.line.y = element_line(colour = "black", size = 1),
axis.text.x = element_text(face="plain", color="black", size=16, angle=0),
axis.text.y = element_text(face="plain", color="black", size=16, angle=0),
axis.title.x = element_text(size=16,face="plain", margin = margin(r=0,10,0,0)),
axis.title.y = element_text(size=16,face="plain", margin = margin(r=10,0,0,0)),
axis.ticks = element_line(size = 1),
axis.ticks.length = unit(.3, "cm"))+guides(colour = guide_legend(override.aes = list(size=4)))
grid.arrange(grobs = list(p6,p6.2), nrow = 1,ncol=2, widths=c(2.3, 2.3))
Figure 6: Effects of group size (A) and area treatment (B) on the
reproductive success of female and male focals. Black bars indicate
means and quartile borders.
Statistical models: Reproductive success (quasi-Poisson
GLM)
Effect of group size on reproductive success in females.
# Statistical models: Reproductive success (quasi-Poisson GLM)
# Sex: Female
# Treatment: Group size
mod6.1=glm(f_RS~Gr_size,data=DB_data_clean,family = quasipoisson)
summary(mod6.1)
Call:
glm(formula = f_RS ~ Gr_size, family = quasipoisson, data = DB_data_clean)
Deviance Residuals:
Min 1Q Median 3Q Max
-11.143 -10.664 1.433 4.942 8.982
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 4.04054 0.12007 33.650 <2e-16 ***
Gr_sizeLG 0.08801 0.18288 0.481 0.632
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
(Dispersion parameter for quasipoisson family taken to be 40.16879)
Null deviance: 4614.9 on 82 degrees of freedom
Residual deviance: 4605.7 on 81 degrees of freedom
(94 observations deleted due to missingness)
AIC: NA
Number of Fisher Scoring iterations: 5
Effect of group size on reproductive success in males.
# Sex: Male
# Treatment: Group size
mod6.2=glm(m_RS~Gr_size,data=DB_data_clean,family = quasipoisson)
summary(mod6.2)
Call:
glm(formula = m_RS ~ Gr_size, family = quasipoisson, data = DB_data_clean)
Deviance Residuals:
Min 1Q Median 3Q Max
-11.7196 -7.9610 -0.7465 4.1631 21.0427
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 4.229 0.127 33.308 <2e-16 ***
Gr_sizeLG -0.203 0.181 -1.121 0.265
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
(Dispersion parameter for quasipoisson family taken to be 47.61251)
Null deviance: 5078.4 on 93 degrees of freedom
Residual deviance: 5018.5 on 92 degrees of freedom
(83 observations deleted due to missingness)
AIC: NA
Number of Fisher Scoring iterations: 5
Effect of area on reproductive success in females.
# Sex: Female
# Treatment: Area
mod6.3=glm(f_RS~Area,data=DB_data_clean,family = quasipoisson)
summary(mod6.3)
Call:
glm(formula = f_RS ~ Area, family = quasipoisson, data = DB_data_clean)
Deviance Residuals:
Min 1Q Median 3Q Max
-10.920 -10.817 1.581 4.922 8.727
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 4.06903 0.12200 33.354 <2e-16 ***
AreaSmall 0.01899 0.18177 0.104 0.917
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
(Dispersion parameter for quasipoisson family taken to be 40.05135)
Null deviance: 4614.9 on 82 degrees of freedom
Residual deviance: 4614.5 on 81 degrees of freedom
(94 observations deleted due to missingness)
AIC: NA
Number of Fisher Scoring iterations: 5
Effect of area on reproductive success in males.
# Sex: Male
# Treatment: Area
mod6.4=glm(m_RS~Area,data=DB_data_clean,family = quasipoisson)
summary(mod6.4)
Call:
glm(formula = m_RS ~ Area, family = quasipoisson, data = DB_data_clean)
Deviance Residuals:
Min 1Q Median 3Q Max
-11.536 -7.447 -0.200 3.954 21.400
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 4.0520 0.1306 31.034 <2e-16 ***
AreaSmall 0.1457 0.1819 0.801 0.425
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
(Dispersion parameter for quasipoisson family taken to be 48.03915)
Null deviance: 5078.4 on 93 degrees of freedom
Residual deviance: 5047.5 on 92 degrees of freedom
(83 observations deleted due to missingness)
AIC: NA
Number of Fisher Scoring iterations: 5Metrics of sexual selection
In this part of our analysis we estimated standardized metrics of
(sexual) selection.
Metrics:
- Opportunity for selection
- Opportunity for sexual selection
- Bateman gradient
- Jones
index
We used bootstrapping (10.000 bootstrap replicates) to
obtain 95% confidence intervals and permutation tests (10.000
permutations) to statistically compare treatments and sexes.
# Opportunity for selection
#Area
DB_data_clean_Large_area_Male_rel_propRS <-as.data.table(DB_data_clean_Large_area$rel_m_RS)
c <- function(d, i){
d2 <- d[i,]
return(var(d2[,1], na.rm=TRUE))
}
I_Large_area_Male_bootvar <- boot(DB_data_clean_Large_area_Male_rel_propRS, c, R=10000)
DB_data_clean_Large_area_Female_rel_propRS <-as.data.table(DB_data_clean_Large_area$rel_f_RS)
I_Large_area_Female_bootvar <- boot(DB_data_clean_Large_area_Female_rel_propRS, c, R=10000)
DB_data_clean_Small_area_Male_rel_propRS <-as.data.table(DB_data_clean_Small_area$rel_m_RS)
I_Small_area_Male_bootvar <- boot(DB_data_clean_Small_area_Male_rel_propRS, c, R=10000)
DB_data_clean_Small_area_Female_rel_propRS <-as.data.table(DB_data_clean_Small_area$rel_f_RS)
I_Small_area_Female_bootvar <- boot(DB_data_clean_Small_area_Female_rel_propRS, c, R=10000)
#Population size
DB_data_clean_Large_pop_Male_rel_propRS <-as.data.table(DB_data_clean_Large_pop$rel_m_RS)
I_Large_pop_Male_bootvar <- boot(DB_data_clean_Large_pop_Male_rel_propRS, c, R=10000)
DB_data_clean_Large_pop_Female_rel_propRS <-as.data.table(DB_data_clean_Large_pop$rel_f_RS)
I_Large_pop_Female_bootvar <- boot(DB_data_clean_Large_pop_Female_rel_propRS, c, R=10000)
DB_data_clean_Small_pop_Male_rel_propRS <-as.data.table(DB_data_clean_Small_pop$rel_m_RS)
I_Small_pop_Male_bootvar <- boot(DB_data_clean_Small_pop_Male_rel_propRS, c, R=10000)
DB_data_clean_Small_pop_Female_rel_propRS <-as.data.table(DB_data_clean_Small_pop$rel_f_RS)
I_Small_pop_Female_bootvar <- boot(DB_data_clean_Small_pop_Female_rel_propRS, c, R=10000)
rm("c")
# Opportunity for sexual selection
#Area
DB_data_clean_Large_area_Male_relMS <-as.data.table(DB_data_clean_Large_area$rel_m_cMS)
c <- function(d, i){
d2 <- d[i,]
return(var(d2[,1], na.rm=TRUE))
}
Is_Large_area_Male_bootvar <- boot(DB_data_clean_Large_area_Male_relMS, c, R=10000)
DB_data_clean_Large_area_Female_relMS <-as.data.table(DB_data_clean_Large_area$rel_f_cMS)
Is_Large_area_Female_bootvar <- boot(DB_data_clean_Large_area_Female_relMS, c, R=10000)
DB_data_clean_Small_area_Male_relMS <-as.data.table(DB_data_clean_Small_area$rel_m_cMS)
Is_Small_area_Male_bootvar <- boot(DB_data_clean_Small_area_Male_relMS, c, R=10000)
DB_data_clean_Small_area_Female_relMS <-as.data.table(DB_data_clean_Small_area$rel_f_cMS)
Is_Small_area_Female_bootvar <- boot(DB_data_clean_Small_area_Female_relMS, c, R=10000)
#Population size
DB_data_clean_Large_pop_Male_relMS <-as.data.table(DB_data_clean_Large_pop$rel_m_cMS)
Is_Large_pop_Male_bootvar <- boot(DB_data_clean_Large_pop_Male_relMS, c, R=10000)
DB_data_clean_Large_pop_Female_relMS <-as.data.table(DB_data_clean_Large_pop$rel_f_cMS)
Is_Large_pop_Female_bootvar <- boot(DB_data_clean_Large_pop_Female_relMS, c, R=10000)
DB_data_clean_Small_pop_Male_relMS <-as.data.table(DB_data_clean_Small_pop$rel_m_cMS)
Is_Small_pop_Male_bootvar <- boot(DB_data_clean_Small_pop_Male_relMS, c, R=10000)
DB_data_clean_Small_pop_Female_relMS <-as.data.table(DB_data_clean_Small_pop$rel_f_cMS)
Is_Small_pop_Female_bootvar <- boot(DB_data_clean_Small_pop_Female_relMS, c, R=10000)
rm("c")
# Bateman Gradient
#Area
DB_data_clean_Large_area_Male_B <-as.data.table(cbind(DB_data_clean_Large_area$rel_m_RS,DB_data_clean_Large_area$rel_m_cMS))
c <- function(d, i){
d2 <- d[i,]
return(lm(V1 ~V2,data=d2)$coefficients[2])
}
B_Large_area_Male_bootvar <- boot(DB_data_clean_Large_area_Male_B, c, R=10000)
DB_data_clean_Small_area_Male_B <-as.data.table(cbind(DB_data_clean_Small_area$rel_m_RS,DB_data_clean_Small_area$rel_m_cMS))
B_Small_area_Male_bootvar <- boot(DB_data_clean_Small_area_Male_B, c, R=10000)
DB_data_clean_Large_area_Female_B <-as.data.table(cbind(DB_data_clean_Large_area$rel_f_RS,DB_data_clean_Large_area$rel_f_cMS))
B_Large_area_Female_bootvar <- boot(DB_data_clean_Large_area_Female_B, c, R=10000)
DB_data_clean_Small_area_Female_B <-as.data.table(cbind(DB_data_clean_Small_area$rel_f_RS,DB_data_clean_Small_area$rel_f_cMS))
B_Small_area_Female_bootvar <- boot(DB_data_clean_Small_area_Female_B, c, R=10000)
#Population size
DB_data_clean_Large_pop_Male_B <-as.data.table(cbind(DB_data_clean_Large_pop$rel_m_RS,DB_data_clean_Large_pop$rel_m_cMS))
B_Large_pop_Male_bootvar <- boot(DB_data_clean_Large_pop_Male_B, c, R=10000)
DB_data_clean_Small_pop_Male_B <-as.data.table(cbind(DB_data_clean_Small_pop$rel_m_RS,DB_data_clean_Small_pop$rel_m_cMS))
B_Small_pop_Male_bootvar <- boot(DB_data_clean_Small_pop_Male_B, c, R=10000)
DB_data_clean_Large_pop_Female_B <-as.data.table(cbind(DB_data_clean_Large_pop$rel_f_RS,DB_data_clean_Large_pop$rel_f_cMS))
B_Large_pop_Female_bootvar <- boot(DB_data_clean_Large_pop_Female_B, c, R=10000)
DB_data_clean_Small_pop_Female_B <-as.data.table(cbind(DB_data_clean_Small_pop$rel_f_RS,DB_data_clean_Small_pop$rel_f_cMS))
B_Small_pop_Female_bootvar <- boot(DB_data_clean_Small_pop_Female_B, c, R=10000)
rm("c")
#Jones index
#Area
c <- function(d, i){
d2 <- d[i,]
return(lm(d2$V1 ~d2$V2)$coefficients[2]*sqrt(var(d2$V2, na.rm=TRUE)))
}
S_Large_area_Male_bootvar <- boot(DB_data_clean_Large_area_Male_B, c, R=10000)
S_Small_area_Male_bootvar <- boot(DB_data_clean_Small_area_Male_B, c, R=10000)
S_Large_area_Female_bootvar <- boot(DB_data_clean_Large_area_Female_B, c, R=10000)
S_Small_area_Female_bootvar <- boot(DB_data_clean_Small_area_Female_B, c, R=10000)
#Population size
S_Large_pop_Male_bootvar <- boot(DB_data_clean_Large_pop_Male_B, c, R=10000)
S_Small_pop_Male_bootvar <- boot(DB_data_clean_Small_pop_Male_B, c, R=10000)
S_Large_pop_Female_bootvar <- boot(DB_data_clean_Large_pop_Female_B, c, R=10000)
S_Small_pop_Female_bootvar <- boot(DB_data_clean_Small_pop_Female_B, c, R=10000)
rm("c")
#Make data table
PhenVarBoot_Table_Male_Small_pop_I <- as.data.frame(cbind("Male", "Small_pop", "Opportunity for selection", as.numeric(mean(I_Small_pop_Male_bootvar$t)), quantile(I_Small_pop_Male_bootvar$t,.025, names = FALSE), quantile(I_Small_pop_Male_bootvar$t,.975, names = FALSE)))
PhenVarBoot_Table_Male_Large_pop_I <- as.data.frame(cbind("Male", "Large_pop", "Opportunity for selection", mean(I_Large_pop_Male_bootvar$t), quantile(I_Large_pop_Male_bootvar$t,.025, names = FALSE), quantile(I_Large_pop_Male_bootvar$t,.975, names = FALSE)))
PhenVarBoot_Table_Male_Large_area_I <- as.data.frame(cbind("Male", "Large_area", "Opportunity for selection", mean(I_Large_area_Male_bootvar$t), quantile(I_Large_area_Male_bootvar$t,.025, names = FALSE), quantile(I_Large_area_Male_bootvar$t,.975, names = FALSE)))
PhenVarBoot_Table_Male_Small_area_I <- as.data.frame(cbind("Male", "Small_area", "Opportunity for selection", mean(I_Small_area_Male_bootvar$t), quantile(I_Small_area_Male_bootvar$t,.025, names = FALSE), quantile(I_Small_area_Male_bootvar$t,.975, names = FALSE)))
PhenVarBoot_Table_Male_Small_pop_Is <- as.data.frame(cbind("Male", "Small_pop", "Opportunity for sexual selection", mean(Is_Small_pop_Male_bootvar$t), quantile(Is_Small_pop_Male_bootvar$t,.025, names = FALSE), quantile(Is_Small_pop_Male_bootvar$t,.975, names = FALSE)))
PhenVarBoot_Table_Male_Large_pop_Is <- as.data.frame(cbind("Male", "Large_pop", "Opportunity for sexual selection", mean(Is_Large_pop_Male_bootvar$t), quantile(Is_Large_pop_Male_bootvar$t,.025, names = FALSE), quantile(Is_Large_pop_Male_bootvar$t,.975, names = FALSE)))
PhenVarBoot_Table_Male_Large_area_Is <- as.data.frame(cbind("Male", "Large_area", "Opportunity for sexual selection", mean(Is_Large_area_Male_bootvar$t), quantile(Is_Large_area_Male_bootvar$t,.025, names = FALSE), quantile(Is_Large_area_Male_bootvar$t,.975, names = FALSE)))
PhenVarBoot_Table_Male_Small_area_Is <- as.data.frame(cbind("Male", "Small_area", "Opportunity for sexual selection", mean(Is_Small_area_Male_bootvar$t), quantile(Is_Small_area_Male_bootvar$t,.025, names = FALSE), quantile(Is_Small_area_Male_bootvar$t,.975, names = FALSE)))
PhenVarBoot_Table_Male_Small_pop_B <- as.data.frame(cbind("Male", "Small_pop", "Bateman gradient", mean(B_Small_pop_Male_bootvar$t), quantile(B_Small_pop_Male_bootvar$t,.025, names = FALSE), quantile(B_Small_pop_Male_bootvar$t,.975, names = FALSE)))
PhenVarBoot_Table_Male_Large_pop_B <- as.data.frame(cbind("Male", "Large_pop", "Bateman gradient", mean(B_Large_pop_Male_bootvar$t), quantile(B_Large_pop_Male_bootvar$t,.025, names = FALSE), quantile(B_Large_pop_Male_bootvar$t,.975, names = FALSE)))
PhenVarBoot_Table_Male_Large_area_B <- as.data.frame(cbind("Male", "Large_area", "Bateman gradient", mean(B_Large_area_Male_bootvar$t), quantile(B_Large_area_Male_bootvar$t,.025, names = FALSE), quantile(B_Large_area_Male_bootvar$t,.975, names = FALSE)))
PhenVarBoot_Table_Male_Small_area_B <- as.data.frame(cbind("Male", "Small_area", "Bateman gradient", mean(B_Small_area_Male_bootvar$t), quantile(B_Small_area_Male_bootvar$t,.025, names = FALSE), quantile(B_Small_area_Male_bootvar$t,.975, names = FALSE)))
PhenVarBoot_Table_Male_Small_pop_S <- as.data.frame(cbind("Male", "Small_pop", "Maximum standardized sexual selection differential", mean(S_Small_pop_Male_bootvar$t,na.rm = T), quantile(S_Small_pop_Male_bootvar$t,.025, names = FALSE,na.rm = T), quantile(S_Small_pop_Male_bootvar$t,.975, names = FALSE,na.rm = T)))
PhenVarBoot_Table_Male_Large_pop_S <- as.data.frame(cbind("Male", "Large_pop", "Maximum standardized sexual selection differential", mean(S_Large_pop_Male_bootvar$t,na.rm = T), quantile(S_Large_pop_Male_bootvar$t,.025, names = FALSE,na.rm = T), quantile(S_Large_pop_Male_bootvar$t,.975, names = FALSE,na.rm = T)))
PhenVarBoot_Table_Male_Large_area_S <- as.data.frame(cbind("Male", "Large_area", "Maximum standardized sexual selection differential", mean(S_Large_area_Male_bootvar$t,na.rm = T), quantile(S_Large_area_Male_bootvar$t,.025, names = FALSE,na.rm = T), quantile(S_Large_area_Male_bootvar$t,.975, names = FALSE,na.rm = T)))
PhenVarBoot_Table_Male_Small_area_S <- as.data.frame(cbind("Male", "Small_area", "Maximum standardized sexual selection differential", mean(S_Small_area_Male_bootvar$t,na.rm = T), quantile(S_Small_area_Male_bootvar$t,.025, names = FALSE,na.rm = T), quantile(S_Small_area_Male_bootvar$t,.975, names = FALSE,na.rm = T)))
PhenVarBoot_Table_Female_Small_pop_I <- as.data.frame(cbind("Female", "Small_pop", "Opportunity for selection", mean(I_Small_pop_Female_bootvar$t), quantile(I_Small_pop_Female_bootvar$t,.025, names = FALSE), quantile(I_Small_pop_Female_bootvar$t,.975, names = FALSE)))
PhenVarBoot_Table_Female_Large_pop_I <- as.data.frame(cbind("Female", "Large_pop", "Opportunity for selection", mean(I_Large_pop_Female_bootvar$t), quantile(I_Large_pop_Female_bootvar$t,.025, names = FALSE), quantile(I_Large_pop_Female_bootvar$t,.975, names = FALSE)))
PhenVarBoot_Table_Female_Large_area_I <- as.data.frame(cbind("Female", "Large_area", "Opportunity for selection", mean(I_Large_area_Female_bootvar$t), quantile(I_Large_area_Female_bootvar$t,.025, names = FALSE), quantile(I_Large_area_Female_bootvar$t,.975, names = FALSE)))
PhenVarBoot_Table_Female_Small_area_I <- as.data.frame(cbind("Female", "Small_area", "Opportunity for selection", mean(I_Small_area_Female_bootvar$t), quantile(I_Small_area_Female_bootvar$t,.025, names = FALSE), quantile(I_Small_area_Female_bootvar$t,.975, names = FALSE)))
PhenVarBoot_Table_Female_Small_pop_Is <- as.data.frame(cbind("Female", "Small_pop", "Opportunity for sexual selection", mean(Is_Small_pop_Female_bootvar$t), quantile(Is_Small_pop_Female_bootvar$t,.025, names = FALSE), quantile(Is_Small_pop_Female_bootvar$t,.975, names = FALSE)))
PhenVarBoot_Table_Female_Large_pop_Is <- as.data.frame(cbind("Female", "Large_pop", "Opportunity for sexual selection", mean(Is_Large_pop_Female_bootvar$t), quantile(Is_Large_pop_Female_bootvar$t,.025, names = FALSE), quantile(Is_Large_pop_Female_bootvar$t,.975, names = FALSE)))
PhenVarBoot_Table_Female_Large_area_Is <- as.data.frame(cbind("Female", "Large_area", "Opportunity for sexual selection", mean(Is_Large_area_Female_bootvar$t), quantile(Is_Large_area_Female_bootvar$t,.025, names = FALSE), quantile(Is_Large_area_Female_bootvar$t,.975, names = FALSE)))
PhenVarBoot_Table_Female_Small_area_Is <- as.data.frame(cbind("Female", "Small_area", "Opportunity for sexual selection", mean(Is_Small_area_Female_bootvar$t), quantile(Is_Small_area_Female_bootvar$t,.025, names = FALSE), quantile(Is_Small_area_Female_bootvar$t,.975, names = FALSE)))
PhenVarBoot_Table_Female_Small_pop_B <- as.data.frame(cbind("Female", "Small_pop", "Bateman gradient", mean(B_Small_pop_Female_bootvar$t), quantile(B_Small_pop_Female_bootvar$t,.025, names = FALSE), quantile(B_Small_pop_Female_bootvar$t,.975, names = FALSE)))
PhenVarBoot_Table_Female_Large_pop_B <- as.data.frame(cbind("Female", "Large_pop", "Bateman gradient", mean(B_Large_pop_Female_bootvar$t), quantile(B_Large_pop_Female_bootvar$t,.025, names = FALSE), quantile(B_Large_pop_Female_bootvar$t,.975, names = FALSE)))
PhenVarBoot_Table_Female_Large_area_B <- as.data.frame(cbind("Female", "Large_area", "Bateman gradient", mean(B_Large_area_Female_bootvar$t), quantile(B_Large_area_Female_bootvar$t,.025, names = FALSE), quantile(B_Large_area_Female_bootvar$t,.975, names = FALSE)))
PhenVarBoot_Table_Female_Small_area_B <- as.data.frame(cbind("Female", "Small_area", "Bateman gradient", mean(B_Small_area_Female_bootvar$t), quantile(B_Small_area_Female_bootvar$t,.025, names = FALSE), quantile(B_Small_area_Female_bootvar$t,.975, names = FALSE)))
PhenVarBoot_Table_Female_Small_pop_S <- as.data.frame(cbind("Female", "Small_pop", "Maximum standardized sexual selection differential", mean(S_Small_pop_Female_bootvar$t,na.rm = T), quantile(S_Small_pop_Female_bootvar$t,.025, names = FALSE,na.rm = T), quantile(S_Small_pop_Female_bootvar$t,.975, names = FALSE,na.rm = T)))
PhenVarBoot_Table_Female_Large_pop_S <- as.data.frame(cbind("Female", "Large_pop", "Maximum standardized sexual selection differential", mean(S_Large_pop_Female_bootvar$t,na.rm = T), quantile(S_Large_pop_Female_bootvar$t,.025, names = FALSE,na.rm = T), quantile(S_Large_pop_Female_bootvar$t,.975, names = FALSE,na.rm = T)))
PhenVarBoot_Table_Female_Large_area_S <- as.data.frame(cbind("Female", "Large_area", "Maximum standardized sexual selection differential", mean(S_Large_area_Female_bootvar$t,na.rm = T), quantile(S_Large_area_Female_bootvar$t,.025, names = FALSE,na.rm = T), quantile(S_Large_area_Female_bootvar$t,.975, names = FALSE,na.rm = T)))
PhenVarBoot_Table_Female_Small_area_S <- as.data.frame(cbind("Female", "Small_area", "Maximum standardized sexual selection differential", mean(S_Small_area_Female_bootvar$t,na.rm = T), quantile(S_Small_area_Female_bootvar$t,.025, names = FALSE,na.rm = T), quantile(S_Small_area_Female_bootvar$t,.975, names = FALSE,na.rm = T)))
Table_BatemanMetrics <- as.data.frame(as.matrix(rbind(PhenVarBoot_Table_Male_Small_pop_I,PhenVarBoot_Table_Male_Large_pop_I,PhenVarBoot_Table_Male_Large_area_I,PhenVarBoot_Table_Male_Small_area_I,
PhenVarBoot_Table_Male_Small_pop_Is,PhenVarBoot_Table_Male_Large_pop_Is,PhenVarBoot_Table_Male_Large_area_Is,PhenVarBoot_Table_Male_Small_area_Is,
PhenVarBoot_Table_Male_Small_pop_B,PhenVarBoot_Table_Male_Large_pop_B,PhenVarBoot_Table_Male_Large_area_B,PhenVarBoot_Table_Male_Small_area_B,
PhenVarBoot_Table_Male_Small_pop_S,PhenVarBoot_Table_Male_Large_pop_S,PhenVarBoot_Table_Male_Large_area_S,PhenVarBoot_Table_Male_Small_area_S,
PhenVarBoot_Table_Female_Small_pop_I,PhenVarBoot_Table_Female_Large_pop_I,PhenVarBoot_Table_Female_Large_area_I,PhenVarBoot_Table_Female_Small_area_I,
PhenVarBoot_Table_Female_Small_pop_Is,PhenVarBoot_Table_Female_Large_pop_Is,PhenVarBoot_Table_Female_Large_area_Is,PhenVarBoot_Table_Female_Small_area_Is,
PhenVarBoot_Table_Female_Small_pop_B,PhenVarBoot_Table_Female_Large_pop_B,PhenVarBoot_Table_Female_Large_area_B,PhenVarBoot_Table_Female_Small_area_B,
PhenVarBoot_Table_Female_Small_pop_S,PhenVarBoot_Table_Female_Large_pop_S,PhenVarBoot_Table_Female_Large_area_S,PhenVarBoot_Table_Female_Small_area_S)),digits=3)
is.table(Table_BatemanMetrics)
colnames(Table_BatemanMetrics)[1] <- "Sex"
colnames(Table_BatemanMetrics)[2] <- "Treatment"
colnames(Table_BatemanMetrics)[3] <- "Variable"
colnames(Table_BatemanMetrics)[4] <- "Variance"
colnames(Table_BatemanMetrics)[5] <- "l95.CI"
colnames(Table_BatemanMetrics)[6] <- "u95.CI"
Table_BatemanMetrics[,4]=as.numeric(Table_BatemanMetrics[,4])
Table_BatemanMetrics[,5]=as.numeric(Table_BatemanMetrics[,5])
Table_BatemanMetrics[,6]=as.numeric(Table_BatemanMetrics[,6])#Bootstrap treatment comparisons
#I
#Area
#Males
Treat_diff_Male_area_I=c(I_Large_area_Male_bootvar$t)-c(I_Small_area_Male_bootvar$t)
t_Treat_diff_Male_area_I=mean(Treat_diff_Male_area_I,na.rm=TRUE)
t_Treat_diff_Male_area_I_lower=quantile(Treat_diff_Male_area_I,.025,na.rm=TRUE)
t_Treat_diff_Male_area_I_upper=quantile(Treat_diff_Male_area_I,.975,na.rm=TRUE)
#Permutation test to calculate p value
comb_data=c(DB_data_clean_Large_area$rel_m_RS,DB_data_clean_Small_area$rel_m_RS)
diff.observed = var(na.omit((DB_data_clean_Large_area$rel_m_RS))) - var(na.omit((DB_data_clean_Small_area$rel_m_RS)))
diff.observed
number_of_permutations = 100000
diff.random = NULL
for (i in 1 : number_of_permutations) {
# Sample from the combined dataset
a.random = sample (na.omit(comb_data), length(c(DB_data_clean_Large_area$rel_m_RS)), TRUE)
b.random = sample (na.omit(comb_data), length(c(DB_data_clean_Small_area$rel_m_RS)), TRUE)
# Null (permuated) difference
diff.random[i] = var(na.omit(b.random)) - var(na.omit(a.random))
}
# P-value is the fraction of how many times the permuted difference is
# equal or more extreme than the observed difference
t_Treat_diff_Male_area_I_p = sum(abs(diff.random) >= as.numeric(abs(diff.observed)))/ number_of_permutations
#Females
Treat_diff_Female_area_I=c(I_Large_area_Female_bootvar$t)-c(I_Small_area_Female_bootvar$t)
t_Treat_diff_Female_area_I=mean(Treat_diff_Female_area_I,na.rm=TRUE)
t_Treat_diff_Female_area_I_lower=quantile(Treat_diff_Female_area_I,.025,na.rm=TRUE)
t_Treat_diff_Female_area_I_upper=quantile(Treat_diff_Female_area_I,.975,na.rm=TRUE)
#Permutation test to calculate p value
comb_data=c(DB_data_clean_Large_area$rel_f_RS,DB_data_clean_Small_area$rel_f_RS)
diff.observed = var(na.omit((DB_data_clean_Large_area$rel_f_RS))) - var(na.omit((DB_data_clean_Small_area$rel_f_RS)))
diff.observed
number_of_permutations = 100000
diff.random = NULL
for (i in 1 : number_of_permutations) {
# Sample from the combined dataset
a.random = sample (na.omit(comb_data), length(c(DB_data_clean_Large_area$rel_f_RS)), TRUE)
b.random = sample (na.omit(comb_data), length(c(DB_data_clean_Small_area$rel_f_RS)), TRUE)
# Null (permuated) difference
diff.random[i] = var(na.omit(b.random)) - var(na.omit(a.random))
}
# P-value is the fraction of how many times the permuted difference is
# equal or more extreme than the observed difference
t_Treat_diff_Female_area_I_p = sum(abs(diff.random) >= as.numeric(abs(diff.observed)))/ number_of_permutations
#Population size ####
#Males
Treat_diff_Male_pop_I=c(I_Small_pop_Male_bootvar$t)-c(I_Large_pop_Male_bootvar$t)
t_Treat_diff_Male_pop_I=mean(Treat_diff_Male_pop_I,na.rm=TRUE)
t_Treat_diff_Male_pop_I_lower=quantile(Treat_diff_Male_pop_I,.025,na.rm=TRUE)
t_Treat_diff_Male_pop_I_upper=quantile(Treat_diff_Male_pop_I,.975,na.rm=TRUE)
#Permutation test to calculate p value
comb_data=c(DB_data_clean_Small_pop$rel_m_RS,DB_data_clean_Large_pop$rel_m_RS)
diff.observed = var(na.omit((DB_data_clean_Small_pop$rel_m_RS))) - var(na.omit((DB_data_clean_Large_pop$rel_m_RS)))
diff.observed
number_of_permutations = 100000
diff.random = NULL
for (i in 1 : number_of_permutations) {
# Sample from the combined dataset
a.random = sample (na.omit(comb_data), length(c(DB_data_clean_Small_pop$rel_m_RS)), TRUE)
b.random = sample (na.omit(comb_data), length(c(DB_data_clean_Large_pop$rel_m_RS)), TRUE)
# Null (permuated) difference
diff.random[i] = var(na.omit(b.random)) - var(na.omit(a.random))
}
# P-value is the fraction of how many times the permuted difference is
# equal or more extreme than the observed difference
t_Treat_diff_Male_pop_I_p = sum(abs(diff.random) >= as.numeric(abs(diff.observed)))/ number_of_permutations
#Females
Treat_diff_Female_pop_I=c(I_Small_pop_Female_bootvar$t)-c(I_Large_pop_Female_bootvar$t)
t_Treat_diff_Female_pop_I=mean(Treat_diff_Female_pop_I,na.rm=TRUE)
t_Treat_diff_Female_pop_I_lower=quantile(Treat_diff_Female_pop_I,.025,na.rm=TRUE)
t_Treat_diff_Female_pop_I_upper=quantile(Treat_diff_Female_pop_I,.975,na.rm=TRUE)
#Permutation test to calculate p value
comb_data=c(DB_data_clean_Small_pop$rel_f_RS,DB_data_clean_Large_pop$rel_f_RS)
diff.observed = var(na.omit(c(DB_data_clean_Small_pop$rel_f_RS))) - var(na.omit(c(DB_data_clean_Large_pop$rel_f_RS)))
diff.observed
number_of_permutations = 100000
diff.random = NULL
for (i in 1 : number_of_permutations) {
# Sample from the combined dataset
a.random = sample (na.omit(comb_data), length(c(DB_data_clean_Small_pop$rel_f_RS)), TRUE)
b.random = sample (na.omit(comb_data), length(c(DB_data_clean_Large_pop$rel_f_RS)), TRUE)
# Null (permuated) difference
diff.random[i] = var(na.omit(b.random)) - var(na.omit(a.random))
}
# P-value is the fraction of how many times the permuted difference is
# equal or more extreme than the observed difference
t_Treat_diff_Female_pop_I_p = sum(abs(diff.random) >= as.numeric(abs(diff.observed)))/ number_of_permutations
#Is ####
#Area ####
#Males
Treat_diff_Male_area_Is=c(Is_Large_area_Male_bootvar$t)-c(Is_Small_area_Male_bootvar$t)
t_Treat_diff_Male_area_Is=mean(Treat_diff_Male_area_Is,na.rm=TRUE)
t_Treat_diff_Male_area_Is_lower=quantile(Treat_diff_Male_area_Is,.025,na.rm=TRUE)
t_Treat_diff_Male_area_Is_upper=quantile(Treat_diff_Male_area_Is,.975,na.rm=TRUE)
#Permutation test to calculate p value
comb_data=c(DB_data_clean_Large_area$rel_m_cMS,DB_data_clean_Small_area$rel_m_cMS)
diff.observed = var(na.omit(c(DB_data_clean_Large_area$rel_m_cMS))) - var(na.omit(c(DB_data_clean_Small_area$rel_m_cMS)))
diff.observed
number_of_permutations = 100000
diff.random = NULL
for (i in 1 : number_of_permutations) {
# Sample from the combined dataset
a.random = sample (na.omit(comb_data), length(c(DB_data_clean_Large_area$rel_m_cMS)), TRUE)
b.random = sample (na.omit(comb_data), length(c(DB_data_clean_Small_area$rel_m_cMS)), TRUE)
# Null (permuated) difference
diff.random[i] = var(na.omit(b.random)) - var(na.omit(a.random))
}
# P-value is the fraction of how many times the permuted difference is
# equal or more extreme than the observed difference
t_Treat_diff_Male_area_Is_p = sum(abs(diff.random) >= as.numeric(abs(diff.observed)))/ number_of_permutations
#Females
Treat_diff_Female_area_Is=c(Is_Large_area_Female_bootvar$t)-c(Is_Small_area_Female_bootvar$t)
t_Treat_diff_Female_area_Is=mean(Treat_diff_Female_area_Is,na.rm=TRUE)
t_Treat_diff_Female_area_Is_lower=quantile(Treat_diff_Female_area_Is,.025,na.rm=TRUE)
t_Treat_diff_Female_area_Is_upper=quantile(Treat_diff_Female_area_Is,.975,na.rm=TRUE)
#Permutation test to calculate p value
comb_data=c(DB_data_clean_Large_area$rel_f_cMS,DB_data_clean_Small_area$rel_f_cMS)
diff.observed = var(na.omit(c(DB_data_clean_Large_area$rel_f_cMS))) - var(na.omit(c(DB_data_clean_Small_area$rel_f_cMS)))
diff.observed
number_of_permutations = 100000
diff.random = NULL
for (i in 1 : number_of_permutations) {
# Sample from the combined dataset
a.random = sample (na.omit(comb_data), length(c(DB_data_clean_Large_area$rel_f_cMS)), TRUE)
b.random = sample (na.omit(comb_data), length(c(DB_data_clean_Small_area$rel_f_cMS)), TRUE)
# Null (permuated) difference
diff.random[i] = var(na.omit(b.random)) - var(na.omit(a.random))
}
# P-value is the fraction of how many times the permuted difference is
# equal or more extreme than the observed difference
t_Treat_diff_Female_area_Is_p = sum(abs(diff.random) >= as.numeric(abs(diff.observed)))/ number_of_permutations
#Population size ####
#Males
Treat_diff_Male_pop_Is=c(Is_Small_pop_Male_bootvar$t)-c(Is_Large_pop_Male_bootvar$t)
t_Treat_diff_Male_pop_Is=mean(Treat_diff_Male_pop_Is,na.rm=TRUE)
t_Treat_diff_Male_pop_Is_lower=quantile(Treat_diff_Male_pop_Is,.025,na.rm=TRUE)
t_Treat_diff_Male_pop_Is_upper=quantile(Treat_diff_Male_pop_Is,.975,na.rm=TRUE)
#Permutation test to calculate p value
comb_data=c(DB_data_clean_Small_pop$rel_m_cMS,DB_data_clean_Large_pop$rel_m_cMS)
diff.observed = var(na.omit(DB_data_clean_Small_pop$rel_m_cMS),na.rm = T) - var(na.omit(DB_data_clean_Large_pop$rel_m_cMS))
diff.observed
number_of_permutations = 100000
diff.random = NULL
for (i in 1 : number_of_permutations) {
# Sample from the combined dataset
a.random = sample (na.omit(comb_data), length(DB_data_clean_Small_pop$rel_m_cMS), TRUE)
b.random = sample (na.omit(comb_data), length(DB_data_clean_Large_pop$rel_m_cMS), TRUE)
# Null (permuated) difference
diff.random[i] = var(na.omit(b.random)) - var(na.omit(a.random))
}
# P-value is the fraction of how many times the permuted difference is
# equal or more extreme than the observed difference
t_Treat_diff_Male_pop_Is_p = sum(abs(diff.random) >= as.numeric(abs(diff.observed)))/ number_of_permutations
#Females
Treat_diff_Female_pop_Is=c(Is_Small_pop_Female_bootvar$t)-c(Is_Large_pop_Female_bootvar$t)
t_Treat_diff_Female_pop_Is=mean(Treat_diff_Female_pop_Is,na.rm=TRUE)
t_Treat_diff_Female_pop_Is_lower=quantile(Treat_diff_Female_pop_Is,.025,na.rm=TRUE)
t_Treat_diff_Female_pop_Is_upper=quantile(Treat_diff_Female_pop_Is,.975,na.rm=TRUE)
#Permutation test to calculate p value
comb_data=c(DB_data_clean_Small_pop$rel_f_cMS,DB_data_clean_Large_pop$rel_f_cMS)
diff.observed = var(na.omit(DB_data_clean_Small_pop$rel_f_cMS)) - var(na.omit(DB_data_clean_Large_pop$rel_f_cMS))
diff.observed
number_of_permutations = 100000
diff.random = NULL
for (i in 1 : number_of_permutations) {
# Sample from the combined dataset
a.random = sample (na.omit(comb_data), length(c(DB_data_clean_Small_pop$rel_f_cMS)), TRUE)
b.random = sample (na.omit(comb_data), length(c(DB_data_clean_Large_pop$rel_f_cMS)), TRUE)
# Null (permuated) difference
diff.random[i] = var(na.omit(b.random)) - var(na.omit(a.random))
}
# P-value is the fraction of how many times the permuted difference is
# equal or more extreme than the observed difference
t_Treat_diff_Female_pop_Is_p = sum(abs(diff.random) >= as.numeric(abs(diff.observed)))/ number_of_permutations
#B ####
#Area ####
#Males
Treat_diff_Male_area_B=c(B_Large_area_Male_bootvar$t)-c(B_Small_area_Male_bootvar$t)
t_Treat_diff_Male_area_B=mean(Treat_diff_Male_area_B,na.rm=TRUE)
t_Treat_diff_Male_area_B_lower=quantile(Treat_diff_Male_area_B,.025,na.rm=TRUE)
t_Treat_diff_Male_area_B_upper=quantile(Treat_diff_Male_area_B,.975,na.rm=TRUE)
#Permutation test to calculate p value
comb_data1=c(DB_data_clean_Large_area$rel_m_RS, DB_data_clean_Small_area$rel_m_RS)
comb_data2=c(DB_data_clean_Large_area$rel_m_cMS,DB_data_clean_Small_area$rel_m_cMS)
diff.observed = lm(DB_data_clean_Large_area$rel_m_RS ~DB_data_clean_Large_area$rel_m_cMS)$coefficients[2] - lm(DB_data_clean_Small_area$rel_m_RS ~DB_data_clean_Small_area$rel_m_cMS)$coefficients[2]
diff.observed
number_of_permutations = 100000
diff.random = NULL
for (i in 1 : number_of_permutations) {
# Sample from the combined dataset
a.random = sample (na.omit(comb_data1), length(DB_data_clean_Large_area$rel_m_RS), TRUE)
b.random = sample (na.omit(comb_data1), length(DB_data_clean_Small_area$rel_m_RS), TRUE)
c.random = sample (na.omit(comb_data2), length(DB_data_clean_Large_area$rel_m_cMS), TRUE)
d.random = sample (na.omit(comb_data2), length(DB_data_clean_Small_area$rel_m_cMS), TRUE)
# Null (permuated) difference
diff.random[i] = lm(a.random ~c.random)$coefficients[2] - lm(b.random ~d.random)$coefficients[2]
}
# P-value is the fraction of how many times the permuted difference is
# equal or more extreme than the observed difference
t_Treat_diff_Male_area_B_p = sum(abs(diff.random) >= as.numeric(abs(diff.observed)))/ number_of_permutations
#Females
Treat_diff_Female_area_B=c(B_Large_area_Female_bootvar$t)-c(B_Small_area_Female_bootvar$t)
t_Treat_diff_Female_area_B=mean(Treat_diff_Female_area_B,na.rm=TRUE)
t_Treat_diff_Female_area_B_lower=quantile(Treat_diff_Female_area_B,.025,na.rm=TRUE)
t_Treat_diff_Female_area_B_upper=quantile(Treat_diff_Female_area_B,.975,na.rm=TRUE)
#Permutation test to calculate p value
comb_data1=c(DB_data_clean_Large_area$rel_f_RS, DB_data_clean_Small_area$rel_f_RS)
comb_data2=c(DB_data_clean_Large_area$rel_f_cMS,DB_data_clean_Small_area$rel_f_cMS)
diff.observed = lm(DB_data_clean_Large_area$rel_f_RS ~DB_data_clean_Large_area$rel_f_cMS)$coefficients[2] - lm(DB_data_clean_Small_area$rel_f_RS ~DB_data_clean_Small_area$rel_f_cMS)$coefficients[2]
diff.observed
number_of_permutations = 100000
diff.random = NULL
for (i in 1 : number_of_permutations) {
# Sample from the combined dataset
a.random = sample (na.omit(comb_data1), length(DB_data_clean_Large_area$rel_f_RS), TRUE)
b.random = sample (na.omit(comb_data1), length(DB_data_clean_Small_area$rel_f_RS), TRUE)
c.random = sample (na.omit(comb_data2), length(DB_data_clean_Large_area$rel_f_cMS), TRUE)
d.random = sample (na.omit(comb_data2), length(DB_data_clean_Small_area$rel_f_cMS), TRUE)
# Null (permuated) difference
diff.random[i] = lm(a.random ~c.random)$coefficients[2] - lm(b.random ~d.random)$coefficients[2]
}
# P-value is the fraction of how many times the permuted difference is
# equal or more extreme than the observed difference
t_Treat_diff_Female_area_B_p = sum(abs(diff.random) >= as.numeric(abs(diff.observed)))/ number_of_permutations
#Population size ####
#Males
Treat_diff_Male_pop_B=c(B_Small_pop_Male_bootvar$t)-c(B_Large_pop_Male_bootvar$t)
t_Treat_diff_Male_pop_B=mean(Treat_diff_Male_pop_B,na.rm=TRUE)
t_Treat_diff_Male_pop_B_lower=quantile(Treat_diff_Male_pop_B,.025,na.rm=TRUE)
t_Treat_diff_Male_pop_B_upper=quantile(Treat_diff_Male_pop_B,.975,na.rm=TRUE)
#Permutation test to calculate p value
comb_data1=c(DB_data_clean_Small_pop$rel_m_RS, DB_data_clean_Large_pop$rel_m_RS)
comb_data2=c(DB_data_clean_Small_pop$rel_m_cMS,DB_data_clean_Large_pop$rel_m_cMS)
diff.observed = lm(DB_data_clean_Small_pop$rel_m_RS ~DB_data_clean_Small_pop$rel_m_cMS)$coefficients[2] - lm(DB_data_clean_Large_pop$rel_m_RS ~DB_data_clean_Large_pop$rel_m_cMS)$coefficients[2]
diff.observed
number_of_permutations = 100000
diff.random = NULL
for (i in 1 : number_of_permutations) {
# Sample from the combined dataset
a.random = sample (na.omit(comb_data1), length(DB_data_clean_Small_pop$rel_m_RS), TRUE)
b.random = sample (na.omit(comb_data1), length(DB_data_clean_Large_pop$rel_m_RS), TRUE)
c.random = sample (na.omit(comb_data2), length(DB_data_clean_Small_pop$rel_m_cMS), TRUE)
d.random = sample (na.omit(comb_data2), length(DB_data_clean_Large_pop$rel_m_cMS), TRUE)
# Null (permuated) difference
diff.random[i] = lm(a.random ~c.random)$coefficients[2] - lm(b.random ~d.random)$coefficients[2]
}
# P-value is the fraction of how many times the permuted difference is
# equal or more extreme than the observed difference
t_Treat_diff_Male_pop_B_p = sum(abs(diff.random) >= as.numeric(abs(diff.observed)))/ number_of_permutations
#Females
Treat_diff_Female_pop_B=c(B_Small_pop_Female_bootvar$t)-c(B_Large_pop_Female_bootvar$t)
t_Treat_diff_Female_pop_B=mean(Treat_diff_Female_pop_B,na.rm=TRUE)
t_Treat_diff_Female_pop_B_lower=quantile(Treat_diff_Female_pop_B,.025,na.rm=TRUE)
t_Treat_diff_Female_pop_B_upper=quantile(Treat_diff_Female_pop_B,.975,na.rm=TRUE)
#Permutation test to calculate p value
comb_data1=c(DB_data_clean_Small_pop$rel_f_RS, DB_data_clean_Large_pop$rel_f_RS)
comb_data2=c(DB_data_clean_Small_pop$rel_f_cMS,DB_data_clean_Large_pop$rel_f_cMS)
diff.observed = lm(DB_data_clean_Small_pop$rel_f_RS ~DB_data_clean_Small_pop$rel_f_cMS)$coefficients[2] - lm(DB_data_clean_Large_pop$rel_f_RS ~DB_data_clean_Large_pop$rel_f_cMS)$coefficients[2]
diff.observed
number_of_permutations = 100000
diff.random = NULL
for (i in 1 : number_of_permutations) {
# Sample from the combined dataset
a.random = sample (na.omit(comb_data1), length(DB_data_clean_Small_pop$rel_f_RS), TRUE)
b.random = sample (na.omit(comb_data1), length(DB_data_clean_Large_pop$rel_f_RS), TRUE)
c.random = sample (na.omit(comb_data2), length(DB_data_clean_Small_pop$rel_f_cMS), TRUE)
d.random = sample (na.omit(comb_data2), length(DB_data_clean_Large_pop$rel_f_cMS), TRUE)
# Null (permuated) difference
diff.random[i] = lm(a.random ~c.random)$coefficients[2] - lm(b.random ~d.random)$coefficients[2]
}
# P-value is the fraction of how many times the permuted difference is
# equal or more extreme than the observed difference
t_Treat_diff_Female_pop_B_p = sum(abs(diff.random) >= as.numeric(abs(diff.observed)))/ number_of_permutations
#S ####
#Area ####
#Males
Treat_diff_Male_area_S=c(S_Large_area_Male_bootvar$t)-c(S_Small_area_Male_bootvar$t)
t_Treat_diff_Male_area_S=mean(Treat_diff_Male_area_S,na.rm=TRUE)
t_Treat_diff_Male_area_S_lower=quantile(Treat_diff_Male_area_S,.025,na.rm=TRUE)
t_Treat_diff_Male_area_S_upper=quantile(Treat_diff_Male_area_S,.975,na.rm=TRUE)
#Permutation test to calculate p value
comb_data1=c(DB_data_clean_Large_area$rel_m_cMS, DB_data_clean_Small_area$rel_m_cMS)
comb_data2=c(DB_data_clean_Large_area$rel_m_RS, DB_data_clean_Small_area$rel_m_RS)
diff.observed = lm(DB_data_clean_Large_area$rel_m_RS ~DB_data_clean_Large_area$rel_m_cMS)$coefficients[2]*sqrt(var(DB_data_clean_Large_area$rel_m_cMS, na.rm=TRUE)) - lm(DB_data_clean_Small_area$rel_m_RS ~DB_data_clean_Small_area$rel_m_cMS)$coefficients[2]*sqrt(var(DB_data_clean_Small_area$rel_m_cMS, na.rm=TRUE))
diff.observed
number_of_permutations = 100000
diff.random = NULL
for (i in 1 : number_of_permutations) {
# Sample from the combined dataset
a.random = sample (na.omit(comb_data1), length(DB_data_clean_Large_area$rel_m_cMS), TRUE)
b.random = sample (na.omit(comb_data1), length(DB_data_clean_Small_area$rel_m_cMS), TRUE)
c.random = sample (na.omit(comb_data2), length(DB_data_clean_Large_area$rel_m_RS), TRUE)
d.random = sample (na.omit(comb_data2), length(DB_data_clean_Small_area$rel_m_RS), TRUE)
# Null (permuated) difference
diff.random[i] = lm(c.random ~a.random)$coefficients[2]*sqrt(var(a.random, na.rm=TRUE)) - lm(d.random ~b.random)$coefficients[2]*sqrt(var(b.random, na.rm=TRUE))
}
# P-value is the fraction of how many times the permuted difference is
# equal or more extreme than the observed difference
t_Treat_diff_Male_area_S_p = sum(abs(diff.random) >= as.numeric(abs(diff.observed)))/ number_of_permutations
#Females
Treat_diff_Female_area_S=c(S_Large_area_Female_bootvar$t)-c(S_Small_area_Female_bootvar$t)
t_Treat_diff_Female_area_S=mean(Treat_diff_Female_area_S,na.rm=TRUE)
t_Treat_diff_Female_area_S_lower=quantile(Treat_diff_Female_area_S,.025,na.rm=TRUE)
t_Treat_diff_Female_area_S_upper=quantile(Treat_diff_Female_area_S,.975,na.rm=TRUE)
#Permutation test to calculate p value
comb_data1=c(DB_data_clean_Large_area$rel_f_cMS, DB_data_clean_Small_area$rel_f_cMS)
comb_data2=c(DB_data_clean_Large_area$rel_f_RS, DB_data_clean_Small_area$rel_f_RS)
diff.observed = lm(DB_data_clean_Large_area$rel_f_RS ~DB_data_clean_Large_area$rel_f_cMS)$coefficients[2]*sqrt(var(DB_data_clean_Large_area$rel_f_cMS, na.rm=TRUE)) - lm(DB_data_clean_Small_area$rel_f_RS ~DB_data_clean_Small_area$rel_f_cMS)$coefficients[2]*sqrt(var(DB_data_clean_Small_area$rel_f_cMS, na.rm=TRUE))
diff.observed
number_of_permutations = 100000
diff.random = NULL
for (i in 1 : number_of_permutations) {
# Sample from the combined dataset
a.random = sample (na.omit(comb_data1), length(DB_data_clean_Large_area$rel_f_cMS), TRUE)
b.random = sample (na.omit(comb_data1), length(DB_data_clean_Small_area$rel_f_cMS), TRUE)
c.random = sample (na.omit(comb_data2), length(DB_data_clean_Large_area$rel_f_RS), TRUE)
d.random = sample (na.omit(comb_data2), length(DB_data_clean_Small_area$rel_f_RS), TRUE)
# Null (permuated) difference
diff.random[i] = lm(c.random ~a.random)$coefficients[2]*sqrt(var(a.random, na.rm=TRUE)) - lm(d.random ~b.random)$coefficients[2]*sqrt(var(b.random, na.rm=TRUE))
}
# P-value is the fraction of how many times the permuted difference is
# equal or more extreme than the observed difference
t_Treat_diff_Female_area_S_p = sum(abs(diff.random) >= as.numeric(abs(diff.observed)))/ number_of_permutations
#Population size ####
#Males
Treat_diff_Male_pop_S=c(S_Small_pop_Male_bootvar$t)-c(S_Large_pop_Male_bootvar$t)
t_Treat_diff_Male_pop_S=mean(Treat_diff_Male_pop_S,na.rm=TRUE)
t_Treat_diff_Male_pop_S_lower=quantile(Treat_diff_Male_pop_S,.025,na.rm=TRUE)
t_Treat_diff_Male_pop_S_upper=quantile(Treat_diff_Male_pop_S,.975,na.rm=TRUE)
#Permutation test to calculate p value
comb_data1=c(DB_data_clean_Small_pop$rel_m_cMS, DB_data_clean_Large_pop$rel_m_cMS)
comb_data2=c(DB_data_clean_Small_pop$rel_m_RS, DB_data_clean_Large_pop$rel_m_RS)
diff.observed = lm(DB_data_clean_Small_pop$rel_m_RS ~DB_data_clean_Small_pop$rel_m_cMS)$coefficients[2]*sqrt(var(DB_data_clean_Small_pop$rel_m_cMS, na.rm=TRUE)) - lm(DB_data_clean_Large_pop$rel_m_RS ~DB_data_clean_Large_pop$rel_m_cMS)$coefficients[2]*sqrt(var(DB_data_clean_Large_pop$rel_m_cMS, na.rm=TRUE))
diff.observed
number_of_permutations = 100000
diff.random = NULL
for (i in 1 : number_of_permutations) {
# Sample from the combined dataset
a.random = sample (na.omit(comb_data1), length(DB_data_clean_Small_pop$rel_m_cMS), TRUE)
b.random = sample (na.omit(comb_data1), length(DB_data_clean_Large_pop$rel_m_cMS), TRUE)
c.random = sample (na.omit(comb_data2), length(DB_data_clean_Small_pop$rel_m_RS), TRUE)
d.random = sample (na.omit(comb_data2), length(DB_data_clean_Large_pop$rel_m_RS), TRUE)
# Null (permuated) difference
diff.random[i] = lm(c.random ~a.random)$coefficients[2]*sqrt(var(a.random, na.rm=TRUE)) - lm(d.random ~b.random)$coefficients[2]*sqrt(var(b.random, na.rm=TRUE))
}
# P-value is the fraction of how many times the permuted difference is
# equal or more extreme than the observed difference
t_Treat_diff_Male_pop_S_p = sum(abs(diff.random) >= as.numeric(abs(diff.observed)))/ number_of_permutations
#Females
Treat_diff_Female_pop_S=c(S_Small_pop_Female_bootvar$t)-c(S_Large_pop_Female_bootvar$t)
t_Treat_diff_Female_pop_S=mean(Treat_diff_Female_pop_S,na.rm=TRUE)
t_Treat_diff_Female_pop_S_lower=quantile(Treat_diff_Female_pop_S,.025,na.rm=TRUE)
t_Treat_diff_Female_pop_S_upper=quantile(Treat_diff_Female_pop_S,.975,na.rm=TRUE)
#Permutation test to calculate p value
comb_data1=c(DB_data_clean_Small_pop$rel_f_cMS, DB_data_clean_Large_pop$rel_f_cMS)
comb_data2=c(DB_data_clean_Small_pop$rel_f_RS, DB_data_clean_Large_pop$rel_f_RS)
diff.observed = lm(DB_data_clean_Small_pop$rel_f_RS ~DB_data_clean_Small_pop$rel_f_cMS)$coefficients[2]*sqrt(var(DB_data_clean_Small_pop$rel_f_cMS, na.rm=TRUE)) - lm(DB_data_clean_Large_pop$rel_f_RS ~DB_data_clean_Large_pop$rel_f_cMS)$coefficients[2]*sqrt(var(DB_data_clean_Large_pop$rel_f_cMS, na.rm=TRUE))
diff.observed
number_of_permutations = 100000
diff.random = NULL
for (i in 1 : number_of_permutations) {
# Sample from the combined dataset
a.random = sample (na.omit(comb_data1), length(DB_data_clean_Small_pop$rel_f_cMS), TRUE)
b.random = sample (na.omit(comb_data1), length(DB_data_clean_Large_pop$rel_f_cMS), TRUE)
c.random = sample (na.omit(comb_data2), length(DB_data_clean_Small_pop$rel_f_RS), TRUE)
d.random = sample (na.omit(comb_data2), length(DB_data_clean_Large_pop$rel_f_RS), TRUE)
# Null (permuated) difference
diff.random[i] = lm(c.random ~a.random)$coefficients[2]*sqrt(var(a.random, na.rm=TRUE)) - lm(d.random ~b.random)$coefficients[2]*sqrt(var(b.random, na.rm=TRUE))
}
# P-value is the fraction of how many times the permuted difference is
# equal or more extreme than the observed difference
t_Treat_diff_Female_pop_S_p = sum(abs(diff.random) >= as.numeric(abs(diff.observed)))/ number_of_permutations
#Save data table ####
CompTreat_Table_Male_area_I <- as.data.frame(cbind("Male", "Area", "Opportunity for selection", t_Treat_diff_Male_area_I, t_Treat_diff_Male_area_I_lower, t_Treat_diff_Male_area_I_upper, t_Treat_diff_Male_area_I_p))
names(CompTreat_Table_Male_area_I)=c('V1','V2','V3','V4','V5','V6','V7')
CompTreat_Table_Male_pop_I <- as.data.frame(cbind("Male", "Population size", "Opportunity for selection", t_Treat_diff_Male_pop_I, t_Treat_diff_Male_pop_I_lower, t_Treat_diff_Male_pop_I_upper, t_Treat_diff_Male_pop_I_p))
names(CompTreat_Table_Male_pop_I)=c('V1','V2','V3','V4','V5','V6','V7')
CompTreat_Table_Male_area_Is <- as.data.frame(cbind("Male", "Area", "Opportunity for sexual selection", t_Treat_diff_Male_area_Is, t_Treat_diff_Male_area_Is_lower, t_Treat_diff_Male_area_Is_upper, t_Treat_diff_Male_area_Is_p))
names(CompTreat_Table_Male_area_Is)=c('V1','V2','V3','V4','V5','V6','V7')
CompTreat_Table_Male_pop_Is <- as.data.frame(cbind("Male", "Population size", "Opportunity for sexual selection", t_Treat_diff_Male_pop_Is, t_Treat_diff_Male_pop_Is_lower, t_Treat_diff_Male_pop_Is_upper, t_Treat_diff_Male_pop_Is_p))
names(CompTreat_Table_Male_pop_Is)=c('V1','V2','V3','V4','V5','V6','V7')
CompTreat_Table_Male_area_B <- as.data.frame(cbind("Male", "Area", "Bateman gradient", t_Treat_diff_Male_area_B, t_Treat_diff_Male_area_B_lower, t_Treat_diff_Male_area_B_upper, t_Treat_diff_Male_area_B_p))
names(CompTreat_Table_Male_area_B)=c('V1','V2','V3','V4','V5','V6','V7')
CompTreat_Table_Male_pop_B <- as.data.frame(cbind("Male", "Population size", "Bateman gradient", t_Treat_diff_Male_pop_B, t_Treat_diff_Male_pop_B_lower, t_Treat_diff_Male_pop_B_upper, t_Treat_diff_Male_pop_B_p))
names(CompTreat_Table_Male_pop_B)=c('V1','V2','V3','V4','V5','V6','V7')
CompTreat_Table_Male_area_S <- as.data.frame(cbind("Male", "Area", "Jones index", t_Treat_diff_Male_area_S, t_Treat_diff_Male_area_S_lower, t_Treat_diff_Male_area_S_upper, t_Treat_diff_Male_area_S_p))
names(CompTreat_Table_Male_area_S)=c('V1','V2','V3','V4','V5','V6','V7')
CompTreat_Table_Male_pop_S <- as.data.frame(cbind("Male", "Population size", "Jones index", t_Treat_diff_Male_pop_S, t_Treat_diff_Male_pop_S_lower, t_Treat_diff_Male_pop_S_upper, t_Treat_diff_Male_pop_S_p))
names(CompTreat_Table_Male_pop_S)=c('V1','V2','V3','V4','V5','V6','V7')
CompTreat_Table_Female_area_I <- as.data.frame(cbind("Female", "Area", "Opportunity for selection", t_Treat_diff_Female_area_I, t_Treat_diff_Female_area_I_lower, t_Treat_diff_Female_area_I_upper, t_Treat_diff_Female_area_I_p))
names(CompTreat_Table_Female_area_I)=c('V1','V2','V3','V4','V5','V6','V7')
CompTreat_Table_Female_pop_I <- as.data.frame(cbind("Female", "Population size", "Opportunity for selection", t_Treat_diff_Female_pop_I, t_Treat_diff_Female_pop_I_lower, t_Treat_diff_Female_pop_I_upper, t_Treat_diff_Female_pop_I_p))
names(CompTreat_Table_Female_pop_I)=c('V1','V2','V3','V4','V5','V6','V7')
CompTreat_Table_Female_area_Is <- as.data.frame(cbind("Female", "Area", "Opportunity for sexual selection", t_Treat_diff_Female_area_Is, t_Treat_diff_Female_area_Is_lower, t_Treat_diff_Female_area_Is_upper, t_Treat_diff_Female_area_Is_p))
names(CompTreat_Table_Female_area_Is)=c('V1','V2','V3','V4','V5','V6','V7')
CompTreat_Table_Female_pop_Is <- as.data.frame(cbind("Female", "Population size", "Opportunity for sexual selection", t_Treat_diff_Female_pop_Is, t_Treat_diff_Female_pop_Is_lower, t_Treat_diff_Female_pop_Is_upper, t_Treat_diff_Female_pop_Is_p))
names(CompTreat_Table_Female_pop_Is)=c('V1','V2','V3','V4','V5','V6','V7')
CompTreat_Table_Female_area_B <- as.data.frame(cbind("Female", "Area", "Bateman gradient", t_Treat_diff_Female_area_B, t_Treat_diff_Female_area_B_lower, t_Treat_diff_Female_area_B_upper, t_Treat_diff_Female_area_B_p))
names(CompTreat_Table_Female_area_B)=c('V1','V2','V3','V4','V5','V6','V7')
CompTreat_Table_Female_pop_B <- as.data.frame(cbind("Female", "Population size", "Bateman gradient", t_Treat_diff_Female_pop_B, t_Treat_diff_Female_pop_B_lower, t_Treat_diff_Female_pop_B_upper, t_Treat_diff_Female_pop_B_p))
names(CompTreat_Table_Female_pop_B)=c('V1','V2','V3','V4','V5','V6','V7')
CompTreat_Table_Female_area_S <- as.data.frame(cbind("Female", "Area", "Jones index", t_Treat_diff_Female_area_S, t_Treat_diff_Female_area_S_lower, t_Treat_diff_Female_area_S_upper, t_Treat_diff_Female_area_S_p))
names(CompTreat_Table_Female_area_S)=c('V1','V2','V3','V4','V5','V6','V7')
CompTreat_Table_Female_pop_S <- as.data.frame(cbind("Female", "Population size", "Jones index", t_Treat_diff_Female_pop_S, t_Treat_diff_Female_pop_S_lower, t_Treat_diff_Female_pop_S_upper, t_Treat_diff_Female_pop_S_p))
names(CompTreat_Table_Female_pop_S)=c('V1','V2','V3','V4','V5','V6','V7')
Table_BatemanMetrics_TreatComp <- as.data.frame(as.matrix(rbind(CompTreat_Table_Male_area_I,CompTreat_Table_Male_area_Is,
CompTreat_Table_Male_area_B,CompTreat_Table_Male_area_S,
CompTreat_Table_Male_pop_I,CompTreat_Table_Male_pop_Is,
CompTreat_Table_Male_pop_B,CompTreat_Table_Male_pop_S,
CompTreat_Table_Female_area_I,CompTreat_Table_Female_area_Is,
CompTreat_Table_Female_area_B,CompTreat_Table_Female_area_S,
CompTreat_Table_Female_pop_I,CompTreat_Table_Female_pop_Is,
CompTreat_Table_Female_pop_B,CompTreat_Table_Female_pop_S
)))
Table_BatemanMetrics_TreatComp[,4]=as.numeric(Table_BatemanMetrics_TreatComp[,4])
Table_BatemanMetrics_TreatComp[,5]=as.numeric(Table_BatemanMetrics_TreatComp[,5])
Table_BatemanMetrics_TreatComp[,6]=as.numeric(Table_BatemanMetrics_TreatComp[,6])
Table_BatemanMetrics_TreatComp[,7]=as.numeric(Table_BatemanMetrics_TreatComp[,7])
colnames(Table_BatemanMetrics_TreatComp)[1] <- "Sex"
colnames(Table_BatemanMetrics_TreatComp)[2] <- "Treatment"
colnames(Table_BatemanMetrics_TreatComp)[3] <- "Selection_metric"
colnames(Table_BatemanMetrics_TreatComp)[4] <- "Variance"
colnames(Table_BatemanMetrics_TreatComp)[5] <- "l95.CI"
colnames(Table_BatemanMetrics_TreatComp)[6] <- "u95.CI"
colnames(Table_BatemanMetrics_TreatComp)[7] <- "p-value"
Table_BatemanMetrics_TreatComp[,4]=as.numeric(Table_BatemanMetrics_TreatComp[,4])
Table_BatemanMetrics_TreatComp[,5]=as.numeric(Table_BatemanMetrics_TreatComp[,5])
Table_BatemanMetrics_TreatComp[,6]=as.numeric(Table_BatemanMetrics_TreatComp[,6])
Table_BatemanMetrics_TreatComp[,7]=as.numeric(Table_BatemanMetrics_TreatComp[,7])
Table_BatemanMetrics_TreatComp_round=cbind(Table_BatemanMetrics_TreatComp[,c(1,2,3)],round(Table_BatemanMetrics_TreatComp[,c(4,5,6,7)],digit=3))
rownames(Table_BatemanMetrics_TreatComp_round) <- NULL#Bootstrap sex comparisons ####
#I ####
#Area ####
#Large
Sex_diff_Large_area_I=c(I_Large_area_Male_bootvar$t)-c(I_Large_area_Female_bootvar$t)
t_Sex_diff_Large_area_I=mean(Sex_diff_Large_area_I,na.rm=TRUE)
t_Sex_diff_Large_area_I_lower=quantile(Sex_diff_Large_area_I,.025,na.rm=TRUE)
t_Sex_diff_Large_area_I_upper=quantile(Sex_diff_Large_area_I,.975,na.rm=TRUE)
#Permutation test to calculate p value
comb_data=c(DB_data_clean_Large_area$rel_m_RS,DB_data_clean_Large_area$rel_f_RS)
diff.observed = var(na.omit((DB_data_clean_Large_area$rel_m_RS))) - var(na.omit((DB_data_clean_Large_area$rel_f_RS)))
diff.observed
number_of_permutations = 100000
diff.random = NULL
for (i in 1 : number_of_permutations) {
# Sample from the combined dataset
a.random = sample (na.omit(comb_data), length(c(DB_data_clean_Large_area$rel_m_RS)), TRUE)
b.random = sample (na.omit(comb_data), length(c(DB_data_clean_Large_area$rel_f_RS)), TRUE)
# Null (permuated) difference
diff.random[i] = var(na.omit(b.random)) - var(na.omit(a.random))
}
# P-value is the fraction of how many times the permuted difference is
# equal or more extreme than the observed difference
t_Sex_diff_Large_area_I_p = sum(abs(diff.random) >= as.numeric(abs(diff.observed)))/ number_of_permutations
#Small
Sex_diff_Small_area_I=c(I_Small_area_Male_bootvar$t)-c(I_Small_area_Female_bootvar$t)
t_Sex_diff_Small_area_I=mean(Sex_diff_Small_area_I,na.rm=TRUE)
t_Sex_diff_Small_area_I_lower=quantile(Sex_diff_Small_area_I,.025,na.rm=TRUE)
t_Sex_diff_Small_area_I_upper=quantile(Sex_diff_Small_area_I,.975,na.rm=TRUE)
#Permutation test to calculate p value
comb_data=c(DB_data_clean_Small_area$rel_m_RS,DB_data_clean_Small_area$rel_f_RS)
diff.observed = var(na.omit((DB_data_clean_Small_area$rel_m_RS))) - var(na.omit((DB_data_clean_Small_area$rel_f_RS)))
diff.observed
number_of_permutations = 100000
diff.random = NULL
for (i in 1 : number_of_permutations) {
# Sample from the combined dataset
a.random = sample (na.omit(comb_data), length(c(DB_data_clean_Small_area$rel_m_RS)), TRUE)
b.random = sample (na.omit(comb_data), length(c(DB_data_clean_Small_area$rel_f_RS)), TRUE)
# Null (permuated) difference
diff.random[i] = var(na.omit(b.random)) - var(na.omit(a.random))
}
# P-value is the fraction of how many times the permuted difference is
# equal or more extreme than the observed difference
t_Sex_diff_Small_area_I_p = sum(abs(diff.random) >= as.numeric(abs(diff.observed)))/ number_of_permutations
#Population size ####
#Small
Sex_diff_Small_pop_I=c(I_Small_pop_Male_bootvar$t)-c(I_Small_pop_Female_bootvar$t)
t_Sex_diff_Small_pop_I=mean(Sex_diff_Small_pop_I,na.rm=TRUE)
t_Sex_diff_Small_pop_I_lower=quantile(Sex_diff_Small_pop_I,.025,na.rm=TRUE)
t_Sex_diff_Small_pop_I_upper=quantile(Sex_diff_Small_pop_I,.975,na.rm=TRUE)
#Permutation test to calculate p value
comb_data=c(DB_data_clean_Small_pop$rel_m_RS,DB_data_clean_Small_pop$rel_f_RS)
diff.observed = var(na.omit((DB_data_clean_Small_pop$rel_m_RS))) - var(na.omit((DB_data_clean_Small_pop$rel_f_RS)))
diff.observed
number_of_permutations = 100000
diff.random = NULL
for (i in 1 : number_of_permutations) {
# Sample from the combined dataset
a.random = sample (na.omit(comb_data), length(c(DB_data_clean_Small_pop$rel_m_RS)), TRUE)
b.random = sample (na.omit(comb_data), length(c(DB_data_clean_Small_pop$rel_f_RS)), TRUE)
# Null (permuated) difference
diff.random[i] = var(na.omit(b.random)) - var(na.omit(a.random))
}
# P-value is the fraction of how many times the permuted difference is
# equal or more extreme than the observed difference
t_Sex_diff_Small_pop_I_p = sum(abs(diff.random) >= as.numeric(abs(diff.observed)))/ number_of_permutations
#Large
Sex_diff_Large_pop_I=c(I_Large_pop_Male_bootvar$t)-c(I_Large_pop_Female_bootvar$t)
t_Sex_diff_Large_pop_I=mean(Sex_diff_Large_pop_I,na.rm=TRUE)
t_Sex_diff_Large_pop_I_lower=quantile(Sex_diff_Large_pop_I,.025,na.rm=TRUE)
t_Sex_diff_Large_pop_I_upper=quantile(Sex_diff_Large_pop_I,.975,na.rm=TRUE)
#Permutation test to calculate p value
comb_data=c(DB_data_clean_Large_pop$rel_m_RS,DB_data_clean_Large_pop$rel_f_RS)
diff.observed = var(na.omit(c(DB_data_clean_Large_pop$rel_m_RS))) - var(na.omit(c(DB_data_clean_Large_pop$rel_f_RS)))
diff.observed
number_of_permutations = 100000
diff.random = NULL
for (i in 1 : number_of_permutations) {
# Sample from the combined dataset
a.random = sample (na.omit(comb_data), length(c(DB_data_clean_Large_pop$rel_m_RS)), TRUE)
b.random = sample (na.omit(comb_data), length(c(DB_data_clean_Large_pop$rel_f_RS)), TRUE)
# Null (permuated) difference
diff.random[i] = var(na.omit(b.random)) - var(na.omit(a.random))
}
# P-value is the fraction of how many times the permuted difference is
# equal or more extreme than the observed difference
t_Sex_diff_Large_pop_I_p = sum(abs(diff.random) >= as.numeric(abs(diff.observed)))/ number_of_permutations
#Is ####
#Area ####
#Small
Sex_diff_Small_area_Is=c(Is_Small_area_Male_bootvar$t)-c(Is_Small_area_Female_bootvar$t)
t_Sex_diff_Small_area_Is=mean(Sex_diff_Small_area_Is,na.rm=TRUE)
t_Sex_diff_Small_area_Is_lower=quantile(Sex_diff_Small_area_Is,.025,na.rm=TRUE)
t_Sex_diff_Small_area_Is_upper=quantile(Sex_diff_Small_area_Is,.975,na.rm=TRUE)
#Permutation test to calculate p value
comb_data=c(DB_data_clean_Small_area$rel_m_cMS,DB_data_clean_Small_area$rel_f_cMS)
diff.observed = var(na.omit(c(DB_data_clean_Small_area$rel_m_cMS))) - var(na.omit(c(DB_data_clean_Small_area$rel_f_cMS)))
diff.observed
number_of_permutations = 100000
diff.random = NULL
for (i in 1 : number_of_permutations) {
# Sample from the combined dataset
a.random = sample (na.omit(comb_data), length(c(DB_data_clean_Small_area$rel_m_cMS)), TRUE)
b.random = sample (na.omit(comb_data), length(c(DB_data_clean_Small_area$rel_f_cMS)), TRUE)
# Null (permuated) difference
diff.random[i] = var(na.omit(b.random)) - var(na.omit(a.random))
}
# P-value is the fraction of how many times the permuted difference is
# equal or more extreme than the observed difference
t_Sex_diff_Small_area_Is_p = sum(abs(diff.random) >= as.numeric(abs(diff.observed)))/ number_of_permutations
#Large
Sex_diff_Large_area_Is=c(Is_Large_area_Male_bootvar$t)-c(Is_Large_area_Female_bootvar$t)
t_Sex_diff_Large_area_Is=mean(Sex_diff_Large_area_Is,na.rm=TRUE)
t_Sex_diff_Large_area_Is_lower=quantile(Sex_diff_Large_area_Is,.025,na.rm=TRUE)
t_Sex_diff_Large_area_Is_upper=quantile(Sex_diff_Large_area_Is,.975,na.rm=TRUE)
#Permutation test to calculate p value
comb_data=c(DB_data_clean_Large_area$rel_m_cMS,DB_data_clean_Large_area$rel_f_cMS)
diff.observed = var(na.omit(c(DB_data_clean_Large_area$rel_m_cMS))) - var(na.omit(c(DB_data_clean_Large_area$rel_f_cMS)))
diff.observed
number_of_permutations = 100000
diff.random = NULL
for (i in 1 : number_of_permutations) {
# Sample from the combined dataset
a.random = sample (na.omit(comb_data), length(c(DB_data_clean_Large_area$rel_m_cMS)), TRUE)
b.random = sample (na.omit(comb_data), length(c(DB_data_clean_Large_area$rel_f_cMS)), TRUE)
# Null (permuated) difference
diff.random[i] = var(na.omit(b.random)) - var(na.omit(a.random))
}
# P-value is the fraction of how many times the permuted difference is
# equal or more extreme than the observed difference
t_Sex_diff_Large_area_Is_p = sum(abs(diff.random) >= as.numeric(abs(diff.observed)))/ number_of_permutations
#Population size ####
#Small
Sex_diff_Small_pop_Is=c(Is_Small_pop_Male_bootvar$t)-c(Is_Small_pop_Female_bootvar$t)
t_Sex_diff_Small_pop_Is=mean(Sex_diff_Small_pop_Is,na.rm=TRUE)
t_Sex_diff_Small_pop_Is_lower=quantile(Sex_diff_Small_pop_Is,.025,na.rm=TRUE)
t_Sex_diff_Small_pop_Is_upper=quantile(Sex_diff_Small_pop_Is,.975,na.rm=TRUE)
#Permutation test to calculate p value
comb_data=c(DB_data_clean_Small_pop$rel_m_cMS,DB_data_clean_Small_pop$rel_f_cMS)
diff.observed = var(na.omit(c(DB_data_clean_Small_pop$rel_m_cMS))) - var(na.omit(c(DB_data_clean_Small_pop$rel_f_cMS)))
diff.observed
number_of_permutations = 100000
diff.random = NULL
for (i in 1 : number_of_permutations) {
# Sample from the combined dataset
a.random = sample (na.omit(comb_data), length(c(DB_data_clean_Small_pop$rel_m_cMS)), TRUE)
b.random = sample (na.omit(comb_data), length(c(DB_data_clean_Small_pop$rel_f_cMS)), TRUE)
# Null (permuated) difference
diff.random[i] = var(na.omit(b.random)) - var(na.omit(a.random))
}
# P-value is the fraction of how many times the permuted difference is
# equal or more extreme than the observed difference
t_Sex_diff_Small_pop_Is_p = sum(abs(diff.random) >= as.numeric(abs(diff.observed)))/ number_of_permutations
#Large
Sex_diff_Large_pop_Is=c(Is_Large_pop_Male_bootvar$t)-c(Is_Large_pop_Female_bootvar$t)
t_Sex_diff_Large_pop_Is=mean(Sex_diff_Large_pop_Is,na.rm=TRUE)
t_Sex_diff_Large_pop_Is_lower=quantile(Sex_diff_Large_pop_Is,.025,na.rm=TRUE)
t_Sex_diff_Large_pop_Is_upper=quantile(Sex_diff_Large_pop_Is,.975,na.rm=TRUE)
#Permutation test to calculate p value
comb_data=c(DB_data_clean_Large_pop$rel_m_cMS,DB_data_clean_Large_pop$rel_f_cMS)
diff.observed = var(na.omit(c(DB_data_clean_Large_pop$rel_m_cMS))) - var(na.omit(c(DB_data_clean_Large_pop$rel_f_cMS)))
diff.observed
number_of_permutations = 100000
diff.random = NULL
for (i in 1 : number_of_permutations) {
# Sample from the combined dataset
a.random = sample (na.omit(comb_data), length(c(DB_data_clean_Large_pop$rel_m_cMS)), TRUE)
b.random = sample (na.omit(comb_data), length(c(DB_data_clean_Large_pop$rel_f_cMS)), TRUE)
# Null (permuated) difference
diff.random[i] = var(na.omit(b.random)) - var(na.omit(a.random))
}
# P-value is the fraction of how many times the permuted difference is
# equal or more extreme than the observed difference
t_Sex_diff_Large_pop_Is_p = sum(abs(diff.random) >= as.numeric(abs(diff.observed)))/ number_of_permutations
#B ####
#Area ####
#Large
Sex_diff_Large_area_B=c(B_Large_area_Male_bootvar$t)-c(B_Large_area_Female_bootvar$t)
t_Sex_diff_Large_area_B=mean(Sex_diff_Large_area_B,na.rm=TRUE)
t_Sex_diff_Large_area_B_lower=quantile(Sex_diff_Large_area_B,.025,na.rm=TRUE)
t_Sex_diff_Large_area_B_upper=quantile(Sex_diff_Large_area_B,.975,na.rm=TRUE)
#Permutation test to calculate p value
comb_data1=c(DB_data_clean_Large_area$rel_m_RS, DB_data_clean_Large_area$rel_f_RS)
comb_data2=c(DB_data_clean_Large_area$rel_m_cMS,DB_data_clean_Large_area$rel_f_cMS)
diff.observed = lm(DB_data_clean_Large_area$rel_m_RS ~DB_data_clean_Large_area$rel_m_cMS)$coefficients[2] - lm(DB_data_clean_Large_area$rel_f_RS ~DB_data_clean_Large_area$rel_f_cMS)$coefficients[2]
diff.observed
number_of_permutations = 100000
diff.random = NULL
for (i in 1 : number_of_permutations) {
# Sample from the combined dataset
a.random = sample (na.omit(comb_data1), length(DB_data_clean_Large_area$rel_m_RS), TRUE)
b.random = sample (na.omit(comb_data1), length(DB_data_clean_Small_area$rel_m_RS), TRUE)
c.random = sample (na.omit(comb_data2), length(DB_data_clean_Large_area$rel_m_cMS), TRUE)
d.random = sample (na.omit(comb_data2), length(DB_data_clean_Small_area$rel_m_cMS), TRUE)
# Null (permuated) difference
diff.random[i] = lm(a.random ~c.random)$coefficients[2] - lm(b.random ~d.random)$coefficients[2]
}
# P-value is the fraction of how many times the permuted difference is
# equal or more extreme than the observed difference
t_Sex_diff_Large_area_B_p = sum(abs(diff.random) >= as.numeric(abs(diff.observed)))/ number_of_permutations
#Small
Sex_diff_Small_area_B=c(B_Small_area_Male_bootvar$t)-c(B_Small_area_Female_bootvar$t)
t_Sex_diff_Small_area_B=mean(Sex_diff_Small_area_B,na.rm=TRUE)
t_Sex_diff_Small_area_B_lower=quantile(Sex_diff_Small_area_B,.025,na.rm=TRUE)
t_Sex_diff_Small_area_B_upper=quantile(Sex_diff_Small_area_B,.975,na.rm=TRUE)
#Permutation test to calculate p value
comb_data1=c(DB_data_clean_Small_area$rel_m_RS, DB_data_clean_Small_area$rel_f_RS)
comb_data2=c(DB_data_clean_Small_area$rel_m_cMS,DB_data_clean_Small_area$rel_f_cMS)
diff.observed = lm(DB_data_clean_Small_area$rel_m_RS ~DB_data_clean_Small_area$rel_m_cMS)$coefficients[2] - lm(DB_data_clean_Small_area$rel_f_RS ~DB_data_clean_Small_area$rel_f_cMS)$coefficients[2]
diff.observed
number_of_permutations = 100000
diff.random = NULL
for (i in 1 : number_of_permutations) {
# Sample from the combined dataset
a.random = sample (na.omit(comb_data1), length(DB_data_clean_Small_area$rel_m_RS), TRUE)
b.random = sample (na.omit(comb_data1), length(DB_data_clean_Small_area$rel_f_RS), TRUE)
c.random = sample (na.omit(comb_data2), length(DB_data_clean_Small_area$rel_m_cMS), TRUE)
d.random = sample (na.omit(comb_data2), length(DB_data_clean_Small_area$rel_f_cMS), TRUE)
# Null (permuated) difference
diff.random[i] = lm(a.random ~c.random)$coefficients[2] - lm(b.random ~d.random)$coefficients[2]
}
# P-value is the fraction of how many times the permuted difference is
# equal or more extreme than the observed difference
t_Sex_diff_Small_area_B_p = sum(abs(diff.random) >= as.numeric(abs(diff.observed)))/ number_of_permutations
#Population size ####
#Large
Sex_diff_Large_pop_B=c(B_Large_pop_Male_bootvar$t)-c(B_Large_pop_Female_bootvar$t)
t_Sex_diff_Large_pop_B=mean(Sex_diff_Large_pop_B,na.rm=TRUE)
t_Sex_diff_Large_pop_B_lower=quantile(Sex_diff_Large_pop_B,.025,na.rm=TRUE)
t_Sex_diff_Large_pop_B_upper=quantile(Sex_diff_Large_pop_B,.975,na.rm=TRUE)
#Permutation test to calculate p value
comb_data1=c(DB_data_clean_Large_pop$rel_m_RS, DB_data_clean_Large_pop$rel_f_RS)
comb_data2=c(DB_data_clean_Large_pop$rel_m_cMS,DB_data_clean_Large_pop$rel_f_cMS)
diff.observed = lm(DB_data_clean_Large_pop$rel_m_RS ~DB_data_clean_Large_pop$rel_m_cMS)$coefficients[2] - lm(DB_data_clean_Large_pop$rel_f_RS ~DB_data_clean_Large_pop$rel_f_cMS)$coefficients[2]
diff.observed
number_of_permutations = 100000
diff.random = NULL
for (i in 1 : number_of_permutations) {
# Sample from the combined dataset
a.random = sample (na.omit(comb_data1), length(DB_data_clean_Small_pop$rel_m_RS), TRUE)
b.random = sample (na.omit(comb_data1), length(DB_data_clean_Large_pop$rel_m_RS), TRUE)
c.random = sample (na.omit(comb_data2), length(DB_data_clean_Small_pop$rel_m_cMS), TRUE)
d.random = sample (na.omit(comb_data2), length(DB_data_clean_Large_pop$rel_m_cMS), TRUE)
# Null (permuated) difference
diff.random[i] = lm(a.random ~c.random)$coefficients[2] - lm(b.random ~d.random)$coefficients[2]
}
# P-value is the fraction of how many times the permuted difference is
# equal or more extreme than the observed difference
t_Sex_diff_Large_pop_B_p = sum(abs(diff.random) >= as.numeric(abs(diff.observed)))/ number_of_permutations
#Small
Sex_diff_Small_pop_B=c(B_Small_pop_Male_bootvar$t)-c(B_Small_pop_Female_bootvar$t)
t_Sex_diff_Small_pop_B=mean(Sex_diff_Small_pop_B,na.rm=TRUE)
t_Sex_diff_Small_pop_B_lower=quantile(Sex_diff_Small_pop_B,.025,na.rm=TRUE)
t_Sex_diff_Small_pop_B_upper=quantile(Sex_diff_Small_pop_B,.975,na.rm=TRUE)
#Permutation test to calculate p value
comb_data1=c(DB_data_clean_Small_pop$rel_m_RS, DB_data_clean_Small_pop$rel_f_RS)
comb_data2=c(DB_data_clean_Small_pop$rel_m_cMS,DB_data_clean_Small_pop$rel_f_cMS)
diff.observed = lm(DB_data_clean_Small_pop$rel_m_RS ~DB_data_clean_Small_pop$rel_m_cMS)$coefficients[2] - lm(DB_data_clean_Small_pop$rel_f_RS ~DB_data_clean_Small_pop$rel_f_cMS)$coefficients[2]
diff.observed
number_of_permutations = 100000
diff.random = NULL
for (i in 1 : number_of_permutations) {
# Sample from the combined dataset
a.random = sample (na.omit(comb_data1), length(DB_data_clean_Small_pop$rel_m_RS), TRUE)
b.random = sample (na.omit(comb_data1), length(DB_data_clean_Small_pop$rel_f_RS), TRUE)
c.random = sample (na.omit(comb_data2), length(DB_data_clean_Small_pop$rel_m_cMS), TRUE)
d.random = sample (na.omit(comb_data2), length(DB_data_clean_Small_pop$rel_f_cMS), TRUE)
# Null (permuated) difference
diff.random[i] = lm(a.random ~c.random)$coefficients[2] - lm(b.random ~d.random)$coefficients[2]
}
# P-value is the fraction of how many times the permuted difference is
# equal or more extreme than the observed difference
t_Sex_diff_Small_pop_B_p = sum(abs(diff.random) >= as.numeric(abs(diff.observed)))/ number_of_permutations
#S ####
#Area ####
#Large
Sex_diff_Large_area_S=c(S_Large_area_Male_bootvar$t)-c(S_Large_area_Female_bootvar$t)
t_Sex_diff_Large_area_S=mean(Sex_diff_Large_area_S,na.rm=TRUE)
t_Sex_diff_Large_area_S_lower=quantile(Sex_diff_Large_area_S,.025,na.rm=TRUE)
t_Sex_diff_Large_area_S_upper=quantile(Sex_diff_Large_area_S,.975,na.rm=TRUE)
#Permutation test to calculate p value
comb_data1=c(DB_data_clean_Large_area$rel_m_cMS, DB_data_clean_Large_area$rel_f_cMS)
comb_data2=c(DB_data_clean_Large_area$rel_m_RS, DB_data_clean_Large_area$rel_f_RS)
diff.observed = lm(DB_data_clean_Large_area$rel_m_RS ~DB_data_clean_Large_area$rel_m_cMS)$coefficients[2]*sqrt(var(DB_data_clean_Large_area$rel_m_cMS, na.rm=TRUE)) - lm(DB_data_clean_Large_area$rel_f_RS ~DB_data_clean_Large_area$rel_f_cMS)$coefficients[2]*sqrt(var(DB_data_clean_Large_area$rel_f_cMS, na.rm=TRUE))
diff.observed
number_of_permutations = 100000
diff.random = NULL
for (i in 1 : number_of_permutations) {
# Sample from the combined dataset
a.random = sample (na.omit(comb_data1), length(DB_data_clean_Large_area$rel_m_cMS), TRUE)
b.random = sample (na.omit(comb_data1), length(DB_data_clean_Large_area$rel_f_cMS), TRUE)
c.random = sample (na.omit(comb_data2), length(DB_data_clean_Large_area$rel_m_RS), TRUE)
d.random = sample (na.omit(comb_data2), length(DB_data_clean_Large_area$rel_f_RS), TRUE)
# Null (permuated) difference
diff.random[i] = lm(c.random ~a.random)$coefficients[2]*sqrt(var(a.random, na.rm=TRUE)) - lm(d.random ~b.random)$coefficients[2]*sqrt(var(b.random, na.rm=TRUE))
}
# P-value is the fraction of how many times the permuted difference is
# equal or more extreme than the observed difference
t_Sex_diff_Large_area_S_p = sum(abs(diff.random) >= as.numeric(abs(diff.observed)))/ number_of_permutations
#Small
Sex_diff_Small_area_S=c(S_Small_area_Male_bootvar$t)-c(S_Small_area_Female_bootvar$t)
t_Sex_diff_Small_area_S=mean(Sex_diff_Small_area_S,na.rm=TRUE)
t_Sex_diff_Small_area_S_lower=quantile(Sex_diff_Small_area_S,.025,na.rm=TRUE)
t_Sex_diff_Small_area_S_upper=quantile(Sex_diff_Small_area_S,.975,na.rm=TRUE)
#Permutation test to calculate p value
comb_data1=c(DB_data_clean_Small_area$rel_m_cMS, DB_data_clean_Small_area$rel_f_cMS)
comb_data2=c(DB_data_clean_Small_area$rel_m_RS, DB_data_clean_Small_area$rel_f_RS)
diff.observed = lm(DB_data_clean_Small_area$rel_m_RS ~DB_data_clean_Small_area$rel_m_cMS)$coefficients[2]*sqrt(var(DB_data_clean_Small_area$rel_m_cMS, na.rm=TRUE)) - lm(DB_data_clean_Small_area$rel_f_RS ~DB_data_clean_Small_area$rel_f_cMS)$coefficients[2]*sqrt(var(DB_data_clean_Small_area$rel_f_cMS, na.rm=TRUE))
diff.observed
number_of_permutations = 100000
diff.random = NULL
for (i in 1 : number_of_permutations) {
# Sample from the combined dataset
a.random = sample (na.omit(comb_data1), length(DB_data_clean_Small_area$rel_m_cMS), TRUE)
b.random = sample (na.omit(comb_data1), length(DB_data_clean_Small_area$rel_f_cMS), TRUE)
c.random = sample (na.omit(comb_data2), length(DB_data_clean_Small_area$rel_m_RS), TRUE)
d.random = sample (na.omit(comb_data2), length(DB_data_clean_Small_area$rel_f_RS), TRUE)
# Null (permuated) difference
diff.random[i] = lm(c.random ~a.random)$coefficients[2]*sqrt(var(a.random, na.rm=TRUE)) - lm(d.random ~b.random)$coefficients[2]*sqrt(var(b.random, na.rm=TRUE))
}
# P-value is the fraction of how many times the permuted difference is
# equal or more extreme than the observed difference
t_Sex_diff_Small_area_S_p = sum(abs(diff.random) >= as.numeric(abs(diff.observed)))/ number_of_permutations
#Population size ####
#Small
Sex_diff_Small_pop_S=c(S_Small_pop_Male_bootvar$t)-c(S_Small_pop_Female_bootvar$t)
t_Sex_diff_Small_pop_S=mean(Sex_diff_Small_pop_S,na.rm=TRUE)
t_Sex_diff_Small_pop_S_lower=quantile(Sex_diff_Small_pop_S,.025,na.rm=TRUE)
t_Sex_diff_Small_pop_S_upper=quantile(Sex_diff_Small_pop_S,.975,na.rm=TRUE)
#Permutation test to calculate p value
comb_data1=c(DB_data_clean_Small_pop$rel_m_cMS, DB_data_clean_Small_pop$rel_f_cMS)
comb_data2=c(DB_data_clean_Small_pop$rel_m_RS, DB_data_clean_Small_pop$rel_f_RS)
diff.observed = lm(DB_data_clean_Small_pop$rel_m_RS ~DB_data_clean_Small_pop$rel_m_cMS)$coefficients[2]*sqrt(var(DB_data_clean_Small_pop$rel_m_cMS, na.rm=TRUE)) - lm(DB_data_clean_Small_pop$rel_f_RS ~DB_data_clean_Small_pop$rel_f_cMS)$coefficients[2]*sqrt(var(DB_data_clean_Small_pop$rel_f_cMS, na.rm=TRUE))
diff.observed
number_of_permutations = 100000
diff.random = NULL
for (i in 1 : number_of_permutations) {
# Sample from the combined dataset
a.random = sample (na.omit(comb_data1), length(DB_data_clean_Small_pop$rel_m_cMS), TRUE)
b.random = sample (na.omit(comb_data1), length(DB_data_clean_Small_pop$rel_f_cMS), TRUE)
c.random = sample (na.omit(comb_data2), length(DB_data_clean_Small_pop$rel_m_RS), TRUE)
d.random = sample (na.omit(comb_data2), length(DB_data_clean_Small_pop$rel_f_RS), TRUE)
# Null (permuated) difference
diff.random[i] = lm(c.random ~a.random)$coefficients[2]*sqrt(var(a.random, na.rm=TRUE)) - lm(d.random ~b.random)$coefficients[2]*sqrt(var(b.random, na.rm=TRUE))
}
# P-value is the fraction of how many times the permuted difference is
# equal or more extreme than the observed difference
t_Sex_diff_Small_pop_S_p = sum(abs(diff.random) >= as.numeric(abs(diff.observed)))/ number_of_permutations
#Large
Sex_diff_Large_pop_S=c(S_Large_pop_Male_bootvar$t)-c(S_Large_pop_Female_bootvar$t)
t_Sex_diff_Large_pop_S=mean(Sex_diff_Large_pop_S,na.rm=TRUE)
t_Sex_diff_Large_pop_S_lower=quantile(Sex_diff_Large_pop_S,.025,na.rm=TRUE)
t_Sex_diff_Large_pop_S_upper=quantile(Sex_diff_Large_pop_S,.975,na.rm=TRUE)
#Permutation test to calculate p value
comb_data1=c(DB_data_clean_Large_pop$rel_m_cMS, DB_data_clean_Large_pop$rel_f_cMS)
comb_data2=c(DB_data_clean_Large_pop$rel_m_RS, DB_data_clean_Large_pop$rel_f_RS)
diff.observed = lm(DB_data_clean_Large_pop$rel_m_RS ~DB_data_clean_Large_pop$rel_m_cMS)$coefficients[2]*sqrt(var(DB_data_clean_Large_pop$rel_m_cMS, na.rm=TRUE)) - lm(DB_data_clean_Large_pop$rel_f_RS ~DB_data_clean_Large_pop$rel_f_cMS)$coefficients[2]*sqrt(var(DB_data_clean_Large_pop$rel_f_cMS, na.rm=TRUE))
diff.observed
number_of_permutations = 100000
diff.random = NULL
for (i in 1 : number_of_permutations) {
# Sample from the combined dataset
a.random = sample (na.omit(comb_data1), length(DB_data_clean_Large_pop$rel_m_cMS), TRUE)
b.random = sample (na.omit(comb_data1), length(DB_data_clean_Large_pop$rel_f_cMS), TRUE)
c.random = sample (na.omit(comb_data2), length(DB_data_clean_Large_pop$rel_f_RS), TRUE)
d.random = sample (na.omit(comb_data2), length(DB_data_clean_Large_pop$rel_m_RS), TRUE)
# Null (permuated) difference
diff.random[i] = lm(c.random ~a.random)$coefficients[2]*sqrt(var(a.random, na.rm=TRUE)) - lm(d.random ~b.random)$coefficients[2]*sqrt(var(b.random, na.rm=TRUE))
}
# P-value is the fraction of how many times the permuted difference is
# equal or more extreme than the observed difference
t_Sex_diff_Large_pop_S_p = sum(abs(diff.random) >= as.numeric(abs(diff.observed)))/ number_of_permutations
#Save data table ####
CompSex_Table_Large_area_I <- as.data.frame(cbind("Large area", "Opportunity for selection", t_Sex_diff_Large_area_I, t_Sex_diff_Large_area_I_lower, t_Sex_diff_Large_area_I_upper, t_Sex_diff_Large_area_I_p))
names(CompSex_Table_Large_area_I)=c('V1','V2','V3','V4','V5','V6')
CompSex_Table_Large_pop_I <- as.data.frame(cbind("Large population size", "Opportunity for selection", t_Sex_diff_Large_pop_I, t_Sex_diff_Large_pop_I_lower, t_Sex_diff_Large_pop_I_upper, t_Sex_diff_Large_pop_I_p))
names(CompSex_Table_Large_pop_I)=c('V1','V2','V3','V4','V5','V6')
CompSex_Table_Large_area_Is <- as.data.frame(cbind("Large area", "Opportunity for sexual selection", t_Sex_diff_Large_area_Is, t_Sex_diff_Large_area_Is_lower, t_Sex_diff_Large_area_Is_upper, t_Sex_diff_Large_area_Is_p))
names(CompSex_Table_Large_area_Is)=c('V1','V2','V3','V4','V5','V6')
CompSex_Table_Large_pop_Is <- as.data.frame(cbind("Large population size", "Opportunity for sexual selection", t_Sex_diff_Large_pop_Is, t_Sex_diff_Large_pop_Is_lower, t_Sex_diff_Large_pop_Is_upper, t_Sex_diff_Large_pop_Is_p))
names(CompSex_Table_Large_pop_Is)=c('V1','V2','V3','V4','V5','V6')
CompSex_Table_Large_area_B <- as.data.frame(cbind("Large area", "Bateman gradient", t_Sex_diff_Large_area_B, t_Sex_diff_Large_area_B_lower, t_Sex_diff_Large_area_B_upper, t_Sex_diff_Large_area_B_p))
names(CompSex_Table_Large_area_B)=c('V1','V2','V3','V4','V5','V6')
CompSex_Table_Large_pop_B <- as.data.frame(cbind("Large population size", "Bateman gradient", t_Sex_diff_Large_pop_B, t_Sex_diff_Large_pop_B_lower, t_Sex_diff_Large_pop_B_upper, t_Sex_diff_Large_pop_B_p))
names(CompSex_Table_Large_pop_B)=c('V1','V2','V3','V4','V5','V6')
CompSex_Table_Large_area_S <- as.data.frame(cbind("Large area", "Jones index", t_Sex_diff_Large_area_S, t_Sex_diff_Large_area_S_lower, t_Sex_diff_Large_area_S_upper, t_Sex_diff_Large_area_S_p))
names(CompSex_Table_Large_area_S)=c('V1','V2','V3','V4','V5','V6')
CompSex_Table_Large_pop_S <- as.data.frame(cbind("Large population size", "Jones index", t_Sex_diff_Large_pop_S, t_Sex_diff_Large_pop_S_lower, t_Sex_diff_Large_pop_S_upper, t_Sex_diff_Large_pop_S_p))
names(CompSex_Table_Large_pop_S)=c('V1','V2','V3','V4','V5','V6')
CompSex_Table_Small_area_I <- as.data.frame(cbind("Small area", "Opportunity for selection", t_Sex_diff_Small_area_I, t_Sex_diff_Small_area_I_lower, t_Sex_diff_Small_area_I_upper, t_Sex_diff_Small_area_I_p))
names(CompSex_Table_Small_area_I)=c('V1','V2','V3','V4','V5','V6')
CompSex_Table_Small_pop_I <- as.data.frame(cbind("Small population size", "Opportunity for selection", t_Sex_diff_Small_pop_I, t_Sex_diff_Small_pop_I_lower, t_Sex_diff_Small_pop_I_upper, t_Sex_diff_Small_pop_I_p))
names(CompSex_Table_Small_pop_I)=c('V1','V2','V3','V4','V5','V6')
CompSex_Table_Small_area_Is <- as.data.frame(cbind("Small area", "Opportunity for sexual selection", t_Sex_diff_Small_area_Is, t_Sex_diff_Small_area_Is_lower, t_Sex_diff_Small_area_Is_upper, t_Sex_diff_Small_area_Is_p))
names(CompSex_Table_Small_area_Is)=c('V1','V2','V3','V4','V5','V6')
CompSex_Table_Small_pop_Is <- as.data.frame(cbind("Small population size", "Opportunity for sexual selection", t_Sex_diff_Small_pop_Is, t_Sex_diff_Small_pop_Is_lower, t_Sex_diff_Small_pop_Is_upper, t_Sex_diff_Small_pop_Is_p))
names(CompSex_Table_Small_pop_Is)=c('V1','V2','V3','V4','V5','V6')
CompSex_Table_Small_area_B <- as.data.frame(cbind("Small area", "Bateman gradient", t_Sex_diff_Small_area_B, t_Sex_diff_Small_area_B_lower, t_Sex_diff_Small_area_B_upper, t_Sex_diff_Small_area_B_p))
names(CompSex_Table_Small_area_B)=c('V1','V2','V3','V4','V5','V6')
CompSex_Table_Small_pop_B <- as.data.frame(cbind("Small population size", "Bateman gradient", t_Sex_diff_Small_pop_B, t_Sex_diff_Small_pop_B_lower, t_Sex_diff_Small_pop_B_upper, t_Sex_diff_Small_pop_B_p))
names(CompSex_Table_Small_pop_B)=c('V1','V2','V3','V4','V5','V6')
CompSex_Table_Small_area_S <- as.data.frame(cbind("Small area", "Jones index", t_Sex_diff_Small_area_S, t_Sex_diff_Small_area_S_lower, t_Sex_diff_Small_area_S_upper, t_Sex_diff_Small_area_S_p))
names(CompSex_Table_Small_area_S)=c('V1','V2','V3','V4','V5','V6')
CompSex_Table_Small_pop_S <- as.data.frame(cbind("Small population size", "Jones index", t_Sex_diff_Small_pop_S, t_Sex_diff_Small_pop_S_lower, t_Sex_diff_Small_pop_S_upper, t_Sex_diff_Small_pop_S_p))
names(CompSex_Table_Small_pop_S)=c('V1','V2','V3','V4','V5','V6')
Table_BatemanMetrics_SexComp <- as.data.frame(as.matrix(rbind(CompSex_Table_Small_pop_I,CompSex_Table_Small_pop_Is,
CompSex_Table_Small_pop_B,CompSex_Table_Small_pop_S,
CompSex_Table_Large_pop_I,CompSex_Table_Large_pop_Is,
CompSex_Table_Large_pop_B,CompSex_Table_Large_pop_S,
CompSex_Table_Large_area_I,CompSex_Table_Large_area_Is,
CompSex_Table_Large_area_B,CompSex_Table_Large_area_S,
CompSex_Table_Small_area_I,CompSex_Table_Small_area_Is,
CompSex_Table_Small_area_B,CompSex_Table_Small_area_S)))
colnames(Table_BatemanMetrics_SexComp)[1] <- "Treatment"
colnames(Table_BatemanMetrics_SexComp)[2] <- "Selection_metric"
colnames(Table_BatemanMetrics_SexComp)[3] <- "Variance"
colnames(Table_BatemanMetrics_SexComp)[4] <- "l95.CI"
colnames(Table_BatemanMetrics_SexComp)[5] <- "u95.CI"
colnames(Table_BatemanMetrics_SexComp)[6] <- "p-value"
Table_BatemanMetrics_SexComp[,3]=as.numeric(Table_BatemanMetrics_SexComp[,3])
Table_BatemanMetrics_SexComp[,4]=as.numeric(Table_BatemanMetrics_SexComp[,4])
Table_BatemanMetrics_SexComp[,5]=as.numeric(Table_BatemanMetrics_SexComp[,5])
Table_BatemanMetrics_SexComp[,6]=as.numeric(Table_BatemanMetrics_SexComp[,6])
Table_BatemanMetrics_SexComp_round=cbind(Table_BatemanMetrics_SexComp[,c(1,2)],round(Table_BatemanMetrics_SexComp[,c(3,4,5,6)],digit=3))
rownames(Table_BatemanMetrics_SexComp_round) <- NULLOpportunity for selection
Figure: Opportunity for selection (variance in reproductive success)
#Plot Selection Metrics
Table_BatemanMetrics$Sex<- factor(Table_BatemanMetrics$Sex, levels=c("Female",'Male'))
Table_BatemanMetrics$Treatment<- factor(Table_BatemanMetrics$Treatment, levels=c("Small_pop",'Large_pop','Large_area','Small_area'))
Table_BatemanMetrics$Variable <- factor(Table_BatemanMetrics$Variable, levels=c("Opportunity for selection",'Opportunity for sexual selection','Bateman gradient','Maximum standardized sexual selection differential'))
Table_BatemanMetrics_area=Table_BatemanMetrics[Table_BatemanMetrics$Treatment!='Large_pop',]
Table_BatemanMetrics_area=Table_BatemanMetrics_area[Table_BatemanMetrics_area$Treatment!='Small_pop',]
Table_BatemanMetrics_pop=Table_BatemanMetrics[Table_BatemanMetrics$Treatment!='Large_area',]
Table_BatemanMetrics_pop=Table_BatemanMetrics_pop[Table_BatemanMetrics_pop$Treatment!='Small_area',]
# Opportunity for selection
BarPlot_1<- ggplot(Table_BatemanMetrics_pop[c(1,2,9,10),], aes(x=Sex, y=Variance, fill=Treatment)) +
scale_y_continuous(limits = c(0, 2), breaks = seq(0,2,0.5), expand = c(0 ,0)) +
geom_hline(yintercept=0, linetype="dashed", color = "black", size=1) +
geom_bar(stat="identity", color="black", position=position_dodge(), alpha=0.8) +
ylab(expression(paste(~italic("I"))))+ggtitle('Opportunity for selection')+labs(tag = "A")+xlab('Sex')+
scale_x_discrete(breaks=waiver(),labels = c("Female","Male"))+
theme(panel.border = element_blank(),
plot.title = element_text(hjust = 0.5),
panel.background = element_blank(),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
plot.tag.position=c(0.01,0.98),
legend.position = c(0.8, 0.9),
legend.title = element_blank(),
legend.text = element_text(colour="black", size=10),
axis.line.x = element_line(colour = "black", size = 1),
axis.line.y = element_line(colour = "black", size = 1),
axis.text.x = element_text(face="plain", color="black", size=16, angle=0),
axis.text.y = element_text(face="plain", color="black", size=16, angle=0),
axis.title.x = element_text(size=16,face="plain", margin = margin(r=0,10,0,0)),
axis.title.y = element_text(size=16,face="plain", margin = margin(r=10,0,0,0)),
axis.ticks = element_line(size = 1),
axis.ticks.length = unit(.3, "cm")) +
geom_errorbar(aes(ymin=l95.CI, ymax=u95.CI), width=.3,size=1, position=position_dodge(.9)) +
scale_fill_manual(values=c(colorESEB[1],colorESEB[2]),name = "Treatment", labels = c('Small group','Large group'))
BarPlot_2<- ggplot(Table_BatemanMetrics_area[c(1,2,9,10),], aes(x=Sex, y=Variance, fill=Treatment)) +
scale_y_continuous(limits = c(0, 2), breaks = seq(0,2,0.5), expand = c(0 ,0)) +
geom_hline(yintercept=0, linetype="dashed", color = "black", size=1) +
geom_bar(stat="identity", color="black", position=position_dodge(), alpha=0.8) +
ylab('')+ggtitle('Opportunity for selection')+labs(tag = "B")+xlab('Sex')+
scale_x_discrete(breaks=waiver(),labels = c("Female","Male"))+
theme(panel.border = element_blank(),
plot.title = element_text(hjust = 0.5),
panel.background = element_blank(),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
plot.tag.position=c(0.01,0.98),
legend.position = c(0.8, 0.9),
legend.title = element_blank(),
legend.text = element_text(colour="black", size=10),
axis.line.x = element_line(colour = "black", size = 1),
axis.line.y = element_line(colour = "black", size = 1),
axis.text.x = element_text(face="plain", color="black", size=16, angle=0),
axis.text.y = element_text(face="plain", color="black", size=16, angle=0),
axis.title.x = element_text(size=16,face="plain", margin = margin(r=0,10,0,0)),
axis.title.y = element_text(size=16,face="plain", margin = margin(r=10,0,0,0)),
axis.ticks = element_line(size = 1),
axis.ticks.length = unit(.3, "cm")) +
geom_errorbar(aes(ymin=l95.CI, ymax=u95.CI), width=.3,size=1, position=position_dodge(.9)) +
scale_fill_manual(values=c(colorESEB2[1],colorESEB2[2]),name = "Treatment", labels = c('Large area','Small area'))
grid.arrange(grobs = list(BarPlot_1,BarPlot_2), nrow = 1,ncol=2, widths=c(2.3, 2.3))
Figure 7: Effects of group size (A) and area treatment (B) on the
opportunity for selection in females and males. Means and 95% confidence
intervals.
Treatement comparisons via permutation test for the
opportunity for selection
Table_BatemanMetrics_TreatComp_round[c(1,5,9,13),] Sex Treatment Selection_metric Variance l95.CI u95.CI
1 Male Area Opportunity for selection -0.261 -0.778 0.161
5 Male Population size Opportunity for selection 0.124 -0.310 0.653
9 Female Area Opportunity for selection 0.050 -0.170 0.258
13 Female Population size Opportunity for selection 0.240 0.043 0.450
p-value
1 0.112
5 0.445
9 0.499
13 0.001Sex comparisons via permutation test for the opportunity for selection
Table_BatemanMetrics_SexComp_round[c(1,5,9,13),] Treatment Selection_metric Variance l95.CI u95.CI
1 Small population size Opportunity for selection 0.023 -0.383 0.534
5 Large population size Opportunity for selection 0.140 -0.087 0.390
9 Large area Opportunity for selection -0.091 -0.322 0.172
13 Small area Opportunity for selection 0.220 -0.184 0.723
p-value
1 0.859
5 0.136
9 0.281
13 0.199Opportunity for sexual selection
Figure: Opportunity for sexual selection (variance in mating success)
# Opportunity for sexual selection
BarPlot_3<- ggplot(Table_BatemanMetrics_pop[c(3,4,11,12),], aes(x=Sex, y=Variance, fill=Treatment)) +
scale_y_continuous(limits = c(0,1), breaks = seq(0,1,0.25), expand = c(0 ,0)) +
geom_hline(yintercept=0, linetype="dashed", color = "black", size=1) +
geom_bar(stat="identity", color="black", position=position_dodge(), alpha=0.8) +
ylab(expression(paste(~italic("I"['s']))))+ggtitle('Opportunity for sexual selection')+labs(tag = "A")+xlab('Sex')+
scale_x_discrete(breaks=waiver(),labels = c("Female","Male"))+
theme(panel.border = element_blank(),
plot.title = element_text(hjust = 0.5),
panel.background = element_blank(),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
plot.tag.position=c(0.01,0.98),
legend.position = c(0.8, 0.9),
legend.title = element_blank(),
legend.text = element_text(colour="black", size=10),
axis.line.x = element_line(colour = "black", size = 1),
axis.line.y = element_line(colour = "black", size = 1),
axis.text.x = element_text(face="plain", color="black", size=16, angle=0),
axis.text.y = element_text(face="plain", color="black", size=16, angle=0),
axis.title.x = element_text(size=16,face="plain", margin = margin(r=0,10,0,0)),
axis.title.y = element_text(size=16,face="plain", margin = margin(r=10,0,0,0)),
axis.ticks = element_line(size = 1),
axis.ticks.length = unit(.3, "cm")) +
geom_errorbar(aes(ymin=l95.CI, ymax=u95.CI), width=.3,size=1, position=position_dodge(.9)) +
scale_fill_manual(values=c(colorESEB[1],colorESEB[2]),name = "Treatment", labels = c('Small group','Large group'))
BarPlot_4<- ggplot(Table_BatemanMetrics_area[c(3,4,11,12),], aes(x=Sex, y=Variance, fill=Treatment)) +
scale_y_continuous(limits = c(0,1), breaks = seq(0,1,0.25), expand = c(0 ,0)) +
geom_hline(yintercept=0, linetype="dashed", color = "black", size=1) +
geom_bar(stat="identity", color="black", position=position_dodge(), alpha=0.8) +
ylab('')+ggtitle('Opportunity for sexual selection')+labs(tag = "B")+xlab('Sex')+
scale_x_discrete(breaks=waiver(),labels = c("Female","Male"))+
theme(panel.border = element_blank(),
plot.title = element_text(hjust = 0.5),
panel.background = element_blank(),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
plot.tag.position=c(0.01,0.98),
legend.position = c(0.8, 0.9),
legend.title = element_blank(),
legend.text = element_text(colour="black", size=10),
axis.line.x = element_line(colour = "black", size = 1),
axis.line.y = element_line(colour = "black", size = 1),
axis.text.x = element_text(face="plain", color="black", size=16, angle=0),
axis.text.y = element_text(face="plain", color="black", size=16, angle=0),
axis.title.x = element_text(size=16,face="plain", margin = margin(r=0,10,0,0)),
axis.title.y = element_text(size=16,face="plain", margin = margin(r=10,0,0,0)),
axis.ticks = element_line(size = 1),
axis.ticks.length = unit(.3, "cm")) +
geom_errorbar(aes(ymin=l95.CI, ymax=u95.CI), width=.3,size=1, position=position_dodge(.9)) +
scale_fill_manual(values=c(colorESEB2[1],colorESEB2[2]),name = "Treatment", labels = c('Large area','Small area'))
grid.arrange(grobs = list(BarPlot_3,BarPlot_4), nrow = 1,ncol=2, widths=c(2.3, 2.3))
Figure 8: Effects of group size (A) and area treatment (B) on the
opportunity for sexual selection in females and males. Means and 95%
confidence intervals.
Treatement comparisons via permutation
test for the opportunity for sexual selection
Table_BatemanMetrics_TreatComp_round[c(2,6,10,14),] Sex Treatment Selection_metric Variance l95.CI
2 Male Area Opportunity for sexual selection 0.067 -0.055
6 Male Population size Opportunity for sexual selection -0.084 -0.216
10 Female Area Opportunity for sexual selection 0.036 -0.075
14 Female Population size Opportunity for sexual selection -0.099 -0.235
u95.CI p-value
2 0.206 0.172
6 0.033 0.090
10 0.155 0.373
14 0.025 0.010Sex comparisons via permutation test for the opportunity for selection
Table_BatemanMetrics_SexComp_round[c(2,6,10,14),] Treatment Selection_metric Variance l95.CI
2 Small population size Opportunity for sexual selection -0.001 -0.064
6 Large population size Opportunity for sexual selection -0.016 -0.187
10 Large area Opportunity for sexual selection 0.020 -0.119
14 Small area Opportunity for sexual selection -0.011 -0.111
u95.CI p-value
2 0.059 0.960
6 0.153 0.770
10 0.172 0.689
14 0.086 0.718Bateman gradient
Figure: Bateman gradient (slope of mating success on reproductive success)
# Bateman gradient
BarPlot_5<- ggplot(Table_BatemanMetrics_pop[c(5,6,13,14),], aes(x=Sex, y=Variance, fill=Treatment)) +
scale_y_continuous(limits = c(0, 2.4), breaks = seq(0,2.4,0.5), expand = c(0 ,0)) +
geom_hline(yintercept=0, linetype="dashed", color = "black", size=1) +
geom_bar(stat="identity", color="black", position=position_dodge(), alpha=0.8) +
ylab(expression(paste(~italic(symbol("b")['ss']))))+ggtitle('Bateman gradient')+labs(tag = "A")+xlab('Sex')+
scale_x_discrete(breaks=waiver(),labels = c("Female","Male"))+
theme(panel.border = element_blank(),
plot.title = element_text(hjust = 0.5),
panel.background = element_blank(),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
plot.tag.position=c(0.01,0.98),
legend.position = c(0.8, 0.9),
legend.title = element_blank(),
legend.text = element_text(colour="black", size=10),
axis.line.x = element_line(colour = "black", size = 1),
axis.line.y = element_line(colour = "black", size = 1),
axis.text.x = element_text(face="plain", color="black", size=16, angle=0),
axis.text.y = element_text(face="plain", color="black", size=16, angle=0),
axis.title.x = element_text(size=16,face="plain", margin = margin(r=0,10,0,0)),
axis.title.y = element_text(size=16,face="plain", margin = margin(r=10,0,0,0)),
axis.ticks = element_line(size = 1),
axis.ticks.length = unit(.3, "cm")) +
geom_errorbar(aes(ymin=l95.CI, ymax=u95.CI), width=.3,size=1, position=position_dodge(.9)) +
scale_fill_manual(values=c(colorESEB[1],colorESEB[2]),name = "Treatment", labels = c('Small group','Large group'))
BarPlot_6<- ggplot(Table_BatemanMetrics_area[c(5,6,13,14),], aes(x=Sex, y=Variance, fill=Treatment)) +
scale_y_continuous(limits = c(0, 2.4), breaks = seq(0,2.4,0.5), expand = c(0 ,0)) +
geom_hline(yintercept=0, linetype="dashed", color = "black", size=1) +
geom_bar(stat="identity", color="black", position=position_dodge(), alpha=0.8) +
ylab('')+ggtitle('Bateman gradient')+labs(tag = "B")+xlab('Sex')+
scale_x_discrete(breaks=waiver(),labels = c("Female","Male"))+
theme(panel.border = element_blank(),
plot.title = element_text(hjust = 0.5),
panel.background = element_blank(),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
plot.tag.position=c(0.01,0.98),
legend.position = c(0.8, 0.9),
legend.title = element_blank(),
legend.text = element_text(colour="black", size=10),
axis.line.x = element_line(colour = "black", size = 1),
axis.line.y = element_line(colour = "black", size = 1),
axis.text.x = element_text(face="plain", color="black", size=16, angle=0),
axis.text.y = element_text(face="plain", color="black", size=16, angle=0),
axis.title.x = element_text(size=16,face="plain", margin = margin(r=0,10,0,0)),
axis.title.y = element_text(size=16,face="plain", margin = margin(r=10,0,0,0)),
axis.ticks = element_line(size = 1),
axis.ticks.length = unit(.3, "cm")) +
geom_errorbar(aes(ymin=l95.CI, ymax=u95.CI), width=.3,size=1, position=position_dodge(.9)) +
scale_fill_manual(values=c(colorESEB2[1],colorESEB2[2]),name = "Treatment", labels = c('Large area','Small area'))
grid.arrange(grobs = list(BarPlot_5,BarPlot_6), nrow = 1,ncol=2, widths=c(2.3, 2.3))
Figure 9: Effects of group size (A) and area treatment (B) on the
Bateman gradient in females and males. Means and 95% confidence
intervals.
Treatement comparisons via permutation test for the
Bateman gradient
Table_BatemanMetrics_TreatComp_round[c(3,7,11,15),] Sex Treatment Selection_metric Variance l95.CI u95.CI p-value
3 Male Area Bateman gradient -1.074 -1.954 -0.270 0.001
7 Male Population size Bateman gradient 0.892 0.025 1.754 0.003
11 Female Area Bateman gradient 0.653 -0.124 1.410 0.031
15 Female Population size Bateman gradient -0.485 -1.258 0.277 0.107Sex comparisons via permutation test for the opportunity for selection
Table_BatemanMetrics_SexComp_round[c(3,7,11,15),] Treatment Selection_metric Variance l95.CI u95.CI p-value
3 Small population size Bateman gradient 0.854 -0.125 1.805 0.012
7 Large population size Bateman gradient -0.523 -1.180 0.077 0.052
11 Large area Bateman gradient -0.655 -1.363 0.025 0.021
15 Small area Bateman gradient 1.072 0.181 2.011 0.003# Bateman gradient (scatter)
p1<-ggplot(DB_data_clean_Small_pop, aes(x=rel_m_cMS, y=rel_m_RS)) +
geom_point(alpha=0.4,shape=16, size = 3,color=colpal2[2]) +
geom_smooth(method=lm, se=TRUE,alpha=0.3) +
theme(plot.tag.position=c(0.1,0.98))+
labs(tag = "A")+xlab('Rel. mating success')+ylab("Rel. reproductive success")+ggtitle('Small group')+ theme(plot.title = element_text(hjust = 0.5))+
theme(axis.text=element_text(size=13),
axis.title=element_text(size=14))+ theme(legend.position="none")+
ylim(0,4.2)+xlim(0,3.2)+
annotate("text",label=expression(paste(beta['female'],' = 0.69')),x=.48,y=4.2,size=4)+
annotate("text",label=expression(paste(beta['male'],' = 1.15')),x=.42,y=3.85,size=4)+
theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank(),panel.background = element_blank(), axis.line = element_line(colour = "black"))
p1=p1+geom_point(aes(x=rel_f_cMS, y=rel_f_RS),color=colpal2[1],alpha=0.4,shape=16, size = 3)+
geom_smooth(aes(x=rel_f_cMS, y=rel_f_RS),color=colpal2[1],method=lm, se=TRUE,alpha=0.3)
p2<-ggplot(DB_data_clean_Large_pop, aes(x=rel_m_cMS, y=rel_m_RS)) +
geom_point(alpha=0.4,shape=16, size = 3,color=colpal2[2]) +
geom_smooth(method=lm, se=TRUE,alpha=0.3) +
theme(plot.tag.position=c(0.1,0.98))+
labs(tag = "B")+xlab('Rel. mating success')+ylab("Rel. reproductive success")+ggtitle('Large group')+ theme(plot.title = element_text(hjust = 0.5))+
theme(axis.text=element_text(size=13),
axis.title=element_text(size=14))+ theme(legend.position="none")+
ylim(0,4.2)+xlim(0,3.2)+
annotate("text",label=expression(paste(beta['female'],' = 0.88')),x=.48,y=4.2,size=4)+
annotate("text",label=expression(paste(beta['male'],' = 0.59')),x=.42,y=3.85,size=4)+
theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank(),panel.background = element_blank(), axis.line = element_line(colour = "black"))
p2=p2+geom_point(aes(x=rel_f_cMS, y=rel_f_RS),color=colpal2[1],alpha=0.4,shape=16, size = 3)+
geom_smooth(aes(x=rel_f_cMS, y=rel_f_RS),color=colpal2[1],method=lm, se=TRUE,alpha=0.3)
p3<-ggplot(DB_data_clean_Large_area, aes(x=rel_m_cMS, y=rel_m_RS)) +
geom_point(alpha=0.4,shape=16, size = 3,color=colpal2[2]) +
geom_smooth(method=lm, se=TRUE,alpha=0.3) +
theme(plot.tag.position=c(0.1,0.98))+
labs(tag = "C")+xlab('Rel. mating success')+ylab("Rel. reproductive success")+ggtitle('Large area')+ theme(plot.title = element_text(hjust = 0.5))+
theme(axis.text=element_text(size=13),
axis.title=element_text(size=14))+ theme(legend.position="none")+
ylim(0,4.2)+xlim(0,3.2)+
annotate("text",label=expression(paste(beta['female'],' = 0.93')),x=.48,y=4.2,size=4)+
annotate("text",label=expression(paste(beta['male'],' = 0.51')),x=.42,y=3.85,size=4)+
theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank(),panel.background = element_blank(), axis.line = element_line(colour = "black"))
p3=p3+geom_point(aes(x=rel_f_cMS, y=rel_f_RS),color=colpal2[1],alpha=0.4,shape=16, size = 3)+
geom_smooth(aes(x=rel_f_cMS, y=rel_f_RS),color=colpal2[1],method=lm, se=TRUE,alpha=0.3)
p4<-ggplot(DB_data_clean_Small_area, aes(x=rel_m_cMS, y=rel_m_RS)) +
geom_point(alpha=0.4,shape=16, size = 3,color=colpal2[2]) +
geom_smooth(method=lm, se=TRUE,alpha=0.3) +
theme(plot.tag.position=c(0.1,0.98))+
labs(tag = "D")+xlab('Rel. mating success')+ylab("Rel. reproductive success")+ggtitle('Small area')+ theme(plot.title = element_text(hjust = 0.5))+
theme(axis.text=element_text(size=13),
axis.title=element_text(size=14))+
ylim(0,4.2)+xlim(0,3.2)+
theme(legend.position="none")+
annotate("text",label=expression(paste(beta['female'],' = 0.72')),x=.48,y=4.2,size=4)+
annotate("text",label=expression(paste(beta['male'],' = 1.19')),x=.42,y=3.85,size=4)+
theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank(),panel.background = element_blank(), axis.line = element_line(colour = "black"))
p4=p4+geom_point(aes(x=rel_f_cMS, y=rel_f_RS),color=colpal2[1],alpha=0.4,shape=16, size = 3)+
geom_smooth(aes(x=rel_f_cMS, y=rel_f_RS),color=colpal2[1],method=lm, se=TRUE,alpha=0.3)
#Create legend
p5<-ggplot(DB_data_clean, aes(x=Total_N_MTP1, y=Total_N_Rd, color=Sex)) +
geom_point(alpha=0.4,shape=16, size = 3, position=position_jitterdodge(jitter.height=0,jitter.width=0,dodge.width = 0)) +
geom_smooth(method=lm, se=TRUE,alpha=0.3) +
scale_color_manual(values=c(colpal2[1],colpal2[2]),name = "Sex", labels = c('Females','Males'))+
xlab('Rel. mating success')+ylab("Rel. reproductive success")+
guides(color=guide_legend(override.aes=list(fill=NA)))+
theme(legend.key = element_rect(fill = "transparent"))
legend <- get_legend(p5)
plot1<-grid.arrange(p1,p2,legend,p3,p4,legend, nrow = 2,ncol=3, widths=c(2.3, 2.3, 0.65))
Figure 10: Scatter plot of the Bateman gradient in females and males.
Means and 95% confidence intervals.
Jones index
Figure: Jones index (maximum strength of sexual selection)
# Jones index
BarPlot_7<- ggplot(Table_BatemanMetrics_pop[c(7,8,14,15),], aes(x=Sex, y=Variance, fill=Treatment)) +
scale_y_continuous(limits = c(0,1.3), breaks = seq(0,1.3,0.4), expand = c(0 ,0)) +
geom_hline(yintercept=0, linetype="dashed", color = "black", size=1) +
geom_bar(stat="identity", color="black", position=position_dodge(), alpha=0.8) +
ylab(expression(paste(~italic("s'"['max']))))+ggtitle('Jones index')+labs(tag = "A")+xlab('Sex')+
scale_x_discrete(breaks=waiver(),labels = c("Female","Male"))+
theme(panel.border = element_blank(),
plot.title = element_text(hjust = 0.5),
panel.background = element_blank(),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
plot.tag.position=c(0.01,0.98),
legend.position = c(0.8, 0.9),
legend.title = element_blank(),
legend.text = element_text(colour="black", size=10),
axis.line.x = element_line(colour = "black", size = 1),
axis.line.y = element_line(colour = "black", size = 1),
axis.text.x = element_text(face="plain", color="black", size=16, angle=0),
axis.text.y = element_text(face="plain", color="black", size=16, angle=0),
axis.title.x = element_text(size=16,face="plain", margin = margin(r=0,10,0,0)),
axis.title.y = element_text(size=16,face="plain", margin = margin(r=10,0,0,0)),
axis.ticks = element_line(size = 1),
axis.ticks.length = unit(.3, "cm")) +
geom_errorbar(aes(ymin=l95.CI, ymax=u95.CI), width=.3,size=1, position=position_dodge(.9)) +
scale_fill_manual(values=c(colorESEB[1],colorESEB[2]),name = "Treatment", labels = c('Small group','Large group'))
BarPlot_8<- ggplot(Table_BatemanMetrics_area[c(7,8,14,15),], aes(x=Sex, y=Variance, fill=Treatment)) +
scale_y_continuous(limits = c(0,1.3), breaks = seq(0,1.3,0.4), expand = c(0 ,0)) +
geom_hline(yintercept=0, linetype="dashed", color = "black", size=1) +
geom_bar(stat="identity", color="black", position=position_dodge(), alpha=0.8) +
ylab('')+ggtitle('Jones index')+labs(tag = "B")+xlab('Sex')+
scale_x_discrete(breaks=waiver(),labels = c("Female","Male"))+
theme(panel.border = element_blank(),
plot.title = element_text(hjust = 0.5),
panel.background = element_blank(),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
plot.tag.position=c(0.01,0.98),
legend.position = c(0.8, 0.9),
legend.title = element_blank(),
legend.text = element_text(colour="black", size=10),
axis.line.x = element_line(colour = "black", size = 1),
axis.line.y = element_line(colour = "black", size = 1),
axis.text.x = element_text(face="plain", color="black", size=16, angle=0),
axis.text.y = element_text(face="plain", color="black", size=16, angle=0),
axis.title.x = element_text(size=16,face="plain", margin = margin(r=0,10,0,0)),
axis.title.y = element_text(size=16,face="plain", margin = margin(r=10,0,0,0)),
axis.ticks = element_line(size = 1),
axis.ticks.length = unit(.3, "cm")) +
geom_errorbar(aes(ymin=l95.CI, ymax=u95.CI), width=.3,size=1, position=position_dodge(.9)) +
scale_fill_manual(values=c(colorESEB2[1],colorESEB2[2]),name = "Treatment", labels = c('Large area','Small area'))
grid.arrange(grobs = list(BarPlot_7,BarPlot_8), nrow = 1,ncol=2, widths=c(2.3, 2.3))
Figure 11: Effects of group size (A) and area treatment (B) on the Jones
index in females and males. Means and 95% confidence intervals.
Treatement comparisons via permutation test for the Jones index
Table_BatemanMetrics_TreatComp_round[c(4,8,12,16),] Sex Treatment Selection_metric Variance l95.CI u95.CI p-value
4 Male Area Jones index -0.389 -0.777 -0.009 0.002
8 Male Population size Jones index 0.297 -0.075 0.694 0.018
12 Female Area Jones index 0.290 -0.026 0.600 0.017
16 Female Population size Jones index -0.269 -0.563 0.035 0.027Sex comparisons via permutation test for the opportunity for selection
Table_BatemanMetrics_SexComp_round[c(4,8,12,16),] Treatment Selection_metric Variance l95.CI u95.CI p-value
4 Small population size Jones index 0.319 -0.060 0.712 0.011
8 Large population size Jones index -0.246 -0.542 0.053 0.039
12 Large area Jones index -0.273 -0.593 0.057 0.018
16 Small area Jones index 0.406 0.046 0.780 0.002Variance decopmposition
We decomposed the variance in reproductive success for males and
females.
Components fro males were:
- Mating success
-
Insemination success
- Fertilization success
- Partner
fecundity
Components for females were:
- Mating success
- Fecundity
We used bootstrapping (10.000 bootstrap
replicates) to obtain 95% confidence intervals and permutation tests
(10.000 permutations) to statistically compare treatments and
sexes.
# Bootstrapping variances + CI
# mMS
# Large area
DB_data_clean_Large_area_M_MS_n <-as.data.table(DB_data_clean_Large_area$rel_m_cMS)
c <- function(d, i){
d2 <- d[i,]
return(var(d2[,1], na.rm=TRUE))
}
Large_area_M_MS_bootvar <- boot(DB_data_clean_Large_area_M_MS_n, c, R=10000)
# Small Area
DB_data_clean_Small_area_M_MS_n <-as.data.table(DB_data_clean_Small_area$rel_m_cMS)
Small_area_M_MS_bootvar <- boot(DB_data_clean_Small_area_M_MS_n, c, R=10000)
# Small group
DB_data_clean_Small_pop_M_MS_n <-as.data.table(DB_data_clean_Small_pop$rel_m_cMS)
Small_pop_M_MS_bootvar <- boot(DB_data_clean_Small_pop_M_MS_n, c, R=10000)
# Large group
DB_data_clean_Large_pop_M_MS_n <-as.data.table(DB_data_clean_Large_pop$rel_m_cMS)
Large_pop_M_MS_bootvar <- boot(DB_data_clean_Large_pop_M_MS_n, c, R=10000)
rm("c")
# InSuc ####
# Large area
DB_data_clean_Large_area_M_InSuc_n <-as.data.table(DB_data_clean_Large_area$rel_m_InSuc)
c <- function(d, i){
d2 <- d[i,]
return(var(d2[,1], na.rm=TRUE))
}
Large_area_M_InSuc_bootvar <- boot(DB_data_clean_Large_area_M_InSuc_n, c, R=10000)
# Small Area
DB_data_clean_Small_area_M_InSuc_n <-as.data.table(DB_data_clean_Small_area$rel_m_InSuc)
Small_area_M_InSuc_bootvar <- boot(DB_data_clean_Small_area_M_InSuc_n, c, R=10000)
# Small group
DB_data_clean_Small_pop_M_InSuc_n <-as.data.table(DB_data_clean_Small_pop$rel_m_InSuc)
Small_pop_M_InSuc_bootvar <- boot(DB_data_clean_Small_pop_M_InSuc_n, c, R=10000)
# Large group
DB_data_clean_Large_pop_M_InSuc_n <-as.data.table(DB_data_clean_Large_pop$rel_m_InSuc)
Large_pop_M_InSuc_bootvar <- boot(DB_data_clean_Large_pop_M_InSuc_n, c, R=10000)
rm("c")
# feSuc ####
# Large area
DB_data_clean_Large_area_M_feSuc_n <-as.data.table(DB_data_clean_Large_area$rel_m_feSuc)
c <- function(d, i){
d2 <- d[i,]
return(var(d2$V1, na.rm=TRUE))
}
Large_area_M_feSuc_bootvar <- boot(DB_data_clean_Large_area_M_feSuc_n, c, R=10000)
# Small Area
DB_data_clean_Small_area_M_feSuc_n <-as.data.table(DB_data_clean_Small_area$rel_m_feSuc)
Small_area_M_feSuc_bootvar <- boot(DB_data_clean_Small_area_M_feSuc_n, c, R=10000)
# Small group
DB_data_clean_Small_pop_M_feSuc_n <-as.data.table(DB_data_clean_Small_pop$rel_m_feSuc)
Small_pop_M_feSuc_bootvar <- boot(DB_data_clean_Small_pop_M_feSuc_n, c, R=10000)
# Large group
DB_data_clean_Large_pop_M_feSuc_n <-as.data.table(DB_data_clean_Large_pop$rel_m_feSuc)
Large_pop_M_feSuc_bootvar <- boot(DB_data_clean_Large_pop_M_feSuc_n, c, R=10000)
rm("c")
# pFec ####
# Large area
DB_data_clean_Large_area_M_pFec_n <-as.data.table(DB_data_clean_Large_area$rel_m_pFec)
c <- function(d, i){
d2 <- d[i,]
return(var(d2[,1], na.rm=TRUE))
}
Large_area_M_pFec_bootvar <- boot(DB_data_clean_Large_area_M_pFec_n, c, R=10000)
# Small Area
DB_data_clean_Small_area_M_pFec_n <-as.data.table(DB_data_clean_Small_area$rel_m_pFec)
Small_area_M_pFec_bootvar <- boot(DB_data_clean_Small_area_M_pFec_n, c, R=10000)
# Small group
DB_data_clean_Small_pop_M_pFec_n <-as.data.table(DB_data_clean_Small_pop$rel_m_pFec)
Small_pop_M_pFec_bootvar <- boot(DB_data_clean_Small_pop_M_pFec_n, c, R=10000)
# Large group
DB_data_clean_Large_pop_M_pFec_n <-as.data.table(DB_data_clean_Large_pop$rel_m_pFec)
Large_pop_M_pFec_bootvar <- boot(DB_data_clean_Large_pop_M_pFec_n, c, R=10000)
rm("c")
# fMS ####
# Large area
DB_data_clean_Large_area_F_fMS_n <-as.data.table(DB_data_clean_Large_area$rel_f_cMS)
c <- function(d, i){
d2 <- d[i,]
return(var(d2[,1], na.rm=TRUE))
}
Large_area_F_fMS_bootvar <- boot(DB_data_clean_Large_area_F_fMS_n, c, R=10000)
# Small Area
DB_data_clean_Small_area_F_fMS_n <-as.data.table(DB_data_clean_Small_area$rel_f_cMS)
Small_area_F_fMS_bootvar <- boot(DB_data_clean_Small_area_F_fMS_n, c, R=10000)
# Small group
DB_data_clean_Small_pop_F_fMS_n <-as.data.table(DB_data_clean_Small_pop$rel_f_cMS)
Small_pop_F_fMS_bootvar <- boot(DB_data_clean_Small_pop_F_fMS_n, c, R=10000)
# Large group
DB_data_clean_Large_pop_F_fMS_n <-as.data.table(DB_data_clean_Large_pop$rel_f_cMS)
Large_pop_F_fMS_bootvar <- boot(DB_data_clean_Large_pop_F_fMS_n, c, R=10000)
rm("c")
# fFec ####
# Large area
DB_data_clean_Large_area_F_fFec_n <-as.data.table(DB_data_clean_Large_area$rel_f_fec_pMate)
c <- function(d, i){
d2 <- d[i,]
return(var(d2[,1], na.rm=TRUE))
}
Large_area_F_fFec_bootvar <- boot(DB_data_clean_Large_area_F_fFec_n, c, R=10000)
# Small Area
DB_data_clean_Small_area_F_fFec_n <-as.data.table(DB_data_clean_Small_area$rel_f_fec_pMate)
Small_area_F_fFec_bootvar <- boot(DB_data_clean_Small_area_F_fFec_n, c, R=10000)
# Small group
DB_data_clean_Small_pop_F_fFec_n <-as.data.table(DB_data_clean_Small_pop$rel_f_fec_pMate)
Small_pop_F_fFec_bootvar <- boot(DB_data_clean_Small_pop_F_fFec_n, c, R=10000)
# Large group
DB_data_clean_Large_pop_F_fFec_n <-as.data.table(DB_data_clean_Large_pop$rel_f_fec_pMate)
Large_pop_F_fFec_bootvar <- boot(DB_data_clean_Large_pop_F_fFec_n, c, R=10000)
rm("c")
#Write Table ####
library(base)
PhenVarBoot_Table_Male_Large_area_MS <- as.data.frame(cbind("Male", "MS", "Large_area", mean(Large_area_M_MS_bootvar$t), quantile(Large_area_M_MS_bootvar$t,.025, names = FALSE), quantile(Large_area_M_MS_bootvar$t,.975, names = FALSE)))
PhenVarBoot_Table_Male_Small_area_MS <- as.data.frame(cbind("Male", "MS", "Small_area", mean(Small_area_M_MS_bootvar$t), quantile(Small_area_M_MS_bootvar$t,.025, names = FALSE), quantile(Small_area_M_MS_bootvar$t,.975, names = FALSE)))
PhenVarBoot_Table_Male_Small_pop_MS <- as.data.frame(cbind("Male", "MS", "Small_pop", mean(Small_pop_M_MS_bootvar$t), quantile(Small_pop_M_MS_bootvar$t,.025, names = FALSE), quantile(Small_pop_M_MS_bootvar$t,.975, names = FALSE)))
PhenVarBoot_Table_Male_Large_pop_MS <- as.data.frame(cbind("Male", "MS", "Large_pop", mean(Large_pop_M_MS_bootvar$t), quantile(Large_pop_M_MS_bootvar$t,.025, names = FALSE), quantile(Large_pop_M_MS_bootvar$t,.975, names = FALSE)))
PhenVarBoot_Table_Male_Large_area_InSuc <- as.data.frame(cbind("Male", "InSuc", "Large_area", mean(Large_area_M_InSuc_bootvar$t), quantile(Large_area_M_InSuc_bootvar$t,.025, names = FALSE), quantile(Large_area_M_InSuc_bootvar$t,.975, names = FALSE)))
PhenVarBoot_Table_Male_Small_area_InSuc <- as.data.frame(cbind("Male", "InSuc", "Small_area", mean(Small_area_M_InSuc_bootvar$t), quantile(Small_area_M_InSuc_bootvar$t,.025, names = FALSE), quantile(Small_area_M_InSuc_bootvar$t,.975, names = FALSE)))
PhenVarBoot_Table_Male_Small_pop_InSuc <- as.data.frame(cbind("Male", "InSuc", "Small_pop", mean(Small_pop_M_InSuc_bootvar$t), quantile(Small_pop_M_InSuc_bootvar$t,.025, names = FALSE), quantile(Small_pop_M_InSuc_bootvar$t,.975, names = FALSE)))
PhenVarBoot_Table_Male_Large_pop_InSuc <- as.data.frame(cbind("Male", "InSuc", "Large_pop", mean(Large_pop_M_InSuc_bootvar$t), quantile(Large_pop_M_InSuc_bootvar$t,.025, names = FALSE), quantile(Large_pop_M_InSuc_bootvar$t,.975, names = FALSE)))
PhenVarBoot_Table_Male_Large_area_feSuc <- as.data.frame(cbind("Male", "feSuc", "Large_area", mean(Large_area_M_feSuc_bootvar$t), quantile(Large_area_M_feSuc_bootvar$t,.025, names = FALSE), quantile(Large_area_M_feSuc_bootvar$t,.975, names = FALSE)))
PhenVarBoot_Table_Male_Small_area_feSuc <- as.data.frame(cbind("Male", "feSuc", "Small_area", mean(Small_area_M_feSuc_bootvar$t), quantile(Small_area_M_feSuc_bootvar$t,.025, names = FALSE), quantile(Small_area_M_feSuc_bootvar$t,.975, names = FALSE)))
PhenVarBoot_Table_Male_Small_pop_feSuc <- as.data.frame(cbind("Male", "feSuc", "Small_pop", mean(Small_pop_M_feSuc_bootvar$t), quantile(Small_pop_M_feSuc_bootvar$t,.025, names = FALSE), quantile(Small_pop_M_feSuc_bootvar$t,.975, names = FALSE)))
PhenVarBoot_Table_Male_Large_pop_feSuc <- as.data.frame(cbind("Male", "feSuc", "Large_pop", mean(Large_pop_M_feSuc_bootvar$t), quantile(Large_pop_M_feSuc_bootvar$t,.025, names = FALSE), quantile(Large_pop_M_feSuc_bootvar$t,.975, names = FALSE)))
PhenVarBoot_Table_Male_Large_area_pFec <- as.data.frame(cbind("Male", "pFec", "Large_area", mean(Large_area_M_pFec_bootvar$t), quantile(Large_area_M_pFec_bootvar$t,.025, names = FALSE), quantile(Large_area_M_pFec_bootvar$t,.975, names = FALSE)))
PhenVarBoot_Table_Male_Small_area_pFec <- as.data.frame(cbind("Male", "pFec", "Small_area", mean(Small_area_M_pFec_bootvar$t), quantile(Small_area_M_pFec_bootvar$t,.025, names = FALSE), quantile(Small_area_M_pFec_bootvar$t,.975, names = FALSE)))
PhenVarBoot_Table_Male_Small_pop_pFec <- as.data.frame(cbind("Male", "pFec", "Small_pop", mean(Small_pop_M_pFec_bootvar$t), quantile(Small_pop_M_pFec_bootvar$t,.025, names = FALSE), quantile(Small_pop_M_pFec_bootvar$t,.975, names = FALSE)))
PhenVarBoot_Table_Male_Large_pop_pFec <- as.data.frame(cbind("Male", "pFec", "Large_pop", mean(Large_pop_M_pFec_bootvar$t), quantile(Large_pop_M_pFec_bootvar$t,.025, names = FALSE), quantile(Large_pop_M_pFec_bootvar$t,.975, names = FALSE)))
PhenVarBoot_Table_Female_Large_area_fMS <- as.data.frame(cbind("Female", "fMS", "Large_area", mean(Large_area_F_fMS_bootvar$t), quantile(Large_area_F_fMS_bootvar$t,.025, names = FALSE), quantile(Large_area_F_fMS_bootvar$t,.975, names = FALSE)))
PhenVarBoot_Table_Female_Small_area_fMS <- as.data.frame(cbind("Female", "fMS", "Small_area", mean(Small_area_F_fMS_bootvar$t), quantile(Small_area_F_fMS_bootvar$t,.025, names = FALSE), quantile(Small_area_F_fMS_bootvar$t,.975, names = FALSE)))
PhenVarBoot_Table_Female_Small_pop_fMS <- as.data.frame(cbind("Female", "fMS", "Small_pop", mean(Small_pop_F_fMS_bootvar$t), quantile(Small_pop_F_fMS_bootvar$t,.025, names = FALSE), quantile(Small_pop_F_fMS_bootvar$t,.975, names = FALSE)))
PhenVarBoot_Table_Female_Large_pop_fMS <- as.data.frame(cbind("Female", "fMS", "Large_pop", mean(Large_pop_F_fMS_bootvar$t), quantile(Large_pop_F_fMS_bootvar$t,.025, names = FALSE), quantile(Large_pop_F_fMS_bootvar$t,.975, names = FALSE)))
PhenVarBoot_Table_Female_Large_area_fFec <- as.data.frame(cbind("Female", "fFec", "Large_area", mean(Large_area_F_fFec_bootvar$t), quantile(Large_area_F_fFec_bootvar$t,.025, names = FALSE), quantile(Large_area_F_fFec_bootvar$t,.975, names = FALSE)))
PhenVarBoot_Table_Female_Small_area_fFec <- as.data.frame(cbind("Female", "fFec", "Small_area", mean(Small_area_F_fFec_bootvar$t), quantile(Small_area_F_fFec_bootvar$t,.025, names = FALSE), quantile(Small_area_F_fFec_bootvar$t,.975, names = FALSE)))
PhenVarBoot_Table_Female_Small_pop_fFec <- as.data.frame(cbind("Female", "fFec", "Small_pop", mean(Small_pop_F_fFec_bootvar$t), quantile(Small_pop_F_fFec_bootvar$t,.025, names = FALSE), quantile(Small_pop_F_fFec_bootvar$t,.975, names = FALSE)))
PhenVarBoot_Table_Female_Large_pop_fFec <- as.data.frame(cbind("Female", "fFec", "Large_pop", mean(Large_pop_F_fFec_bootvar$t,na.rm=T), quantile(Large_pop_F_fFec_bootvar$t,.025, names = FALSE,na.rm=T), quantile(Large_pop_F_fFec_bootvar$t,.975, names = FALSE,na.rm=T)))
PhenVarBoot_Table <- as.data.frame(as.matrix(rbind(PhenVarBoot_Table_Male_Small_pop_MS,PhenVarBoot_Table_Male_Large_pop_MS,
PhenVarBoot_Table_Male_Large_area_MS,PhenVarBoot_Table_Male_Small_area_MS,
PhenVarBoot_Table_Male_Small_pop_InSuc,PhenVarBoot_Table_Male_Large_pop_InSuc,
PhenVarBoot_Table_Male_Large_area_InSuc,PhenVarBoot_Table_Male_Small_area_InSuc,
PhenVarBoot_Table_Male_Small_pop_feSuc,PhenVarBoot_Table_Male_Large_pop_feSuc,
PhenVarBoot_Table_Male_Large_area_feSuc,PhenVarBoot_Table_Male_Small_area_feSuc,
PhenVarBoot_Table_Male_Small_pop_pFec,PhenVarBoot_Table_Male_Large_pop_pFec,
PhenVarBoot_Table_Male_Large_area_pFec,PhenVarBoot_Table_Male_Small_area_pFec,
PhenVarBoot_Table_Female_Small_pop_fMS,PhenVarBoot_Table_Female_Large_pop_fMS,
PhenVarBoot_Table_Female_Large_area_fMS,PhenVarBoot_Table_Female_Small_area_fMS,
PhenVarBoot_Table_Female_Small_pop_fFec,PhenVarBoot_Table_Female_Large_pop_fFec,
PhenVarBoot_Table_Female_Large_area_fFec,PhenVarBoot_Table_Female_Small_area_fFec)))
is.table(PhenVarBoot_Table)
colnames(PhenVarBoot_Table)[1] <- "Sex"
colnames(PhenVarBoot_Table)[2] <- "Variance_component"
colnames(PhenVarBoot_Table)[3] <- "Treatment"
colnames(PhenVarBoot_Table)[4] <- "Variance"
colnames(PhenVarBoot_Table)[5] <- "l95.CI"
colnames(PhenVarBoot_Table)[6] <- "u95.CI"
PhenVarBoot_Table[,4]=as.numeric(PhenVarBoot_Table[,4])
PhenVarBoot_Table[,5]=as.numeric(PhenVarBoot_Table[,5])
PhenVarBoot_Table[,6]=as.numeric(PhenVarBoot_Table[,6])
PhenVarBoot_Table_round=cbind(PhenVarBoot_Table[,c(1,2,3)],round(PhenVarBoot_Table[,c(4,5,6)],digit=3))
rownames(PhenVarBoot_Table_round) <- NULL# Treatment comparison
#mMS
#Area
Treat_diff_Male_area_mMS=c(Large_area_M_MS_bootvar$t)-c(Small_area_M_MS_bootvar$t)
t_Treat_diff_Male_area_mMS=mean(Treat_diff_Male_area_mMS,na.rm=TRUE)
t_Treat_diff_Male_area_mMS_lower=quantile(Treat_diff_Male_area_mMS,.025,na.rm=TRUE)
t_Treat_diff_Male_area_mMS_upper=quantile(Treat_diff_Male_area_mMS,.975,na.rm=TRUE)
#Permutation test to calculate p value
comb_data=c(DB_data_clean_Large_area$rel_m_cMS,DB_data_clean_Small_area$rel_m_cMS)
diff.observed = var(na.omit((DB_data_clean_Large_area$rel_m_cMS))) - var(na.omit((DB_data_clean_Small_area$rel_m_cMS)))
diff.observed
number_of_permutations = 100000
diff.random = NULL
for (i in 1 : number_of_permutations) {
# Sample from the combined dataset
a.random = sample (na.omit(comb_data), length(c(DB_data_clean_Large_area$rel_m_cMS)), TRUE)
b.random = sample (na.omit(comb_data), length(c(DB_data_clean_Small_area$rel_m_cMS)), TRUE)
# Null (permuated) difference
diff.random[i] = var(na.omit(b.random)) - var(na.omit(a.random))
}
# P-value is the fraction of how many times the permuted difference is
# equal or more extreme than the observed difference
t_Treat_diff_Male_area_mMS_p = sum(abs(diff.random) >= as.numeric(abs(diff.observed)))/ number_of_permutations
#Population size ####
Treat_diff_Male_pop_mMS=c(Small_pop_M_MS_bootvar$t)-c(Large_pop_M_MS_bootvar$t)
t_Treat_diff_Male_pop_mMS=mean(Treat_diff_Male_pop_mMS,na.rm=TRUE)
t_Treat_diff_Male_pop_mMS_lower=quantile(Treat_diff_Male_pop_mMS,.025,na.rm=TRUE)
t_Treat_diff_Male_pop_mMS_upper=quantile(Treat_diff_Male_pop_mMS,.975,na.rm=TRUE)
#Permutation test to calculate p value
comb_data=c(DB_data_clean_Small_pop$rel_m_cMS,DB_data_clean_Large_pop$rel_m_cMS)
diff.observed = var(na.omit((DB_data_clean_Small_pop$rel_m_cMS))) - var(na.omit((DB_data_clean_Large_pop$rel_m_cMS)))
diff.observed
number_of_permutations = 100000
diff.random = NULL
for (i in 1 : number_of_permutations) {
# Sample from the combined dataset
a.random = sample (na.omit(comb_data), length(c(DB_data_clean_Small_pop$rel_m_cMS)), TRUE)
b.random = sample (na.omit(comb_data), length(c(DB_data_clean_Large_pop$rel_m_cMS)), TRUE)
# Null (permuated) difference
diff.random[i] = var(na.omit(b.random)) - var(na.omit(a.random))
}
# P-value is the fraction of how many times the permuted difference is
# equal or more extreme than the observed difference
t_Treat_diff_Male_pop_mMS_p = sum(abs(diff.random) >= as.numeric(abs(diff.observed)))/ number_of_permutations
#InSuc ####
#Area ####
Treat_diff_Male_area_InSuc=c(Large_area_M_InSuc_bootvar$t)-c(Small_area_M_InSuc_bootvar$t)
t_Treat_diff_Male_area_InSuc=mean(Treat_diff_Male_area_InSuc,na.rm=TRUE)
t_Treat_diff_Male_area_InSuc_lower=quantile(Treat_diff_Male_area_InSuc,.025,na.rm=TRUE)
t_Treat_diff_Male_area_InSuc_upper=quantile(Treat_diff_Male_area_InSuc,.975,na.rm=TRUE)
#Permutation test to calculate p value
comb_data=c(DB_data_clean_Large_area$rel_m_InSuc,DB_data_clean_Small_area$rel_m_InSuc)
diff.observed = var(na.omit((DB_data_clean_Large_area$rel_m_InSuc))) - var(na.omit((DB_data_clean_Small_area$rel_m_InSuc)))
diff.observed
number_of_permutations = 100000
diff.random = NULL
for (i in 1 : number_of_permutations) {
# Sample from the combined dataset
a.random = sample (na.omit(comb_data), length(c(DB_data_clean_Large_area$rel_m_InSuc)), TRUE)
b.random = sample (na.omit(comb_data), length(c(DB_data_clean_Small_area$rel_m_InSuc)), TRUE)
# Null (permuated) difference
diff.random[i] = var(na.omit(b.random)) - var(na.omit(a.random))
}
# P-value is the fraction of how many times the permuted difference is
# equal or more extreme than the observed difference
t_Treat_diff_Male_area_InSuc_p = sum(abs(diff.random) >= as.numeric(abs(diff.observed)))/ number_of_permutations
#Population size ####
Treat_diff_Male_pop_InSuc=c(Small_pop_M_InSuc_bootvar$t)-c(Large_pop_M_InSuc_bootvar$t)
t_Treat_diff_Male_pop_InSuc=mean(Treat_diff_Male_pop_InSuc,na.rm=TRUE)
t_Treat_diff_Male_pop_InSuc_lower=quantile(Treat_diff_Male_pop_InSuc,.025,na.rm=TRUE)
t_Treat_diff_Male_pop_InSuc_upper=quantile(Treat_diff_Male_pop_InSuc,.975,na.rm=TRUE)
#Permutation test to calculate p value
comb_data=c(DB_data_clean_Small_pop$rel_m_InSuc,DB_data_clean_Large_pop$rel_m_InSuc)
diff.observed = var(na.omit((DB_data_clean_Small_pop$rel_m_InSuc))) - var(na.omit((DB_data_clean_Large_pop$rel_m_InSuc)))
diff.observed
number_of_permutations = 100000
diff.random = NULL
for (i in 1 : number_of_permutations) {
# Sample from the combined dataset
a.random = sample (na.omit(comb_data), length(c(DB_data_clean_Small_pop$rel_m_InSuc)), TRUE)
b.random = sample (na.omit(comb_data), length(c(DB_data_clean_Large_pop$rel_m_InSuc)), TRUE)
# Null (permuated) difference
diff.random[i] = var(na.omit(b.random)) - var(na.omit(a.random))
}
# P-value is the fraction of how many times the permuted difference is
# equal or more extreme than the observed difference
t_Treat_diff_Male_pop_InSuc_p = sum(abs(diff.random) >= as.numeric(abs(diff.observed)))/ number_of_permutations
#feSuc ####
#Area ####
Treat_diff_Male_area_feSuc=c(Large_area_M_feSuc_bootvar$t)-c(Small_area_M_feSuc_bootvar$t)
t_Treat_diff_Male_area_feSuc=mean(Treat_diff_Male_area_feSuc,na.rm=TRUE)
t_Treat_diff_Male_area_feSuc_lower=quantile(Treat_diff_Male_area_feSuc,.025,na.rm=TRUE)
t_Treat_diff_Male_area_feSuc_upper=quantile(Treat_diff_Male_area_feSuc,.975,na.rm=TRUE)
#Permutation test to calculate p value
comb_data=c(DB_data_clean_Large_area$rel_m_feSuc,DB_data_clean_Small_area$rel_m_feSuc)
diff.observed = var(na.omit((DB_data_clean_Large_area$rel_m_feSuc))) - var(na.omit((DB_data_clean_Small_area$rel_m_feSuc)))
diff.observed
number_of_permutations = 100000
diff.random = NULL
for (i in 1 : number_of_permutations) {
# Sample from the combined dataset
a.random = sample (na.omit(comb_data), length(c(DB_data_clean_Large_area$rel_m_feSuc)), TRUE)
b.random = sample (na.omit(comb_data), length(c(DB_data_clean_Small_area$rel_m_feSuc)), TRUE)
# Null (permuated) difference
diff.random[i] = var(na.omit(b.random)) - var(na.omit(a.random))
}
# P-value is the fraction of how many times the permuted difference is
# equal or more extreme than the observed difference
t_Treat_diff_Male_area_feSuc_p = sum(abs(diff.random) >= as.numeric(abs(diff.observed)))/ number_of_permutations
#Population size ####
Treat_diff_Male_pop_feSuc=c(Small_pop_M_feSuc_bootvar$t)-c(Large_pop_M_feSuc_bootvar$t)
t_Treat_diff_Male_pop_feSuc=mean(Treat_diff_Male_pop_feSuc,na.rm=TRUE)
t_Treat_diff_Male_pop_feSuc_lower=quantile(Treat_diff_Male_pop_feSuc,.025,na.rm=TRUE)
t_Treat_diff_Male_pop_feSuc_upper=quantile(Treat_diff_Male_pop_feSuc,.975,na.rm=TRUE)
#Permutation test to calculate p value
comb_data=c(DB_data_clean_Small_pop$rel_m_feSuc,DB_data_clean_Large_pop$rel_m_feSuc)
diff.observed = var(na.omit((DB_data_clean_Small_pop$rel_m_feSuc))) - var(na.omit((DB_data_clean_Large_pop$rel_m_feSuc)))
diff.observed
number_of_permutations = 100000
diff.random = NULL
for (i in 1 : number_of_permutations) {
# Sample from the combined dataset
a.random = sample (na.omit(comb_data), length(c(DB_data_clean_Small_pop$rel_m_feSuc)), TRUE)
b.random = sample (na.omit(comb_data), length(c(DB_data_clean_Large_pop$rel_m_feSuc)), TRUE)
# Null (permuated) difference
diff.random[i] = var(na.omit(b.random)) - var(na.omit(a.random))
}
# P-value is the fraction of how many times the permuted difference is
# equal or more extreme than the observed difference
t_Treat_diff_Male_pop_feSuc_p = sum(abs(diff.random) >= as.numeric(abs(diff.observed)))/ number_of_permutations
#pFec ####
#Area ####
Treat_diff_Male_area_mFec=c(Large_area_M_pFec_bootvar$t)-c(Small_area_M_pFec_bootvar$t)
t_Treat_diff_Male_area_mFec=mean(Treat_diff_Male_area_mFec,na.rm=TRUE)
t_Treat_diff_Male_area_mFec_lower=quantile(Treat_diff_Male_area_mFec,.025,na.rm=TRUE)
t_Treat_diff_Male_area_mFec_upper=quantile(Treat_diff_Male_area_mFec,.975,na.rm=TRUE)
#Permutation test to calculate p value
comb_data=c(DB_data_clean_Large_area$rel_m_pFec,DB_data_clean_Small_area$rel_m_pFec)
diff.observed = var(na.omit((DB_data_clean_Large_area$rel_m_pFec))) - var(na.omit((DB_data_clean_Small_area$rel_m_pFec)))
diff.observed
number_of_permutations = 100000
diff.random = NULL
for (i in 1 : number_of_permutations) {
# Sample from the combined dataset
a.random = sample (na.omit(comb_data), length(c(DB_data_clean_Large_area$rel_m_pFec)), TRUE)
b.random = sample (na.omit(comb_data), length(c(DB_data_clean_Small_area$rel_m_pFec)), TRUE)
# Null (permuated) difference
diff.random[i] = var(na.omit(b.random)) - var(na.omit(a.random))
}
# P-value is the fraction of how many times the permuted difference is
# equal or more extreme than the observed difference
t_Treat_diff_Male_area_mFec_p = sum(abs(diff.random) >= as.numeric(abs(diff.observed)))/ number_of_permutations
#Population size ####
Treat_diff_Male_pop_mFec=c(Small_pop_M_pFec_bootvar$t)-c(Large_pop_M_pFec_bootvar$t)
t_Treat_diff_Male_pop_mFec=mean(Treat_diff_Male_pop_mFec,na.rm=TRUE)
t_Treat_diff_Male_pop_mFec_lower=quantile(Treat_diff_Male_pop_mFec,.025,na.rm=TRUE)
t_Treat_diff_Male_pop_mFec_upper=quantile(Treat_diff_Male_pop_mFec,.975,na.rm=TRUE)
#Permutation test to calculate p value
comb_data=c(DB_data_clean_Small_pop$rel_m_pFec,DB_data_clean_Large_pop$rel_m_pFec)
diff.observed = var(na.omit((DB_data_clean_Small_pop$rel_m_pFec))) - var(na.omit((DB_data_clean_Large_pop$rel_m_pFec)))
diff.observed
number_of_permutations = 100000
diff.random = NULL
for (i in 1 : number_of_permutations) {
# Sample from the combined dataset
a.random = sample (na.omit(comb_data), length(c(DB_data_clean_Small_pop$rel_m_pFec)), TRUE)
b.random = sample (na.omit(comb_data), length(c(DB_data_clean_Large_pop$rel_m_pFec)), TRUE)
# Null (permuated) difference
diff.random[i] = var(na.omit(b.random)) - var(na.omit(a.random))
}
# P-value is the fraction of how many times the permuted difference is
# equal or more extreme than the observed difference
t_Treat_diff_Male_pop_mFec_p = sum(abs(diff.random) >= as.numeric(abs(diff.observed)))/ number_of_permutations
#fMS ####
#Area ####
Treat_diff_Female_area_fMS=c(Large_area_F_fMS_bootvar$t)-c(Small_area_F_fMS_bootvar$t)
t_Treat_diff_Female_area_fMS=mean(Treat_diff_Female_area_fMS,na.rm=TRUE)
t_Treat_diff_Female_area_fMS_lower=quantile(Treat_diff_Female_area_fMS,.025,na.rm=TRUE)
t_Treat_diff_Female_area_fMS_upper=quantile(Treat_diff_Female_area_fMS,.975,na.rm=TRUE)
#Permutation test to calculate p value
comb_data=c(DB_data_clean_Large_area$rel_f_cMS,DB_data_clean_Small_area$rel_f_cMS)
diff.observed = var(na.omit((DB_data_clean_Large_area$rel_f_cMS))) - var(na.omit((DB_data_clean_Small_area$rel_f_cMS)))
diff.observed
number_of_permutations = 100000
diff.random = NULL
for (i in 1 : number_of_permutations) {
# Sample from the combined dataset
a.random = sample (na.omit(comb_data), length(c(DB_data_clean_Large_area$rel_f_cMS)), TRUE)
b.random = sample (na.omit(comb_data), length(c(DB_data_clean_Small_area$rel_f_cMS)), TRUE)
# Null (permuated) difference
diff.random[i] = var(na.omit(b.random)) - var(na.omit(a.random))
}
# P-value is the fraction of how many times the permuted difference is
# equal or more extreme than the observed difference
t_Treat_diff_Female_area_fMS_p = sum(abs(diff.random) >= as.numeric(abs(diff.observed)))/ number_of_permutations
#Population size ####
Treat_diff_Female_pop_fMS=c(Small_pop_F_fMS_bootvar$t)-c(Large_pop_F_fMS_bootvar$t)
t_Treat_diff_Female_pop_fMS=mean(Treat_diff_Female_pop_fMS,na.rm=TRUE)
t_Treat_diff_Female_pop_fMS_lower=quantile(Treat_diff_Female_pop_fMS,.025,na.rm=TRUE)
t_Treat_diff_Female_pop_fMS_upper=quantile(Treat_diff_Female_pop_fMS,.975,na.rm=TRUE)
#Permutation test to calculate p value
comb_data=c(DB_data_clean_Small_pop$rel_f_cMS,DB_data_clean_Large_pop$rel_f_cMS)
diff.observed = var(na.omit((DB_data_clean_Small_pop$rel_f_cMS))) - var(na.omit((DB_data_clean_Large_pop$rel_f_cMS)))
diff.observed
number_of_permutations = 100000
diff.random = NULL
for (i in 1 : number_of_permutations) {
# Sample from the combined dataset
a.random = sample (na.omit(comb_data), length(c(DB_data_clean_Small_pop$rel_f_cMS)), TRUE)
b.random = sample (na.omit(comb_data), length(c(DB_data_clean_Large_pop$rel_f_cMS)), TRUE)
# Null (permuated) difference
diff.random[i] = var(na.omit(b.random)) - var(na.omit(a.random))
}
# P-value is the fraction of how many times the permuted difference is
# equal or more extreme than the observed difference
t_Treat_diff_Female_pop_fMS_p = sum(abs(diff.random) >= as.numeric(abs(diff.observed)))/ number_of_permutations
#fFec ####
#Area ####
Treat_diff_Female_area_fFec=c(Large_area_F_fFec_bootvar$t)-c(Small_area_F_fFec_bootvar$t)
t_Treat_diff_Female_area_fFec=mean(Treat_diff_Female_area_fFec,na.rm=TRUE)
t_Treat_diff_Female_area_fFec_lower=quantile(Treat_diff_Female_area_fFec,.025,na.rm=TRUE)
t_Treat_diff_Female_area_fFec_upper=quantile(Treat_diff_Female_area_fFec,.975,na.rm=TRUE)
#Permutation test to calculate p value
comb_data=c(DB_data_clean_Large_area$rel_f_fec_pMate,DB_data_clean_Small_area$rel_f_fec_pMate)
diff.observed = var(na.omit((DB_data_clean_Large_area$rel_f_fec_pMate))) - var(na.omit((DB_data_clean_Small_area$rel_f_fec_pMate)))
diff.observed
number_of_permutations = 100000
diff.random = NULL
for (i in 1 : number_of_permutations) {
# Sample from the combined dataset
a.random = sample (na.omit(comb_data), length(c(DB_data_clean_Large_area$rel_f_fec_pMate)), TRUE)
b.random = sample (na.omit(comb_data), length(c(DB_data_clean_Small_area$rel_f_fec_pMate)), TRUE)
# Null (permuated) difference
diff.random[i] = var(na.omit(b.random)) - var(na.omit(a.random))
}
# P-value is the fraction of how many times the permuted difference is
# equal or more extreme than the observed difference
t_Treat_diff_Female_area_fFec_p = sum(abs(diff.random) >= as.numeric(abs(diff.observed)))/ number_of_permutations
#Population size ####
Treat_diff_Female_pop_fFec=c(Small_pop_F_fFec_bootvar$t)-c(Large_pop_F_fFec_bootvar$t)
t_Treat_diff_Female_pop_fFec=mean(Treat_diff_Female_pop_fFec,na.rm=TRUE)
t_Treat_diff_Female_pop_fFec_lower=quantile(Treat_diff_Female_pop_fFec,.025,na.rm=TRUE)
t_Treat_diff_Female_pop_fFec_upper=quantile(Treat_diff_Female_pop_fFec,.975,na.rm=TRUE)
#Permutation test to calculate p value
comb_data=c(DB_data_clean_Small_pop$rel_f_fec_pMate,DB_data_clean_Large_pop$rel_f_fec_pMate)
diff.observed = var(na.omit((DB_data_clean_Small_pop$rel_f_fec_pMate))) - var(na.omit((DB_data_clean_Large_pop$rel_f_fec_pMate)))
diff.observed
number_of_permutations = 100000
diff.random = NULL
for (i in 1 : number_of_permutations) {
# Sample from the combined dataset
a.random = sample (na.omit(comb_data), length(c(DB_data_clean_Small_pop$rel_f_fec_pMate)), TRUE)
b.random = sample (na.omit(comb_data), length(c(DB_data_clean_Large_pop$rel_f_fec_pMate)), TRUE)
# Null (permuated) difference
diff.random[i] = var(na.omit(b.random)) - var(na.omit(a.random))
}
# P-value is the fraction of how many times the permuted difference is
# equal or more extreme than the observed difference
t_Treat_diff_Female_pop_fFec_p = sum(abs(diff.random) >= as.numeric(abs(diff.observed)))/ number_of_permutations
#Save data table ####
CompTreat_Table_Male_area_mMS <- as.data.frame(cbind("Male", "Area", "mMS", t_Treat_diff_Male_area_mMS, t_Treat_diff_Male_area_mMS_lower, t_Treat_diff_Male_area_mMS_upper, t_Treat_diff_Male_area_mMS_p))
names(CompTreat_Table_Male_area_mMS)=c('V1','V2','V3','V4','V5','V6','V7')
CompTreat_Table_Male_area_InSuc <- as.data.frame(cbind("Male", "Area", "InSuc", t_Treat_diff_Male_area_InSuc, t_Treat_diff_Male_area_InSuc_lower, t_Treat_diff_Male_area_InSuc_upper, t_Treat_diff_Male_area_InSuc_p))
names(CompTreat_Table_Male_area_InSuc)=c('V1','V2','V3','V4','V5','V6','V7')
CompTreat_Table_Male_area_feSuc <- as.data.frame(cbind("Male", "Area", "feSuc", t_Treat_diff_Male_area_feSuc, t_Treat_diff_Male_area_feSuc_lower, t_Treat_diff_Male_area_feSuc_upper, t_Treat_diff_Male_area_feSuc_p))
names(CompTreat_Table_Male_area_feSuc)=c('V1','V2','V3','V4','V5','V6','V7')
CompTreat_Table_Male_area_mFec <- as.data.frame(cbind("Male", "Area", "mFec", t_Treat_diff_Male_area_mFec, t_Treat_diff_Male_area_mFec_lower, t_Treat_diff_Male_area_mFec_upper, t_Treat_diff_Male_area_mFec_p))
names(CompTreat_Table_Male_area_mFec)=c('V1','V2','V3','V4','V5','V6','V7')
CompTreat_Table_Female_area_fMS <- as.data.frame(cbind("Female", "Area", "fMS", t_Treat_diff_Female_area_fMS, t_Treat_diff_Female_area_fMS_lower, t_Treat_diff_Female_area_fMS_upper, t_Treat_diff_Female_area_fMS_p))
names(CompTreat_Table_Female_area_fMS)=c('V1','V2','V3','V4','V5','V6','V7')
CompTreat_Table_Female_area_fFec <- as.data.frame(cbind("Female", "Area", "fFec", t_Treat_diff_Female_area_fFec, t_Treat_diff_Female_area_fFec_lower, t_Treat_diff_Female_area_fFec_upper, t_Treat_diff_Female_area_fFec_p))
names(CompTreat_Table_Female_area_fFec)=c('V1','V2','V3','V4','V5','V6','V7')
CompTreat_Table_Male_pop_mMS <- as.data.frame(cbind("Male", "pop", "mMS", t_Treat_diff_Male_pop_mMS, t_Treat_diff_Male_pop_mMS_lower, t_Treat_diff_Male_pop_mMS_upper, t_Treat_diff_Male_pop_mMS_p))
names(CompTreat_Table_Male_pop_mMS)=c('V1','V2','V3','V4','V5','V6','V7')
CompTreat_Table_Male_pop_InSuc <- as.data.frame(cbind("Male", "pop", "InSuc", t_Treat_diff_Male_pop_InSuc, t_Treat_diff_Male_pop_InSuc_lower, t_Treat_diff_Male_pop_InSuc_upper, t_Treat_diff_Male_pop_InSuc_p))
names(CompTreat_Table_Male_pop_InSuc)=c('V1','V2','V3','V4','V5','V6','V7')
CompTreat_Table_Male_pop_feSuc <- as.data.frame(cbind("Male", "pop", "feSuc", t_Treat_diff_Male_pop_feSuc, t_Treat_diff_Male_pop_feSuc_lower, t_Treat_diff_Male_pop_feSuc_upper, t_Treat_diff_Male_pop_feSuc_p))
names(CompTreat_Table_Male_pop_feSuc)=c('V1','V2','V3','V4','V5','V6','V7')
CompTreat_Table_Male_pop_mFec <- as.data.frame(cbind("Male", "pop", "mFec", t_Treat_diff_Male_pop_mFec, t_Treat_diff_Male_pop_mFec_lower, t_Treat_diff_Male_pop_mFec_upper, t_Treat_diff_Male_pop_mFec_p))
names(CompTreat_Table_Male_pop_mFec)=c('V1','V2','V3','V4','V5','V6','V7')
CompTreat_Table_Female_pop_fMS <- as.data.frame(cbind("Female", "pop", "fMS", t_Treat_diff_Female_pop_fMS, t_Treat_diff_Female_pop_fMS_lower, t_Treat_diff_Female_pop_fMS_upper, t_Treat_diff_Female_pop_fMS_p))
names(CompTreat_Table_Female_pop_fMS)=c('V1','V2','V3','V4','V5','V6','V7')
CompTreat_Table_Female_pop_fFec <- as.data.frame(cbind("Female", "pop", "fFec", t_Treat_diff_Female_pop_fFec, t_Treat_diff_Female_pop_fFec_lower, t_Treat_diff_Female_pop_fFec_upper, t_Treat_diff_Female_pop_fFec_p))
names(CompTreat_Table_Female_pop_fFec)=c('V1','V2','V3','V4','V5','V6','V7')
Table_VarianceDecomposition_TreatComp <- as.data.frame(as.matrix(rbind(CompTreat_Table_Male_pop_mMS,CompTreat_Table_Male_area_mMS,
CompTreat_Table_Male_pop_InSuc,CompTreat_Table_Male_area_InSuc,
CompTreat_Table_Male_pop_feSuc,CompTreat_Table_Male_area_feSuc,
CompTreat_Table_Male_pop_mFec,CompTreat_Table_Male_area_mFec,
CompTreat_Table_Female_pop_fMS,CompTreat_Table_Female_area_fMS,
CompTreat_Table_Female_pop_fFec,CompTreat_Table_Female_area_fFec)))
colnames(Table_VarianceDecomposition_TreatComp)[1] <- "Sex"
colnames(Table_VarianceDecomposition_TreatComp)[2] <- "Treatment"
colnames(Table_VarianceDecomposition_TreatComp)[3] <- "Variance_component"
colnames(Table_VarianceDecomposition_TreatComp)[4] <- "Variance"
colnames(Table_VarianceDecomposition_TreatComp)[5] <- "l95.CI"
colnames(Table_VarianceDecomposition_TreatComp)[6] <- "u95.CI"
colnames(Table_VarianceDecomposition_TreatComp)[7] <- "p-value"
Table_VarianceDecomposition_TreatComp[,4]=as.numeric(Table_VarianceDecomposition_TreatComp[,4])
Table_VarianceDecomposition_TreatComp[,5]=as.numeric(Table_VarianceDecomposition_TreatComp[,5])
Table_VarianceDecomposition_TreatComp[,6]=as.numeric(Table_VarianceDecomposition_TreatComp[,6])
Table_VarianceDecomposition_TreatComp[,7]=as.numeric(Table_VarianceDecomposition_TreatComp[,7])
Table_VarianceDecomposition_TreatComp_round=cbind(Table_VarianceDecomposition_TreatComp[,c(1,2,3)],round(Table_VarianceDecomposition_TreatComp[,c(4,5,6,7)],digit=3))
rownames(Table_VarianceDecomposition_TreatComp_round) <- NULLVariance decomposition for males
Figure: Variance decomposition for males
#Figure ####
PhenVarBoot_Table$Treatment<- factor(PhenVarBoot_Table$Treatment, levels=c("Small_pop",'Large_pop','Large_area','Small_area'))
PhenVarBoot_Table$Variance_component <- factor(PhenVarBoot_Table$Variance_component, levels=c("MS",'InSuc','feSuc','pFec','cov_mMS_PS','cov_mMS_pFec','cov_PS_pFec','fMS','fFec','cov_fMS_fFec'))
PhenVarBoot_Table_area=PhenVarBoot_Table[PhenVarBoot_Table$Treatment!='Large_pop',]
PhenVarBoot_Table_area=PhenVarBoot_Table_area[PhenVarBoot_Table_area$Treatment!='Small_pop',]
PhenVarBoot_Table_pop=PhenVarBoot_Table[PhenVarBoot_Table$Treatment!='Large_area',]
PhenVarBoot_Table_pop=PhenVarBoot_Table_pop[PhenVarBoot_Table_pop$Treatment!='Small_area',]
BarPlot_1<- ggplot(PhenVarBoot_Table_pop[1:8,], aes(x=Variance_component, y=Variance, fill=Treatment)) +
scale_y_continuous(limits = c(0, 0.6), breaks = seq(0,0.6,0.15), expand = c(0 ,0)) +
geom_hline(yintercept=0, linetype="solid", color = "black", size=1) +
geom_bar(stat="identity", color="black", position=position_dodge(), alpha=0.8) +
geom_errorbar(aes(ymin=l95.CI, ymax=u95.CI), width=.3,size=1, position=position_dodge(.9)) +
ylab('Variance') +xlab('Variance component') +ggtitle('Male')+labs(tag = "A")+
scale_x_discrete(breaks=waiver(),labels = c('MS','inSuc','feSuc','Fec'))+
theme(panel.border = element_blank(),
plot.title = element_text(hjust = 0.5),
panel.background = element_blank(),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
plot.tag.position=c(0.01,0.98),
legend.position = c(0.8, 0.9),
legend.title = element_blank(),
legend.text = element_text(colour="black", size=10),
axis.line.x = element_line(colour = "black", size = 1),
axis.line.y = element_line(colour = "black", size = 1),
axis.text.x = element_text(face="plain", color="black", size=16, angle=0),
axis.text.y = element_text(face="plain", color="black", size=16, angle=0),
axis.title.x = element_text(size=16,face="plain", margin = margin(r=0,10,0,0)),
axis.title.y = element_text(size=16,face="plain", margin = margin(r=10,0,0,0)),
axis.ticks = element_line(size = 1),
axis.ticks.length = unit(.3, "cm"))+
scale_fill_manual(values=c(colorESEB[1],colorESEB[2]),name = "Treatment", labels = c('Small group','Large group'))
BarPlot_2<-ggplot(PhenVarBoot_Table_area[1:8,], aes(x=Variance_component, y=Variance, fill=Treatment)) +
scale_y_continuous(limits = c(0, 0.6), breaks = seq(0,0.6,0.15), expand = c(0 ,0)) +
geom_hline(yintercept=0, linetype="solid", color = "black", size=1) +
geom_bar(stat="identity", color="black", position=position_dodge(), alpha=0.8) +
geom_errorbar(aes(ymin=l95.CI, ymax=u95.CI), width=.3,size=1, position=position_dodge(.9)) +
ylab('') +xlab('Variance component') +ggtitle('Male')+labs(tag = "B")+
scale_x_discrete(breaks=waiver(),labels = c('MS','inSuc','feSuc','Fec'))+
scale_fill_manual(values=c(colorESEB2[1],colorESEB2[2]),name = "Treatment", labels = c('Large area','Small area'))+
theme(panel.border = element_blank(),
plot.title = element_text(hjust = 0.5),
panel.background = element_blank(),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
legend.position = c(0.8, 0.9),
plot.tag.position=c(0.01,0.98),
legend.title = element_blank(),
legend.text = element_text(colour="black", size=10),
axis.line.x = element_line(colour = "black", size = 1),
axis.line.y = element_line(colour = "black", size = 1),
axis.text.x = element_text(face="plain", color="black", size=16, angle=0),
axis.text.y = element_text(face="plain", color="black", size=16, angle=0),
axis.title.x = element_text(size=16,face="plain", margin = margin(r=0,10,0,0)),
axis.title.y = element_text(size=16,face="plain", margin = margin(r=10,0,0,0)),
axis.ticks = element_line(size = 1),
axis.ticks.length = unit(.3, "cm"))
grid.arrange(grobs = list(BarPlot_1,BarPlot_2), nrow = 1,ncol=2, widths=c(2.3, 2.3))
Figure 12: Variance decomposition for males into mating success,
insemination success, fertilization success and fecundity of the
partners. Means and 95% confidence intervals.
Treatement comparisons via permutation test for the variance decomposition of male reproductive success.
Table_VarianceDecomposition_TreatComp_round[c(1:8),] Sex Treatment Variance_component Variance l95.CI u95.CI p-value
1 Male pop mMS -0.084 -0.218 0.033 0.091
2 Male Area mMS 0.066 -0.059 0.208 0.171
3 Male pop InSuc 0.016 -0.122 0.152 0.709
4 Male Area InSuc -0.106 -0.242 0.036 0.033
5 Male pop feSuc -0.125 -0.206 -0.043 0.000
6 Male Area feSuc 0.048 -0.047 0.137 0.094
7 Male pop mFec 0.026 -0.053 0.109 0.307
8 Male Area mFec -0.011 -0.096 0.067 0.663Variance decomposition for females
Figure: Variance decomposition for females
BarPlot_3<- ggplot(PhenVarBoot_Table_pop[9:12,], aes(x=Variance_component, y=Variance, fill=Treatment)) +
scale_y_continuous(limits = c(0, 2.7), breaks = seq(0,2.7,0.5), expand = c(0 ,0)) +
geom_hline(yintercept=0, linetype="solid", color = "black", size=1) +
geom_bar(stat="identity", color="black", position=position_dodge(), alpha=0.8) +
ylab('Variance') +xlab('Variance component') +ggtitle('Female')+labs(tag = "A")+
scale_x_discrete(breaks=waiver(),labels = c('MS','Fec'))+
theme(panel.border = element_blank(),
plot.title = element_text(hjust = 0.5),
panel.background = element_blank(),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
legend.position = c(0.8, 0.9),
plot.tag.position=c(0.01,0.98),
legend.title = element_blank(),
legend.text = element_text(colour="black", size=10),
axis.line.x = element_line(colour = "black", size = 1),
axis.line.y = element_line(colour = "black", size = 1),
axis.text.x = element_text(face="plain", color="black", size=16, angle=0),
axis.text.y = element_text(face="plain", color="black", size=16, angle=0),
axis.title.x = element_text(size=16,face="plain", margin = margin(r=0,10,0,0)),
axis.title.y = element_text(size=16,face="plain", margin = margin(r=10,0,0,0)),
axis.ticks = element_line(size = 1),
axis.ticks.length = unit(.3, "cm")) +
geom_errorbar(aes(ymin=l95.CI, ymax=u95.CI), width=.3,size=1, position=position_dodge(.9)) +
scale_fill_manual(values=c(colorESEB[1],colorESEB[2]),name = "Treatment", labels = c('Small group','Large group'))
BarPlot_4<- ggplot(PhenVarBoot_Table_area[9:12,], aes(x=Variance_component, y=Variance, fill=Treatment)) +
scale_y_continuous(limits = c(0, 2.7), breaks = seq(0,2.7,0.5), expand = c(0 ,0)) +
geom_hline(yintercept=0, linetype="solid", color = "black", size=1) +
geom_bar(stat="identity", color="black", position=position_dodge(), alpha=0.8) +
ylab('') +xlab('Variance component') +ggtitle('Female')+labs(tag = "B")+
scale_x_discrete(breaks=waiver(),labels = c('MS','Fec','cov\n(MS, Fec)'))+
theme(panel.border = element_blank(),
plot.title = element_text(hjust = 0.5),
panel.background = element_blank(),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
legend.position = c(0.8, 0.9),
plot.tag.position=c(0.01,0.98),
legend.title = element_blank(),
legend.text = element_text(colour="black", size=10),
axis.line.x = element_line(colour = "black", size = 1),
axis.line.y = element_line(colour = "black", size = 1),
axis.text.x = element_text(face="plain", color="black", size=16, angle=0),
axis.text.y = element_text(face="plain", color="black", size=16, angle=0),
axis.title.x = element_text(size=16,face="plain", margin = margin(r=0,10,0,0)),
axis.title.y = element_text(size=16,face="plain", margin = margin(r=10,0,0,0)),
axis.ticks = element_line(size = 1),
axis.ticks.length = unit(.3, "cm")) +
geom_errorbar(aes(ymin=l95.CI, ymax=u95.CI), width=.3,size=1, position=position_dodge(.9)) +
scale_fill_manual(values=c(colorESEB2[1],colorESEB2[2]),name = "Treatment", labels = c('Large area','Small area'))
grid.arrange(grobs = list(BarPlot_3,BarPlot_4), nrow = 1,ncol=2, widths=c(2.3, 2.3))
Figure 13: Variance decomposition for females into mating success,
fecundity and their covariance. Means and 95% confidence
intervals.
Treatement comparisons via permutation test for the
variance decomposition of female reproductive success.
Table_VarianceDecomposition_TreatComp_round[c(9:10),] Sex Treatment Variance_component Variance l95.CI u95.CI p-value
9 Female pop fMS -0.098 -0.236 0.023 0.011
10 Female Area fMS 0.036 -0.076 0.151 0.372Covariances for male variance decomposition
#Compute covariace matrices ####
# Large Area ####
#Covariance mMS x inSuc
x5=as.data.frame(cbind(DB_data_clean_Large_area_M_MS_n,DB_data_clean_Large_area_M_InSuc_n))
c <- function(d, i){
d2 <- d[i,]
return(cov(d2[1],d2[2],use='pairwise.complete.obs'))
}
Large_area_M_cov_mMS_inSuc_bootvar <- boot(x5, c, R=10000)
#Covariance mMS x feSuc
x6=as.data.frame(cbind(DB_data_clean_Large_area_M_MS_n,DB_data_clean_Large_area_M_feSuc_n))
Large_area_M_cov_mMS_feSuc_bootvar <- boot(x6, c, R=10000)
#Covariance mMS x pFec
x7=as.data.frame(cbind(DB_data_clean_Large_area_M_MS_n,DB_data_clean_Large_area_M_pFec_n))
Large_area_M_cov_mMS_pFec_bootvar <- boot(x7, c, R=10000)
#Covariance inSuc x feSuc
x8=as.data.frame(cbind(DB_data_clean_Large_area_M_InSuc_n,DB_data_clean_Large_area_M_feSuc_n))
Large_area_M_cov_inSuc_feSuc_bootvar <- boot(x8, c, R=10000)
#Covariance inSuc x pFec
x9=as.data.frame(cbind(DB_data_clean_Large_area_M_InSuc_n,DB_data_clean_Large_area_M_pFec_n))
Large_area_M_cov_inSuc_pFec_bootvar <- boot(x9, c, R=10000)
#Covariance feSuc x pFec
x10=as.data.frame(cbind(DB_data_clean_Large_area_M_feSuc_n,DB_data_clean_Large_area_M_pFec_n))
Large_area_M_cov_feSuc_pFec_bootvar <- boot(x10, c, R=10000)
#Covariance fMS x fFec
x13=as.data.frame(cbind(DB_data_clean_Large_area_F_fMS_n,DB_data_clean_Large_area_F_fFec_n))
Large_area_F_cov_fMS_fFec_bootvar <- boot(x13, c, R=10000)
rm("c")
#Write Table ####
PhenVarBoot_Table_Male_Large_area_cov_mMS_inSuc <- as.data.frame(cbind("Male", "cov_mMS_inSuc", "Large_area", mean(Large_area_M_cov_mMS_inSuc_bootvar$t), quantile(Large_area_M_cov_mMS_inSuc_bootvar$t,.025, names = FALSE), quantile(Large_area_M_cov_mMS_inSuc_bootvar$t,.975, names = FALSE)))
PhenVarBoot_Table_Male_Large_area_cov_mMS_feSuc <- as.data.frame(cbind("Male", "cov_mMS_feSuc", "Large_area", mean(Large_area_M_cov_mMS_feSuc_bootvar$t), quantile(Large_area_M_cov_mMS_feSuc_bootvar$t,.025, names = FALSE), quantile(Large_area_M_cov_mMS_feSuc_bootvar$t,.975, names = FALSE)))
PhenVarBoot_Table_Male_Large_area_cov_mMS_pFec <- as.data.frame(cbind("Male", "cov_mMS_pFec", "Large_area", mean(Large_area_M_cov_mMS_pFec_bootvar$t), quantile(Large_area_M_cov_mMS_pFec_bootvar$t,.025, names = FALSE), quantile(Large_area_M_cov_mMS_pFec_bootvar$t,.975, names = FALSE)))
PhenVarBoot_Table_Male_Large_area_cov_inSuc_feSuc <- as.data.frame(cbind("Male", "cov_inSuc_feSuc", "Large_area", mean(Large_area_M_cov_inSuc_feSuc_bootvar$t), quantile(Large_area_M_cov_inSuc_feSuc_bootvar$t,.025, names = FALSE), quantile(Large_area_M_cov_inSuc_feSuc_bootvar$t,.975, names = FALSE)))
PhenVarBoot_Table_Male_Large_area_cov_inSuc_pFec <- as.data.frame(cbind("Male", "cov_inSuc_pFec", "Large_area", mean(Large_area_M_cov_inSuc_pFec_bootvar$t), quantile(Large_area_M_cov_inSuc_pFec_bootvar$t,.025, names = FALSE), quantile(Large_area_M_cov_inSuc_pFec_bootvar$t,.975, names = FALSE)))
PhenVarBoot_Table_Male_Large_area_cov_feSuc_pFec <- as.data.frame(cbind("Male", "cov_feSuc_pFec", "Large_area", mean(Large_area_M_cov_feSuc_pFec_bootvar$t), quantile(Large_area_M_cov_feSuc_pFec_bootvar$t,.025, names = FALSE), quantile(Large_area_M_cov_feSuc_pFec_bootvar$t,.975, names = FALSE)))
PhenVarBoot_Table_Female_Large_area_cov_fMS_fFec <- as.data.frame(cbind("Female", "cov_fMS_fFec", "Large_area", mean(Large_area_F_cov_fMS_fFec_bootvar$t), quantile(Large_area_F_cov_fMS_fFec_bootvar$t,.025, names = FALSE), quantile(Large_area_F_cov_fMS_fFec_bootvar$t,.975, names = FALSE)))
PhenVarBoot_Cov_Table_Large_area <- as.data.frame(as.matrix(rbind(PhenVarBoot_Table_Male_Large_area_cov_mMS_inSuc,PhenVarBoot_Table_Male_Large_area_cov_mMS_feSuc,
PhenVarBoot_Table_Male_Large_area_cov_mMS_pFec,PhenVarBoot_Table_Male_Large_area_cov_inSuc_feSuc,
PhenVarBoot_Table_Male_Large_area_cov_inSuc_pFec,PhenVarBoot_Table_Male_Large_area_cov_feSuc_pFec,
PhenVarBoot_Table_Female_Large_area_cov_fMS_fFec)),digits=3)
is.table(PhenVarBoot_Cov_Table_Large_area)
colnames(PhenVarBoot_Cov_Table_Large_area)[1] <- "Sex"
colnames(PhenVarBoot_Cov_Table_Large_area)[2] <- "Trait"
colnames(PhenVarBoot_Cov_Table_Large_area)[3] <- "Density"
colnames(PhenVarBoot_Cov_Table_Large_area)[4] <- "Variance"
colnames(PhenVarBoot_Cov_Table_Large_area)[5] <- "l95.CI"
colnames(PhenVarBoot_Cov_Table_Large_area)[6] <- "u95.CI"
PhenVarBoot_Cov_Table_Large_area[,4]=as.numeric(PhenVarBoot_Cov_Table_Large_area[,4])
PhenVarBoot_Cov_Table_Large_area[,5]=as.numeric(PhenVarBoot_Cov_Table_Large_area[,5])
PhenVarBoot_Cov_Table_Large_area[,6]=as.numeric(PhenVarBoot_Cov_Table_Large_area[,6])
PhenVarBoot_Cov_Table_Large_area_round=cbind(PhenVarBoot_Cov_Table_Large_area[,1:3],round(PhenVarBoot_Cov_Table_Large_area[,4:6],digit=3))
# Small Area ####
#Covariance mMS x inSuc
x5=as.data.frame(cbind(DB_data_clean_Small_area_M_MS_n,DB_data_clean_Small_area_M_InSuc_n))
c <- function(d, i){
d2 <- d[i,]
return(cov(d2[1],d2[2],use='pairwise.complete.obs'))
}
Small_area_M_cov_mMS_inSuc_bootvar <- boot(x5, c, R=10000)
#Covariance mMS x feSuc
x6=as.data.frame(cbind(DB_data_clean_Small_area_M_MS_n,DB_data_clean_Small_area_M_feSuc_n))
Small_area_M_cov_mMS_feSuc_bootvar <- boot(x6, c, R=10000)
#Covariance mMS x pFec
x7=as.data.frame(cbind(DB_data_clean_Small_area_M_MS_n,DB_data_clean_Small_area_M_pFec_n))
Small_area_M_cov_mMS_pFec_bootvar <- boot(x7, c, R=10000)
#Covariance inSuc x feSuc
x8=as.data.frame(cbind(DB_data_clean_Small_area_M_InSuc_n,DB_data_clean_Small_area_M_feSuc_n))
Small_area_M_cov_inSuc_feSuc_bootvar <- boot(x8, c, R=10000)
#Covariance inSuc x pFec
x9=as.data.frame(cbind(DB_data_clean_Small_area_M_InSuc_n,DB_data_clean_Small_area_M_pFec_n))
Small_area_M_cov_inSuc_pFec_bootvar <- boot(x9, c, R=10000)
#Covariance feSuc x pFec
x10=as.data.frame(cbind(DB_data_clean_Small_area_M_feSuc_n,DB_data_clean_Small_area_M_pFec_n))
Small_area_M_cov_feSuc_pFec_bootvar <- boot(x10, c, R=10000)
#Covariance fMS x fFec
x13=as.data.frame(cbind(DB_data_clean_Small_area_F_fMS_n,DB_data_clean_Small_area_F_fFec_n))
Small_area_F_cov_fMS_fFec_bootvar <- boot(x13, c, R=10000)
rm("c")
#Write Table ####
PhenVarBoot_Table_Male_Small_area_cov_mMS_inSuc <- as.data.frame(cbind("Male", "cov_mMS_inSuc", "Small_area", mean(Small_area_M_cov_mMS_inSuc_bootvar$t), quantile(Small_area_M_cov_mMS_inSuc_bootvar$t,.025, names = FALSE), quantile(Small_area_M_cov_mMS_inSuc_bootvar$t,.975, names = FALSE)))
PhenVarBoot_Table_Male_Small_area_cov_mMS_feSuc <- as.data.frame(cbind("Male", "cov_mMS_feSuc", "Small_area", mean(Small_area_M_cov_mMS_feSuc_bootvar$t), quantile(Small_area_M_cov_mMS_feSuc_bootvar$t,.025, names = FALSE), quantile(Small_area_M_cov_mMS_feSuc_bootvar$t,.975, names = FALSE)))
PhenVarBoot_Table_Male_Small_area_cov_mMS_pFec <- as.data.frame(cbind("Male", "cov_mMS_pFec", "Small_area", mean(Small_area_M_cov_mMS_pFec_bootvar$t), quantile(Small_area_M_cov_mMS_pFec_bootvar$t,.025, names = FALSE), quantile(Small_area_M_cov_mMS_pFec_bootvar$t,.975, names = FALSE)))
PhenVarBoot_Table_Male_Small_area_cov_inSuc_feSuc <- as.data.frame(cbind("Male", "cov_inSuc_feSuc", "Small_area", mean(Small_area_M_cov_inSuc_feSuc_bootvar$t), quantile(Small_area_M_cov_inSuc_feSuc_bootvar$t,.025, names = FALSE), quantile(Small_area_M_cov_inSuc_feSuc_bootvar$t,.975, names = FALSE)))
PhenVarBoot_Table_Male_Small_area_cov_inSuc_pFec <- as.data.frame(cbind("Male", "cov_inSuc_pFec", "Small_area", mean(Small_area_M_cov_inSuc_pFec_bootvar$t), quantile(Small_area_M_cov_inSuc_pFec_bootvar$t,.025, names = FALSE), quantile(Small_area_M_cov_inSuc_pFec_bootvar$t,.975, names = FALSE)))
PhenVarBoot_Table_Male_Small_area_cov_feSuc_pFec <- as.data.frame(cbind("Male", "cov_feSuc_pFec", "Small_area", mean(Small_area_M_cov_feSuc_pFec_bootvar$t), quantile(Small_area_M_cov_feSuc_pFec_bootvar$t,.025, names = FALSE), quantile(Small_area_M_cov_feSuc_pFec_bootvar$t,.975, names = FALSE)))
PhenVarBoot_Table_Female_Small_area_cov_fMS_fFec <- as.data.frame(cbind("Female", "cov_fMS_fFec", "Small_area", mean(Small_area_F_cov_fMS_fFec_bootvar$t), quantile(Small_area_F_cov_fMS_fFec_bootvar$t,.025, names = FALSE), quantile(Small_area_F_cov_fMS_fFec_bootvar$t,.975, names = FALSE)))
PhenVarBoot_Cov_Table_Small_area <- as.data.frame(as.matrix(rbind(PhenVarBoot_Table_Male_Small_area_cov_mMS_inSuc,PhenVarBoot_Table_Male_Small_area_cov_mMS_feSuc,
PhenVarBoot_Table_Male_Small_area_cov_mMS_pFec,PhenVarBoot_Table_Male_Small_area_cov_inSuc_feSuc,
PhenVarBoot_Table_Male_Small_area_cov_inSuc_pFec,PhenVarBoot_Table_Male_Small_area_cov_feSuc_pFec,
PhenVarBoot_Table_Female_Small_area_cov_fMS_fFec)),digits=3)
is.table(PhenVarBoot_Cov_Table_Small_area)
colnames(PhenVarBoot_Cov_Table_Small_area)[1] <- "Sex"
colnames(PhenVarBoot_Cov_Table_Small_area)[2] <- "Trait"
colnames(PhenVarBoot_Cov_Table_Small_area)[3] <- "Density"
colnames(PhenVarBoot_Cov_Table_Small_area)[4] <- "Variance"
colnames(PhenVarBoot_Cov_Table_Small_area)[5] <- "l95.CI"
colnames(PhenVarBoot_Cov_Table_Small_area)[6] <- "u95.CI"
PhenVarBoot_Cov_Table_Small_area[,4]=as.numeric(PhenVarBoot_Cov_Table_Small_area[,4])
PhenVarBoot_Cov_Table_Small_area[,5]=as.numeric(PhenVarBoot_Cov_Table_Small_area[,5])
PhenVarBoot_Cov_Table_Small_area[,6]=as.numeric(PhenVarBoot_Cov_Table_Small_area[,6])
PhenVarBoot_Cov_Table_Small_area_round=cbind(PhenVarBoot_Cov_Table_Small_area[,1:3],round(PhenVarBoot_Cov_Table_Small_area[,4:6],digit=3))
# Small group ####
#Covariance mMS x inSuc
x5=as.data.frame(cbind(DB_data_clean_Small_pop_M_MS_n,DB_data_clean_Small_pop_M_InSuc_n))
c <- function(d, i){
d2 <- d[i,]
return(cov(d2[1],d2[2],use='pairwise.complete.obs'))
}
Small_pop_M_cov_mMS_inSuc_bootvar <- boot(x5, c, R=10000)
#Covariance mMS x feSuc
x6=as.data.frame(cbind(DB_data_clean_Small_pop_M_MS_n,DB_data_clean_Small_pop_M_feSuc_n))
Small_pop_M_cov_mMS_feSuc_bootvar <- boot(x6, c, R=10000)
#Covariance mMS x pFec
x7=as.data.frame(cbind(DB_data_clean_Small_pop_M_MS_n,DB_data_clean_Small_pop_M_pFec_n))
Small_pop_M_cov_mMS_pFec_bootvar <- boot(x7, c, R=10000)
#Covariance inSuc x feSuc
x8=as.data.frame(cbind(DB_data_clean_Small_pop_M_InSuc_n,DB_data_clean_Small_pop_M_feSuc_n))
Small_pop_M_cov_inSuc_feSuc_bootvar <- boot(x8, c, R=10000)
#Covariance inSuc x pFec
x9=as.data.frame(cbind(DB_data_clean_Small_pop_M_InSuc_n,DB_data_clean_Small_pop_M_pFec_n))
Small_pop_M_cov_inSuc_pFec_bootvar <- boot(x9, c, R=10000)
#Covariance feSuc x pFec
x10=as.data.frame(cbind(DB_data_clean_Small_pop_M_feSuc_n,DB_data_clean_Small_pop_M_pFec_n))
Small_pop_M_cov_feSuc_pFec_bootvar <- boot(x10, c, R=10000)
#Covariance fMS x fFec
x13=as.data.frame(cbind(DB_data_clean_Small_pop_F_fMS_n,DB_data_clean_Small_pop_F_fFec_n))
Small_pop_F_cov_fMS_fFec_bootvar <- boot(x13, c, R=10000)
rm("c")
#Write Table ####
PhenVarBoot_Table_Male_Small_pop_cov_mMS_inSuc <- as.data.frame(cbind("Male", "cov_mMS_inSuc", "Small_pop", mean(Small_pop_M_cov_mMS_inSuc_bootvar$t), quantile(Small_pop_M_cov_mMS_inSuc_bootvar$t,.025, names = FALSE), quantile(Small_pop_M_cov_mMS_inSuc_bootvar$t,.975, names = FALSE)))
PhenVarBoot_Table_Male_Small_pop_cov_mMS_feSuc <- as.data.frame(cbind("Male", "cov_mMS_feSuc", "Small_pop", mean(Small_pop_M_cov_mMS_feSuc_bootvar$t), quantile(Small_pop_M_cov_mMS_feSuc_bootvar$t,.025, names = FALSE), quantile(Small_pop_M_cov_mMS_feSuc_bootvar$t,.975, names = FALSE)))
PhenVarBoot_Table_Male_Small_pop_cov_mMS_pFec <- as.data.frame(cbind("Male", "cov_mMS_pFec", "Small_pop", mean(Small_pop_M_cov_mMS_pFec_bootvar$t), quantile(Small_pop_M_cov_mMS_pFec_bootvar$t,.025, names = FALSE), quantile(Small_pop_M_cov_mMS_pFec_bootvar$t,.975, names = FALSE)))
PhenVarBoot_Table_Male_Small_pop_cov_inSuc_feSuc <- as.data.frame(cbind("Male", "cov_inSuc_feSuc", "Small_pop", mean(Small_pop_M_cov_inSuc_feSuc_bootvar$t), quantile(Small_pop_M_cov_inSuc_feSuc_bootvar$t,.025, names = FALSE), quantile(Small_pop_M_cov_inSuc_feSuc_bootvar$t,.975, names = FALSE)))
PhenVarBoot_Table_Male_Small_pop_cov_inSuc_pFec <- as.data.frame(cbind("Male", "cov_inSuc_pFec", "Small_pop", mean(Small_pop_M_cov_inSuc_pFec_bootvar$t), quantile(Small_pop_M_cov_inSuc_pFec_bootvar$t,.025, names = FALSE), quantile(Small_pop_M_cov_inSuc_pFec_bootvar$t,.975, names = FALSE)))
PhenVarBoot_Table_Male_Small_pop_cov_feSuc_pFec <- as.data.frame(cbind("Male", "cov_feSuc_pFec", "Small_pop", mean(Small_pop_M_cov_feSuc_pFec_bootvar$t), quantile(Small_pop_M_cov_feSuc_pFec_bootvar$t,.025, names = FALSE), quantile(Small_pop_M_cov_feSuc_pFec_bootvar$t,.975, names = FALSE)))
PhenVarBoot_Table_Female_Small_pop_cov_fMS_fFec <- as.data.frame(cbind("Female", "cov_fMS_fFec", "Small_pop", mean(Small_pop_F_cov_fMS_fFec_bootvar$t), quantile(Small_pop_F_cov_fMS_fFec_bootvar$t,.025, names = FALSE), quantile(Small_pop_F_cov_fMS_fFec_bootvar$t,.975, names = FALSE)))
PhenVarBoot_Cov_Table_Small_pop <- as.data.frame(as.matrix(rbind(PhenVarBoot_Table_Male_Small_pop_cov_mMS_inSuc,PhenVarBoot_Table_Male_Small_pop_cov_mMS_feSuc,
PhenVarBoot_Table_Male_Small_pop_cov_mMS_pFec,PhenVarBoot_Table_Male_Small_pop_cov_inSuc_feSuc,
PhenVarBoot_Table_Male_Small_pop_cov_inSuc_pFec,PhenVarBoot_Table_Male_Small_pop_cov_feSuc_pFec,
PhenVarBoot_Table_Female_Small_pop_cov_fMS_fFec)),digits=3)
is.table(PhenVarBoot_Cov_Table_Small_pop)
colnames(PhenVarBoot_Cov_Table_Small_pop)[1] <- "Sex"
colnames(PhenVarBoot_Cov_Table_Small_pop)[2] <- "Trait"
colnames(PhenVarBoot_Cov_Table_Small_pop)[3] <- "Density"
colnames(PhenVarBoot_Cov_Table_Small_pop)[4] <- "Variance"
colnames(PhenVarBoot_Cov_Table_Small_pop)[5] <- "l95.CI"
colnames(PhenVarBoot_Cov_Table_Small_pop)[6] <- "u95.CI"
PhenVarBoot_Cov_Table_Small_pop[,4]=as.numeric(PhenVarBoot_Cov_Table_Small_pop[,4])
PhenVarBoot_Cov_Table_Small_pop[,5]=as.numeric(PhenVarBoot_Cov_Table_Small_pop[,5])
PhenVarBoot_Cov_Table_Small_pop[,6]=as.numeric(PhenVarBoot_Cov_Table_Small_pop[,6])
PhenVarBoot_Cov_Table_Small_pop_round=cbind(PhenVarBoot_Cov_Table_Small_pop[,1:3],round(PhenVarBoot_Cov_Table_Small_pop[,4:6],digit=3))
# Large group ####
#Covariance mMS x inSuc
x5=as.data.frame(cbind(DB_data_clean_Large_pop_M_MS_n,DB_data_clean_Large_pop_M_InSuc_n))
c <- function(d, i){
d2 <- d[i,]
return(cov(d2[1],d2[2],use='pairwise.complete.obs'))
}
Large_pop_M_cov_mMS_inSuc_bootvar <- boot(x5, c, R=10000)
#Covariance mMS x feSuc
x6=as.data.frame(cbind(DB_data_clean_Large_pop_M_MS_n,DB_data_clean_Large_pop_M_feSuc_n))
Large_pop_M_cov_mMS_feSuc_bootvar <- boot(x6, c, R=10000)
#Covariance mMS x pFec
x7=as.data.frame(cbind(DB_data_clean_Large_pop_M_MS_n,DB_data_clean_Large_pop_M_pFec_n))
Large_pop_M_cov_mMS_pFec_bootvar <- boot(x7, c, R=10000)
#Covariance inSuc x feSuc
x8=as.data.frame(cbind(DB_data_clean_Large_pop_M_InSuc_n,DB_data_clean_Large_pop_M_feSuc_n))
Large_pop_M_cov_inSuc_feSuc_bootvar <- boot(x8, c, R=10000)
#Covariance inSuc x pFec
x9=as.data.frame(cbind(DB_data_clean_Large_pop_M_InSuc_n,DB_data_clean_Large_pop_M_pFec_n))
Large_pop_M_cov_inSuc_pFec_bootvar <- boot(x9, c, R=10000)
#Covariance feSuc x pFec
x10=as.data.frame(cbind(DB_data_clean_Large_pop_M_feSuc_n,DB_data_clean_Large_pop_M_pFec_n))
Large_pop_M_cov_feSuc_pFec_bootvar <- boot(x10, c, R=10000)
#Covariance fMS x fFec
x13=as.data.frame(cbind(DB_data_clean_Large_pop_F_fMS_n,DB_data_clean_Large_pop_F_fFec_n))
Large_pop_F_cov_fMS_fFec_bootvar <- boot(x13, c, R=10000)
rm("c")
#Write Table ####
PhenVarBoot_Table_Male_Large_pop_cov_mMS_inSuc <- as.data.frame(cbind("Male", "cov_mMS_inSuc", "Large_pop", mean(Large_pop_M_cov_mMS_inSuc_bootvar$t), quantile(Large_pop_M_cov_mMS_inSuc_bootvar$t,.025, names = FALSE), quantile(Large_pop_M_cov_mMS_inSuc_bootvar$t,.975, names = FALSE)))
PhenVarBoot_Table_Male_Large_pop_cov_mMS_feSuc <- as.data.frame(cbind("Male", "cov_mMS_feSuc", "Large_pop", mean(Large_pop_M_cov_mMS_feSuc_bootvar$t), quantile(Large_pop_M_cov_mMS_feSuc_bootvar$t,.025, names = FALSE), quantile(Large_pop_M_cov_mMS_feSuc_bootvar$t,.975, names = FALSE)))
PhenVarBoot_Table_Male_Large_pop_cov_mMS_pFec <- as.data.frame(cbind("Male", "cov_mMS_pFec", "Large_pop", mean(Large_pop_M_cov_mMS_pFec_bootvar$t), quantile(Large_pop_M_cov_mMS_pFec_bootvar$t,.025, names = FALSE), quantile(Large_pop_M_cov_mMS_pFec_bootvar$t,.975, names = FALSE)))
PhenVarBoot_Table_Male_Large_pop_cov_inSuc_feSuc <- as.data.frame(cbind("Male", "cov_inSuc_feSuc", "Large_pop", mean(Large_pop_M_cov_inSuc_feSuc_bootvar$t), quantile(Large_pop_M_cov_inSuc_feSuc_bootvar$t,.025, names = FALSE), quantile(Large_pop_M_cov_inSuc_feSuc_bootvar$t,.975, names = FALSE)))
PhenVarBoot_Table_Male_Large_pop_cov_inSuc_pFec <- as.data.frame(cbind("Male", "cov_inSuc_pFec", "Large_pop", mean(Large_pop_M_cov_inSuc_pFec_bootvar$t), quantile(Large_pop_M_cov_inSuc_pFec_bootvar$t,.025, names = FALSE), quantile(Large_pop_M_cov_inSuc_pFec_bootvar$t,.975, names = FALSE)))
PhenVarBoot_Table_Male_Large_pop_cov_feSuc_pFec <- as.data.frame(cbind("Male", "cov_feSuc_pFec", "Large_pop", mean(Large_pop_M_cov_feSuc_pFec_bootvar$t), quantile(Large_pop_M_cov_feSuc_pFec_bootvar$t,.025, names = FALSE), quantile(Large_pop_M_cov_feSuc_pFec_bootvar$t,.975, names = FALSE)))
PhenVarBoot_Table_Female_Large_pop_cov_fMS_fFec <- as.data.frame(cbind("Female", "cov_fMS_fFec", "Large_pop", mean(Large_pop_F_cov_fMS_fFec_bootvar$t), quantile(Large_pop_F_cov_fMS_fFec_bootvar$t,.025, names = FALSE), quantile(Large_pop_F_cov_fMS_fFec_bootvar$t,.975, names = FALSE)))
PhenVarBoot_Cov_Table_Large_pop <- as.data.frame(as.matrix(rbind(PhenVarBoot_Table_Male_Large_pop_cov_mMS_inSuc,PhenVarBoot_Table_Male_Large_pop_cov_mMS_feSuc,
PhenVarBoot_Table_Male_Large_pop_cov_mMS_pFec,PhenVarBoot_Table_Male_Large_pop_cov_inSuc_feSuc,
PhenVarBoot_Table_Male_Large_pop_cov_inSuc_pFec,PhenVarBoot_Table_Male_Large_pop_cov_feSuc_pFec,
PhenVarBoot_Table_Female_Large_pop_cov_fMS_fFec)),digits=3)
is.table(PhenVarBoot_Cov_Table_Large_pop)
colnames(PhenVarBoot_Cov_Table_Large_pop)[1] <- "Sex"
colnames(PhenVarBoot_Cov_Table_Large_pop)[2] <- "Trait"
colnames(PhenVarBoot_Cov_Table_Large_pop)[3] <- "Density"
colnames(PhenVarBoot_Cov_Table_Large_pop)[4] <- "Variance"
colnames(PhenVarBoot_Cov_Table_Large_pop)[5] <- "l95.CI"
colnames(PhenVarBoot_Cov_Table_Large_pop)[6] <- "u95.CI"
PhenVarBoot_Cov_Table_Large_pop[,4]=as.numeric(PhenVarBoot_Cov_Table_Large_pop[,4])
PhenVarBoot_Cov_Table_Large_pop[,5]=as.numeric(PhenVarBoot_Cov_Table_Large_pop[,5])
PhenVarBoot_Cov_Table_Large_pop[,6]=as.numeric(PhenVarBoot_Cov_Table_Large_pop[,6])
PhenVarBoot_Cov_Table_Large_pop_round=cbind(PhenVarBoot_Cov_Table_Large_pop[,1:3],round(PhenVarBoot_Cov_Table_Large_pop[,4:6],digit=3))
#Figure ####
PhenVarBoot_Table_plot_cov <- as.data.frame(as.matrix(rbind( PhenVarBoot_Table_Male_Small_pop_cov_mMS_inSuc,PhenVarBoot_Table_Male_Large_pop_cov_mMS_inSuc,
PhenVarBoot_Table_Male_Large_area_cov_mMS_inSuc,PhenVarBoot_Table_Male_Small_area_cov_mMS_inSuc,
PhenVarBoot_Table_Male_Small_pop_cov_mMS_feSuc,PhenVarBoot_Table_Male_Large_pop_cov_mMS_feSuc,
PhenVarBoot_Table_Male_Large_area_cov_mMS_feSuc,PhenVarBoot_Table_Male_Small_area_cov_mMS_feSuc,
PhenVarBoot_Table_Male_Small_pop_cov_mMS_pFec,PhenVarBoot_Table_Male_Large_pop_cov_mMS_pFec,
PhenVarBoot_Table_Male_Large_area_cov_mMS_pFec,PhenVarBoot_Table_Male_Small_area_cov_mMS_pFec,
PhenVarBoot_Table_Male_Small_pop_cov_inSuc_feSuc,PhenVarBoot_Table_Male_Large_pop_cov_inSuc_feSuc,
PhenVarBoot_Table_Male_Large_area_cov_inSuc_feSuc,PhenVarBoot_Table_Male_Small_area_cov_inSuc_feSuc,
PhenVarBoot_Table_Male_Small_pop_cov_inSuc_pFec,PhenVarBoot_Table_Male_Large_pop_cov_inSuc_pFec,
PhenVarBoot_Table_Male_Large_area_cov_inSuc_pFec,PhenVarBoot_Table_Male_Small_area_cov_inSuc_pFec,
PhenVarBoot_Table_Male_Small_pop_cov_feSuc_pFec,PhenVarBoot_Table_Male_Large_pop_cov_feSuc_pFec,
PhenVarBoot_Table_Male_Large_area_cov_feSuc_pFec,PhenVarBoot_Table_Male_Small_area_cov_feSuc_pFec,
PhenVarBoot_Table_Female_Small_pop_cov_fMS_fFec,PhenVarBoot_Table_Female_Large_pop_cov_fMS_fFec,
PhenVarBoot_Table_Female_Large_area_cov_fMS_fFec,PhenVarBoot_Table_Female_Small_area_cov_fMS_fFec
)))
is.table(PhenVarBoot_Table_plot_cov)
colnames(PhenVarBoot_Table_plot_cov)[1] <- "Sex"
colnames(PhenVarBoot_Table_plot_cov)[2] <- "Variance_component"
colnames(PhenVarBoot_Table_plot_cov)[3] <- "Treatment"
colnames(PhenVarBoot_Table_plot_cov)[4] <- "Variance"
colnames(PhenVarBoot_Table_plot_cov)[5] <- "l95.CI"
colnames(PhenVarBoot_Table_plot_cov)[6] <- "u95.CI"
PhenVarBoot_Table_plot_cov[,4]=as.numeric(PhenVarBoot_Table_plot_cov[,4])
PhenVarBoot_Table_plot_cov[,5]=as.numeric(PhenVarBoot_Table_plot_cov[,5])
PhenVarBoot_Table_plot_cov[,6]=as.numeric(PhenVarBoot_Table_plot_cov[,6])
PhenVarBoot_Table_plot_cov_round=cbind(PhenVarBoot_Table_plot_cov[,1:3],round(PhenVarBoot_Table_plot_cov[,4:6],digit=3))
PhenVarBoot_Table_plot_cov$Treatment<- factor(PhenVarBoot_Table_plot_cov$Treatment, levels=c("Small_pop",'Large_pop','Large_area','Small_area'))
PhenVarBoot_Table_plot_cov$Variance_component <- factor(PhenVarBoot_Table_plot_cov$Variance_component, levels=c("cov_mMS_inSuc",'cov_mMS_feSuc','cov_mMS_pFec','cov_inSuc_feSuc','cov_inSuc_pFec','cov_feSuc_pFec','cov_fMS_fFec'))
PhenVarBoot_Table_plot_cov_area=PhenVarBoot_Table_plot_cov[PhenVarBoot_Table_plot_cov$Treatment!='Large_pop',]
PhenVarBoot_Table_plot_cov_area=PhenVarBoot_Table_plot_cov_area[PhenVarBoot_Table_plot_cov_area$Treatment!='Small_pop',]
PhenVarBoot_Table_plot_cov_pop=PhenVarBoot_Table_plot_cov[PhenVarBoot_Table_plot_cov$Treatment!='Large_area',]
PhenVarBoot_Table_plot_cov_pop=PhenVarBoot_Table_plot_cov_pop[PhenVarBoot_Table_plot_cov_pop$Treatment!='Small_area',]Figure: Covariances of variance decomposition for females
BarPlot_5<- ggplot(PhenVarBoot_Table_plot_cov_pop[1:12,], aes(x=Variance_component, y=Variance, fill=Treatment)) +
scale_y_continuous(limits = c(-0.25, 0.2), breaks = seq(-0.25,0.2,0.1), expand = c(0 ,0)) +
geom_hline(yintercept=0, linetype="solid", color = "black", size=1) +
geom_bar(stat="identity", color="black", position=position_dodge(), alpha=0.8) +
geom_errorbar(aes(ymin=l95.CI, ymax=u95.CI), width=.3,size=1, position=position_dodge(.9)) +
ylab('Variance') +xlab('Variance component') +ggtitle('Male')+labs(tag = "A")+
scale_x_discrete(breaks=waiver(),labels = c('cov\n(MS, inSuc)','cov\n(MS, feSuc)','cov\n(MS, Fec)','cov\n(inSuc, feSuc)','cov\n(inSuc,Fec)','cov\n(feSuc, Fec)'))+
theme(panel.border = element_blank(),
plot.title = element_text(hjust = 0.5),
panel.background = element_blank(),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
plot.tag.position=c(0.01,0.98),
legend.position = c(0.8, 0.9),
legend.title = element_blank(),
legend.text = element_text(colour="black", size=10),
axis.line.x = element_line(colour = "black", size = 1),
axis.line.y = element_line(colour = "black", size = 1),
axis.text.x = element_text(face="plain", color="black", size=16, angle=0),
axis.text.y = element_text(face="plain", color="black", size=16, angle=0),
axis.title.x = element_text(size=16,face="plain", margin = margin(r=0,10,0,0)),
axis.title.y = element_text(size=16,face="plain", margin = margin(r=10,0,0,0)),
axis.ticks = element_line(size = 1),
axis.ticks.length = unit(.3, "cm"))+
scale_fill_manual(values=c(colorESEB[1],colorESEB[2]),name = "Treatment", labels = c('Small group','Large group'))
BarPlot_6<-ggplot(PhenVarBoot_Table_plot_cov_area[1:12,], aes(x=Variance_component, y=Variance, fill=Treatment)) +
scale_y_continuous(limits = c(-0.25, 0.2), breaks = seq(-0.25,0.2,0.1), expand = c(0 ,0)) +
geom_hline(yintercept=0, linetype="solid", color = "black", size=1) +
geom_bar(stat="identity", color="black", position=position_dodge(), alpha=0.8) +
geom_errorbar(aes(ymin=l95.CI, ymax=u95.CI), width=.3,size=1, position=position_dodge(.9)) +
ylab('') +xlab('Variance component') +ggtitle('Male')+labs(tag = "B")+
scale_x_discrete(breaks=waiver(),labels = c('cov\n(MS, inSuc)','cov\n(MS, feSuc)','cov\n(MS, Fec)','cov\n(inSuc, feSuc)','cov\n(inSuc,Fec)','cov\n(feSuc, Fec)'))+
scale_fill_manual(values=c(colorESEB2[1],colorESEB2[2]),name = "Treatment", labels = c('Large area','Small area'))+
theme(panel.border = element_blank(),
plot.title = element_text(hjust = 0.5),
panel.background = element_blank(),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
legend.position = c(0.8, 0.9),
plot.tag.position=c(0.01,0.98),
legend.title = element_blank(),
legend.text = element_text(colour="black", size=10),
axis.line.x = element_line(colour = "black", size = 1),
axis.line.y = element_line(colour = "black", size = 1),
axis.text.x = element_text(face="plain", color="black", size=16, angle=0),
axis.text.y = element_text(face="plain", color="black", size=16, angle=0),
axis.title.x = element_text(size=16,face="plain", margin = margin(r=0,10,0,0)),
axis.title.y = element_text(size=16,face="plain", margin = margin(r=10,0,0,0)),
axis.ticks = element_line(size = 1),
axis.ticks.length = unit(.3, "cm"))
grid.arrange(grobs = list(BarPlot_5,BarPlot_6), nrow = 1,ncol=2, widths=c(2.3, 2.3))
Figure 14: Covariance components for variance decomposition in males
into mating success, insemination success, fertilization success and
fecundity of the partners. Means and 95% confidence
intervals.
sessionInfo()R version 4.0.2 (2020-06-22)
Platform: x86_64-w64-mingw32/x64 (64-bit)
Running under: Windows 10 x64 (build 19042)
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] grid stats graphics grDevices utils datasets methods
[8] base
other attached packages:
[1] ICC_2.4.0 tidyr_1.2.0 data.table_1.14.2 boot_1.3-25
[5] RColorBrewer_1.1-3 car_3.1-0 carData_3.0-5 gridGraphics_0.5-1
[9] cowplot_1.1.1 EnvStats_2.7.0 dplyr_1.0.9 readr_2.1.2
[13] lmerTest_3.1-3 lme4_1.1-30 Matrix_1.2-18 gridExtra_2.3
[17] ggplot2_3.3.6 ggeffects_1.1.2 workflowr_1.7.0
loaded via a namespace (and not attached):
[1] httr_1.4.3 sass_0.4.1 bit64_4.0.5
[4] vroom_1.5.7 jsonlite_1.8.0 splines_4.0.2
[7] bslib_0.3.1 assertthat_0.2.1 getPass_0.2-2
[10] highr_0.9 yaml_2.3.5 numDeriv_2016.8-1.1
[13] pillar_1.7.0 lattice_0.20-41 glue_1.6.2
[16] digest_0.6.29 promises_1.2.0.1 minqa_1.2.4
[19] colorspace_2.0-3 htmltools_0.5.2 httpuv_1.6.5
[22] pkgconfig_2.0.3 purrr_0.3.4 scales_1.2.0
[25] processx_3.7.0 whisker_0.4 later_1.3.0
[28] tzdb_0.3.0 git2r_0.30.1 tibble_3.1.7
[31] mgcv_1.8-31 farver_2.1.1 generics_0.1.3
[34] ellipsis_0.3.2 withr_2.5.0 cli_3.3.0
[37] magrittr_2.0.3 crayon_1.5.1 evaluate_0.15
[40] ps_1.7.1 fs_1.5.2 fansi_1.0.3
[43] nlme_3.1-148 MASS_7.3-51.6 tools_4.0.2
[46] hms_1.1.1 lifecycle_1.0.1 stringr_1.4.0
[49] munsell_0.5.0 callr_3.7.1 compiler_4.0.2
[52] jquerylib_0.1.4 rlang_1.0.4 nloptr_2.0.3
[55] rstudioapi_0.13 labeling_0.4.2 rmarkdown_2.14
[58] gtable_0.3.0 abind_1.4-5 DBI_1.1.3
[61] R6_2.5.1 knitr_1.39 bit_4.0.4
[64] fastmap_1.1.0 utf8_1.2.2 rprojroot_2.0.3
[67] stringi_1.7.6 parallel_4.0.2 Rcpp_1.0.9
[70] vctrs_0.4.1 tidyselect_1.1.2 xfun_0.31