Population density affects sexual selection in the red flour beetle
Supplementary material reporting R code
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: 2022-07-22
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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
setwd(".")
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$excluded!=1,]
#Calculate total offspring number ####
DB_data$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'))
# 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(slava_ukrajini[1],slava_ukrajini[2]),name = "Treatment", labels = c('Small group','Large group'))+
scale_fill_manual(values=c(slava_ukrajini[1],slava_ukrajini[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(slava_ukrajini[1],slava_ukrajini[2]),name = "Treatment", labels = c('Large area','Small area'))+
scale_fill_manual(values=c(slava_ukrajini[1],slava_ukrajini[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))
Statistical models: Number of matings (quasi-Poisson GLM)
Effect of
group size on number of matings in females.
# Statistical models: Number of matings (quasi-Poisson GLM)
# Sex: Female
# Treatment: Group size
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
-2.4254 -1.3133 0.0342 0.5850 3.5920
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.07881 0.08504 12.685 < 2e-16 ***
Gr_sizeLG -0.45211 0.14503 -3.117 0.00218 **
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
(Dispersion parameter for quasipoisson family taken to be 1.80809)
Null deviance: 298.54 on 154 degrees of freedom
Residual deviance: 280.27 on 153 degrees of freedom
(161 observations deleted due to missingness)
AIC: NA
Number of Fisher Scoring iterations: 5Effect 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
-2.4547 -0.8515 -0.0753 0.5769 3.1654
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.10288 0.07349 15.008 < 2e-16 ***
Gr_sizeLG -0.35693 0.11248 -3.173 0.00181 **
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
(Dispersion parameter for quasipoisson family taken to be 1.269097)
Null deviance: 219.60 on 160 degrees of freedom
Residual deviance: 206.67 on 159 degrees of freedom
(155 observations deleted due to missingness)
AIC: NA
Number of Fisher Scoring iterations: 5Effect 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
-2.3360 -1.2039 -0.1117 0.5379 4.2560
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.00371 0.09386 10.694 <2e-16 ***
AreaSmall -0.23260 0.14484 -1.606 0.11
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
(Dispersion parameter for quasipoisson family taken to be 1.946974)
Null deviance: 298.54 on 154 degrees of freedom
Residual deviance: 293.47 on 153 degrees of freedom
(161 observations deleted due to missingness)
AIC: NA
Number of Fisher Scoring iterations: 5Effect 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
-2.3131 -1.0405 -0.2838 0.3535 3.6281
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.88730 0.08263 10.74 <2e-16 ***
AreaSmall 0.09677 0.11657 0.83 0.408
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
(Dispersion parameter for quasipoisson family taken to be 1.392725)
Null deviance: 219.60 on 160 degrees of freedom
Residual deviance: 218.64 on 159 degrees of freedom
(155 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(slava_ukrajini[1],slava_ukrajini[2]),name = "Treatment", labels = c('Small group','Large group'))+
scale_fill_manual(values=c(slava_ukrajini[1],slava_ukrajini[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(slava_ukrajini[1],slava_ukrajini[2]),name = "Treatment", labels = c('Large area','Small area'))+
scale_fill_manual(values=c(slava_ukrajini[1],slava_ukrajini[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))
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
-1.8343 -0.5695 0.2377 0.3639 2.2154
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.52019 0.07049 7.38 9.43e-12 ***
Gr_sizeLG -0.09586 0.10774 -0.89 0.375
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
(Dispersion parameter for quasipoisson family taken to be 0.7104776)
Null deviance: 133.68 on 154 degrees of freedom
Residual deviance: 133.12 on 153 degrees of freedom
(161 observations deleted due to missingness)
AIC: NA
Number of Fisher Scoring iterations: 5Effect 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
-1.8367 -0.5726 0.2343 0.2708 2.0591
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.49532 0.06415 7.722 1.2e-12 ***
Gr_sizeLG 0.02748 0.08875 0.310 0.757
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
(Dispersion parameter for quasipoisson family taken to be 0.526679)
Null deviance: 97.278 on 160 degrees of freedom
Residual deviance: 97.228 on 159 degrees of freedom
(155 observations deleted due to missingness)
AIC: NA
Number of Fisher Scoring iterations: 5Effect 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
-1.8526 -0.5933 0.2112 0.3882 2.2449
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.54002 0.07078 7.629 2.35e-12 ***
AreaSmall -0.13456 0.10623 -1.267 0.207
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
(Dispersion parameter for quasipoisson family taken to be 0.6964311)
Null deviance: 133.68 on 154 degrees of freedom
Residual deviance: 132.56 on 153 degrees of freedom
(161 observations deleted due to missingness)
AIC: NA
Number of Fisher Scoring iterations: 5Effect 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
-1.8305 -0.5497 0.2433 0.2598 2.0898
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.50366 0.06170 8.163 9.43e-14 ***
AreaSmall 0.01235 0.08893 0.139 0.89
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
(Dispersion parameter for quasipoisson family taken to be 0.5291461)
Null deviance: 97.278 on 160 degrees of freedom
Residual deviance: 97.268 on 159 degrees of freedom
(155 observations deleted due to missingness)
AIC: NA
Number of Fisher Scoring iterations: 5Mating 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(slava_ukrajini[1],slava_ukrajini[2]),name = "Treatment", labels = c('Small group','Large group'))+
scale_fill_manual(values=c(slava_ukrajini[1],slava_ukrajini[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(slava_ukrajini[1],slava_ukrajini[2]),name = "Treatment", labels = c('Large area','Small area'))+
scale_fill_manual(values=c(slava_ukrajini[1],slava_ukrajini[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))
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
(186 observations deleted due to missingness)
AIC: 1297.3
Number of Fisher Scoring iterations: 2Effect 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
(168 observations deleted due to missingness)
AIC: 1510.3
Number of Fisher Scoring iterations: 2Effect 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
(186 observations deleted due to missingness)
AIC: 1298.8
Number of Fisher Scoring iterations: 2Effect 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
(168 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(slava_ukrajini[1],slava_ukrajini[2]),name = "Treatment", labels = c('Small group','Large group'))+
scale_fill_manual(values=c(slava_ukrajini[1],slava_ukrajini[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(slava_ukrajini[1],slava_ukrajini[2]),name = "Treatment", labels = c('Large area','Small area'))+
scale_fill_manual(values=c(slava_ukrajini[1],slava_ukrajini[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))
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
-8.9059 -2.9059 -0.9059 2.6522 16.0941
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 8.9059 0.4518 19.71 < 2e-16 ***
Gr_sizeLG -2.5581 0.6749 -3.79 0.000217 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
(Dispersion parameter for gaussian family taken to be 17.34802)
Null deviance: 2886.1 on 153 degrees of freedom
Residual deviance: 2636.9 on 152 degrees of freedom
(162 observations deleted due to missingness)
AIC: 880.46
Number of Fisher Scoring iterations: 2Effect 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
-9.577 -3.253 -1.253 1.747 21.423
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 10.5769 0.5630 18.787 < 2e-16 ***
Gr_sizeLG -3.3239 0.7841 -4.239 3.79e-05 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
(Dispersion parameter for gaussian family taken to be 24.72154)
Null deviance: 4375.0 on 160 degrees of freedom
Residual deviance: 3930.7 on 159 degrees of freedom
(155 observations deleted due to missingness)
AIC: 977.32
Number of Fisher Scoring iterations: 2Effect 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
-7.9012 -2.9012 -0.6027 2.3973 17.3973
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 7.9012 0.4839 16.329 <2e-16 ***
AreaSmall -0.2985 0.7028 -0.425 0.672
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
(Dispersion parameter for gaussian family taken to be 18.96506)
Null deviance: 2886.1 on 153 degrees of freedom
Residual deviance: 2882.7 on 152 degrees of freedom
(162 observations deleted due to missingness)
AIC: 894.18
Number of Fisher Scoring iterations: 2Effect 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
-8.351 -3.417 -1.351 2.583 22.649
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 8.4167 0.5700 14.765 <2e-16 ***
AreaSmall 0.9340 0.8243 1.133 0.259
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
(Dispersion parameter for gaussian family taken to be 27.29528)
Null deviance: 4375.0 on 160 degrees of freedom
Residual deviance: 4339.9 on 159 degrees of freedom
(155 observations deleted due to missingness)
AIC: 993.27
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(slava_ukrajini[1],slava_ukrajini[2]),name = "Treatment", labels = c('Small group','Large group'))+
scale_fill_manual(values=c(slava_ukrajini[1],slava_ukrajini[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(slava_ukrajini[1],slava_ukrajini[2]),name = "Treatment", labels = c('Large area','Small area'))+
scale_fill_manual(values=c(slava_ukrajini[1],slava_ukrajini[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))
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
-2.7270 -1.0918 -0.0776 0.8393 3.4972
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -0.70705 0.09781 -7.229 2.26e-11 ***
Gr_sizeLG -0.14460 0.16436 -0.880 0.38
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
(Dispersion parameter for quasibinomial family taken to be 1.601925)
Null deviance: 275.29 on 152 degrees of freedom
Residual deviance: 274.05 on 151 degrees of freedom
(162 observations deleted due to missingness)
AIC: NA
Number of Fisher Scoring iterations: 4Effect 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
-3.6620 -0.8327 0.0646 0.7745 4.3686
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -0.92054 0.09330 -9.866 <2e-16 ***
Gr_sizeLG 0.02854 0.14315 0.199 0.842
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
(Dispersion parameter for quasibinomial family taken to be 1.463058)
Null deviance: 254.50 on 160 degrees of freedom
Residual deviance: 254.44 on 159 degrees of freedom
(155 observations deleted due to missingness)
AIC: NA
Number of Fisher Scoring iterations: 4Effect 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
-2.9107 -1.0039 -0.0619 0.8640 3.3743
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -0.6397 0.1045 -6.120 7.69e-09 ***
AreaSmall -0.2640 0.1575 -1.676 0.0958 .
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
(Dispersion parameter for quasibinomial family taken to be 1.580745)
Null deviance: 275.29 on 152 degrees of freedom
Residual deviance: 270.83 on 151 degrees of freedom
(162 observations deleted due to missingness)
AIC: NA
Number of Fisher Scoring iterations: 4Effect 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
-3.6902 -0.8210 0.0797 0.7876 4.3402
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -0.90247 0.10036 -8.992 6.84e-16 ***
AreaSmall -0.01188 0.14146 -0.084 0.933
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
(Dispersion parameter for quasibinomial family taken to be 1.461793)
Null deviance: 254.50 on 160 degrees of freedom
Residual deviance: 254.49 on 159 degrees of freedom
(155 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(slava_ukrajini[1],slava_ukrajini[2]),name = "Treatment", labels = c('Small group','Large group'))+
scale_fill_manual(values=c(slava_ukrajini[1],slava_ukrajini[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(slava_ukrajini[1],slava_ukrajini[2]),name = "Treatment", labels = c('Large area','Small area'))+
scale_fill_manual(values=c(slava_ukrajini[1],slava_ukrajini[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)))
plot6<-grid.arrange(grobs = list(p6,p6.2), nrow = 1,ncol=2, widths=c(2.3, 2.3))
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
-10.1580 -9.8928 0.9873 5.8238 9.8374
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 3.94338 0.13188 29.902 <2e-16 ***
Gr_sizeLG -0.05292 0.19848 -0.267 0.79
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
(Dispersion parameter for quasipoisson family taken to be 48.45676)
Null deviance: 6268.7 on 98 degrees of freedom
Residual deviance: 6265.2 on 97 degrees of freedom
(104 observations deleted due to missingness)
AIC: NA
Number of Fisher Scoring iterations: 5Effect 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
-11.3310 -9.9291 -0.9812 4.8487 21.8002
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 4.1619 0.1340 31.051 <2e-16 ***
Gr_sizeLG -0.2642 0.1911 -1.382 0.17
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
(Dispersion parameter for quasipoisson family taken to be 53.05143)
Null deviance: 6253.5 on 103 degrees of freedom
Residual deviance: 6152.3 on 102 degrees of freedom
(99 observations deleted due to missingness)
AIC: NA
Number of Fisher Scoring iterations: 5Effect 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
-10.2728 -9.7831 0.9745 5.6124 9.6417
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 3.96584 0.13417 29.557 <2e-16 ***
AreaSmall -0.09768 0.19772 -0.494 0.622
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
(Dispersion parameter for quasipoisson family taken to be 48.44755)
Null deviance: 6268.7 on 98 degrees of freedom
Residual deviance: 6256.8 on 97 degrees of freedom
(104 observations deleted due to missingness)
AIC: NA
Number of Fisher Scoring iterations: 5Effect 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
-11.0546 -10.1229 -0.0872 4.5784 22.3469
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 3.9364 0.1381 28.509 <2e-16 ***
AreaSmall 0.1761 0.1924 0.915 0.362
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
(Dispersion parameter for quasipoisson family taken to be 53.72577)
Null deviance: 6253.5 on 103 degrees of freedom
Residual deviance: 6208.5 on 102 degrees of freedom
(99 observations deleted due to missingness)
AIC: NA
Number of Fisher Scoring iterations: 5
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] farver_2.1.1 generics_0.1.3 ellipsis_0.3.2
[34] withr_2.5.0 cli_3.3.0 magrittr_2.0.3
[37] crayon_1.5.1 evaluate_0.15 ps_1.7.1
[40] fs_1.5.2 fansi_1.0.3 nlme_3.1-148
[43] MASS_7.3-51.6 tools_4.0.2 hms_1.1.1
[46] lifecycle_1.0.1 stringr_1.4.0 munsell_0.5.0
[49] callr_3.7.1 compiler_4.0.2 jquerylib_0.1.4
[52] rlang_1.0.4 nloptr_2.0.3 rstudioapi_0.13
[55] labeling_0.4.2 rmarkdown_2.14 gtable_0.3.0
[58] abind_1.4-5 DBI_1.1.3 R6_2.5.1
[61] knitr_1.39 bit_4.0.4 fastmap_1.1.0
[64] utf8_1.2.2 rprojroot_2.0.3 stringi_1.7.6
[67] parallel_4.0.2 Rcpp_1.0.9 vctrs_0.4.1
[70] tidyselect_1.1.2 xfun_0.31