Population density affects sexual selection in an insect model

Part 2: Testing the effect of density on mating behaviour and reproductive success

workflowr

• Summary
• Checks
• Past versions

Last updated: 2023-05-04

Checks: 7 0

Knit directory: Density_and_sexual_selection_2023/

This reproducible R Markdown analysis was created with workflowr (version 1.7.0). The Checks tab describes the reproducibility checks that were applied when the results were created. The Past versions tab lists the development history.

---

R Markdown file: up-to-date

Great! Since the R Markdown file has been committed to the Git repository, you know the exact version of the code that produced these results.

Environment: empty

Great job! The global environment was empty. Objects defined in the global environment can affect the analysis in your R Markdown file in unknown ways. For reproduciblity it’s best to always run the code in an empty environment.

Seed: set.seed(20210613)

The command set.seed(20210613) was run prior to running the code in the R Markdown file. Setting a seed ensures that any results that rely on randomness, e.g. subsampling or permutations, are reproducible.

Session information: recorded

Great job! Recording the operating system, R version, and package versions is critical for reproducibility.

Cache: none

Nice! There were no cached chunks for this analysis, so you can be confident that you successfully produced the results during this run.

File paths: relative

Great job! Using relative paths to the files within your workflowr project makes it easier to run your code on other machines.

Repository version: fe2f665

Great! You are using Git for version control. Tracking code development and connecting the code version to the results is critical for reproducibility.

The results in this page were generated with repository version fe2f665. See the Past versions tab to see a history of the changes made to the R Markdown and HTML files.

Note that you need to be careful to ensure that all relevant files for the analysis have been committed to Git prior to generating the results (you can use wflow_publish or wflow_git_commit). workflowr only checks the R Markdown file, but you know if there are other scripts or data files that it depends on. Below is the status of the Git repository when the results were generated:

Ignored files:
    Ignored:    .Rhistory
    Ignored:    .Rproj.user/

Note that any generated files, e.g. HTML, png, CSS, etc., are not included in this status report because it is ok for generated content to have uncommitted changes.

---

These are the previous versions of the repository in which changes were made to the R Markdown (analysis/index3.Rmd) and HTML (docs/index3.html) files. If you’ve configured a remote Git repository (see ?wflow_git_remote), click on the hyperlinks in the table below to view the files as they were in that past version.

File	Version	Author	Date	Message
Rmd	fe2f665	LennartWinkler	2023-05-04	wflow_publish(all = T)
html	af34366	LennartWinkler	2023-04-27	Build site.
Rmd	e4a0f09	LennartWinkler	2023-04-27	wflow_publish(all = T)
html	b81114e	LennartWinkler	2023-04-20	Build site.
Rmd	fe8c52c	LennartWinkler	2023-04-20	wflow_publish(all = T)
html	fe8c52c	LennartWinkler	2023-04-20	wflow_publish(all = T)
html	55df7ff	LennartWinkler	2023-04-12	Build site.
html	b0a3f15	LennartWinkler	2023-04-12	Build site.
Rmd	c8bd540	Lennart Winkler	2023-04-12	set up
html	c8bd540	Lennart Winkler	2023-04-12	set up
html	39e340c	LennartWinkler	2022-08-13	new
Rmd	f54f022	LennartWinkler	2022-08-13	wflow_publish(republish = TRUE, all = T)
html	f54f022	LennartWinkler	2022-08-13	wflow_publish(republish = TRUE, all = T)
Rmd	f89f7c1	LennartWinkler	2022-08-10	Build site.
html	f89f7c1	LennartWinkler	2022-08-10	Build site.

---

Supplementary material reporting R code for the manuscript ‘Population density affects sexual selection in an insect model’.

Part 2: Testing the effect of density on mating behaviour and reproductive success

Here we tested the effect that the treatments (group and arena size) had on the mating behaviour and reproductive success of focal beetles.

Behavioural variables:
- Copulation attempts
- Copulations per encounter
- Number of partners
- Number copulations
- Copulations per partner
- Copulation duration

Load and prepare data

Before we started the analyses, we loaded all necessary packages and data.

rm(list = ls()) # Clear work environment

# Load R-packages ####
list_of_packages=cbind('ggeffects','ggplot2','gridExtra','lme4','lmerTest','readr','dplyr','EnvStats','cowplot','gridGraphics','car','RColorBrewer','boot','data.table','base','ICC','knitr')
lapply(list_of_packages, require, character.only = TRUE) 

# Load data set ####
D_data=read_delim("./data/Data_Winkler_et_al_2023_Denstiy.csv",";", escape_double = FALSE, trim_ws = TRUE)

# Set factors and levels for factors
D_data$Week=as.factor(D_data$Week)
D_data$Sex=as.factor(D_data$Sex)
D_data$Gr_size=as.factor(D_data$Gr_size)
D_data$Gr_size <- factor(D_data$Gr_size, levels=c("SG","LG"))
D_data$Arena=as.factor(D_data$Arena)

## Subset data set ####
### Data according to denstiy ####
D_data_0.26=D_data[D_data$Treatment=='D = 0.26',]
D_data_0.52=D_data[D_data$Treatment=='D = 0.52',]
D_data_0.67=D_data[D_data$Treatment=='D = 0.67',]
D_data_1.33=D_data[D_data$Treatment=='D = 1.33',]

### Subset data by sex ####
D_data_m=D_data[D_data$Sex=='M',]
D_data_f=D_data[D_data$Sex=='F',]

### Calculate data relativized within treatment and sex ####
# Small group + large Area
D_data_0.26=D_data[D_data$Treatment=='D = 0.26',]

D_data_0.26$rel_m_RS=NA
D_data_0.26$rel_m_prop_RS=NA
D_data_0.26$rel_m_cMS=NA
D_data_0.26$rel_m_InSuc=NA
D_data_0.26$rel_m_feSuc=NA
D_data_0.26$rel_m_pFec=NA
D_data_0.26$rel_m_PS=NA
D_data_0.26$rel_m_pFec_compl=NA

D_data_0.26$rel_f_RS=NA
D_data_0.26$rel_f_prop_RS=NA
D_data_0.26$rel_f_cMS=NA
D_data_0.26$rel_f_fec_pMate=NA

D_data_0.26$rel_m_RS=D_data_0.26$m_RS/mean(D_data_0.26$m_RS,na.rm=T)
D_data_0.26$rel_m_prop_RS=D_data_0.26$m_prop_RS/mean(D_data_0.26$m_prop_RS,na.rm=T)
D_data_0.26$rel_m_cMS=D_data_0.26$m_cMS/mean(D_data_0.26$m_cMS,na.rm=T)
D_data_0.26$rel_m_InSuc=D_data_0.26$m_InSuc/mean(D_data_0.26$m_InSuc,na.rm=T)
D_data_0.26$rel_m_feSuc=D_data_0.26$m_feSuc/mean(D_data_0.26$m_feSuc,na.rm=T)
D_data_0.26$rel_m_pFec=D_data_0.26$m_pFec/mean(D_data_0.26$m_pFec,na.rm=T)
D_data_0.26$rel_m_PS=D_data_0.26$m_PS/mean(D_data_0.26$m_PS,na.rm=T)
D_data_0.26$rel_m_pFec_compl=D_data_0.26$m_pFec_compl/mean(D_data_0.26$m_pFec_compl,na.rm=T)

D_data_0.26$rel_f_RS=D_data_0.26$f_RS/mean(D_data_0.26$f_RS,na.rm=T)
D_data_0.26$rel_f_prop_RS=D_data_0.26$f_prop_RS/mean(D_data_0.26$f_prop_RS,na.rm=T)
D_data_0.26$rel_f_cMS=D_data_0.26$f_cMS/mean(D_data_0.26$f_cMS,na.rm=T)
D_data_0.26$rel_f_fec_pMate=D_data_0.26$f_fec_pMate/mean(D_data_0.26$f_fec_pMate,na.rm=T)

# Large group + large Area
D_data_0.52=D_data[D_data$Treatment=='D = 0.52',]
#Relativize data

D_data_0.52$rel_m_RS=NA
D_data_0.52$rel_m_prop_RS=NA
D_data_0.52$rel_m_cMS=NA
D_data_0.52$rel_m_InSuc=NA
D_data_0.52$rel_m_feSuc=NA
D_data_0.52$rel_m_pFec=NA
D_data_0.52$rel_m_PS=NA
D_data_0.52$rel_m_pFec_compl=NA

D_data_0.52$rel_f_RS=NA
D_data_0.52$rel_f_prop_RS=NA
D_data_0.52$rel_f_cMS=NA
D_data_0.52$rel_f_fec_pMate=NA

D_data_0.52$rel_m_RS=D_data_0.52$m_RS/mean(D_data_0.52$m_RS,na.rm=T)
D_data_0.52$rel_m_prop_RS=D_data_0.52$m_prop_RS/mean(D_data_0.52$m_prop_RS,na.rm=T)
D_data_0.52$rel_m_cMS=D_data_0.52$m_cMS/mean(D_data_0.52$m_cMS,na.rm=T)
D_data_0.52$rel_m_InSuc=D_data_0.52$m_InSuc/mean(D_data_0.52$m_InSuc,na.rm=T)
D_data_0.52$rel_m_feSuc=D_data_0.52$m_feSuc/mean(D_data_0.52$m_feSuc,na.rm=T)
D_data_0.52$rel_m_pFec=D_data_0.52$m_pFec/mean(D_data_0.52$m_pFec,na.rm=T)
D_data_0.52$rel_m_PS=D_data_0.52$m_PS/mean(D_data_0.52$m_PS,na.rm=T)
D_data_0.52$rel_m_pFec_compl=D_data_0.52$m_pFec_compl/mean(D_data_0.52$m_pFec_compl,na.rm=T)

D_data_0.52$rel_f_RS=D_data_0.52$f_RS/mean(D_data_0.52$f_RS,na.rm=T)
D_data_0.52$rel_f_prop_RS=D_data_0.52$f_prop_RS/mean(D_data_0.52$f_prop_RS,na.rm=T)
D_data_0.52$rel_f_cMS=D_data_0.52$f_cMS/mean(D_data_0.52$f_cMS,na.rm=T)
D_data_0.52$rel_f_fec_pMate=D_data_0.52$f_fec_pMate/mean(D_data_0.52$f_fec_pMate,na.rm=T)

# Small group + small Area
D_data_0.67=D_data[D_data$Treatment=='D = 0.67',]
#Relativize data
D_data_0.67$rel_m_RS=NA
D_data_0.67$rel_m_prop_RS=NA
D_data_0.67$rel_m_cMS=NA
D_data_0.67$rel_m_InSuc=NA
D_data_0.67$rel_m_feSuc=NA
D_data_0.67$rel_m_pFec=NA
D_data_0.67$rel_m_PS=NA
D_data_0.67$rel_m_pFec_compl=NA

D_data_0.67$rel_f_RS=NA
D_data_0.67$rel_f_prop_RS=NA
D_data_0.67$rel_f_cMS=NA
D_data_0.67$rel_f_fec_pMate=NA

D_data_0.67$rel_m_RS=D_data_0.67$m_RS/mean(D_data_0.67$m_RS,na.rm=T)
D_data_0.67$rel_m_prop_RS=D_data_0.67$m_prop_RS/mean(D_data_0.67$m_prop_RS,na.rm=T)
D_data_0.67$rel_m_cMS=D_data_0.67$m_cMS/mean(D_data_0.67$m_cMS,na.rm=T)
D_data_0.67$rel_m_InSuc=D_data_0.67$m_InSuc/mean(D_data_0.67$m_InSuc,na.rm=T)
D_data_0.67$rel_m_feSuc=D_data_0.67$m_feSuc/mean(D_data_0.67$m_feSuc,na.rm=T)
D_data_0.67$rel_m_pFec=D_data_0.67$m_pFec/mean(D_data_0.67$m_pFec,na.rm=T)
D_data_0.67$rel_m_PS=D_data_0.67$m_PS/mean(D_data_0.67$m_PS,na.rm=T)
D_data_0.67$rel_m_pFec_compl=D_data_0.67$m_pFec_compl/mean(D_data_0.67$m_pFec_compl,na.rm=T)

D_data_0.67$rel_f_RS=D_data_0.67$f_RS/mean(D_data_0.67$f_RS,na.rm=T)
D_data_0.67$rel_f_prop_RS=D_data_0.67$f_prop_RS/mean(D_data_0.67$f_prop_RS,na.rm=T)
D_data_0.67$rel_f_cMS=D_data_0.67$f_cMS/mean(D_data_0.67$f_cMS,na.rm=T)
D_data_0.67$rel_f_fec_pMate=D_data_0.67$f_fec_pMate/mean(D_data_0.67$f_fec_pMate,na.rm=T)

# Large group + small Area
D_data_1.33=D_data[D_data$Treatment=='D = 1.33',]
#Relativize data

D_data_1.33$rel_m_RS=NA
D_data_1.33$rel_m_prop_RS=NA
D_data_1.33$rel_m_cMS=NA
D_data_1.33$rel_m_InSuc=NA
D_data_1.33$rel_m_feSuc=NA
D_data_1.33$rel_m_pFec=NA
D_data_1.33$rel_m_PS=NA
D_data_1.33$rel_m_pFec_compl=NA

D_data_1.33$rel_f_RS=NA
D_data_1.33$rel_f_prop_RS=NA
D_data_1.33$rel_f_cMS=NA
D_data_1.33$rel_f_fec_pMate=NA

D_data_1.33$rel_m_RS=D_data_1.33$m_RS/mean(D_data_1.33$m_RS,na.rm=T)
D_data_1.33$rel_m_prop_RS=D_data_1.33$m_prop_RS/mean(D_data_1.33$m_prop_RS,na.rm=T)
D_data_1.33$rel_m_cMS=D_data_1.33$m_cMS/mean(D_data_1.33$m_cMS,na.rm=T)
D_data_1.33$rel_m_InSuc=D_data_1.33$m_InSuc/mean(D_data_1.33$m_InSuc,na.rm=T)
D_data_1.33$rel_m_feSuc=D_data_1.33$m_feSuc/mean(D_data_1.33$m_feSuc,na.rm=T)
D_data_1.33$rel_m_pFec=D_data_1.33$m_pFec/mean(D_data_1.33$m_pFec,na.rm=T)
D_data_1.33$rel_m_PS=D_data_1.33$m_PS/mean(D_data_1.33$m_PS,na.rm=T)
D_data_1.33$rel_m_pFec_compl=D_data_1.33$m_pFec_compl/mean(D_data_1.33$m_pFec_compl,na.rm=T)

D_data_1.33$rel_f_RS=D_data_1.33$f_RS/mean(D_data_1.33$f_RS,na.rm=T)
D_data_1.33$rel_f_prop_RS=D_data_1.33$f_prop_RS/mean(D_data_1.33$f_prop_RS,na.rm=T)
D_data_1.33$rel_f_cMS=D_data_1.33$f_cMS/mean(D_data_1.33$f_cMS,na.rm=T)
D_data_1.33$rel_f_fec_pMate=D_data_1.33$f_fec_pMate/mean(D_data_1.33$f_fec_pMate,na.rm=T)

### Reduce treatments to arena and population size ####
# Arena size
D_data_Large_arena=rbind(D_data_0.26,D_data_0.52)
D_data_Small_arena=rbind(D_data_0.67,D_data_1.33)

# Population size
D_data_Small_pop=rbind(D_data_0.26,D_data_0.67)
D_data_Large_pop=rbind(D_data_0.52,D_data_1.33)

## Set figure schemes ####
# Set color-sets for figures
colpal=brewer.pal(4, 'Dark2')
colpal2=c("#b2182b","#2166AC")
colpal3=brewer.pal(4, 'Paired')

# Set theme for ggplot2 figures
fig_theme=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(1.25, 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"))

## Create customized functions for analysis ####
# Create function to calculate standard error and upper/lower standard deviation
standard_error <- function(x) sd(x,na.rm=T) / sqrt(length(na.exclude(x)))
upper_CI <- function(x) mean(x,na.rm=T)+((standard_error(x))*qnorm(0.975))
lower_CI <- function(x) mean(x,na.rm=T)-((standard_error(x))*qnorm(0.975))

upper_SD <- function(x) mean(x,na.rm=T)+(sd(x)/2)
lower_SD <- function(x) mean(x,na.rm=T)-(sd(x)/2)

Model the effect of density on mating behaviour

Copulation attempts

Males

First, we calculated means and SE for each treatment:

Mean number of copulation attempts in small groups (SE) = 11.04 (0.97)

mean(D_data$m_Total_Encounters[D_data$Gr_size=='SG'],na.rm=T)
standard_error(D_data$m_Total_Encounters[D_data$Gr_size=='SG'])

Mean number of copulation attempts in large groups (SE) = 7.64 (0.49)

mean(D_data$m_Total_Encounters[D_data$Gr_size=='LG'],na.rm=T)
standard_error(D_data$m_Total_Encounters[D_data$Gr_size=='LG'])

Mean number of copulation attempts in large arena size (SE) = 8.98 (0.72)

mean(D_data$m_Total_Encounters[D_data$Arena=='Large'],na.rm=T)
standard_error(D_data$m_Total_Encounters[D_data$Arena=='Large'])

Mean number of copulation attempts in small arena size (SE) = 9.33 (0.8)

mean(D_data$m_Total_Encounters[D_data$Arena=='Small'],na.rm=T)
standard_error(D_data$m_Total_Encounters[D_data$Arena=='Small'])

GLM for copulation attempts including interaction between group and arena size treatment:

mod3=glm(m_Total_Encounters~Gr_size*Arena,data=D_data,family = quasipoisson)
Anova(mod3,type=2)

Analysis of Deviance Table (Type II tests)

Response: m_Total_Encounters
              LR Chisq Df Pr(>Chisq)    
Gr_size        11.4368  1  0.0007201 ***
Arena           0.0520  1  0.8195638    
Gr_size:Arena   0.2226  1  0.6370758    
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

GLM for copulation attempts excluding interaction between group and arena size treatment:

mod3.1=glm(m_Total_Encounters~Gr_size+Arena,data=D_data,family = quasipoisson)
summary(mod3.1)

Call:
glm(formula = m_Total_Encounters ~ Gr_size + Arena, family = quasipoisson, 
    data = D_data)

Deviance Residuals: 
    Min       1Q   Median       3Q      Max  
-3.8812  -1.4114  -0.2269   0.8185   5.1613  

Coefficients:
            Estimate Std. Error t value Pr(>|t|)    
(Intercept)  2.41598    0.09612  25.135  < 2e-16 ***
Gr_sizeLG   -0.37295    0.10996  -3.392 0.000992 ***
ArenaSmall  -0.02515    0.10978  -0.229 0.819291    
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

(Dispersion parameter for quasipoisson family taken to be 2.780109)

    Null deviance: 302.24  on 103  degrees of freedom
Residual deviance: 269.83  on 101  degrees of freedom
  (99 Beobachtungen als fehlend gelöscht)
AIC: NA

Number of Fisher Scoring iterations: 5

Anova(mod3.1,type=2)

Analysis of Deviance Table (Type II tests)

Response: m_Total_Encounters
        LR Chisq Df Pr(>Chisq)    
Gr_size  11.5351  1  0.0006829 ***
Arena     0.0525  1  0.8188029    
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Females

We calculated means and SE for each treatment:

Mean number of copulation attempts in small groups (SE) = 8.8 (0.68)

mean(D_data$f_Total_Encounters[D_data$Gr_size=='SG'],na.rm=T)
standard_error(D_data$f_Total_Encounters[D_data$Gr_size=='SG'])

Mean number of copulation attempts in large groups (SE) = 6.36 (0.44)

mean(D_data$f_Total_Encounters[D_data$Gr_size=='LG'],na.rm=T)
standard_error(D_data$f_Total_Encounters[D_data$Gr_size=='LG'])

Mean number of copulation attempts in large arena size (SE) = 8.1 (0.65)

mean(D_data$f_Total_Encounters[D_data$Arena=='Large'],na.rm=T)
standard_error(D_data$f_Total_Encounters[D_data$Arena=='Large'])

Mean number of copulation attempts in small arena size (SE) = 7.25 (0.58)

mean(D_data$f_Total_Encounters[D_data$Arena=='Small'],na.rm=T)
standard_error(D_data$f_Total_Encounters[D_data$Arena=='Small'])

GLM for copulation attempts including interaction between group and arena size treatment:

mod4=glm(f_Total_Encounters~Gr_size*Arena,data=D_data,family = quasipoisson)
Anova(mod4,type=2)

Analysis of Deviance Table (Type II tests)

Response: f_Total_Encounters
              LR Chisq Df Pr(>Chisq)   
Gr_size         8.0855  1   0.004462 **
Arena           0.2888  1   0.590966   
Gr_size:Arena   0.5385  1   0.463060   
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

GLM for copulation attempts excluding interaction between group and arena size treatment:

mod4.1=glm(f_Total_Encounters~Gr_size+Arena,data=D_data,family = quasipoisson)
summary(mod4.1)

Call:
glm(formula = f_Total_Encounters ~ Gr_size + Arena, family = quasipoisson, 
    data = D_data)

Deviance Residuals: 
    Min       1Q   Median       3Q      Max  
-4.2437  -1.0822  -0.2262   0.9494   3.6489  

Coefficients:
            Estimate Std. Error t value Pr(>|t|)    
(Intercept)  2.19774    0.07997  27.483  < 2e-16 ***
Gr_sizeLG   -0.31505    0.11178  -2.819  0.00586 ** 
ArenaSmall  -0.05846    0.10867  -0.538  0.59183    
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

(Dispersion parameter for quasipoisson family taken to be 2.169405)

    Null deviance: 241.05  on 98  degrees of freedom
Residual deviance: 221.13  on 96  degrees of freedom
  (104 Beobachtungen als fehlend gelöscht)
AIC: NA

Number of Fisher Scoring iterations: 5

Anova(mod4.1,type=2)

Analysis of Deviance Table (Type II tests)

Response: f_Total_Encounters
        LR Chisq Df Pr(>Chisq)   
Gr_size   8.1140  1   0.004392 **
Arena     0.2899  1   0.590313   
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Copulations per attempt

Males

We calculated means and SE for each treatment:

Number of copulations per attempt in small groups (SE) = 0.32 (0.03)

mean(D_data$m_TotMatings[D_data$Gr_size=='SG']/(D_data$m_TotMatings[D_data$Gr_size=='SG']+D_data$m_Attempts_number[D_data$Gr_size=='SG']),na.rm=T)
standard_error(D_data$m_TotMatings[D_data$Gr_size=='SG']/(D_data$m_TotMatings[D_data$Gr_size=='SG']+D_data$m_Attempts_number[D_data$Gr_size=='SG']))

Number of copulations per attempt in large groups (SE) = 0.29 (0.02)

mean(D_data$m_TotMatings[D_data$Gr_size=='LG']/(D_data$m_TotMatings[D_data$Gr_size=='LG']+D_data$m_Attempts_number[D_data$Gr_size=='LG']),na.rm=T)
standard_error(D_data$m_TotMatings[D_data$Gr_size=='LG']/(D_data$m_TotMatings[D_data$Gr_size=='LG']+D_data$m_Attempts_number[D_data$Gr_size=='LG']))

Number of copulations per attempt in large arena size (SE) = 0.31 (0.03)

mean(D_data$m_TotMatings[D_data$Arena=='Large']/(D_data$m_TotMatings[D_data$Arena=='Large']+D_data$m_Attempts_number[D_data$Arena=='Large']),na.rm=T)
standard_error(D_data$m_TotMatings[D_data$Arena=='Large']/(D_data$m_TotMatings[D_data$Arena=='Large']+D_data$m_Attempts_number[D_data$Arena=='Large']))

Number of copulations per attempt in small arena size (SE) = 0.3 (0.03)

mean(D_data$m_TotMatings[D_data$Arena=='Small']/(D_data$m_TotMatings[D_data$Arena=='Small']+D_data$m_Attempts_number[D_data$Arena=='Small']),na.rm=T)
standard_error(D_data$m_TotMatings[D_data$Arena=='Small']/(D_data$m_TotMatings[D_data$Arena=='Small']+D_data$m_Attempts_number[D_data$Arena=='Small']))

GLM for copulations per attempt including interaction between group and arena size treatment:

mod5=glm(cbind(m_TotMatings,m_Attempts_number)~Gr_size*Arena,data=D_data,family = quasibinomial)
Anova(mod5,type=2)

Analysis of Deviance Table (Type II tests)

Response: cbind(m_TotMatings, m_Attempts_number)
              LR Chisq Df Pr(>Chisq)
Gr_size        0.18848  1     0.6642
Arena          0.28809  1     0.5914
Gr_size:Arena  0.11436  1     0.7352

GLM for copulations per attempt excluding interaction between group and arena size treatment:

mod5.1=glm(cbind(m_TotMatings,m_Attempts_number)~Gr_size+Arena,data=D_data,family = quasibinomial)
summary(mod5.1)

Call:
glm(formula = cbind(m_TotMatings, m_Attempts_number) ~ Gr_size + 
    Arena, family = quasibinomial, data = D_data)

Deviance Residuals: 
    Min       1Q   Median       3Q      Max  
-3.6291  -0.8482  -0.0073   0.7556   4.4019  

Coefficients:
            Estimate Std. Error t value Pr(>|t|)    
(Intercept) -0.94176    0.16198  -5.814 7.17e-08 ***
Gr_sizeLG    0.08134    0.18650   0.436    0.664    
ArenaSmall  -0.10058    0.18667  -0.539    0.591    
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

(Dispersion parameter for quasibinomial family taken to be 1.623469)

    Null deviance: 180.01  on 103  degrees of freedom
Residual deviance: 179.10  on 101  degrees of freedom
  (99 Beobachtungen als fehlend gelöscht)
AIC: NA

Number of Fisher Scoring iterations: 4

Anova(mod5.1,type=2)

Analysis of Deviance Table (Type II tests)

Response: cbind(m_TotMatings, m_Attempts_number)
        LR Chisq Df Pr(>Chisq)
Gr_size  0.19011  1     0.6628
Arena    0.29059  1     0.5898

Females

We calculated means and SE for each treatment:

Number of copulations per attempt in small groups (SE) = 0.37 (0.04)

mean(D_data$f_TotMatings[D_data$Gr_size=='SG']/(D_data$f_TotMatings[D_data$Gr_size=='SG']+D_data$f_Attempts_number[D_data$Gr_size=='SG']),na.rm=T)
standard_error(D_data$f_TotMatings[D_data$Gr_size=='SG']/(D_data$f_TotMatings[D_data$Gr_size=='SG']+D_data$f_Attempts_number[D_data$Gr_size=='SG']))

Number of copulations per attempt in large groups (SE) = 0.32 (0.04)

mean(D_data$f_TotMatings[D_data$Gr_size=='LG']/(D_data$f_TotMatings[D_data$Gr_size=='LG']+D_data$f_Attempts_number[D_data$Gr_size=='LG']),na.rm=T)
standard_error(D_data$f_TotMatings[D_data$Gr_size=='LG']/(D_data$f_TotMatings[D_data$Gr_size=='LG']+D_data$f_Attempts_number[D_data$Gr_size=='LG']))

Number of copulations per attempt in large arena size (SE) = 0.39 (0.04)

mean(D_data$f_TotMatings[D_data$Arena=='Large']/(D_data$f_TotMatings[D_data$Arena=='Large']+D_data$f_Attempts_number[D_data$Arena=='Large']),na.rm=T)
standard_error(D_data$f_TotMatings[D_data$Arena=='Large']/(D_data$f_TotMatings[D_data$Arena=='Large']+D_data$f_Attempts_number[D_data$Arena=='Large']))

Number of copulations per attempt in small arena size (SE) = 0.3 (0.04)

mean(D_data$f_TotMatings[D_data$Arena=='Small']/(D_data$f_TotMatings[D_data$Arena=='Small']+D_data$f_Attempts_number[D_data$Arena=='Small']),na.rm=T)
standard_error(D_data$f_TotMatings[D_data$Arena=='Small']/(D_data$f_TotMatings[D_data$Arena=='Small']+D_data$f_Attempts_number[D_data$Arena=='Small']))

GLM for copulations per attempt including interaction between group and arena size treatment:

mod6=glm(cbind(f_TotMatings,f_Attempts_number)~Gr_size*Arena,data=D_data,family = quasibinomial)
Anova(mod6,type=2)

Analysis of Deviance Table (Type II tests)

Response: cbind(f_TotMatings, f_Attempts_number)
              LR Chisq Df Pr(>Chisq)
Gr_size        0.00646  1     0.9359
Arena          1.62223  1     0.2028
Gr_size:Arena  1.63103  1     0.2016

GLM for copulations per attempt excluding interaction between group and arena size treatment:

mod6.1=glm(cbind(f_TotMatings,f_Attempts_number)~Gr_size+Arena,data=D_data,family = quasibinomial)
summary(mod6.1)

Call:
glm(formula = cbind(f_TotMatings, f_Attempts_number) ~ Gr_size + 
    Arena, family = quasibinomial, data = D_data)

Deviance Residuals: 
     Min        1Q    Median        3Q       Max  
-2.88927  -0.92593  -0.04092   0.98199   2.72954  

Coefficients:
            Estimate Std. Error t value Pr(>|t|)    
(Intercept) -0.64091    0.14996  -4.274 4.57e-05 ***
Gr_sizeLG   -0.01687    0.21061  -0.080    0.936    
ArenaSmall  -0.25990    0.20536  -1.266    0.209    
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

(Dispersion parameter for quasibinomial family taken to be 1.67619)

    Null deviance: 183.58  on 97  degrees of freedom
Residual deviance: 180.78  on 95  degrees of freedom
  (104 Beobachtungen als fehlend gelöscht)
AIC: NA

Number of Fisher Scoring iterations: 4

Anova(mod6.1,type=2)

Analysis of Deviance Table (Type II tests)

Response: cbind(f_TotMatings, f_Attempts_number)
        LR Chisq Df Pr(>Chisq)
Gr_size  0.00642  1     0.9362
Arena    1.61064  1     0.2044

Number of partners

Males

We calculated means and SE for each treatment:

Mean number of partners in small groups (SE) = 1.63 (0.11)

mean(D_data$m_cMS[D_data$Gr_size=='SG'],na.rm=T)
standard_error(D_data$m_cMS[D_data$Gr_size=='SG'])

Mean number of partners in large groups (SE) = 1.69 (0.14)

mean(D_data$m_cMS[D_data$Gr_size=='LG'],na.rm=T)
standard_error(D_data$m_cMS[D_data$Gr_size=='LG'])

Mean number of partners in large arena size (SE) = 1.69 (0.14)

mean(D_data$m_cMS[D_data$Arena=='Large'],na.rm=T)
standard_error(D_data$m_cMS[D_data$Arena=='Large'])

Mean number of partners in small arena size (SE) = 1.63 (0.12)

mean(D_data$m_cMS[D_data$Arena=='Small'],na.rm=T)
standard_error(D_data$m_cMS[D_data$Arena=='Small'])

GLM for number of partners including interaction between group and arena size treatment

mod7=glm(m_cMS~Gr_size*Arena,data=D_data,family = quasipoisson)
Anova(mod7,type=2)

Analysis of Deviance Table (Type II tests)

Response: m_cMS
              LR Chisq Df Pr(>Chisq)
Gr_size       0.071366  1     0.7894
Arena         0.069055  1     0.7927
Gr_size:Arena 0.004674  1     0.9455

GLM for number of partners excluding interaction between group and arena size treatment

mod7.1=glm(m_cMS~Gr_size+Arena,data=D_data,family = quasipoisson)
summary(mod7.1)

Call:
glm(formula = m_cMS ~ Gr_size + Arena, family = quasipoisson, 
    data = D_data)

Deviance Residuals: 
    Min       1Q   Median       3Q      Max  
-1.8491  -0.5529   0.2162   0.2670   2.0373  

Coefficients:
            Estimate Std. Error t value Pr(>|t|)    
(Intercept)  0.50567    0.10574   4.782 5.92e-06 ***
Gr_sizeLG    0.03063    0.11416   0.268    0.789    
ArenaSmall  -0.02995    0.11346  -0.264    0.792    
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

(Dispersion parameter for quasipoisson family taken to be 0.5381483)

    Null deviance: 65.695  on 103  degrees of freedom
Residual deviance: 65.603  on 101  degrees of freedom
  (99 Beobachtungen als fehlend gelöscht)
AIC: NA

Number of Fisher Scoring iterations: 5

Anova(mod7.1,type=2)

Analysis of Deviance Table (Type II tests)

Response: m_cMS
        LR Chisq Df Pr(>Chisq)
Gr_size 0.072077  1     0.7883
Arena   0.069742  1     0.7917

Number of partners

Females

We calculated means and SE for each treatment:

Mean number of partners in small groups (SE) = 1.69 (0.12)

mean(D_data$f_cMS[D_data$Gr_size=='SG'],na.rm=T)
standard_error(D_data$f_cMS[D_data$Gr_size=='SG'])

Mean number of partners in large groups (SE) = 1.73 (0.2)

mean(D_data$f_cMS[D_data$Gr_size=='LG'],na.rm=T)
standard_error(D_data$f_cMS[D_data$Gr_size=='LG'])

Mean number of partners in large arena size (SE) = 1.86 (0.15)

mean(D_data$f_cMS[D_data$Arena=='Large'],na.rm=T)
standard_error(D_data$f_cMS[D_data$Arena=='Large'])

Mean number of partners in small arena size (SE) = 1.54 (0.17)

mean(D_data$f_cMS[D_data$Arena=='Small'],na.rm=T)
standard_error(D_data$f_cMS[D_data$Arena=='Small'])

GLM for number of partners including interaction between group and arena size treatment:

mod8=glm(f_cMS~Gr_size*Arena,data=D_data,family = quasipoisson)
Anova(mod8,type=2)

Analysis of Deviance Table (Type II tests)

Response: f_cMS
              LR Chisq Df Pr(>Chisq)
Gr_size        0.21523  1     0.6427
Arena          2.22688  1     0.1356
Gr_size:Arena  1.06086  1     0.3030

GLM for number of partners excluding interaction between group and arena size treatment:

mod8.1=glm(f_cMS~Gr_size+Arena,data=D_data,family = quasipoisson)
summary(mod8.1)

Call:
glm(formula = f_cMS ~ Gr_size + Arena, family = quasipoisson, 
    data = D_data)

Deviance Residuals: 
    Min       1Q   Median       3Q      Max  
-1.9676  -0.6646   0.1318   0.3968   2.1580  

Coefficients:
            Estimate Std. Error t value Pr(>|t|)    
(Intercept)  0.59849    0.10194   5.871 6.19e-08 ***
Gr_sizeLG    0.06204    0.13393   0.463    0.644    
ArenaSmall  -0.19970    0.13457  -1.484    0.141    
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

(Dispersion parameter for quasipoisson family taken to be 0.7318014)

    Null deviance: 89.305  on 98  degrees of freedom
Residual deviance: 87.650  on 96  degrees of freedom
  (104 Beobachtungen als fehlend gelöscht)
AIC: NA

Number of Fisher Scoring iterations: 5

Anova(mod8.1,type=2)

Analysis of Deviance Table (Type II tests)

Response: f_cMS
        LR Chisq Df Pr(>Chisq)
Gr_size   0.2142  1     0.6435
Arena     2.2162  1     0.1366

Number copulations

Males

We calculated means and SE for each treatment:

Mean number of copulations in small groups (SE) = 2.98 (0.31)

mean(D_data$m_TotMatings[D_data$Gr_size=='SG'],na.rm=T)
standard_error(D_data$m_TotMatings[D_data$Gr_size=='SG'])

Mean number of copulations in large groups (SE) = 2.21 (0.21)

mean(D_data$m_TotMatings[D_data$Gr_size=='LG'],na.rm=T)
standard_error(D_data$m_TotMatings[D_data$Gr_size=='LG'])

Mean number of copulations in large arena size (SE) = 2.6 (0.29)

mean(D_data$m_TotMatings[D_data$Arena=='Large'],na.rm=T)
standard_error(D_data$m_TotMatings[D_data$Arena=='Large'])

Mean number of copulations in small arena size (SE) = 2.49 (0.22)

mean(D_data$m_TotMatings[D_data$Arena=='Small'],na.rm=T)
standard_error(D_data$m_TotMatings[D_data$Arena=='Small'])

GLM for number of copulations including interaction between group and arena size treatment:

mod9=glm(m_TotMatings~Gr_size*Arena,data=D_data,family = quasipoisson)
Anova(mod9,type=2)

Analysis of Deviance Table (Type II tests)

Response: m_TotMatings
              LR Chisq Df Pr(>Chisq)  
Gr_size         4.8747  1    0.02725 *
Arena           0.4538  1    0.50056  
Gr_size:Arena   0.4756  1    0.49043  
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

GLM for number of copulations excluding interaction between group and arena size treatment:

mod9.1=glm(m_TotMatings~Gr_size+Arena,data=D_data,family = quasipoisson)
summary(mod9.1)

Call:
glm(formula = m_TotMatings ~ Gr_size + Arena, family = quasipoisson, 
    data = D_data)

Deviance Residuals: 
    Min       1Q   Median       3Q      Max  
-2.5067  -0.9610  -0.1962   0.4978   3.0725  

Coefficients:
            Estimate Std. Error t value Pr(>|t|)    
(Intercept)  1.14475    0.12532   9.135 7.21e-15 ***
Gr_sizeLG   -0.31599    0.14338  -2.204   0.0298 *  
ArenaSmall  -0.09653    0.14375  -0.672   0.5034    
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

(Dispersion parameter for quasipoisson family taken to be 1.321896)

    Null deviance: 146.28  on 103  degrees of freedom
Residual deviance: 139.74  on 101  degrees of freedom
  (99 Beobachtungen als fehlend gelöscht)
AIC: NA

Number of Fisher Scoring iterations: 5

Anova(mod9.1,type=2)

Analysis of Deviance Table (Type II tests)

Response: m_TotMatings
        LR Chisq Df Pr(>Chisq)  
Gr_size   4.8548  1    0.02757 *
Arena     0.4519  1    0.50143  
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Females

We calculated means and SE for each treatment:

Mean number of copulations in small groups (SE) = 2.83 (0.3)

mean(D_data$f_TotMatings[D_data$Gr_size=='SG'],na.rm=T)
standard_error(D_data$f_TotMatings[D_data$Gr_size=='SG'])

Mean number of copulations in large groups (SE) = 1.98 (0.25)

mean(D_data$f_TotMatings[D_data$Gr_size=='LG'],na.rm=T)
standard_error(D_data$f_TotMatings[D_data$Gr_size=='LG'])

Mean number of copulations in large arena size (SE) = 2.78 (0.29)

mean(D_data$f_TotMatings[D_data$Arena=='Large'],na.rm=T)
standard_error(D_data$f_TotMatings[D_data$Arena=='Large'])

Mean number of copulations in small arena size (SE) = 2.08 (0.27)

mean(D_data$f_TotMatings[D_data$Arena=='Small'],na.rm=T)
standard_error(D_data$f_TotMatings[D_data$Arena=='Small'])

GLM for number of copulations including interaction between group and arena size treatment:

mod10=glm(f_TotMatings~Gr_size*Arena,data=D_data,family = quasipoisson)
Anova(mod10,type=2)

Analysis of Deviance Table (Type II tests)

Response: f_TotMatings
              LR Chisq Df Pr(>Chisq)  
Gr_size         3.6584  1    0.05579 .
Arena           2.0796  1    0.14928  
Gr_size:Arena   0.3714  1    0.54226  
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

GLM for number of copulations excluding interaction between group and arena size treatment:

mod10.1=glm(f_TotMatings~Gr_size+Arena,data=D_data,family = quasipoisson)
summary(mod10.1)

Call:
glm(formula = f_TotMatings ~ Gr_size + Arena, family = quasipoisson, 
    data = D_data)

Deviance Residuals: 
    Min       1Q   Median       3Q      Max  
-2.4900  -1.0500  -0.1716   0.6617   3.6136  

Coefficients:
            Estimate Std. Error t value Pr(>|t|)    
(Intercept)   1.1314     0.1161   9.742 5.33e-16 ***
Gr_sizeLG    -0.3193     0.1683  -1.898   0.0607 .  
ArenaSmall   -0.2372     0.1648  -1.440   0.1532    
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

(Dispersion parameter for quasipoisson family taken to be 1.550111)

    Null deviance: 171.30  on 98  degrees of freedom
Residual deviance: 160.59  on 96  degrees of freedom
  (104 Beobachtungen als fehlend gelöscht)
AIC: NA

Number of Fisher Scoring iterations: 5

Anova(mod10.1,type=2)

Analysis of Deviance Table (Type II tests)

Response: f_TotMatings
        LR Chisq Df Pr(>Chisq)  
Gr_size   3.6862  1    0.05486 .
Arena     2.0954  1    0.14774  
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Copulations per partner

Males

We calculated means and SE for each treatment:

Copulations per partner in small groups (SE) = 1.79 (0.12)

mean(D_data$m_meanCop[D_data$Gr_size=='SG'],na.rm=T)
standard_error(D_data$m_meanCop[D_data$Gr_size=='SG'])

Copulations per partner in large groups (SE) = 1.33 (0.09)

mean(D_data$m_meanCop[D_data$Gr_size=='LG'],na.rm=T)
standard_error(D_data$m_meanCop[D_data$Gr_size=='LG'])

Copulations per partner in large arena size (SE) = 1.51 (0.11)

mean(D_data$m_meanCop[D_data$Arena=='Large'],na.rm=T)
standard_error(D_data$m_meanCop[D_data$Arena=='Large'])

Copulations per partner in small arena size (SE) = 1.57 (0.1)

mean(D_data$m_meanCop[D_data$Arena=='Small'],na.rm=T)
standard_error(D_data$m_meanCop[D_data$Arena=='Small'])

GLM for copulations per partner including interaction between group and arena size treatment:

mod11=glm(cbind(m_cMS,m_TotMatings)~Gr_size*Arena,data=D_data,family = quasibinomial)
Anova(mod11,type=2)

Analysis of Deviance Table (Type II tests)

Response: cbind(m_cMS, m_TotMatings)
              LR Chisq Df Pr(>Chisq)    
Gr_size        17.5682  1  2.772e-05 ***
Arena           0.4728  1     0.4917    
Gr_size:Arena   1.6703  1     0.1962    
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

GLM for copulations per partner excluding interaction between group and arena size treatment:

mod11.1=glm(cbind(m_cMS,m_TotMatings)~Gr_size+Arena,data=D_data,family = quasibinomial)
summary(mod11.1)

Call:
glm(formula = cbind(m_cMS, m_TotMatings) ~ Gr_size + Arena, family = quasibinomial, 
    data = D_data)

Deviance Residuals: 
    Min       1Q   Median       3Q      Max  
-1.1268  -0.1276   0.1645   0.2889   0.7831  

Coefficients:
            Estimate Std. Error t value Pr(>|t|)    
(Intercept) -0.63273    0.07417  -8.531 3.02e-13 ***
Gr_sizeLG    0.34336    0.08231   4.172 6.90e-05 ***
ArenaSmall   0.05651    0.08236   0.686    0.494    
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

(Dispersion parameter for quasibinomial family taken to be 0.1717382)

    Null deviance: 18.871  on 93  degrees of freedom
Residual deviance: 15.865  on 91  degrees of freedom
  (99 Beobachtungen als fehlend gelöscht)
AIC: NA

Number of Fisher Scoring iterations: 3

Anova(mod11.1,type=2)

Analysis of Deviance Table (Type II tests)

Response: cbind(m_cMS, m_TotMatings)
        LR Chisq Df Pr(>Chisq)    
Gr_size  17.4924  1  2.885e-05 ***
Arena     0.4708  1     0.4926    
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Females

We calculated means and SE for each treatment:

Copulations per partner in small groups (SE) = 1.61 (0.1)

mean(D_data$f_meanCop[D_data$Gr_size=='SG'],na.rm=T)
standard_error(D_data$f_meanCop[D_data$Gr_size=='SG'])

Copulations per partner in large groups (SE) = 1.13 (0.06)

mean(D_data$f_meanCop[D_data$Gr_size=='LG'],na.rm=T)
standard_error(D_data$f_meanCop[D_data$Gr_size=='LG'])

Copulations per partner in large arena size (SE) = 1.42 (0.07)

mean(D_data$f_meanCop[D_data$Arena=='Large'],na.rm=T)
standard_error(D_data$f_meanCop[D_data$Arena=='Large'])

Copulations per partner in small arena size (SE) = 1.4 (0.13)

mean(D_data$f_meanCop[D_data$Arena=='Small'],na.rm=T)
standard_error(D_data$f_meanCop[D_data$Arena=='Small'])

GLM for copulations per partner including interaction between group and arena size treatment:

mod12=glm(cbind(f_cMS,f_TotMatings)~Gr_size*Arena,data=D_data,family = quasibinomial)
Anova(mod12,type=2)

Analysis of Deviance Table (Type II tests)

Response: cbind(f_cMS, f_TotMatings)
              LR Chisq Df Pr(>Chisq)    
Gr_size        25.4678  1  4.498e-07 ***
Arena           0.0412  1     0.8391    
Gr_size:Arena   0.2179  1     0.6407    
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

GLM for copulations per partner excluding interaction between group and arena size treatment:

mod12.1=glm(cbind(f_cMS,f_TotMatings)~Gr_size+Arena,data=D_data,family = quasibinomial)
summary(mod12.1)

Call:
glm(formula = cbind(f_cMS, f_TotMatings) ~ Gr_size + Arena, family = quasibinomial, 
    data = D_data)

Deviance Residuals: 
     Min        1Q    Median        3Q       Max  
-1.11605  -0.14258   0.09933   0.15315   1.23097  

Coefficients:
            Estimate Std. Error t value Pr(>|t|)    
(Intercept) -0.52471    0.05415  -9.690 3.45e-15 ***
Gr_sizeLG    0.38418    0.07577   5.070 2.46e-06 ***
ArenaSmall   0.01544    0.07563   0.204    0.839    
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

(Dispersion parameter for quasibinomial family taken to be 0.1311458)

    Null deviance: 14.763  on 83  degrees of freedom
Residual deviance: 11.140  on 81  degrees of freedom
  (104 Beobachtungen als fehlend gelöscht)
AIC: NA

Number of Fisher Scoring iterations: 3

Anova(mod12.1,type=2)

Analysis of Deviance Table (Type II tests)

Response: cbind(f_cMS, f_TotMatings)
        LR Chisq Df Pr(>Chisq)    
Gr_size  25.7447  1  3.897e-07 ***
Arena     0.0417  1     0.8383    
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Copulation duration

Males

We calculated means and SE for each treatment:

Copulation duration in small groups (SE) = 78.98 (4.88)

mean(D_data$m_MatingDuration_av[D_data$Gr_size=='SG'],na.rm=T)
standard_error(D_data$m_MatingDuration_av[D_data$Gr_size=='SG'])

Copulation duration in large groups (SE) = 74.8 (4.7)

mean(D_data$m_MatingDuration_av[D_data$Gr_size=='LG'],na.rm=T)
standard_error(D_data$m_MatingDuration_av[D_data$Gr_size=='LG'])

Copulation duration in large arena size (SE) = 77.41 (5.15)

mean(D_data$m_MatingDuration_av[D_data$Arena=='Large'],na.rm=T)
standard_error(D_data$m_MatingDuration_av[D_data$Arena=='Large'])

Copulation duration in small arena size (SE) = 75.95 (4.35)

mean(D_data$m_MatingDuration_av[D_data$Arena=='Small'],na.rm=T)
standard_error(D_data$m_MatingDuration_av[D_data$Arena=='Small'])

GLM for copulation duration including interaction between group and arena size treatment:

mod13=glm(m_MatingDuration_av~Gr_size*Arena,data=D_data,family = gaussian)
Anova(mod13,type=2)

Analysis of Deviance Table (Type II tests)

Response: m_MatingDuration_av
              LR Chisq Df Pr(>Chisq)
Gr_size        0.43450  1     0.5098
Arena          0.11108  1     0.7389
Gr_size:Arena  0.04691  1     0.8285

GLM for copulation duration excluding interaction between group and arena size treatment:

mod13.1=glm(m_MatingDuration_av~Gr_size+Arena,data=D_data,family = gaussian)
summary(mod13.1)

Call:
glm(formula = m_MatingDuration_av ~ Gr_size + Arena, family = gaussian, 
    data = D_data)

Deviance Residuals: 
    Min       1Q   Median       3Q      Max  
-44.333  -21.092   -9.408   10.981  111.783  

Coefficients:
            Estimate Std. Error t value Pr(>|t|)    
(Intercept)   80.333      6.457  12.441   <2e-16 ***
Gr_sizeLG     -4.615      6.965  -0.663    0.509    
ArenaSmall    -2.327      6.946  -0.335    0.738    
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

(Dispersion parameter for gaussian family taken to be 1091.532)

    Null deviance: 99859  on 93  degrees of freedom
Residual deviance: 99329  on 91  degrees of freedom
  (109 Beobachtungen als fehlend gelöscht)
AIC: 929.27

Number of Fisher Scoring iterations: 2

Anova(mod13.1,type=2)

Analysis of Deviance Table (Type II tests)

Response: m_MatingDuration_av
        LR Chisq Df Pr(>Chisq)
Gr_size  0.43909  1     0.5076
Arena    0.11225  1     0.7376

Females

We calculated means and SE for each treatment:

Copulation duration in small groups (SE) = 84.13 (6.9)

mean(D_data$f_MatingDuration_av[D_data$Gr_size=='SG'],na.rm=T)
standard_error(D_data$f_MatingDuration_av[D_data$Gr_size=='SG'])

Copulation duration in large groups (SE) = 68.47 (3.35)

mean(D_data$f_MatingDuration_av[D_data$Gr_size=='LG'],na.rm=T)
standard_error(D_data$f_MatingDuration_av[D_data$Gr_size=='LG'])

Copulation duration in large arena size (SE) = 80.27 (7.07)

mean(D_data$f_MatingDuration_av[D_data$Arena=='Large'],na.rm=T)
standard_error(D_data$f_MatingDuration_av[D_data$Arena=='Large'])

Copulation duration in small arena size (SE) = 74.55 (4.38)

mean(D_data$f_MatingDuration_av[D_data$Arena=='Small'],na.rm=T)
standard_error(D_data$f_MatingDuration_av[D_data$Arena=='Small'])

GLM for copulation duration including interaction between group and arena size treatment:

mod14=glm(f_MatingDuration_av~Gr_size*Arena,data=D_data,family = gaussian)
Anova(mod14,type=2)

Analysis of Deviance Table (Type II tests)

Response: f_MatingDuration_av
              LR Chisq Df Pr(>Chisq)  
Gr_size        2.90863  1    0.08811 .
Arena          0.17022  1    0.67991  
Gr_size:Arena  1.20855  1    0.27162  
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

GLM for copulation duration excluding interaction between group and arena size treatment:

mod14.1=glm(f_MatingDuration_av~Gr_size+Arena,data=D_data,family = gaussian)
summary(mod14.1)

Call:
glm(formula = f_MatingDuration_av ~ Gr_size + Arena, family = gaussian, 
    data = D_data)

Deviance Residuals: 
    Min       1Q   Median       3Q      Max  
-45.913  -21.965   -7.868   11.425  252.462  

Coefficients:
            Estimate Std. Error t value Pr(>|t|)    
(Intercept)   85.538      6.587  12.986   <2e-16 ***
Gr_sizeLG    -15.145      8.892  -1.703   0.0924 .  
ArenaSmall    -3.625      8.797  -0.412   0.6814    
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

(Dispersion parameter for gaussian family taken to be 1555.818)

    Null deviance: 129651  on 82  degrees of freedom
Residual deviance: 124465  on 80  degrees of freedom
  (120 Beobachtungen als fehlend gelöscht)
AIC: 850.52

Number of Fisher Scoring iterations: 2

Anova(mod14.1,type=2)

Analysis of Deviance Table (Type II tests)

Response: f_MatingDuration_av
        LR Chisq Df Pr(>Chisq)  
Gr_size  2.90107  1    0.08852 .
Arena    0.16978  1    0.68031  
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Reproductive success

Males

We calculated means and SE for each treatment:

Reproductive success in small groups (SE) = 64.2 (9.45)

mean(D_data$m_RS[D_data$Gr_size=='SG'],na.rm=T)
standard_error(D_data$m_RS[D_data$Gr_size=='SG'])

Reproductive success in large groups (SE) = 49.29 (6.14)

mean(D_data$m_RS[D_data$Gr_size=='LG'],na.rm=T)
standard_error(D_data$m_RS[D_data$Gr_size=='LG'])

Reproductive success in large arena size (SE) = 51.24 (6.2)

mean(D_data$m_RS[D_data$Arena=='Large'],na.rm=T)
standard_error(D_data$m_RS[D_data$Arena=='Large'])

Reproductive success in small arena size (SE) = 61.1 (9.19)

mean(D_data$m_RS[D_data$Arena=='Small'],na.rm=T)
standard_error(D_data$m_RS[D_data$Arena=='Small'])

GLM for reproductive success including interaction between group and arena size treatment:

mod15=glm(m_RS~Gr_size*Arena,data=D_data,family = quasipoisson)
Anova(mod15,type=2)

Analysis of Deviance Table (Type II tests)

Response: m_RS
              LR Chisq Df Pr(>Chisq)
Gr_size        1.56524  1     0.2109
Arena          0.49398  1     0.4822
Gr_size:Arena  1.22773  1     0.2678

GLM for reproductive success excluding interaction between group and arena size treatment:

mod15.1=glm(m_RS~Gr_size+Arena,data=D_data,family = quasipoisson)
summary(mod15.1)

Call:
glm(formula = m_RS ~ Gr_size + Arena, family = quasipoisson, 
    data = D_data)

Deviance Residuals: 
     Min        1Q    Median        3Q       Max  
-11.6566   -9.6552   -0.6194    4.4727   21.1646  

Coefficients:
            Estimate Std. Error t value Pr(>|t|)    
(Intercept)   4.0831     0.1767  23.111   <2e-16 ***
Gr_sizeLG    -0.2413     0.1932  -1.249    0.215    
ArenaSmall    0.1355     0.1933   0.701    0.485    
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

(Dispersion parameter for quasipoisson family taken to be 52.69429)

    Null deviance: 6253.5  on 103  degrees of freedom
Residual deviance: 6126.4  on 101  degrees of freedom
  (99 Beobachtungen als fehlend gelöscht)
AIC: NA

Number of Fisher Scoring iterations: 5

Anova(mod15.1,type=2)

Analysis of Deviance Table (Type II tests)

Response: m_RS
        LR Chisq Df Pr(>Chisq)
Gr_size  1.55751  1     0.2120
Arena    0.49155  1     0.4832

Females

We calculated means and SE for each treatment:

Reproductive success in small groups (SE) = 51.59 (6.91)

mean(D_data$f_RS[D_data$Gr_size=='SG'],na.rm=T)
standard_error(D_data$f_RS[D_data$Gr_size=='SG'])

Reproductive success in large groups (SE) = 48.93 (7.11)

mean(D_data$f_RS[D_data$Gr_size=='LG'],na.rm=T)
standard_error(D_data$f_RS[D_data$Gr_size=='LG'])

Reproductive success in large arena size (SE) = 52.76 (7.04)

mean(D_data$f_RS[D_data$Arena=='Large'],na.rm=T)
standard_error(D_data$f_RS[D_data$Arena=='Large'])

Reproductive success in small arena size (SE) = 47.85 (7)

mean(D_data$f_RS[D_data$Arena=='Small'],na.rm=T)
standard_error(D_data$f_RS[D_data$Arena=='Small'])

GLM for reproductive success including interaction between group and arena size treatment:

mod16=glm(f_RS~Gr_size*Arena,data=D_data,family = quasipoisson)
Anova(mod16,type=2)

Analysis of Deviance Table (Type II tests)

Response: f_RS
              LR Chisq Df Pr(>Chisq)
Gr_size        0.03445  1     0.8528
Arena          0.20764  1     0.6486
Gr_size:Arena  1.54223  1     0.2143

GLM for reproductive success excluding interaction between group and arena size treatment:

mod16.1=glm(f_RS~Gr_size+Arena,data=D_data,family = quasipoisson)
summary(mod16.1)

Call:
glm(formula = f_RS ~ Gr_size + Arena, family = quasipoisson, 
    data = D_data)

Deviance Residuals: 
     Min        1Q    Median        3Q       Max  
-10.3437   -9.8817    0.8721    5.6151    9.5213  

Coefficients:
            Estimate Std. Error t value Pr(>|t|)    
(Intercept)  3.97960    0.15361  25.907   <2e-16 ***
Gr_sizeLG   -0.03737    0.20234  -0.185    0.854    
ArenaSmall  -0.09137    0.20157  -0.453    0.651    
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

(Dispersion parameter for quasipoisson family taken to be 48.91479)

    Null deviance: 6268.7  on 98  degrees of freedom
Residual deviance: 6255.2  on 96  degrees of freedom
  (104 Beobachtungen als fehlend gelöscht)
AIC: NA

Number of Fisher Scoring iterations: 5

Anova(mod16.1,type=2)

Analysis of Deviance Table (Type II tests)

Response: f_RS
        LR Chisq Df Pr(>Chisq)
Gr_size  0.03416  1     0.8534
Arena    0.20588  1     0.6500

Calculate adjusted p-values

We calculated adjusted p-values within sex and treatment using the false discovery rate correction (FDR).

Adjusted p-values for population size treatment effect in females:

tab1=data.table(round(p.adjust(cbind(Anova(mod4.1,type=2)[1,3],Anova(mod6.1,type=2)[1,3],Anova(mod8.1,type=2)[1,3],Anova(mod10.1,type=2)[1,3],Anova(mod12.1,type=2)[1,3],Anova(mod14.1,type=2)[1,3],Anova(mod16.1,type=2)[1,3]), method = 'fdr'),digit=3)) # Compute FDR corrected p-values
traits=rbind('Copulation attempts','Copulations per attempt','Number of partners','Number copulations','Copulations per partner','Copulation duration','Reproductive success')
tab1= cbind(traits,tab1)
colnames(tab1)= cbind('Trait','Adj. p-value')
tab1

                     Trait Adj. p-value
1:     Copulation attempts        0.015
2: Copulations per attempt        0.936
3:      Number of partners        0.901
4:      Number copulations        0.128
5: Copulations per partner        0.000
6:     Copulation duration        0.155
7:    Reproductive success        0.936

Adjusted p-values for arena size treatment effect in females:

tab2=data.table(round(p.adjust(cbind(Anova(mod4.1,type=2)[2,3],Anova(mod6.1,type=2)[2,3],Anova(mod8.1,type=2)[2,3],Anova(mod10.1,type=2)[2,3],Anova(mod12.1,type=2)[2,3],Anova(mod14.1,type=2)[2,3],Anova(mod16.1,type=2)[2,3]), method = 'fdr'),digit=3)) # Compute FDR corrected p-values
tab2= cbind(traits,tab2)
colnames(tab2)= cbind('Trait','Adj. p-value')
tab2

                     Trait Adj. p-value
1:     Copulation attempts        0.794
2: Copulations per attempt        0.477
3:      Number of partners        0.477
4:      Number copulations        0.477
5: Copulations per partner        0.838
6:     Copulation duration        0.794
7:    Reproductive success        0.794

Adjusted p-values for population size treatment effect in males:

tab3=data.table(round(p.adjust(cbind(Anova(mod3.1,type=2)[1,3],Anova(mod5.1,type=2)[1,3],Anova(mod7.1,type=2)[1,3],Anova(mod9.1,type=2)[1,3],Anova(mod11.1,type=2)[1,3],Anova(mod13.1,type=2)[1,3],Anova(mod15.1,type=2)[1,3]), method = 'fdr'),digit=3)) # Compute FDR corrected p-values
tab3= cbind(traits,tab3)
colnames(tab3)= cbind('Trait','Adj. p-value')
tab3

                     Trait Adj. p-value
1:     Copulation attempts        0.002
2: Copulations per attempt        0.773
3:      Number of partners        0.788
4:      Number copulations        0.064
5: Copulations per partner        0.000
6:     Copulation duration        0.711
7:    Reproductive success        0.371

Adjusted p-values for population size treatment effect in males:

tab4=data.table(round(p.adjust(cbind(Anova(mod3.1,type=2)[2,3],Anova(mod5.1,type=2)[2,3],Anova(mod7.1,type=2)[2,3],Anova(mod9.1,type=2)[2,3],Anova(mod11.1,type=2)[2,3],Anova(mod13.1,type=2)[2,3],Anova(mod15.1,type=2)[2,3]), method = 'fdr'),digit=3)) # Compute FDR corrected p-values
tab4= cbind(traits,tab4)
colnames(tab4)= cbind('Trait','Adj. p-value')
tab4

                     Trait Adj. p-value
1:     Copulation attempts        0.819
2: Copulations per attempt        0.819
3:      Number of partners        0.819
4:      Number copulations        0.819
5: Copulations per partner        0.819
6:     Copulation duration        0.819
7:    Reproductive success        0.819

Figures for mating behaviour and reproductive success (Figure S2-S4)

Here we plotted the mating behaviour and reproductive success per treatment and sex.

## Figures for mating behaviour and reproductive success (Figure S2-S4) ####

# Create factor for treatment categories
D_data$TreatCgroup <- factor(paste(D_data$Sex,D_data$Gr_size,  sep=" "), levels = c("F SG", "F LG", "M SG",'M LG'))
D_data$TreatCarena <- factor(paste(D_data$Sex,D_data$Arena,  sep=" "), levels = c("F Large", "F Small", "M Large",'M Small'))

p3<-ggplot(D_data, aes(x=Sex, y=as.numeric(Total_Encounters),fill=TreatCgroup, col=TreatCgroup,alpha=TreatCgroup)) +
  geom_point(position=position_jitterdodge(jitter.width=0.5,jitter.height = 0,dodge.width=1.2),shape=19, size = 2)+
  stat_summary(fun.min =lower_CI ,
               fun.max = upper_CI ,fun = mean,
               position=position_dodge2(0.3),col=c("#b2182b","#b2182b","#2166AC","#2166AC"),alpha=c(0.5,0.75,0.5,0.75),show.legend = F, linewidth = 1.15)+
  stat_summary(fun = mean,
               position=position_dodge2(0.3), size = 1,col=c("white","white","white","white"),alpha=c(1,1,1,1),show.legend = F, stroke = 0,linewidth = 1.2)+
  stat_summary(fun = mean,
               position=position_dodge2(0.3), size = 1,col=c("#b2182b","#b2182b","#2166AC","#2166AC"),alpha=c(0.5,0.75,0.5,0.75),show.legend = F, stroke = 0,linewidth = 1.2)+
 scale_color_manual(values=c(colpal2[1],colpal2[1],colpal2[2],colpal2[2]),name = "Treatment", labels = c('Small group','Large group','Small group','Large group'))+
  scale_fill_manual(values=c(colpal2[1],colpal2[1],colpal2[2],colpal2[2]),name = "Treatment", labels = c('Small group','Large group','Small group','Large group'))+
  scale_alpha_manual(values=c(0.5,0.75,0.5,0.75),name = "Treatment", labels = c('Small group','Large group','Small group','Large group'))+
  xlab('Sex')+ylab("Copulation attempts")+ggtitle('')+ theme(plot.title = element_text(hjust = 0.5))+
  scale_x_discrete(labels = c('Female','Male'),drop=FALSE)+ ylim(0,30)+labs(tag = "C")+
  annotate("text",label='n =',x=0.55,y=30,size=4)+
  annotate("text",label='54',x=0.78,y=30,size=4)+
  annotate("text",label='45',x=1.23,y=30,size=4)+
  annotate("text",label='46',x=1.78,y=30,size=4)+
  annotate("text",label='58',x=2.23,y=30,size=4)+
  guides(colour = guide_legend(override.aes = list(size=4)))+
  fig_theme

p3.2<-ggplot(D_data, aes(x=Sex, y=as.numeric(Total_Encounters),fill=TreatCarena, col=TreatCarena,alpha=TreatCarena)) +
  geom_point(position=position_jitterdodge(jitter.width=0.5,jitter.height = 0,dodge.width=1.2),shape=19, size = 2)+
  stat_summary(fun.min =lower_CI ,
               fun.max = upper_CI ,fun = mean,
               position=position_dodge2(0.3),col=c("#b2182b","#b2182b","#2166AC","#2166AC"),alpha=c(0.5,0.75,0.5,0.75),show.legend = F, linewidth = 1.15)+
  stat_summary(fun = mean,
               position=position_dodge2(0.3), size = 1,col=c("white","white","white","white"),alpha=c(1,1,1,1),show.legend = F, stroke = 0,linewidth = 1.2)+
  stat_summary(fun = mean,
               position=position_dodge2(0.3), size = 1,col=c("#b2182b","#b2182b","#2166AC","#2166AC"),alpha=c(0.5,0.75,0.5,0.75),show.legend = F, stroke = 0,linewidth = 1.2)+
scale_color_manual(values=c(colpal2[1],colpal2[1],colpal2[2],colpal2[2]),name = "Treatment", labels = c('Small arena','Large arena','Small arena','Large arena'))+
  scale_fill_manual(values=c(colpal2[1],colpal2[1],colpal2[2],colpal2[2]),name = "Treatment", labels = c('Small arena','Large arena','Small arena','Large arena'))+
  scale_alpha_manual(values=c(0.5,0.75,0.5,0.75),name = "Treatment", labels = c('Small arena','Large arena','Small arena','Large arena'))+
  xlab('Sex')+ylab("")+ggtitle('')+ theme(plot.title = element_text(hjust = 0.5))+
  scale_x_discrete(labels = c('Female','Male'),drop=FALSE)+ ylim(0,30)+labs(tag = "D")+
  annotate("text",label='n =',x=0.55,y=30,size=4)+
  annotate("text",label='51',x=0.78,y=30,size=4)+
  annotate("text",label='48',x=1.23,y=30,size=4)+
  annotate("text",label='55',x=1.78,y=30,size=4)+
  annotate("text",label='49',x=2.23,y=30,size=4)+
  guides(colour = guide_legend(override.aes = list(size=4)))+
  fig_theme

### Plot: Copulations per attempt ####
p4<-ggplot(D_data, aes(x=Sex, y=as.numeric(Prop_MS),fill=TreatCgroup, col=TreatCgroup,alpha=TreatCgroup)) +
  geom_point(position=position_jitterdodge(jitter.width=0.5,jitter.height = 0,dodge.width=1.2),shape=19, size = 2)+
  stat_summary(fun.min =lower_CI ,
               fun.max = upper_CI ,fun = mean,
               position=position_dodge2(0.3),col=c("#b2182b","#b2182b","#2166AC","#2166AC"),alpha=c(0.5,0.75,0.5,0.75),show.legend = F, linewidth = 1.15)+
  stat_summary(fun = mean,
               position=position_dodge2(0.3), size = 1,col=c("white","white","white","white"),alpha=c(1,1,1,1),show.legend = F, stroke = 0,linewidth = 1.2)+
  stat_summary(fun = mean,
               position=position_dodge2(0.3), size = 1,col=c("#b2182b","#b2182b","#2166AC","#2166AC"),alpha=c(0.5,0.75,0.5,0.75),show.legend = F, stroke = 0,linewidth = 1.2)+
  scale_color_manual(values=c(colpal2[1],colpal2[1],colpal2[2],colpal2[2]),name = "Treatment", labels = c('Small group','Large group','Small group','Large group'))+
  scale_fill_manual(values=c(colpal2[1],colpal2[1],colpal2[2],colpal2[2]),name = "Treatment", labels = c('Small group','Large group','Small group','Large group'))+
  scale_alpha_manual(values=c(0.5,0.75,0.5,0.75),name = "Treatment", labels = c('Small group','Large group','Small group','Large group'))+
  xlab('')+ylab("Copulations per attempt")+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='54',x=0.78,y=1.1,size=4)+
  annotate("text",label='45',x=1.23,y=1.1,size=4)+
  annotate("text",label='46',x=1.78,y=1.1,size=4)+
  annotate("text",label='58',x=2.23,y=1.1,size=4)+
  guides(colour = guide_legend(override.aes = list(size=4)))+
  fig_theme

p4.2<-ggplot(D_data, aes(x=Sex, y=as.numeric(Prop_MS),fill=TreatCarena, col=TreatCarena,alpha=TreatCarena)) +
  geom_point(position=position_jitterdodge(jitter.width=0.5,jitter.height = 0,dodge.width=1.2),shape=19, size = 2)+
  stat_summary(fun.min =lower_CI ,
               fun.max = upper_CI ,fun = mean,
               position=position_dodge2(0.3),col=c("#b2182b","#b2182b","#2166AC","#2166AC"),alpha=c(0.5,0.75,0.5,0.75),show.legend = F, linewidth = 1.15)+
  stat_summary(fun = mean,
               position=position_dodge2(0.3), size = 1,col=c("white","white","white","white"),alpha=c(1,1,1,1),show.legend = F, stroke = 0,linewidth = 1.2)+
  stat_summary(fun = mean,
               position=position_dodge2(0.3), size = 1,col=c("#b2182b","#b2182b","#2166AC","#2166AC"),alpha=c(0.5,0.75,0.5,0.75),show.legend = F, stroke = 0,linewidth = 1.2)+
  scale_color_manual(values=c(colpal2[1],colpal2[1],colpal2[2],colpal2[2]),name = "Treatment", labels = c('Small arena','Large arena','Small arena','Large arena'))+
  scale_fill_manual(values=c(colpal2[1],colpal2[1],colpal2[2],colpal2[2]),name = "Treatment", labels = c('Small arena','Large arena','Small arena','Large arena'))+
  scale_alpha_manual(values=c(0.5,0.75,0.5,0.75),name = "Treatment", labels = c('Small arena','Large arena','Small arena','Large arena'))+
  xlab('')+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='51',x=0.78,y=1.1,size=4)+
  annotate("text",label='48',x=1.23,y=1.1,size=4)+
  annotate("text",label='55',x=1.78,y=1.1,size=4)+
  annotate("text",label='49',x=2.23,y=1.1,size=4)+
  guides(colour = guide_legend(override.aes = list(size=4)))+
  fig_theme

### Plot: Mating partners focal ####
p5<-ggplot(D_data, aes(x=Sex, y=as.numeric(MatingPartners_number),fill=TreatCgroup, col=TreatCgroup,alpha=TreatCgroup)) +
  geom_point(position=position_jitterdodge(jitter.width=0.5,jitter.height = 0,dodge.width=1.2),shape=19, size = 2)+
  stat_summary(fun.min =lower_CI ,
               fun.max = upper_CI ,fun = mean,
               position=position_dodge2(0.3),col=c("#b2182b","#b2182b","#2166AC","#2166AC"),alpha=c(0.5,0.75,0.5,0.75),show.legend = F, linewidth = 1.15)+
  stat_summary(fun = mean,
               position=position_dodge2(0.3), size = 1,col=c("white","white","white","white"),alpha=c(1,1,1,1),show.legend = F, stroke = 0,linewidth = 1.2)+
  stat_summary(fun = mean,
               position=position_dodge2(0.3), size = 1,col=c("#b2182b","#b2182b","#2166AC","#2166AC"),alpha=c(0.5,0.75,0.5,0.75),show.legend = F, stroke = 0,linewidth = 1.2)+
  scale_color_manual(values=c(colpal2[1],colpal2[1],colpal2[2],colpal2[2]),name = "Treatment", labels = c('Small group','Large group','Small group','Large group'))+
  scale_fill_manual(values=c(colpal2[1],colpal2[1],colpal2[2],colpal2[2]),name = "Treatment", labels = c('Small group','Large group','Small group','Large group'))+
  scale_alpha_manual(values=c(0.5,0.75,0.5,0.75),name = "Treatment", labels = c('Small group','Large group','Small group','Large group'))+
  xlab('')+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 = "C")+
  annotate("text",label='n =',x=0.55,y=5.4,size=4)+
  annotate("text",label='54',x=0.78,y=5.4,size=4)+
  annotate("text",label='45',x=1.23,y=5.4,size=4)+
  annotate("text",label='46',x=1.78,y=5.4,size=4)+
  annotate("text",label='58',x=2.23,y=5.4,size=4)+
  guides(colour = guide_legend(override.aes = list(size=4)))+
  fig_theme

p5.2<-ggplot(D_data, aes(x=Sex, y=as.numeric(MatingPartners_number),fill=TreatCarena, col=TreatCarena,alpha=TreatCarena)) +
  geom_point(position=position_jitterdodge(jitter.width=0.5,jitter.height = 0,dodge.width=1.2),shape=19, size = 2)+
  stat_summary(fun.min =lower_CI ,
               fun.max = upper_CI ,fun = mean,
               position=position_dodge2(0.3),col=c("#b2182b","#b2182b","#2166AC","#2166AC"),alpha=c(0.5,0.75,0.5,0.75),show.legend = F, linewidth = 1.15)+
  stat_summary(fun = mean,
               position=position_dodge2(0.3), size = 1,col=c("white","white","white","white"),alpha=c(1,1,1,1),show.legend = F, stroke = 0,linewidth = 1.2)+
  stat_summary(fun = mean,
               position=position_dodge2(0.3), size = 1,col=c("#b2182b","#b2182b","#2166AC","#2166AC"),alpha=c(0.5,0.75,0.5,0.75),show.legend = F, stroke = 0,linewidth = 1.2)+
  scale_color_manual(values=c(colpal2[1],colpal2[1],colpal2[2],colpal2[2]),name = "Treatment", labels = c('Small arena','Large arena','Small arena','Large arena'))+
  scale_fill_manual(values=c(colpal2[1],colpal2[1],colpal2[2],colpal2[2]),name = "Treatment", labels = c('Small arena','Large arena','Small arena','Large arena'))+
  scale_alpha_manual(values=c(0.5,0.75,0.5,0.75),name = "Treatment", labels = c('Small arena','Large arena','Small arena','Large arena'))+
  xlab('')+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 = "D")+
  annotate("text",label='n =',x=0.55,y=5.4,size=4)+
  annotate("text",label='51',x=0.78,y=5.4,size=4)+
  annotate("text",label='48',x=1.23,y=5.4,size=4)+
  annotate("text",label='55',x=1.78,y=5.4,size=4)+
  annotate("text",label='49',x=2.23,y=5.4,size=4)+
  guides(colour = guide_legend(override.aes = list(size=4)))+
  fig_theme

### Plot: Number copulations ####
p6<-ggplot(D_data, aes(x=Sex, y=as.numeric(Matings_number),fill=TreatCgroup, col=TreatCgroup,alpha=TreatCgroup)) +
  geom_point(position=position_jitterdodge(jitter.width=0.5,jitter.height = 0,dodge.width=1.2),shape=19, size = 2)+
  stat_summary(fun.min =lower_CI ,
               fun.max = upper_CI ,fun = mean,
               position=position_dodge2(0.3),col=c("#b2182b","#b2182b","#2166AC","#2166AC"),alpha=c(0.5,0.75,0.5,0.75),show.legend = F, linewidth = 1.15)+
  stat_summary(fun = mean,
               position=position_dodge2(0.3), size = 1,col=c("white","white","white","white"),alpha=c(1,1,1,1),show.legend = F, stroke = 0,linewidth = 1.2)+
  stat_summary(fun = mean,
               position=position_dodge2(0.3), size = 1,col=c("#b2182b","#b2182b","#2166AC","#2166AC"),alpha=c(0.5,0.75,0.5,0.75),show.legend = F, stroke = 0,linewidth = 1.2)+
  scale_color_manual(values=c(colpal2[1],colpal2[1],colpal2[2],colpal2[2]),name = "Treatment", labels = c('Small group','Large group','Small group','Large group'))+
  scale_fill_manual(values=c(colpal2[1],colpal2[1],colpal2[2],colpal2[2]),name = "Treatment", labels = c('Small group','Large group','Small group','Large group'))+
  scale_alpha_manual(values=c(0.5,0.75,0.5,0.75),name = "Treatment", labels = c('Small group','Large group','Small group','Large group'))+
  xlab('')+ylab("Number of copulations")+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='54',x=0.78,y=12,size=4)+
  annotate("text",label='45',x=1.23,y=12,size=4)+
  annotate("text",label='46',x=1.78,y=12,size=4)+
  annotate("text",label='58',x=2.23,y=12,size=4)+
  guides(colour = guide_legend(override.aes = list(size=4)))+
  fig_theme

p6.2<-ggplot(D_data, aes(x=Sex, y=as.numeric(Matings_number),fill=TreatCarena, col=TreatCarena,alpha=TreatCarena)) +
  geom_point(position=position_jitterdodge(jitter.width=0.5,jitter.height = 0,dodge.width=1.2),shape=19, size = 2)+
  stat_summary(fun.min =lower_CI ,
               fun.max = upper_CI ,fun = mean,
               position=position_dodge2(0.3),col=c("#b2182b","#b2182b","#2166AC","#2166AC"),alpha=c(0.5,0.75,0.5,0.75),show.legend = F, linewidth = 1.15)+
  stat_summary(fun = mean,
               position=position_dodge2(0.3), size = 1,col=c("white","white","white","white"),alpha=c(1,1,1,1),show.legend = F, stroke = 0,linewidth = 1.2)+
  stat_summary(fun = mean,
               position=position_dodge2(0.3), size = 1,col=c("#b2182b","#b2182b","#2166AC","#2166AC"),alpha=c(0.5,0.75,0.5,0.75),show.legend = F, stroke = 0,linewidth = 1.2)+
  scale_color_manual(values=c(colpal2[1],colpal2[1],colpal2[2],colpal2[2]),name = "Treatment", labels = c('Small arena','Large arena','Small arena','Large arena'))+
  scale_fill_manual(values=c(colpal2[1],colpal2[1],colpal2[2],colpal2[2]),name = "Treatment", labels = c('Small arena','Large arena','Small arena','Large arena'))+
  scale_alpha_manual(values=c(0.5,0.75,0.5,0.75),name = "Treatment", labels = c('Small arena','Large arena','Small arena','Large arena'))+
  xlab('')+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='51',x=0.78,y=12,size=4)+
  annotate("text",label='48',x=1.23,y=12,size=4)+
  annotate("text",label='55',x=1.78,y=12,size=4)+
  annotate("text",label='49',x=2.23,y=12,size=4)+
  guides(colour = guide_legend(override.aes = list(size=4)))+
  fig_theme

###Plot: Copulations per partner ####
p7<-ggplot(D_data, aes(x=Sex, y=as.numeric(meanCop),fill=TreatCgroup, col=TreatCgroup,alpha=TreatCgroup)) +
  geom_point(position=position_jitterdodge(jitter.width=0.5,jitter.height = 0,dodge.width=1.2),shape=19, size = 2)+
  stat_summary(fun.min =lower_CI ,
               fun.max = upper_CI ,fun = mean,
               position=position_dodge2(0.3),col=c("#b2182b","#b2182b","#2166AC","#2166AC"),alpha=c(0.5,0.75,0.5,0.75),show.legend = F, linewidth = 1.15)+
  stat_summary(fun = mean,
               position=position_dodge2(0.3), size = 1,col=c("white","white","white","white"),alpha=c(1,1,1,1),show.legend = F, stroke = 0,linewidth = 1.2)+
  stat_summary(fun = mean,
               position=position_dodge2(0.3), size = 1,col=c("#b2182b","#b2182b","#2166AC","#2166AC"),alpha=c(0.5,0.75,0.5,0.75),show.legend = F, stroke = 0,linewidth = 1.2)+
  scale_color_manual(values=c(colpal2[1],colpal2[1],colpal2[2],colpal2[2]),name = "Treatment", labels = c('Small group','Large group','Small group','Large group'))+
  scale_fill_manual(values=c(colpal2[1],colpal2[1],colpal2[2],colpal2[2]),name = "Treatment", labels = c('Small group','Large group','Small group','Large group'))+
  scale_alpha_manual(values=c(0.5,0.75,0.5,0.75),name = "Treatment", labels = c('Small group','Large group','Small group','Large group'))+
  xlab('Sex')+ylab("Copulations per partner")+ggtitle('')+ theme(plot.title = element_text(hjust = 0.5))+
  scale_x_discrete(labels = c('Female','Male'),drop=FALSE)+ labs(tag = "C")+ylim(1,4.5)+
  annotate("text",label='n =',x=0.55,y=4.5,size=4)+
  annotate("text",label='54',x=0.78,y=4.5,size=4)+
  annotate("text",label='45',x=1.23,y=4.5,size=4)+
  annotate("text",label='46',x=1.78,y=4.5,size=4)+
  annotate("text",label='58',x=2.23,y=4.5,size=4)+
  guides(colour = guide_legend(override.aes = list(size=4)))+
  fig_theme

p7.2<-ggplot(D_data, aes(x=Sex, y=as.numeric(meanCop),fill=TreatCarena, col=TreatCarena,alpha=TreatCarena)) +
  geom_point(position=position_jitterdodge(jitter.width=0.5,jitter.height = 0,dodge.width=1.2),shape=19, size = 2)+
  stat_summary(fun.min =lower_CI ,
               fun.max = upper_CI ,fun = mean,
               position=position_dodge2(0.3),col=c("#b2182b","#b2182b","#2166AC","#2166AC"),alpha=c(0.5,0.75,0.5,0.75),show.legend = F, linewidth = 1.15)+
  stat_summary(fun = mean,
               position=position_dodge2(0.3), size = 1,col=c("white","white","white","white"),alpha=c(1,1,1,1),show.legend = F, stroke = 0,linewidth = 1.2)+
  stat_summary(fun = mean,
               position=position_dodge2(0.3), size = 1,col=c("#b2182b","#b2182b","#2166AC","#2166AC"),alpha=c(0.5,0.75,0.5,0.75),show.legend = F, stroke = 0,linewidth = 1.2)+
  scale_color_manual(values=c(colpal2[1],colpal2[1],colpal2[2],colpal2[2]),name = "Treatment", labels = c('Small arena','Large arena','Small arena','Large arena'))+
  scale_fill_manual(values=c(colpal2[1],colpal2[1],colpal2[2],colpal2[2]),name = "Treatment", labels = c('Small arena','Large arena','Small arena','Large arena'))+
  scale_alpha_manual(values=c(0.5,0.75,0.5,0.75),name = "Treatment", labels = c('Small arena','Large arena','Small arena','Large arena'))+
  xlab('Sex')+ylab("")+ggtitle('')+ theme(plot.title = element_text(hjust = 0.5))+
  scale_x_discrete(labels = c('Female','Male'),drop=FALSE)+ ylim(1,4.5)+labs(tag = "D")+
  annotate("text",label='n =',x=0.55,y=4.5,size=4)+
  annotate("text",label='51',x=0.78,y=4.5,size=4)+
  annotate("text",label='48',x=1.23,y=4.5,size=4)+
  annotate("text",label='55',x=1.78,y=4.5,size=4)+
  annotate("text",label='49',x=2.23,y=4.5,size=4)+
  guides(colour = guide_legend(override.aes = list(size=4)))+
  fig_theme

### Plot: Copulation duration ####
p8<-ggplot(D_data, aes(x=Sex, y=as.numeric(MatingDuration_av),fill=TreatCgroup, col=TreatCgroup,alpha=TreatCgroup)) +
  geom_point(position=position_jitterdodge(jitter.width=0.5,jitter.height = 0,dodge.width=1.2),shape=19, size = 2)+
  stat_summary(fun.min =lower_CI ,
               fun.max = upper_CI ,fun = mean,
               position=position_dodge2(0.3),col=c("#b2182b","#b2182b","#2166AC","#2166AC"),alpha=c(0.5,0.75,0.5,0.75),show.legend = F, linewidth = 1.15)+
  stat_summary(fun = mean,
               position=position_dodge2(0.3), size = 1,col=c("white","white","white","white"),alpha=c(1,1,1,1),show.legend = F, stroke = 0,linewidth = 1.2)+
  stat_summary(fun = mean,
               position=position_dodge2(0.3), size = 1,col=c("#b2182b","#b2182b","#2166AC","#2166AC"),alpha=c(0.5,0.75,0.5,0.75),show.legend = F, stroke = 0,linewidth = 1.2)+
  scale_color_manual(values=c(colpal2[1],colpal2[1],colpal2[2],colpal2[2]),name = "Treatment", labels = c('Small group','Large group','Small group','Large group'))+
  scale_fill_manual(values=c(colpal2[1],colpal2[1],colpal2[2],colpal2[2]),name = "Treatment", labels = c('Small group','Large group','Small group','Large group'))+
  scale_alpha_manual(values=c(0.5,0.75,0.5,0.75),name = "Treatment", labels = c('Small group','Large group','Small group','Large group'))+
  xlab('Sex')+ylab("Mean copulation duration")+ggtitle('')+ theme(plot.title = element_text(hjust = 0.5))+
  scale_x_discrete(labels = c('Female','Male'),drop=FALSE)+ ylim(0,390)+labs(tag = "E")+
  annotate("text",label='n =',x=0.55,y=390,size=4)+
  annotate("text",label='49',x=0.78,y=390,size=4)+
  annotate("text",label='34',x=1.23,y=390,size=4)+
  annotate("text",label='43',x=1.78,y=390,size=4)+
  annotate("text",label='51',x=2.23,y=390,size=4)+
  guides(colour = guide_legend(override.aes = list(size=4)))+
  fig_theme

p8.2<-ggplot(D_data, aes(x=Sex, y=as.numeric(MatingDuration_av),fill=TreatCarena, col=TreatCarena,alpha=TreatCarena)) +
  geom_point(position=position_jitterdodge(jitter.width=0.5,jitter.height = 0,dodge.width=1.2),shape=19, size = 2)+
  stat_summary(fun.min =lower_CI ,
               fun.max = upper_CI ,fun = mean,
               position=position_dodge2(0.3),col=c("#b2182b","#b2182b","#2166AC","#2166AC"),alpha=c(0.5,0.75,0.5,0.75),show.legend = F, linewidth = 1.15)+
  stat_summary(fun = mean,
               position=position_dodge2(0.3), size = 1,col=c("white","white","white","white"),alpha=c(1,1,1,1),show.legend = F, stroke = 0,linewidth = 1.2)+
  stat_summary(fun = mean,
               position=position_dodge2(0.3), size = 1,col=c("#b2182b","#b2182b","#2166AC","#2166AC"),alpha=c(0.5,0.75,0.5,0.75),show.legend = F, stroke = 0,linewidth = 1.2)+
  scale_color_manual(values=c(colpal2[1],colpal2[1],colpal2[2],colpal2[2]),name = "Treatment", labels = c('Small arena','Large arena','Small arena','Large arena'))+
  scale_fill_manual(values=c(colpal2[1],colpal2[1],colpal2[2],colpal2[2]),name = "Treatment", labels = c('Small arena','Large arena','Small arena','Large arena'))+
  scale_alpha_manual(values=c(0.5,0.75,0.5,0.75),name = "Treatment", labels = c('Small arena','Large arena','Small arena','Large arena'))+
  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 = "F")+
  annotate("text",label='n =',x=0.55,y=390,size=4)+
  annotate("text",label='46',x=0.78,y=390,size=4)+
  annotate("text",label='37',x=1.23,y=390,size=4)+
  annotate("text",label='49',x=1.78,y=390,size=4)+
  annotate("text",label='45',x=2.23,y=390,size=4)+
  guides(colour = guide_legend(override.aes = list(size=4)))+
  fig_theme

###Plot: Reproductive success ####
p9<-ggplot(D_data, aes(x=Sex, y=as.numeric(Total_N_MTP1),fill=TreatCgroup, col=TreatCgroup,alpha=TreatCgroup)) +
  geom_point(position=position_jitterdodge(jitter.width=0.5,jitter.height = 0,dodge.width=1.2),shape=19, size = 2)+
  stat_summary(fun.min =lower_CI ,
               fun.max = upper_CI ,fun = mean,
               position=position_dodge2(0.3),col=c("#b2182b","#b2182b","#2166AC","#2166AC"),alpha=c(0.5,0.75,0.5,0.75),show.legend = F, linewidth = 1.15)+
  stat_summary(fun = mean,
               position=position_dodge2(0.3), size = 1,col=c("white","white","white","white"),alpha=c(1,1,1,1),show.legend = F, stroke = 0,linewidth = 1.2)+
  stat_summary(fun = mean,
               position=position_dodge2(0.3), size = 1,col=c("#b2182b","#b2182b","#2166AC","#2166AC"),alpha=c(0.5,0.75,0.5,0.75),show.legend = F, stroke = 0,linewidth = 1.2)+
 scale_color_manual(values=c(colpal2[1],colpal2[1],colpal2[2],colpal2[2]),name = "Treatment", labels = c('Small group','Large group','Small group','Large group'))+
  scale_fill_manual(values=c(colpal2[1],colpal2[1],colpal2[2],colpal2[2]),name = "Treatment", labels = c('Small group','Large group','Small group','Large group'))+
  scale_alpha_manual(values=c(0.5,0.75,0.5,0.75),name = "Treatment", labels = c('Small group','Large group','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='54',x=0.78,y=320,size=4)+
  annotate("text",label='45',x=1.23,y=320,size=4)+
  annotate("text",label='46',x=1.78,y=320,size=4)+
  annotate("text",label='58',x=2.23,y=320,size=4)+
  guides(colour = guide_legend(override.aes = list(size=4)))+
  fig_theme

p9.2<-ggplot(D_data, aes(x=Sex, y=as.numeric(Total_N_MTP1),fill=TreatCarena, col=TreatCarena,alpha=TreatCarena)) +
  geom_point(position=position_jitterdodge(jitter.width=0.5,jitter.height = 0,dodge.width=1.2),shape=19, size = 2)+
  stat_summary(fun.min =lower_CI ,
               fun.max = upper_CI ,fun = mean,
               position=position_dodge2(0.3),col=c("#b2182b","#b2182b","#2166AC","#2166AC"),alpha=c(0.5,0.75,0.5,0.75),show.legend = F, linewidth = 1.15)+
  stat_summary(fun = mean,
               position=position_dodge2(0.3), size = 1,col=c("white","white","white","white"),alpha=c(1,1,1,1),show.legend = F, stroke = 0,linewidth = 1.2)+
  stat_summary(fun = mean,
               position=position_dodge2(0.3), size = 1,col=c("#b2182b","#b2182b","#2166AC","#2166AC"),alpha=c(0.5,0.75,0.5,0.75),show.legend = F, stroke = 0,linewidth = 1.2)+
  scale_color_manual(values=c(colpal2[1],colpal2[1],colpal2[2],colpal2[2]),name = "Treatment", labels = c('Small arena','Large arena','Small arena','Large arena'))+
  scale_fill_manual(values=c(colpal2[1],colpal2[1],colpal2[2],colpal2[2]),name = "Treatment", labels = c('Small arena','Large arena','Small arena','Large arena'))+
  scale_alpha_manual(values=c(0.5,0.75,0.5,0.75),name = "Treatment", labels = c('Small arena','Large arena','Small arena','Large arena'))+
  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='51',x=0.78,y=320,size=4)+
  annotate("text",label='48',x=1.23,y=320,size=4)+
  annotate("text",label='55',x=1.78,y=320,size=4)+
  annotate("text",label='49',x=2.23,y=320,size=4)+
  guides(colour = guide_legend(override.aes = list(size=4)))+
  fig_theme

Figure S2

#Behaviour 1 (Figure S2)
Figure_S2<-grid.arrange(p3+labs(tag = "A")+xlab('')+theme(legend.position = c(1.25, 0.8),plot.margin = unit(c(0.2,3.5,0,0.1), "cm"),legend.text = element_text(size=9)),p3.2+
                          labs(tag = "B")+xlab('')+theme(legend.position = c(1.25, 0.8),plot.margin = unit(c(0.2,3.5,0,0.1), "cm"),legend.text = element_text(size=9)),
                        p4+labs(tag = "C")+xlab('')+theme(legend.position = 'none',plot.margin = unit(c(0.2,3.5,0,0.1), "cm")),p4.2+labs(tag = "D")+xlab('')+theme(legend.position = 'none',plot.margin = unit(c(0.2,3.5,0,0.1), "cm")),
                        p5+labs(tag = "E")+xlab('Sex')+theme(legend.position = 'none',plot.margin = unit(c(0.2,3.5,0,0.1), "cm")),p5.2+labs(tag = "F")+xlab('Sex')+theme(legend.position = 'none',plot.margin = unit(c(0.2,3.5,0,0.1), "cm"))
                        ,nrow = 3,ncol=2)

Figure S2: Mating behavior of males and females under low and high density manipulation via group (left) and arena size (right). Bars indicate means and 95% CI.

Figure_S2 = plot_grid(Figure_S2, ncol=1, rel_heights=c(0.1, 1)) # rel_heights values control title margins

Figure S3

#Behavior 2 (Figure S3)
Figure_S3<-grid.arrange(p6+labs(tag = "A")+xlab('')+theme(legend.position = c(1.25, 0.8),plot.margin = unit(c(0.2,3.5,0,0.1), "cm"),legend.text = element_text(size=9)),p6.2+labs(tag = "B")+xlab('')+theme(legend.position = c(1.25, 0.8),plot.margin = unit(c(0.2,3.5,0,0.1), "cm"),legend.text = element_text(size=9)),
                        p7+labs(tag = "C")+xlab('')+theme(legend.position = 'none',plot.margin = unit(c(0.2,3.5,0,0.1), "cm")),p7.2+labs(tag = "D")+xlab('')+theme(legend.position = 'none',plot.margin = unit(c(0.2,3.5,0,0.1), "cm")),
                        p8+labs(tag = "E")+xlab('Sex')+theme(legend.position = 'none',plot.margin = unit(c(0.2,3.5,0,0.1), "cm")),p8.2+labs(tag = "F")+xlab('Sex')+theme(legend.position = 'none',plot.margin = unit(c(0.2,3.5,0,0.1), "cm")),nrow = 3,ncol=2)

Figure S3: Mating behavior of males and females under low and high density manipulation via group (left) and arena size (right). Bars indicate means and 95% CI.

Figure_S3 = plot_grid(Figure_S3, ncol=1, rel_heights=c(0.1, 1)) # rel_heights values control title margins

Figure S4

# Reproductive success (Figure S4)
Figure_S4<-grid.arrange(grobs = list(p9+theme(legend.position = c(1.2, 0.8),plot.margin = unit(c(0.2,4,0,0.3), "cm")),p9.2+theme(legend.position = c(1.2, 0.8),plot.margin = unit(c(0.2,4,0,0.3), "cm"))), nrow = 1,ncol=2, widths=c(2.3, 2.3))

Figure S4: Reproductive success of males and females under density manipulation via group (left) and arena size (right). Bars indicate means and 95% CI.

Figure_S4 = plot_grid(Figure_S4, ncol=1, rel_heights=c(0.1, 1)) # rel_heights values control title margins

Session information

sessionInfo()

R version 4.2.0 (2022-04-22 ucrt)
Platform: x86_64-w64-mingw32/x64 (64-bit)
Running under: Windows 10 x64 (build 19045)

Matrix products: default

locale:
[1] LC_COLLATE=German_Germany.utf8  LC_CTYPE=German_Germany.utf8   
[3] LC_MONETARY=German_Germany.utf8 LC_NUMERIC=C                   
[5] LC_TIME=German_Germany.utf8    

attached base packages:
[1] grid      stats     graphics  grDevices utils     datasets  methods  
[8] base     

other attached packages:
 [1] knitr_1.42         ICC_2.4.0          data.table_1.14.8  boot_1.3-28       
 [5] RColorBrewer_1.1-3 car_3.1-1          carData_3.0-5      gridGraphics_0.5-1
 [9] cowplot_1.1.1      EnvStats_2.7.0     dplyr_1.1.0        readr_2.1.4       
[13] lmerTest_3.1-3     lme4_1.1-31        Matrix_1.5-3       gridExtra_2.3     
[17] ggplot2_3.4.1      ggeffects_1.2.0    workflowr_1.7.0   

loaded via a namespace (and not attached):
 [1] httr_1.4.5          sass_0.4.5          bit64_4.0.5        
 [4] vroom_1.6.1         jsonlite_1.8.4      splines_4.2.0      
 [7] bslib_0.4.2         getPass_0.2-2       highr_0.10         
[10] yaml_2.3.7          numDeriv_2016.8-1.1 pillar_1.8.1       
[13] lattice_0.20-45     glue_1.6.2          digest_0.6.31      
[16] promises_1.2.0.1    minqa_1.2.5         colorspace_2.1-0   
[19] htmltools_0.5.4     httpuv_1.6.9        pkgconfig_2.0.3    
[22] scales_1.2.1        processx_3.8.0      whisker_0.4.1      
[25] later_1.3.0         tzdb_0.3.0          git2r_0.31.0       
[28] tibble_3.2.0        generics_0.1.3      farver_2.1.1       
[31] ellipsis_0.3.2      cachem_1.0.7        withr_2.5.0        
[34] cli_3.6.1           magrittr_2.0.3      crayon_1.5.2       
[37] evaluate_0.20       ps_1.7.2            fs_1.6.1           
[40] fansi_1.0.4         nlme_3.1-157        MASS_7.3-56        
[43] tools_4.2.0         hms_1.1.2           lifecycle_1.0.3    
[46] stringr_1.5.0       munsell_0.5.0       callr_3.7.3        
[49] compiler_4.2.0      jquerylib_0.1.4     rlang_1.0.6        
[52] nloptr_2.0.3        rstudioapi_0.14     labeling_0.4.2     
[55] rmarkdown_2.20      gtable_0.3.1        abind_1.4-5        
[58] R6_2.5.1            fastmap_1.1.1       bit_4.0.5          
[61] utf8_1.2.3          rprojroot_2.0.3     stringi_1.7.12     
[64] parallel_4.2.0      Rcpp_1.0.10         vctrs_0.5.2        
[67] tidyselect_1.2.0    xfun_0.37