Foraging Figures
Lauren E Johnson
12/12/2021
Figures
Fig.1 Across both sexes, AH fish at significantly more than AL fish
# population alone
fig1 <- data_forage %>% group_by(Sex) %>%
ggplot(aes(Population, Attempts)) +
stat_summary(fun = mean, geom = "point") +
stat_summary(fun.data = mean_se, geom = "errorbar", width = 0.5) +
geom_jitter(aes(color = Sex, shape = Sex), alpha = 0.5, width = 0.25) +
theme_bw() +
facet_wrap(~Sex)
fig1
Fig.2 AH and AL females reared in pred+ water ate more in pred+ test water than those reared in pred- water
fig2 <- data_forage %>% group_by(Sex, Water, Pred, Population) %>%
ggplot(aes(x = Water, y = Attempts, color = Pred)) +
stat_summary(fun = mean, geom = "point") +
stat_summary(fun.data = mean_se, geom = "errorbar", width = 0.07) +
scale_color_grey() +
theme_bw() +
facet_grid(Sex~Population) +
xlab('Test Water') +
labs(color = 'Rearing Water', title = '')
fig2## Warning: Removed 3 rows containing non-finite values (stat_summary).
## Warning: Removed 3 rows containing non-finite values (stat_summary).
# Version without male panel
# fig2 <- subset(data_forage, Sex == 'F') %>% group_by(Water, Pred, Population) %>%
# ggplot(aes(x = Water, y = Attempts, color = Pred)) +
# stat_summary(fun = mean, geom = "point") +
# stat_summary(fun = mean, geom = "line") +
# stat_summary(fun.data = mean_se, geom = "errorbar", width = 0.07) +
# scale_color_grey() +
# theme_classic() +
# facet_grid(~Population) +
# xlab('Test Water') +
# labs(color = 'Rearing Water') +
# ggtitle('Female')
#
# fig2Fig.3 Although not statistically significant, there is a trend where AH and AL males raised with AL tutors ate more in pred+ water than AH and AL males raised with AH tutors
fig3 <- data_forage %>% group_by(Sex, Water, Tutor_pop, Population) %>%
ggplot(aes(x = Water, y = Attempts, color = Tutor_pop)) +
stat_summary(fun = mean, geom = "point") +
stat_summary(fun.data = mean_se, geom = "errorbar", width = 0.07) +
scale_color_grey() +
theme_bw() +
facet_grid(Sex~Population) +
xlab('Test Water')
fig3## Warning: Removed 3 rows containing non-finite values (stat_summary).
## Warning: Removed 3 rows containing non-finite values (stat_summary).
# Version without female panel
# fig3 <- subset(data_forage, Sex == 'M') %>% group_by(Water, Tutor_pop, Population) %>%
# ggplot(aes(x = Water, y = Attempts, color = Tutor_pop)) +
# stat_summary(fun = mean, geom = "point") +
# stat_summary(fun = mean, geom = "line") +
# stat_summary(fun.data = mean_se, geom = "errorbar", width = 0.07) +
# scale_color_grey() +
# theme_classic() +
# facet_grid(~Population) +
# xlab('Test Water') +
# ggtitle('Male')
#
# fig3Latency…stuck on what the best graphs for this sections will be…
Fig.4 Across both sexes, AH fish were quicker to eat than AL fish
fig4 <- data_forage %>% group_by(Sex) %>%
ggplot(aes(Population, Latency)) +
stat_summary(fun = mean, geom = "point") +
stat_summary(fun.data = mean_se, geom = "errorbar", width = 0.5) +
geom_jitter(aes(color = Sex, shape = Sex), alpha = 0.5, width = 0.25) +
theme_bw() +
facet_wrap(~Sex)
fig4## Warning: Removed 3 rows containing non-finite values (stat_summary).
## Warning: Removed 3 rows containing non-finite values (stat_summary).## Warning: Removed 3 rows containing missing values (geom_point).
Fig.5 AH males reared in pred + water were quicker to peck in both pred- and pred + test water. However, there were no effects of rearing water on time to first peck for the AL males. Although not statistically signficant, there is a trend whre AL females reared in pred+ water were quicker to peck when tested in pred+ water, but there were no differences in time to first peck between pred- and pred+ reared females when tested in pred- water.
fig5 <- data_forage %>% group_by(Sex, Water, Pred, Population) %>%
ggplot(aes(x = Water, y = Latency, color = Pred)) +
stat_summary(fun = mean, geom = "point") +
stat_summary(fun.data = mean_se, geom = "errorbar", width = 0.07) +
scale_color_grey() +
theme_bw() +
facet_grid(Sex~Population) +
xlab('Test Water') +
labs(color = 'Rearing Water', title = '')
fig5## Warning: Removed 3 rows containing non-finite values (stat_summary).
## Warning: Removed 3 rows containing non-finite values (stat_summary).
# fig5 <- data_forage %>% group_by(Sex, Water, Pred, Population) %>%
# ggplot(aes(x = Pred, y = Latency, color = Water)) +
# stat_summary(fun = mean, geom = "point") +
# stat_summary(fun.data = mean_se, geom = "errorbar", width = 0.07) +
# scale_color_grey() +
# theme_bw() +
# facet_grid(Sex~Population) +
# xlab('Rearing Water')
# #labs(color = 'Rearing Water', title = '')
#
# fig5Fig.6 AH females raised with AH tutors in pred + water were quicker to peck than AH females raised with AL tutors in pred+ water. There were no differences in latency to peck between tutor pops for AH females raised in pred- water. AL females raised with AH tutors in prd- water were more quicker to peck than AL females raised with AL tutors. There were no differences in latency between tutor popos for AL feamels raised in pred+ water.
fig6 <- data_forage %>% group_by(Sex, Water, Tutor_pop, Population) %>%
ggplot(aes(x = Pred, y = Latency, color = Tutor_pop)) +
stat_summary(fun = mean, geom = "point") +
stat_summary(fun.data = mean_se, geom = "errorbar", width = 0.07) +
scale_color_grey() +
theme_bw() +
facet_grid(Sex~Population) +
xlab('Rearing Water')
fig6## Warning: Removed 3 rows containing non-finite values (stat_summary).
## Warning: Removed 3 rows containing non-finite values (stat_summary).
Fig1. Comparison of foraging attempts (attakcs and latency) among the two populations of origin.
fig1a <- ggplot(data_forage, aes(Population, Attempts)) +
stat_summary(fun = mean, geom = "point") +
stat_summary(fun.data = mean_se, geom = "errorbar", width = 0.07) +
geom_jitter(aes(color = Sex, shape = Sex), alpha = 0.5, width = 0.25) +
theme_bw()
fig1a
fig1b <- ggplot(data_forage, aes(Population, Latency)) +
stat_summary(fun = mean, geom = "point") +
stat_summary(fun.data = mean_se, geom = "errorbar", width = 0.07) +
geom_jitter(aes(color = Sex, shape = Sex), alpha = 0.5, width = 0.25) +
theme_bw()
fig1b
# Trying to facet wrap instead
fig1facet <- data_forage %>% group_by(Sex) %>%
ggplot(aes(Population, Attempts)) +
stat_summary(fun = mean, geom = "point") +
stat_summary(fun.data = mean_se, geom = "errorbar", width = 0.5) +
geom_jitter(aes(color = Sex, shape = Sex), alpha = 0.5, width = 0.25) +
theme_bw() +
facet_wrap(~Sex)
fig1facet
fig2facet <- data_forage %>% group_by(Sex) %>%
ggplot(aes(Population, Latency)) +
stat_summary(fun = mean, geom = "point") +
stat_summary(fun.data = mean_se, geom = "errorbar", width = 0.5) +
geom_jitter(aes(color = Sex, shape = Sex), alpha = 0.5, width = 0.25) +
theme_bw() +
facet_wrap(~Sex)
fig2facet
Fig2. Effect of testing water on the feeding attempts of fish from different origins, sexes, and treatments.
Plotting population differences with test water
# library(ggpubr)
# library(survminer)
# library(survival)
#
# data_forage <- data_forage %>%
# mutate(popwater = factor(paste(Population, Water)))
#
# latency_female <- survfit(time2peck ~ popwater, subset(data_forage, Sex == 'F'))
#
# ggsurvplot(latency_female, subset(data_forage, Sex == 'F'),
# conf.int = T,
# conf.int.style = c('ribbon'),
# conf.int.alpha = .25,
# palette = c("#A6CEE3", "#1F78B4", "#FDBF6F" ,"#FF7F00"),
# ylab = "Proportion of fish that have not foraged",
# title = "Females")# library(tidyverse)
#
# plot_forag <- data_forage %>%
# filter(Sex == 'F') %>%
# group_by(Population) %>%
# ggplot(aes(y = Attempts, x = Pred, fill = Water)) +
# geom_violin(alpha = 0.25, position = position_dodge(0.7)) +
# geom_boxplot(width = 0.1, position = position_dodge(0.7)) +
# facet_wrap(~ Population) +
# labs(title = "Females") +
# stat_summary(fun.y=mean, geom="point", color = "black",
# shape = 4, show.legend = F, position = position_dodge(0.7)) +
# theme_bw()
#
# plot_forag