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variance-activity-models-v3.R
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###project: Mesopredator Behavior
###author(s): MW
###goal(s): Running some variance models on temperature only
###date(s): January 2025
###note(s):
###########################################################################
# Housekeeping ------------------------------------------------------------
###########################################################################
### load necessary libraries
### install.packages("librarian")
# remotes::install_github('m-clark/mixedup')
librarian::shelf(tidyverse, readr, readxl, MuMIn, visreg, mgcv, corrplot, glmmTMB, DHARMa,
performance, ggeffects, lsmeans, emmeans, ggpubr)
### read in necessary data---
acc_bin <- read_rds('data/binned-accelerometer-model-data-012025.RDS') |>
select(datetime_est, date, year, month, day, time, id, tl,
mean_acceleration, sd_acceleration, daily_sd_acceleration, everything())
glimpse(acc_bin)
### selected data for models---
mod_df <- acc_bin |>
select(-sd_acceleration) |>
rename(temp = mean_temp_c,
stage = marsh_stage,
# temp_mean = mean_temp_c,
temp_sd = sd_temp_c,
temp_min = min_temp_c,
temp_max = max_temp_c,
sd_acceleration = daily_sd_acceleration) |>
select(species, id, tl, sd_acceleration, temp, temp_sd, temp_min, temp_max, stage) |>
rename(act_sd = sd_acceleration) |>
filter(act_sd >= 1) |>
mutate(id = as.factor(id),
species = as.factor(species)) |>
distinct()
### clean environment ---
keep <- c("mod_df")
rm(list = setdiff(ls(), keep))
hist(mod_df$act_sd)
mod_df$logsdact <- log(mod_df$act_sd)
hist(mod_df$logsdact)
glimpse(mod_df)
keep <- c("mod_df")
rm(list = setdiff(ls(), keep))
# temperature effects on snook activity ---------------------------
snook_df <- mod_df |>
filter(species == "common snook")
### null model---
m1 <- mgcv::gam(act_sd ~ 1 + s(id, bs="re"),
data = snook_df,
family = gaussian(link = 'log'),
method = 'REML')
### temperature models---
m2 <- mgcv::gam(act_sd ~ s(temp) + s(id, bs="re"),
data = snook_df,
family = gaussian(link = 'log'),
method = 'REML')
m3 <- mgcv::gam(act_sd ~ s(temp_sd) + s(id, bs="re"),
data = snook_df,
family = gaussian(link = 'log'),
method = 'REML')
m4 <- mgcv::gam(act_sd ~ s(temp_min) + s(id, bs="re"),
data = snook_df,
family = gaussian(link = 'log'),
method = 'REML')
m5 <- mgcv::gam(act_sd ~ s(temp_max) + s(id, bs="re"),
data = snook_df,
family = gaussian(link = 'log'),
method = 'REML')
model_performance <- compare_performance(m1,m2,m3,m4,m5) |>
mutate(dAICc = AICc-min(AICc))
summary(m2)
summary(m4)
summary(m5)
### visualize temperature effects---
temp_vis <- visreg(m4, "temp_max", type = "conditional", scale = "response")
temp_visfit <- temp_vis$fit |>
rename(fit = visregFit,
fit_lower = visregLwr,
fit_upper = visregUpr) |>
select(fit, fit_lower, fit_upper, temp)
temp_visfit |>
ggplot(aes(temp, fit))+
geom_ribbon(aes(ymin = fit_lower, ymax = fit_upper), fill = "grey60", alpha = 0.3)+
geom_line(linewidth = 2, color= "black") + theme_bw()+
labs(x = "Water Temperature (°C)", y = expression(bold("SD Acceleration (m/s"^2*")"))) +
scale_x_continuous(breaks = c(16, 19, 22, 25, 28, 31, 34)) +
scale_y_continuous(breaks = c(5,6,7,8,9,10,11,12)) +
geom_vline(xintercept = 27.89917, linetype = "dashed", color = "black", size = 1) +
theme(axis.text = element_text(size = 14, face = "bold", colour = "black"),
axis.title = element_text(size = 16, face = "bold", colour = "black"),
plot.title = element_text(size = 16, face = "bold", colour = "black"),
panel.grid.major = element_blank(),
axis.line = element_line(colour = "black"),
panel.grid.minor = element_blank(),
panel.border = element_blank(),
panel.background = element_blank())
### visualize max temperature effects---
temp_vis <- visreg(m5, "temp_max", type = "conditional", scale = "response")
temp_visfit <- temp_vis$fit |>
rename(fit = visregFit,
fit_lower = visregLwr,
fit_upper = visregUpr) |>
select(fit, fit_lower, fit_upper, temp_max)
temp_visfit |>
ggplot(aes(temp_max, fit))+
geom_ribbon(aes(ymin = fit_lower, ymax = fit_upper), fill = "grey60", alpha = 0.3)+
geom_line(linewidth = 2, color= "black") + theme_bw()+
labs(x = "Water Temperature (°C)", y = expression(bold("SD Acceleration (m/s"^2*")"))) +
scale_x_continuous(breaks = c(16, 19, 22, 25, 28, 31, 34)) +
scale_y_continuous(breaks = c(5,6,7,8,9,10,11,12)) +
geom_vline(xintercept = 29.592, linetype = "dashed", color = "black", size = 1) +
theme(axis.text = element_text(size = 14, face = "bold", colour = "black"),
axis.title = element_text(size = 16, face = "bold", colour = "black"),
plot.title = element_text(size = 16, face = "bold", colour = "black"),
panel.grid.major = element_blank(),
axis.line = element_line(colour = "black"),
panel.grid.minor = element_blank(),
panel.border = element_blank(),
panel.background = element_blank())
# temperature effects on bass activity ---------------------------
bass_df <- mod_df |>
filter(species == "largemouth bass")
### null model---
m1 <- mgcv::gam(act_sd ~ 1 + s(id, bs="re"),
data = bass_df,
family = gaussian(link = 'log'),
method = 'REML')
### temperature models---
m2 <- mgcv::gam(act_sd ~ s(temp) + s(id, bs="re"),
data = bass_df,
family = gaussian(link = 'log'),
method = 'REML')
m3 <- mgcv::gam(act_sd ~ s(temp_sd) + s(id, bs="re"),
data = bass_df,
family = gaussian(link = 'log'),
method = 'REML')
m4 <- mgcv::gam(act_sd ~ s(temp_min) + s(id, bs="re"),
data = bass_df,
family = gaussian(link = 'log'),
method = 'REML')
m5 <- mgcv::gam(act_sd ~ s(temp_max) + s(id, bs="re"),
data = bass_df,
family = gaussian(link = 'log'),
method = 'REML')
model_performance <- compare_performance(m1,m2,m3,m4,m5) |>
mutate(dAICc = AICc-min(AICc))
summary(m4)
### visualize temperature effects---
temp_vis <- visreg(m4, "temp_min", type = "conditional", scale = "response")
temp_visfit <- temp_vis$fit |>
rename(fit = visregFit,
fit_lower = visregLwr,
fit_upper = visregUpr) |>
select(fit, fit_lower, fit_upper, temp_min)
temp_visfit |>
ggplot(aes(temp_min, fit))+
geom_ribbon(aes(ymin = fit_lower, ymax = fit_upper), fill = "grey60", alpha = 0.3)+
geom_line(linewidth = 2, color= "black") + theme_bw()+
labs(x = "Water Temperature (°C)", y = expression(bold("SD Acceleration (m/s"^2*")"))) +
scale_x_continuous(breaks = c(18, 22, 26, 30)) +
scale_y_continuous(breaks = c(6,10,14,18,22,26)) +
geom_vline(xintercept = 20.748, linetype = "dashed", color = "black", size = 1) +
theme(axis.text = element_text(size = 14, face = "bold", colour = "black"),
axis.title = element_text(size = 16, face = "bold", colour = "black"),
plot.title = element_text(size = 16, face = "bold", colour = "black"),
panel.grid.major = element_blank(),
axis.line = element_line(colour = "black"),
panel.grid.minor = element_blank(),
panel.border = element_blank(),
panel.background = element_blank())