First install/load packages:

x <- c("readxl", "brms", "viridis", "ggplot2", "pbapply", "cowplot", "kableExtra", "warbleR", github = "maRce10/brmsish")

source("https://raw.githubusercontent.com/maRce10/sketchy/main/R/load_packages.R")

load_packages(x)

Read and prepare data:

total_dat <- read.csv("https://raw.githubusercontent.com/maRce10/Roost-finding-behavior-in-Thyroptera-tricolor/main/data/raw/time_entering_roost_thyroptera.csv")

total_dat$sensory_input <- factor(total_dat$sensory_input, levels = c("Sound & vision", "Noise control", "Sound", "Vision", "Lessen input"))

Sensory input treatments:

  • Sound & vision: diurnal experiments with no noise playback
  • Noise control: diurnal experiments with control (no echolocation-masking) playback
  • Sound: nocturnal experiments with no playback
  • Vision: diurnal experiments with echolocation-masking playback
  • Lessen input: nocturnal experiments with echolocation-masking playback

Sample sizes

First, exclude individuals with 1 experiment:

tab <- table(total_dat$individual[!duplicated(paste(total_dat$sensory_input, total_dat$individual))])

dat <- total_dat[!total_dat$individual %in% names(tab)[tab == 1], ]

Experiments per date:

# Dates
table(dat$date)
## 
## 2019-11-06 2019-11-09 2019-11-10 2019-11-11 2019-11-12 2019-11-13 2019-11-14 
##          4          3          4          3          4          7          4 
## 2019-11-16 2019-11-17 2019-11-19 2019-11-25 2019-11-26 2019-11-27 2019-11-28 
##          3          7          6          3          6          9          4 
## 2019-11-29 2019-12-01 2019-12-07 2020-01-24 2020-01-28 2020-01-29 
##          6          3          2          4          4         10

Test per individual:

table(dat$individual)
## 
## 982.126051278486 982.126051278498 982.126051278508 982.126051278509 
##                3                7                2                4 
## 982.126051278515 982.126051278518 982.126051278520 982.126051278530 
##                3                3                4                3 
## 982.126051278531 982.126051278554 982.126051278558 982.126051278559 
##                6                3                3                3 
## 982.126051278588 982.126057845184 982.126057845188 982.126057845230 
##                3                4                3                3 
## 982.126058484283 982.126058484309 982.126058484311 982.126058484344 
##                3                3                3                6 
## 982.126058484345 982.126058484348  982126052945834  982126052945878 
##                3                3                2                2 
##  982126052945887  982126052945892  982126052945893  982126052945904 
##                2                2                2                2 
##  982126052945905  982126058484333  982126058484340 
##                2                2                2

Number of individuals by number of tests:

aggregate(individual ~ sensory_input, dat, function(x) length(unique(x)))
## # A tibble: 5 × 2
##   sensory_input  individual
##   <fct>               <int>
## 1 Sound & vision         22
## 2 Noise control          21
## 3 Sound                   9
## 4 Vision                 22
## 5 Lessen input            9

Experiments per individual:

table(dat$individual[!duplicated(paste(dat$sensory_input, dat$individual))])
## 
## 982.126051278486 982.126051278498 982.126051278508 982.126051278509 
##                3                3                2                3 
## 982.126051278515 982.126051278518 982.126051278520 982.126051278530 
##                3                3                3                3 
## 982.126051278531 982.126051278554 982.126051278558 982.126051278559 
##                3                3                3                3 
## 982.126051278588 982.126057845184 982.126057845188 982.126057845230 
##                3                3                3                3 
## 982.126058484283 982.126058484309 982.126058484311 982.126058484344 
##                3                3                3                3 
## 982.126058484345 982.126058484348  982126052945834  982126052945878 
##                3                3                2                2 
##  982126052945887  982126052945892  982126052945893  982126052945904 
##                2                2                2                2 
##  982126052945905  982126058484333  982126058484340 
##                2                2                2

Number of individuals by number of experiments:

table(table(dat$individual[!duplicated(paste(dat$sensory_input, dat$individual))]))
## 
##  2  3 
## 10 21

Tests per treatment:

table(dat$sensory_input, dat$time_of_the_day)
##                 
##                  day night
##   Sound & vision  24     0
##   Noise control   30     0
##   Sound            0     9
##   Vision          24     0
##   Lessen input     0     9

Experiments per treatment:

table(dat$sensory_input[!duplicated(paste(dat$sensory_input, dat$individual))], dat$time_of_the_day[!duplicated(paste(dat$sensory_input, dat$individual))])
##                 
##                  day night
##   Sound & vision  22     0
##   Noise control   21     0
##   Sound            0     9
##   Vision          22     0
##   Lessen input     0     9

 

  • 31 individuals were tested

  • The 2 individuals with only 1 treatment were excluded so the final individual sample size was 31

  • The mean number of tests per individual (after excluding those with 1 treatment) was 3.1 (range = 2, 7)

  • The mean number of experimental treatments in which each individual was tested (after excluding those with 1 treatment) was 2.68

 

Effect of sensory input on the time to find the roost

Bayesian generalized linear models on time (in s) to enter the roost, with individual as a random effect and sensory input treatment as predictors. An intercept-only (null) model was also included in the analysis:

  1. Sensory input as a categorical variable: \[Time\ to\ enter\ roost \sim + categorical\ input + (1 | individual)\]
  2. Null model with no predictor: \[Time\ to\ enter\ roost \sim 1 + (1 | individual)\]

A loop is used to run these models, 3 chains per model, 10000 iterations each. Models were run on the complete data set and on a subset including only trials in which individuals entered the roost.

Models using all the data

Raw data plot

cols <- viridis(10)

agg_dat <- aggregate(time_to_enter ~ sensory_input, dat, mean)
agg_dat$n <- sapply(1:nrow(agg_dat), function(x) length(unique(dat$individual[dat$sensory_input == agg_dat$sensory_input[x]]))) 
agg_dat$n.labels <- paste("n =", agg_dat$n)
agg_dat$sensory_input <- factor(agg_dat$sensory_input)

# raincoud plot:
fill_color <- adjustcolor("#e85307", 0.6)

ggplot(dat, aes(y = time_to_enter, x = sensory_input)) +
  # add half-violin from {ggdist} package
  ggdist::stat_halfeye(
    fill = fill_color,
    alpha = 0.5,
    # custom bandwidth
    adjust = .5,
    # adjust height
    width = .6,
    .width = 0,
    # move geom to the cright
    justification = -.2,
    point_colour = NA
  ) +
  geom_boxplot(fill = fill_color,
    width = .15,
    # remove outliers
    outlier.shape = NA # `outlier.shape = NA` works as well
  ) +
  # add justified jitter from the {gghalves} package
  gghalves::geom_half_point(
    color = fill_color,
    # draw jitter on the left
    side = "l",
    # control range of jitter
    range_scale = .4,
    # add some transparency
    alpha = .5,
  ) +
   ylim(c(-30, 310)) +
  geom_text(data = agg_dat, aes(y = rep(-25, 5), x = sensory_input, label = n.labels), nudge_x = -0.13, size = 6) + 
   scale_x_discrete(labels=c("Control" = "Noise control", "Sound vision" = "Sound & vision", "Vision" = "Vision", "Lessen input" = "Lessen input")) +
  labs(x = "Sensory input       ", y = "Time to enter roost (s)") + theme(axis.text.x = element_text(angle = 15, hjust = 1))

# ggsave(filename = "./output/time_to_enter_by_treatment.tiff", dpi = 300, width = 3000, height = 1500, units = "px")
model_formulas <- c("time_to_enter ~ sensory_input + (1 | individual)", "time_to_enter ~ 1 + (1 | individual)")

iter <- 5000
chains <- 4

priors <- c(set_prior("student_t(10,0,1)", class = "sigma"),
           set_prior("student_t(10,0,1)", class = "sd"))


# Run loops with models
brms_models <- lapply(model_formulas, function(x){

  mod <- brm(
          formula = x,
          iter = iter,
          thin = 1,
          data = dat,
          family = lognormal(),
          silent = 2,
          chains = chains,
          cores = chains,
          prior = priors
          )
  
  mod <- add_criterion(mod, c("loo"), save_pars = save_pars(all = TRUE))

  return(mod)
  })

names(brms_models) <- model_formulas

saveRDS(brms_models, "./data/processed/regresion_models_brms.RDS")
brms_models <- readRDS("./data/processed/regresion_models_brms.RDS")

comp_mods <- loo_compare(brms_models[[1]], brms_models[[2]], model_names = names(brms_models))

df1 <- kbl(comp_mods, row.names = TRUE, escape = FALSE, format = "html", digits = 3)

cat("Compare models:")

Compare models:

kable_styling(df1, bootstrap_options = c("striped", "hover", "condensed", "responsive"), full_width = FALSE, font_size = 12)
elpd_diff se_diff elpd_loo se_elpd_loo p_loo se_p_loo looic se_looic
time_to_enter ~ sensory_input + (1 | individual) 0.000 0.000 -510.740 16.182 20.972 1.890 1021.481 32.364
time_to_enter ~ 1 + (1 | individual) -7.225 3.742 -517.965 16.101 19.136 1.764 1035.930 32.202 
cat("Best model:\n")

Best model:

cat(paste("-  ", rownames(comp_mods)[1], "\n"))
  • time_to_enter ~ sensory_input + (1 | individual)
# best model
if (!grepl("1 +", rownames(comp_mods)[1], fixed = TRUE))
html_summary(model = brms_models[[rownames(comp_mods)[1]]], gsub.pattern = "sensory_input", gsub.replacement = "", fill = "#e85307", trace.palette = terrain.colors)

brms_models[[rownames(comp_mods)[1]]]

priors formula iterations chains thinning warmup diverg_transitions rhats > 1.05 min_bulk_ESS min_tail_ESS seed
1 Intercept-student_t(3, 3.5, 2.5) sd-student_t(10,0,1) sigma-student_t(10,0,1) time_to_enter ~ sensory_input + (1 | individual) 5000 4 1 2500 0 0 7632.569 7283.589 652773362
Estimate l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
b_Intercept 3.228 2.643 3.827 1 7632.569 7360.967
b_Noisecontrol 0.208 -0.424 0.854 1 13237.365 7884.717
b_Sound 0.476 -0.647 1.636 1 8482.597 7968.398
b_Vision 0.604 -0.075 1.269 1 13132.351 7283.589
b_Lesseninput 2.407 1.284 3.534 1.001 8279.037 7899.409

Compare all contrasts

# contrasts
contrasts(model = brms_models[[rownames(comp_mods)[1]]], predictor = "sensory_input", n.posterior = 2000, level.sep = " VS ", fill = "#e85307", gsub.pattern = c("Lesseninput", "Soundvision", "Noisecontrol"), gsub.replacement = c("Lessen input", "Sound & vision", "Noise control"), html.table = TRUE, plot = TRUE,  sort.levels = c("Lessen input","Vision", "Sound", "Sound & vision", "Noise control"))
Hypothesis Estimate Est.Error l-95% CI u-95% CI
1 Lessen input VS Vision 1.803 0.571 0.669 2.918
2 Lessen input VS Sound 1.931 0.570 0.811 3.062
3 Lessen input VS Sound & vision 2.407 0.572 1.284 3.534
4 Lessen input VS Noise control 2.198 0.561 1.079 3.262
5 Vision VS Sound 0.128 0.577 -1.022 1.257
6 Vision VS Sound & vision 0.604 0.344 -0.075 1.269
7 Vision VS Noise control 0.395 0.331 -0.245 1.044
8 Sound VS Sound & vision 0.476 0.574 -0.647 1.636
9 Sound VS Noise control 0.267 0.568 -0.859 1.405
10 Sound & vision VS Noise control 0.208 0.325 -0.424 0.854 

ggsave(filename = "./output/contrasts_between_treatments.tiff", dpi = 300, width = 2000, height = 1000, units = "px")

 

Takeaways

  • “lessen input” was the only sensory input treatment in which a increase in time was detected: it show a longer time to enter the roost than all other treatments

 

Models on the data excluding bats that did not enter the roost

Raw data plot

agg_dat <- aggregate(time_to_enter ~ sensory_input, dat[dat$time_to_enter < 300, ], mean)
agg_dat$n <- sapply(1:nrow(agg_dat), function(x) length(unique(dat$individual[dat$sensory_input == agg_dat$sensory_input[x] & dat$time_to_enter < 300]))) 
agg_dat$labels <- c("a", "a", "a", "a", "b")
agg_dat$n.labels <- paste("n =", agg_dat$n)
agg_dat$sensory_input <- factor(agg_dat$sensory_input)

# raincoud plot:

ggplot(dat[dat$time_to_enter < 300, ], aes(y = time_to_enter, x = sensory_input)) +
  # add half-violin from {ggdist} package
  ggdist::stat_halfeye(
    fill = fill_color,
    alpha = 0.5,
    # custom bandwidth
    adjust = .5,
    # adjust height
    width = .6,
    .width = 0,
    # move geom to the cright
    justification = -.2,
    point_colour = NA
  ) +
  geom_boxplot(fill = fill_color,
    width = .15,
    # remove outliers
    outlier.shape = NA # `outlier.shape = NA` works as well
  ) +
  # add justified jitter from the {gghalves} package
  gghalves::geom_half_point(
    color = fill_color,
    # draw jitter on the left
    side = "l",
    # control range of jitter
    range_scale = .4,
    # add some transparency
    alpha = .5,
  ) +
   ylim(c(-30, 310)) +
  # geom_text(data = agg_dat, aes(y = rep(340, 5), x = sensory_input, label = labels), size = 7) +
  geom_text(data = agg_dat, aes(y = rep(-25, 5), x = sensory_input, label = n.labels), nudge_x = -0.13, size = 6) + 
   scale_x_discrete(labels=c("Control" = "Noise control", "Sound vision" = "Sound & vision", "Vision" = "Vision", "Lessen input" = "Lessen input")) +
  labs(x = "Sensory input       ", y = "Time to enter roost (s)") + theme(axis.text.x = element_text(angle = 15, hjust = 1))

model_formulas <- c("time_to_enter ~ sensory_input + (1 | individual)", "time_to_enter ~ 1 + (1 | individual)")

iter <- 5000
chains <- 4
priors <- c(set_prior("student_t(10,0,1)", class = "sigma"),
           set_prior("student_t(10,0,1)", class = "sd"))


# Run loops with models
brms_models <- lapply(model_formulas, function(x){

  mod <- brm(
          formula = x,
          iter = iter,
          thin = 1,
          data = dat[dat$time_to_enter < 300, ],
          family = lognormal(),
          silent = 2,
          chains = chains,
          cores = chains,
          prior = priors,
          control=list(adapt_delta=0.99, max_treedepth=15)
          )
  
  mod <- add_criterion(mod, c("loo"), save_pars = save_pars(all = TRUE))

  return(mod)
  })

names(brms_models) <- model_formulas

saveRDS(brms_models, "./data/processed/regression_models_brms_subset.RDS")
brms_models <- readRDS("./data/processed/regression_models_brms_subset.RDS")

comp_mods <- loo_compare(brms_models[[1]], brms_models[[2]], model_names = names(brms_models))


df1 <- kbl(comp_mods, row.names = TRUE, escape = FALSE, format = "html", digits = 3)

cat("Compare models:")

Compare models:

kable_styling(df1, bootstrap_options = c("striped", "hover", "condensed", "responsive"), full_width = FALSE, font_size = 12)
elpd_diff se_diff elpd_loo se_elpd_loo p_loo se_p_loo looic se_looic
time_to_enter ~ sensory_input + (1 | individual) 0.000 0.000 -398.054 14.044 18.956 1.898 796.107 28.087
time_to_enter ~ 1 + (1 | individual) -2.318 2.771 -400.372 14.170 15.192 1.660 800.744 28.340 
cat("Best model:\n")

Best model:

cat(paste("-  ", rownames(comp_mods)[1], "\n"))
  • time_to_enter ~ sensory_input + (1 | individual)
# best model
if (!grepl("1 +", rownames(comp_mods)[1], fixed = TRUE))
html_summary(model = brms_models[[rownames(comp_mods)[1]]], gsub.pattern = "sensory_input", gsub.replacement = "",  fill = "#e85307", trace.palette = terrain.colors)

brms_models[[rownames(comp_mods)[1]]]

priors formula iterations chains thinning warmup diverg_transitions rhats > 1.05 min_bulk_ESS min_tail_ESS seed
1 Intercept-student_t(3, 3.1, 2.5) sd-student_t(10,0,1) sigma-student_t(10,0,1) time_to_enter ~ sensory_input + (1 | individual) 5000 4 1 2500 0 0 5171.14 6488.326 1582467647
Estimate l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
b_Intercept 3.018 2.417 3.634 1 5171.140 6488.326
b_Noisecontrol 0.124 -0.554 0.803 1 8844.558 7129.572
b_Sound 0.693 -0.388 1.814 1 6254.619 6711.689
b_Vision 0.518 -0.186 1.225 1 9433.443 7726.126
b_Lesseninput 2.948 0.985 4.924 1 7428.895 7424.103

Compare all contrasts

# contrasts
contrasts(model = brms_models[[rownames(comp_mods)[1]]], predictor = "sensory_input", n.posterior = 2000, level.sep = " VS ", fill = "#e85307", gsub.pattern = c("Lesseninput", "Soundvision", "Noisecontrol"), gsub.replacement = c("Lessen input", "Sound & vision", "Noise control"), html.table = TRUE, plot = TRUE, sort.levels = c("Lessen input", "Vision", "Sound", "Sound & vision", "Noise control"))
Hypothesis Estimate Est.Error l-95% CI u-95% CI
1 Lessen input VS Vision 2.429 1.008 0.472 4.434
2 Lessen input VS Sound 2.255 1.008 0.256 4.221
3 Lessen input VS Sound & vision 2.948 1.000 0.985 4.924
4 Lessen input VS Noise control 2.824 0.992 0.906 4.778
5 Vision VS Sound -0.175 0.555 -1.277 0.922
6 Vision VS Sound & vision 0.518 0.356 -0.186 1.225
7 Vision VS Noise control 0.394 0.345 -0.285 1.081
8 Sound VS Sound & vision 0.693 0.560 -0.388 1.814
9 Sound VS Noise control 0.569 0.552 -0.516 1.656
10 Sound & vision VS Noise control 0.124 0.343 -0.554 0.803

 

Takeaways

  • Results remain qualitatively similar after excluding individuals that did not enter the roost

Treatment diagram

reverse.viridis <- function (...) viridis(..., direction = -1) 

wv <- read_wave("https://github.com/maRce10/Roost-finding-behavior-in-Thyroptera-tricolor/raw/main/data/raw/thyroptera_echolocation_clip.wav")


wv <- ffilter(wv, from = 40000, to = 170000, bandpass = TRUE, output = "Wave")

wv1 <- cutw(wv, from = 0.005, to = 0.009, output = "Wave")
wv2 <- cutw(wv, from = 0.036, to = duration(wv) - 0.004, output = "Wave")

subwv <- pastew(wv1, wv2, output = "Wave")

ns <- seewave::noisew(f = subwv@samp.rate, output = "Wave", d = duration(subwv))


mask <- ffilter(ns, from = 0, to = 45000, bandpass = FALSE, output = "Wave")

no_mask <- ffilter(ns, from = 0, to = 45000, bandpass = TRUE, output = "Wave")

subwv@left <- subwv@left[1:length(mask)]

ovlp <- 99
cex <- 2
res <- 200
bl <- 4
hgh <- wdh <- 480 * 4

lf <- c(0, 2/14, 6/14, 10/14, 0, 1/6, 1/2, 5/6)
rgh <- c(2/14, 6/14, 10/14, 1, 1/6, 1/2, 5/6, 1)
btm <- c(1/2, 1/2, 1/2, 1/2, 0, 0, 0, 0)  
tp <- c(1, 1, 1, 1, 1/2, 1/2, 1/2, 1/2)  
m <- cbind(lf, rgh, btm, tp)

# graphics.off()
invisible(close.screen(all.screens = TRUE))

# tiff(filename = "./output/diagram_experimental_design.tiff", res = 300, width = 3500, height = 2000)

sc <- split.screen(figs = m)

# sound and vision
screen(2)

par(mar = c(3, 6, 1, 1))
warbleR:::spectro_wrblr_int2(subwv, grid = FALSE, collev.min = -35, wl = 120, palette = reverse.viridis, ovlp = ovlp, zp = 1000, tlim = c(0.001, 0.0085), axisX = FALSE, tlab = NULL, axisY = FALSE, flab = NULL, flim = c(0, 220))

box(lwd = bl)

axis(side = 2, cex.axis = cex, labels = c(0, 100, 200), at = c(0, 100, 200))

mtext(side = 2, text = "Frequency (kHz)", line = 4, cex = cex)

mtext(side = 1, text = "Sound & vision  ", line = 1.5, cex = cex)


# vision
screen(3)
par(mar = c(3, 6, 1, 1))
warbleR:::spectro_wrblr_int2(subwv + mask * 1000, grid = FALSE, collev.min = -35, wl = 120, palette = reverse.viridis, ovlp = ovlp, zp = 1000, tlim = c(0.001, 0.0085), axisX = FALSE, tlab = NULL, axisY = FALSE, flab = NULL, flim = c(0, 220))

box(lwd = bl)

axis(side = 2, cex.axis = cex, labels = c(0, 100, 200), at = c(0, 100, 200))

mtext(side = 1, text = "Vision", line = 1.5, cex = cex)

# Noise control

screen(4)

par(mar = c(3, 6, 1, 1), new = TRUE)
warbleR:::spectro_wrblr_int2(subwv  + no_mask * 1000, grid = FALSE, collev.min = -35, wl = 120, palette = reverse.viridis, ovlp = ovlp, zp = 1000, tlim = c(0.001, 0.0085), axisX = FALSE, tlab = NULL, axisY = FALSE, flab = NULL, flim = c(0, 220))

box(lwd = bl)

axis(side = 2, cex.axis = cex, labels = c(0, 100, 200), at = c(0, 100, 200))

mtext(side = 1, text = "Noise control", line = 1.5, cex = cex)


# sound
par(bg = "black", new = TRUE)
screen(6)

par(mar = c(3, 8.5, 1, 1))
warbleR:::spectro_wrblr_int2(subwv, grid = FALSE, collev.min = -35, wl = 120, palette = reverse.viridis, ovlp = ovlp, zp = 1000, tlim = c(0.001, 0.0085), axisX = FALSE, tlab = NULL, axisY = FALSE, flab = NULL, flim = c(0, 220))

box(lwd = bl, col = "white")

axis(side = 2, cex.axis = cex, labels = c(0, 100, 200), at = c(0, 100, 200), col.ticks = "white", col = "white", col.axis = "white")

mtext(side = 1, text = "Sound", line = 1.5, cex = cex, col = "white")


# lessen input
par(bg = "black", new = TRUE)
screen(7)

par(mar = c(3, 8.5, 1, 1))

# par(mar = c(1, 9, 1, 1), bg = "black", new = TRUE)
warbleR:::spectro_wrblr_int2(subwv + mask * 1000, grid = FALSE, collev.min = -35, wl = 120, palette = reverse.viridis, ovlp = ovlp, zp = 1000, tlim = c(0.001, 0.0085), axisX = FALSE, tlab = NULL, axisY = FALSE, flab = NULL, flim = c(0, 220))

box(lwd = bl, col = "white")

axis(side = 2, cex.axis = cex, labels = c(0, 100, 200), at = c(0, 100, 200), col.ticks = "white", col = "white", col.axis = "white")

mtext(side = 1, text = "Lessen input", line = 1.5, cex = cex, col = "white")
# dev.off()

par(bg = "black", new = TRUE)
screen(5)
par(mar = c(0, 0, 0, 0))
plot(1, frame.plot = FALSE, type = "n")
text(x = 1, y = 1.05, "Nighttime", srt = 90, 
     cex = 1.2 * cex, col = "white")

par(bg = "black", new = TRUE)
screen(8)

par(bg = "white", new = TRUE)
screen(1)
par(mar = c(0, 0, 0, 0))
plot(1, frame.plot = FALSE, type = "n")
text(x = 1, y = 1.05, "Daytime", srt = 90, 
     cex = 1.2 * cex)

# dev.off()

 

Session information

## R version 4.1.0 (2021-05-18)
## Platform: x86_64-pc-linux-gnu (64-bit)
## Running under: Ubuntu 20.04.2 LTS
## 
## Matrix products: default
## BLAS:   /usr/lib/x86_64-linux-gnu/atlas/libblas.so.3.10.3
## LAPACK: /usr/lib/x86_64-linux-gnu/atlas/liblapack.so.3.10.3
## 
## locale:
##  [1] LC_CTYPE=pt_BR.UTF-8       LC_NUMERIC=C              
##  [3] LC_TIME=es_CR.UTF-8        LC_COLLATE=pt_BR.UTF-8    
##  [5] LC_MONETARY=es_CR.UTF-8    LC_MESSAGES=pt_BR.UTF-8   
##  [7] LC_PAPER=es_CR.UTF-8       LC_NAME=C                 
##  [9] LC_ADDRESS=C               LC_TELEPHONE=C            
## [11] LC_MEASUREMENT=es_CR.UTF-8 LC_IDENTIFICATION=C       
## 
## attached base packages:
## [1] stats     graphics  grDevices utils     datasets  methods   base     
## 
## other attached packages:
##  [1] brmsish_1.0.0      warbleR_1.1.28     NatureSounds_1.0.4 knitr_1.42        
##  [5] seewave_2.2.0      tuneR_1.4.1        kableExtra_1.3.4   cowplot_1.1.1     
##  [9] pbapply_1.6-0      ggplot2_3.4.0      viridis_0.6.2      viridisLite_0.4.1 
## [13] brms_2.18.0        Rcpp_1.0.9         readxl_1.3.1      
## 
## loaded via a namespace (and not attached):
##   [1] backports_1.4.1      systemfonts_1.0.4    plyr_1.8.7          
##   [4] igraph_1.3.5         splines_4.1.0        crosstalk_1.2.0     
##   [7] TH.data_1.1-0        rstantools_2.2.0     inline_0.3.19       
##  [10] digest_0.6.31        htmltools_0.5.4      fansi_1.0.3         
##  [13] magrittr_2.0.3       checkmate_2.1.0      RcppParallel_5.1.5  
##  [16] matrixStats_0.62.0   xts_0.12.2           sandwich_3.0-1      
##  [19] svglite_2.1.0        prettyunits_1.1.1    colorspace_2.0-3    
##  [22] signal_0.7-7         rvest_1.0.3          ggdist_3.2.0        
##  [25] textshaping_0.3.5    xfun_0.36            dplyr_1.0.10        
##  [28] callr_3.7.3          crayon_1.5.2         RCurl_1.98-1.9      
##  [31] jsonlite_1.8.4       lme4_1.1-27.1        ape_5.6-2           
##  [34] survival_3.2-11      zoo_1.8-11           glue_1.6.2          
##  [37] gtable_0.3.1         emmeans_1.8.1-1      webshot_0.5.4       
##  [40] distributional_0.3.1 pkgbuild_1.4.0       rstan_2.21.7        
##  [43] abind_1.4-5          scales_1.2.1         mvtnorm_1.1-3       
##  [46] DBI_1.1.1            miniUI_0.1.1.1       dtw_1.23-1          
##  [49] xtable_1.8-4         diffobj_0.3.4        proxy_0.4-27        
##  [52] stats4_4.1.0         StanHeaders_2.21.0-7 DT_0.26             
##  [55] htmlwidgets_1.5.4    httr_1.4.4           threejs_0.3.3       
##  [58] posterior_1.3.1      ellipsis_0.3.2       pkgconfig_2.0.3     
##  [61] loo_2.4.1.9000       farver_2.1.1         sass_0.4.5          
##  [64] utf8_1.2.2           labeling_0.4.2       tidyselect_1.2.0    
##  [67] rlang_1.0.6          reshape2_1.4.4       later_1.3.0         
##  [70] munsell_0.5.0        cellranger_1.1.0     tools_4.1.0         
##  [73] cachem_1.0.6         cli_3.6.0            generics_0.1.3      
##  [76] ggridges_0.5.4       evaluate_0.20        stringr_1.5.0       
##  [79] fastmap_1.1.0        ragg_1.1.3           yaml_2.3.7          
##  [82] processx_3.8.0       nlme_3.1-152         mime_0.12           
##  [85] projpred_2.0.2       xml2_1.3.3           compiler_4.1.0      
##  [88] bayesplot_1.9.0      shinythemes_1.2.0    rstudioapi_0.14     
##  [91] gamm4_0.2-6          tibble_3.1.8         bslib_0.4.2         
##  [94] stringi_1.7.12       highr_0.10           ps_1.7.2            
##  [97] Brobdingnag_1.2-9    lattice_0.20-44      Matrix_1.5-1        
## [100] nloptr_1.2.2.2       markdown_1.3         fftw_1.0-7          
## [103] shinyjs_2.1.0        tensorA_0.36.2       vctrs_0.5.2         
## [106] pillar_1.8.1         lifecycle_1.0.3      jquerylib_0.1.4     
## [109] bridgesampling_1.1-2 estimability_1.4.1   bitops_1.0-7        
## [112] httpuv_1.6.6         R6_2.5.1             promises_1.2.0.1    
## [115] gridExtra_2.3        codetools_0.2-18     boot_1.3-28         
## [118] colourpicker_1.2.0   MASS_7.3-54          gtools_3.9.3        
## [121] assertthat_0.2.1     rjson_0.2.21         withr_2.5.0         
## [124] shinystan_2.6.0      multcomp_1.4-17      mgcv_1.8-36         
## [127] parallel_4.1.0       gghalves_0.1.3       grid_4.1.0          
## [130] coda_0.19-4          minqa_1.2.4          rmarkdown_2.20      
## [133] shiny_1.7.3          base64enc_0.1-3      dygraphs_1.1.1.6