Effect of background noise on schroeder structure detection v3
Fine scale acoustic perception in zebra finches
Source code and data found at https://github.com/maRce10/acoustic-fine-features-zebra-finch
1 Purpose
- Evaluate the effect of degradation on the detection of acoustic fine structural variation in Schroeders
2 Data analysis workflow
flowchart LR A[Raw Schroeder\nrecordings] --> B(Aritfically\nDecrease SNR) B --> C(measure\nSchroeder\nsimilarity) C --> D(Matrix\nregression\nmodels) style A fill:#44015466 style B fill:#3E4A894D style C fill:#6DCD594D style D fill:#FDE7254D
3 Degradation as a function of signal-to-noise ratio
- Adding synthetic noise to the master sound file annotations to decrease the signal-to-noise
- Evaluate at which signal-to-noise ratio Schroeder sign can still be distinguished
3.1 On 200ms schroeders
3.1.1 Add synthetic noise
3.1.1.1 Repeated Schroeders
Code
master_annotations <- imp_raven(path = "./data/processed", files = "200ms_schroeders.txt",
warbler.format = TRUE, all.data = TRUE)
master_annotations$sound.id <- paste0("f0:", master_annotations$f0,
"_comp:", master_annotations$label)
master_annotations$sound.id[1] <- "start_marker"
master_annotations$sound.id[nrow(master_annotations)] <- "end_marker"
est_master <- selection_table(master_annotations[grep("marker", master_annotations$sound.id,
invert = TRUE), ], extended = TRUE, mar = 0.1, path = "./data/processed")
# est_schr <-
# readRDS('./data/processed/extended_selection_table_schroeders.RDS')
# est_schr <- readRDS(file.path(path,
# 'extended_sel_table_degradation_exp.RDS')) est_schr$treatment
# <- ifelse(grepl('inside', est_schr$sound.files), 'inside',
# 'outside') est_schr$sound.id <- 1:nrow(est_schr) est_in <-
# est_schr[est_schr$treatment == 'inside', ]
est_master <- signal_to_noise_ratio(est_master, mar = 0.07, cores = 20)
hist(est_master$signal.to.noise.ratio)
adj_snr_schroeder_white <- lapply(30:1, function(i) {
print(i)
est_schr_adj <- baRulho::add_noise(X = est_master, target.snr = i,
precision = 0.1, mar = 0.07, cores = 20, kind = "white", seed = NULL)
est_schr_adj$target.snr <- i
return(est_schr_adj)
})
beepr::beep(2)
saveRDS(adj_snr_schroeder_white, "./data/processed/200ms_schroeders_adjusted_snr_white_noise.RDS")3.1.1.2 Single Schroders
Code
est_schr <- readRDS("./data/processed/extended_selection_table_single_schroeders.RDS")
est_schr$sound.id <- "a"
est_schr <- signal_to_noise_ratio(X = est_schr, mar = 0.01, cores = 20,
bp = NULL)
hist(est_schr$signal.to.noise.ratio)
adj_snr_schroeder_white <- lapply(30:1, function(i) {
print(i)
est_schr_adj <- add_noise(X = est_schr, target.snr = i, precision = 0.1,
mar = 0.01, cores = 1, bp = NULL, kind = "white", seed = NULL)
est_schr_adj$target.snr <- i
return(est_schr_adj)
})
beepr::beep(2)
saveRDS(adj_snr_schroeder_white, "./data/processed/single_schroeder_adjusted_snr_white_noise.RDS")3.1.2 Check SNR adjusted waves
3.1.2.1 Examples
3.1.2.1.1 Repeated Schroeders
200 Hz, 9 components, positive phase
Code
adj_snr_schroeder <- readRDS("./data/processed/200ms_schroeders_adjusted_snr_white_noise.RDS")
# print(adj_snr_schroeder[[1]]$sound.id[10])
par(mar = c(0, 0, 0, 0), mfrow = c(5, 6))
for (i in 1:30) {
warbleR:::spectro_wrblr_int(wave = read_sound_file(adj_snr_schroeder[[i]],
10, from = 0, to = Inf), flim = c(0, 5), osc = FALSE, scale = FALSE,
axisY = FALSE, axisX = FALSE, grid = FALSE, palette = viridis::mako,
collevels = seq(-110, 0, 5))
text(0.2, 4, paste("SNR:", adj_snr_schroeder[[i]]$target.snr[1]),
col = "black", cex = 1.3)
}
500 Hz, 16 components, positive phase
Code
# print(adj_snr_schroeder[[1]]$sound.id[150])
par(mar = c(0, 0, 0, 0), mfrow = c(5, 6))
for (i in 1:30) {
warbleR:::spectro_wrblr_int(wave = read_sound_file(adj_snr_schroeder[[i]],
150, from = 0, to = Inf), flim = c(0, 12), osc = FALSE, scale = FALSE,
axisY = FALSE, axisX = FALSE, grid = FALSE, palette = viridis::mako,
collevels = seq(-110, 0, 5))
text(0.2, 11, paste("SNR:", adj_snr_schroeder[[i]]$target.snr[1]),
col = "black", cex = 1.3)
}
700 Hz, 24 components, positive phase
Code
# print(adj_snr_schroeder[[1]]$sound.id[250])
par(mar = c(0, 0, 0, 0), mfrow = c(5, 6))
for (i in 1:30) {
warbleR:::spectro_wrblr_int(wave = read_sound_file(adj_snr_schroeder[[i]],
250, from = 0, to = Inf), flim = c(0, 20), osc = FALSE, scale = FALSE,
axisY = FALSE, axisX = FALSE, grid = FALSE, palette = viridis::mako,
collevels = seq(-110, 0, 5))
text(0.2, 18, paste("SNR:", adj_snr_schroeder[[i]]$target.snr[1]),
col = "black", cex = 1.3)
}
3.1.2.1.2 Single Schroeders
200 Hz, 9 components, positive phase
Code
adj_snr_schroeder <- readRDS("./data/processed/single_schroeder_adjusted_snr_white_noise.RDS")
# print(adj_snr_schroeder[[1]]$sound.id[10])
par(mar = c(0, 0, 0, 0), mfrow = c(5, 6))
for (i in 1:30) {
wv <- read_sound_file(adj_snr_schroeder[[i]], 10)@left
plot(wv, type = "l", lwd = 2, col = mako(10)[6], xlab = "", ylab = "",
axes = FALSE, ylim = c(-2, 2))
box()
text(length(wv)/2, 1.7, paste("SNR:", adj_snr_schroeder[[i]]$target.snr[1]),
col = "black", cex = 1.3)
}
200 Hz, 9 components, negative phase
Code
# print(adj_snr_schroeder[[1]]$sound.id[9])
par(mar = c(0, 0, 0, 0), mfrow = c(5, 6))
for (i in 1:30) {
wv <- read_sound_file(adj_snr_schroeder[[i]], 9)@left
plot(wv, type = "l", lwd = 2, col = mako(10)[6], xlab = "", ylab = "",
axes = FALSE, ylim = c(-2, 2))
box()
text(length(wv)/2, 1.7, paste("SNR:", adj_snr_schroeder[[i]]$target.snr[1]),
col = "black", cex = 1.3)
}
500 Hz, 16 components, positive phase
Code
# print(adj_snr_schroeder[[1]]$sound.files[150])
par(mar = c(0, 0, 0, 0), mfrow = c(5, 6))
for (i in 1:30) {
wv <- read_sound_file(adj_snr_schroeder[[i]], 150)@left
plot(wv, type = "l", lwd = 2, col = mako(10)[6], xlab = "", ylab = "",
axes = FALSE, ylim = c(-2, 2))
box()
text(length(wv)/2, 1.7, paste("SNR:", adj_snr_schroeder[[i]]$target.snr[1]),
col = "black", cex = 1.3)
}
500 Hz, 16 components, negative phase
Code
# print(adj_snr_schroeder[[1]]$sound.files[149])
par(mar = c(0, 0, 0, 0), mfrow = c(5, 6))
for (i in 1:30) {
wv <- read_sound_file(adj_snr_schroeder[[i]], 149)@left
plot(wv, type = "l", lwd = 2, col = mako(10)[6], xlab = "", ylab = "",
axes = FALSE, ylim = c(-2, 2))
box()
text(length(wv)/2, 1.7, paste("SNR:", adj_snr_schroeder[[i]]$target.snr[1]),
col = "black", cex = 1.3)
}
700 Hz, 24 components, positive phase
Code
# print(adj_snr_schroeder[[1]]$sound.files[250])
par(mar = c(0, 0, 0, 0), mfrow = c(5, 6))
for (i in 1:30) {
wv <- read_sound_file(adj_snr_schroeder[[i]], 250)@left
plot(wv, type = "l", lwd = 2, col = mako(10)[6], xlab = "", ylab = "",
axes = FALSE, ylim = c(-2, 2))
box()
text(length(wv)/2, 1.7, paste("SNR:", adj_snr_schroeder[[i]]$target.snr[1]),
col = "black", cex = 1.3)
}
700 Hz, 24 components, negative phase
Code
# print(adj_snr_schroeder[[1]]$sound.files[249])
par(mar = c(0, 0, 0, 0), mfrow = c(5, 6))
for (i in 1:30) {
wv <- read_sound_file(adj_snr_schroeder[[i]], 249)@left
plot(wv, type = "l", lwd = 2, col = mako(10)[6], xlab = "", ylab = "",
axes = FALSE, ylim = c(-2, 2))
box()
text(length(wv)/2, 1.7, paste("SNR:", adj_snr_schroeder[[i]]$target.snr[1]),
col = "black", cex = 1.3)
}
3.1.3 Measure waveform similarity
3.1.3.1 On repeated Schroeders
Code
adj_snr_schroeder <- readRDS("./data/processed/200ms_schroeders_adjusted_snr_white_noise.RDS")
dist_white_noise <- lapply(adj_snr_schroeder, function(schroeders) {
print(schroeders$target.snr[1])
dtw_dist <- waveform_similarity(X = schroeders, parallel = 22,
output = "rectangular", sim.method = "DTW", type = "sliding",
wl = 32)
cor_dist <- waveform_similarity(X = schroeders, parallel = 22,
output = "rectangular", sim.method = "correlation", type = "sliding",
wl = 32)
dtw_dist$cor <- cor_dist$similarity
names(dtw_dist) <- c("schr1", "schr2", "dtw", "cor")
dtw_dist$target.snr <- schroeders$target.snr[1]
return(dtw_dist)
})
beepr::beep(2)
saveRDS(dist_white_noise, "./data/processed/waveform_similarity_adjusted_snr_200ms_schroeders_white_noise.RDS")3.1.3.2 On single Schroeders
Code
adj_snr_schroeder <- readRDS("./data/processed/single_schroeder_adjusted_snr_white_noise.RDS")
rep_schroeder <- readRDS("./data/processed/200ms_schroeders_adjusted_snr_white_noise.RDS")[[1]]
dist_white_noise <- lapply(adj_snr_schroeder, function(schroeders) {
schroeders$sound.id <- sapply(schroeders$sound.files, function(x) {
a <- strsplit(x, "_")[[1]]
paste(paste0("f0:", gsub("-", " ", a[2])), gsub("ncomp-",
"comp:", a[3]), substr(a[4], 0, 1), sep = "_")
})
schroeders$bottom.freq <- sapply(schroeders$sound.id, function(x) rep_schroeder$bottom.freq[rep_schroeder$sound.id ==
x])
schroeders$top.freq <- sapply(schroeders$sound.id, function(x) rep_schroeder$top.freq[rep_schroeder$sound.id ==
x])
print(schroeders$target.snr[1])
dtw_dist <- waveform_similarity(X = schroeders, parallel = 22,
output = "rectangular", sim.method = "DTW", type = "sliding",
wl = 32)
cor_dist <- waveform_similarity(X = schroeders, parallel = 22,
output = "rectangular", sim.method = "correlation", type = "sliding",
wl = 32)
dtw_dist$cor <- cor_dist$similarity
names(dtw_dist) <- c("schr1", "schr2", "dtw", "cor")
dtw_dist$target.snr <- schroeders$target.snr[1]
return(dtw_dist)
})
beepr::beep(2)
saveRDS(dist_white_noise, "./data/processed/waveform_similarity_adjusted_snr_single_schroeder_white_noise.RDS")3.1.3.3 Statistical analysis
Modeling: - Multiple Regression on distance Matrices - Model:
\[\begin{align*}
Dissimilarity &\sim frequency + components + phase
\end{align*}\] - Response values scaled to make effect sizes comparable across models - Predictors were coded as pairwise binary matrices in which 0 means that calls in a dyad belong to the same level and 1 means calls belong to different levels - One model for each environment treatment as well as on the original model sounds
3.1.3.3.1 On repeated Schroeders
3.1.3.3.1.1 DTW
Code
dtw_dists_snr_list <- readRDS("./data/processed/waveform_similarity_adjusted_snr_200ms_schroeders_white_noise_v2.RDS")
adj_snr_schroeder1 <- readRDS("./data/processed/200ms_schroeders_adjusted_snr_white_noise.RDS")[[1]]
adj_snr_schroeder1$sound.id <- gsub("f0:f0 ", "f0:", adj_snr_schroeder1$sound.id)
dtw_wv_mod_list <- warbleR:::pblapply_wrblr_int(dtw_dists_snr_list,
cl = 20, function(x) {
# scale between 0 and 1
x$dtw <- (x$dtw - min(x$dtw))/(max(x$dtw) - min(x$dtw))
target_snr <- x$target.snr
x$target.snr <- NULL
x$schr1 <- sapply(x$schr1, function(x) adj_snr_schroeder1$sound.id[adj_snr_schroeder1$sound.files ==
gsub("-1$", "", x)])
x$schr2 <- sapply(x$schr2, function(x) adj_snr_schroeder1$sound.id[adj_snr_schroeder1$sound.files ==
gsub("-1$", "", x)])
dtw_tri <- PhenotypeSpace::rectangular_to_triangular(x[, c("schr1",
"schr2", "dtw")])
freq_bi_tri <- as.dist(binary_triangular_matrix(group = sapply(strsplit(rownames(dtw_tri),
"_"), "[[", 1)))
comp_bi_tri <- as.dist(binary_triangular_matrix(group = gsub("_n|_p",
"", sapply(strsplit(rownames(dtw_tri), "_"), "[[", 2))))
sign_bi_tri <- as.dist(binary_triangular_matrix(group = sapply(strsplit(rownames(dtw_tri),
"_"), "[[", 3)))
rect_var <- cbind(as.dist(dtw_tri), freq_bi_tri, comp_bi_tri,
sign_bi_tri)
colnames(rect_var) <- c("dtw_dist", "frequency", "components",
"phase")
dtw_wv_mod <- MRM2(formula = dtw_dist ~ frequency + components +
phase, nperm = 10000, mat = rect_var)
return(dtw_wv_mod)
})
names(dtw_wv_mod_list) <- paste0("snr:", sapply(dtw_dists_snr_list,
function(x) x$target.snr[1]))
saveRDS(dtw_wv_mod_list, "./data/processed/matrix_regression_dtw_distance_varying_snr_white_noise_32wl.RDS")3.1.3.3.1.2 Correlation
Code
dtw_dists_snr_list <- readRDS("./data/processed/waveform_similarity_adjusted_snr_200ms_schroeders_white_noise.RDS")
adj_snr_schroeder1 <- readRDS("./data/processed/200ms_schroeders_adjusted_snr_white_noise.RDS")[[1]]
adj_snr_schroeder1$sound.id <- gsub("f0:f0 ", "f0:", adj_snr_schroeder1$sound.id)
cor_wv_mod_list <- warbleR:::pblapply_wrblr_int(dtw_dists_snr_list,
cl = 20, function(x) {
# convert to distance
x$cor <- 1 - x$cor
# scale between 0 and 1
x$cor <- (x$cor - min(x$cor))/(max(x$cor) - min(x$cor))
target_snr <- x$target.snr
x$target.snr <- NULL
x$schr1 <- sapply(x$schr1, function(x) adj_snr_schroeder1$sound.id[adj_snr_schroeder1$sound.files ==
gsub("-1$", "", x)])
x$schr2 <- sapply(x$schr2, function(x) adj_snr_schroeder1$sound.id[adj_snr_schroeder1$sound.files ==
gsub("-1$", "", x)])
cor_tri <- PhenotypeSpace::rectangular_to_triangular(x[, c("schr1",
"schr2", "cor")])
freq_bi_tri <- as.dist(binary_triangular_matrix(group = sapply(strsplit(rownames(cor_tri),
"_"), "[[", 1)))
comp_bi_tri <- as.dist(binary_triangular_matrix(group = gsub("_n|_p",
"", sapply(strsplit(rownames(cor_tri), "_"), "[[", 2))))
sign_bi_tri <- as.dist(binary_triangular_matrix(group = sapply(strsplit(rownames(cor_tri),
"_"), "[[", 3)))
rect_var <- cbind(as.dist(cor_tri), freq_bi_tri, comp_bi_tri,
sign_bi_tri)
colnames(rect_var) <- c("cor_dist", "frequency", "components",
"phase")
cor_wv_mod <- MRM2(formula = cor_dist ~ frequency + components +
phase, nperm = 10000, mat = rect_var)
return(cor_wv_mod)
})
names(cor_wv_mod_list) <- paste0("snr:", sapply(dtw_dists_snr_list,
function(x) x$target.snr[1]))
saveRDS(cor_wv_mod_list, "./data/processed/matrix_regression_correlation_varying_snr_white_noise.RDS")3.1.3.3.2 On single Schroeders
3.1.3.3.2.1 DTW
Code
sim_dists_snr_list <- readRDS("./data/processed/waveform_similarity_adjusted_snr_single_schroeder_white_noise.RDS")
adj_snr_schroeder1 <- readRDS("./data/processed/200ms_schroeders_adjusted_snr_white_noise.RDS")[[1]]
adj_snr_schroeder1$sound.id <- gsub("f0:f0 ", "f0:", adj_snr_schroeder1$sound.id)
dtw_wv_mod_list <- warbleR:::pblapply_wrblr_int(sim_dists_snr_list,
cl = 20, function(x) {
# scale between 0 and 1
x$dtw <- (x$dtw - min(x$dtw))/(max(x$dtw) - min(x$dtw))
target_snr <- x$target.snr
x$target.snr <- NULL
x$schr1 <- substr(sapply(strsplit(x$schr1, ".wav"), "[[",
1), start = 5, stop = 1000)
x$schr2 <- substr(sapply(strsplit(x$schr2, ".wav"), "[[",
1), start = 5, stop = 1000)
dtw_tri <- PhenotypeSpace::rectangular_to_triangular(x[, c("schr1",
"schr2", "dtw")])
freq_bi_tri <- as.dist(binary_triangular_matrix(group = sapply(strsplit(rownames(dtw_tri),
"_"), "[[", 1)))
comp_bi_tri <- as.dist(binary_triangular_matrix(group = gsub("_n|_p",
"", sapply(strsplit(rownames(dtw_tri), "_"), "[[", 2))))
sign_bi_tri <- as.dist(binary_triangular_matrix(group = sapply(strsplit(rownames(dtw_tri),
"_"), "[[", 3)))
rect_var <- cbind(as.dist(dtw_tri), freq_bi_tri, comp_bi_tri,
sign_bi_tri)
colnames(rect_var) <- c("dtw_dist", "frequency", "components",
"phase")
dtw_wv_mod <- MRM2(formula = dtw_dist ~ frequency + components +
phase, nperm = 10000, mat = rect_var)
return(dtw_wv_mod)
})
names(dtw_wv_mod_list) <- paste0("snr:", sapply(sim_dists_snr_list,
function(x) x$target.snr[1]))
saveRDS(dtw_wv_mod_list, "./data/processed/matrix_regression_dtw_distance_varying_snr_white_noise_single_shroeder_32wl.RDS")3.1.3.3.2.2 Correlation
Code
cor_snr_list <- readRDS("./data/processed/waveform_similarity_adjusted_snr_single_schroeder_white_noise.RDS")
adj_snr_schroeder1 <- readRDS("./data/processed/200ms_schroeders_adjusted_snr_white_noise.RDS")[[1]]
adj_snr_schroeder1$sound.id <- gsub("f0:f0 ", "f0:", adj_snr_schroeder1$sound.id)
cor_wv_mod_list <- warbleR:::pblapply_wrblr_int(cor_snr_list, cl = 20,
function(x) {
# convert to distance
x$cor <- 1 - x$cor
# scale between 0 and 1
x$cor <- (x$cor - min(x$cor))/(max(x$cor) - min(x$cor))
target_snr <- x$target.snr
x$target.snr <- NULL
x$schr1 <- substr(sapply(strsplit(x$schr1, ".wav"), "[[",
1), start = 5, stop = 1000)
x$schr2 <- substr(sapply(strsplit(x$schr2, ".wav"), "[[",
1), start = 5, stop = 1000)
cor_tri <- PhenotypeSpace::rectangular_to_triangular(x[, c("schr1",
"schr2", "cor")])
freq_bi_tri <- as.dist(binary_triangular_matrix(group = sapply(strsplit(rownames(cor_tri),
"_"), "[[", 1)))
comp_bi_tri <- as.dist(binary_triangular_matrix(group = gsub("_n|_p",
"", sapply(strsplit(rownames(cor_tri), "_"), "[[", 2))))
sign_bi_tri <- as.dist(binary_triangular_matrix(group = sapply(strsplit(rownames(cor_tri),
"_"), "[[", 3)))
rect_var <- cbind(as.dist(cor_tri), freq_bi_tri, comp_bi_tri,
sign_bi_tri)
colnames(rect_var) <- c("cor_dist", "frequency", "components",
"phase")
cor_wv_mod <- MRM2(formula = cor_dist ~ frequency + components +
phase, nperm = 10000, mat = rect_var)
return(cor_wv_mod)
})
names(cor_wv_mod_list) <- paste0("snr:", sapply(cor_snr_list, function(x) x$target.snr[1]))
saveRDS(cor_wv_mod_list, "./data/processed/matrix_regression_correlation_varying_snr_white_noise_single_shroeder.RDS")3.2 Results
3.2.1 Read model fits
Code
mod_list <- c(dtw_rep = "matrix_regression_dtw_distance_varying_snr_white_noise_32wl.RDS",
cor_rep = "matrix_regression_correlation_varying_snr_white_noise.RDS",
dtw_single = "matrix_regression_dtw_distance_varying_snr_white_noise_single_shroeder_32wl.RDS",
cor_single = "matrix_regression_correlation_varying_snr_white_noise_single_shroeder.RDS")
out <- lapply(names(mod_list), function(y) {
# print(y)
x <- mod_list[[y]]
# repeat Schroeder DTW models
mod <- readRDS(file.path("./data/processed/", x))
estimates <- do.call(rbind, lapply(seq_along(mod), function(x) {
Y <- data.frame(mod[[x]]$coef[-1, ])
Y$predictor <- rownames(Y)
Y$model <- as.numeric(gsub("snr:", "", names(mod)[x]))
names(Y) <- c("coef", "p", "predictor", "model")
return(Y)
}))
estimates$model_f <- factor(estimates$model, levels = 1:30)
estimates$type <- y
return(estimates)
})
estimates <- do.call(rbind, out)
estimates$meth <- ifelse(grepl("dtw", estimates$type), "waveform DTW",
"waveform correlation")
estimates$type <- ifelse(grepl("single", estimates$type), "single cycle",
"repeated cycle")
ggplot(estimates[estimates$predictor == "phase", ], aes(y = coef,
x = model, color = meth, lty = type)) + geom_line(linewidth = 1.6) +
# geom_smooth() +
labs(x = "Signal-to-noise ratio (dB)", y = "Effect size", color = "Metric",
lty = "Input data") + scale_color_viridis_d(alpha = 0.7, begin = 0.2,
end = 0.8, option = "G") + theme_classic()
4 Takeaways
Session information
R version 4.5.0 (2025-04-11)
Platform: x86_64-pc-linux-gnu
Running under: Ubuntu 22.04.5 LTS
Matrix products: default
BLAS: /usr/lib/x86_64-linux-gnu/blas/libblas.so.3.10.0
LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.10.0 LAPACK version 3.10.0
locale:
[1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
[3] LC_TIME=es_CR.UTF-8 LC_COLLATE=en_US.UTF-8
[5] LC_MONETARY=es_CR.UTF-8 LC_MESSAGES=en_US.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
time zone: America/Costa_Rica
tzcode source: system (glibc)
attached base packages:
[1] stats graphics grDevices utils datasets methods base
other attached packages:
[1] numform_0.7.0 ecodist_2.1.3 PhenotypeSpace_0.1.0
[4] ggplot2_3.5.2 baRulho_2.1.5 ohun_1.0.2
[7] Rraven_1.0.14 viridis_0.6.5 viridisLite_0.4.2
[10] warbleR_1.1.35 NatureSounds_1.0.5 tuneR_1.4.7
[13] seewave_2.2.3 formatR_1.14 knitr_1.50
[16] kableExtra_1.4.0
loaded via a namespace (and not attached):
[1] DBI_1.2.3 bitops_1.0-9 deldir_2.0-4
[4] pbapply_1.7-2 gridExtra_2.3 remotes_2.5.0
[7] permute_0.9-7 testthat_3.2.3 rlang_1.1.6
[10] magrittr_2.0.3 e1071_1.7-16 compiler_4.5.0
[13] spatstat.geom_3.3-6 mgcv_1.9-3 png_0.1-8
[16] systemfonts_1.2.3 vctrs_0.6.5 Sim.DiffProc_4.9
[19] stringr_1.5.1 pkgconfig_2.0.3 crayon_1.5.3
[22] fastmap_1.2.0 backports_1.5.0 labeling_0.4.3
[25] rmarkdown_2.29 xfun_0.52 jsonlite_2.0.0
[28] goftest_1.2-3 spatstat.utils_3.1-4 terra_1.8-42
[31] Deriv_4.1.6 parallel_4.5.0 cluster_2.1.8.1
[34] R6_2.6.1 stringi_1.8.7 RColorBrewer_1.1-3
[37] spatstat.data_3.1-6 spatstat.univar_3.1-2 brio_1.1.5
[40] Rcpp_1.0.14 tensor_1.5 Matrix_1.7-3
[43] splines_4.5.0 igraph_2.1.4 tidyselect_1.2.1
[46] rstudioapi_0.17.1 abind_1.4-8 yaml_2.3.10
[49] vegan_2.6-10 codetools_0.2-20 dtw_1.23-1
[52] spatstat.random_3.4-1 spatstat.explore_3.4-2 curl_6.3.0
[55] lattice_0.22-7 tibble_3.3.0 withr_3.0.2
[58] evaluate_1.0.3 signal_1.8-1 sf_1.0-20
[61] sketchy_1.0.5 units_0.8-7 proxy_0.4-27
[64] polyclip_1.10-7 xml2_1.3.8 pillar_1.10.2
[67] packrat_0.9.2 KernSmooth_2.23-26 checkmate_2.3.2
[70] generics_0.1.4 sp_2.2-0 RCurl_1.98-1.17
[73] scales_1.4.0 class_7.3-23 glue_1.8.0
[76] tools_4.5.0 xaringanExtra_0.8.0 grid_4.5.0
[79] nlme_3.1-168 raster_3.6-32 cli_3.6.5
[82] spatstat.sparse_3.1-0 svglite_2.1.3 dplyr_1.1.4
[85] gtable_0.3.6 fftw_1.0-9 digest_0.6.37
[88] classInt_0.4-11 rjson_0.2.23 htmlwidgets_1.6.4
[91] farver_2.1.2 htmltools_0.5.8.1 lifecycle_1.0.4
[94] httr_1.4.7 MASS_7.3-65