Code
# options to customize chunk outputs
knitr::opts_chunk$set(
tidy.opts = list(width.cutoff = 65),
tidy = TRUE,
message = FALSE
)
# ggplot font size
bs <- 12Vocal variation in monk parakeets
Acoustic analysis
Code
# print link to github repo if any
if (file.exists("./.git/config")){
config <- readLines("./.git/config")
url <- grep("url", config, value = TRUE)
url <- gsub("\\turl = |.git$", "", url)
cat("\nSource code and data found at [", url, "](", url, ")", sep = "")
}Source code and data found at https://github.com/maRce10/thick_billed_parrots_vocal_variation
Purpose
- Quantify contribution of different social levels of organization in the geographic variation of monk parakeets calls.
Report overview
Analysis flowchart
flowchart LR A(Read data) --> B(Measure acoustic<br>features) B --> C(Explore acoustic spaces) style A fill:#44015466 style B fill:#3E4A894D style C fill:#26828E4D
Load packages
Code
# knitr is require for creating html/pdf/word reports formatR is
# used for soft-wrapping code
# install/ load packages
sketchy::load_packages(packages = c("knitr", "formatR", "warbleR",
"ggplot2", "maRce10/PhenotypeSpace"))1 Measure acoustic features
Five sets of acoustic features are measured: - Cross-correlation - MFCCs - Spectral features - Spectral + harmonic features - Wavelet features
Code
mpa_calls <- readRDS("./data/processed/monk_parakeet_contactCalls_rangeComparison_extSelTable.RDS")
# cross correlation
mpa_xc <- warbleR::cross_correlation(X = mpa_calls, ovlp = 70, bp = c(0.1,
8), parallel = 20)
color = type
# save results
saveRDS(mpa_xc, file = "./data/processed/cross_correlation_mpa_calls.rds")
# mfccs
mpa_mfcc <- warbleR::mfcc_stats(X = mpa_calls, ovlp = 70, bp = c(0.1,
8), parallel = 20)
# save results
saveRDS(mpa_mfcc, file = "./data/processed/mfcc_mpa_calls.rds")
# spectral feautures
mpa_spft <- warbleR::spectro_analysis(X = mpa_calls, ovlp = 70, bp = c(0.1,
8), parallel = 20)
# save results
saveRDS(mpa_spft, file = "./data/processed/spectral_features_mpa_calls.rds")
# spectral + harmonic features
mpa_spft_harm <- warbleR::spectro_analysis(X = mpa_calls, ovlp = 70,
bp = c(0.1, 8), parallel = 20, harmonicity = TRUE)
# save results
saveRDS(mpa_spft_harm, file = "./data/processed/spectral_features_harmonics_mpa_calls.rds")
mpa_wpd <- warbleR::wpd_features(X = mpa_calls, parallel = 20)
# save results
saveRDS(mpa_wpd, file = "./data/processed/wavelets_mpa_calls.rds")2 Explore acoustic spaces
2.1 Cross-correlation
Code
mpa_xc <- readRDS("./data/processed/cross_correlation_mpa_calls.rds")
mpa_calls <- readRDS("./data/processed/monk_parakeet_contactCalls_rangeComparison_extSelTable.RDS")
mpa_calls$country.region <- paste(mpa_calls$country, mpa_calls$region,
sep = "-")
mpa_mds <- cmdscale(mpa_xc, k = 2)
mpa_calls$MDS1 <- mpa_mds[, 1]
mpa_calls$MDS2 <- mpa_mds[, 2]
ss_type <- space_similarity(country ~ MDS1 + MDS2, data = as.data.frame(mpa_calls),
method = "density.overlap")
# graph bidimensional space with gpplot coloring by type
ggplot(mpa_calls, aes(x = MDS1, y = MDS2, color = country)) + geom_point() +
scale_colour_viridis_d(option = "G", begin = 0.2, end = 0.8, name = "Country") +
theme_classic(base_size = bs) + labs(title = paste0("Mean density overlap:",
round(ss_type$mean.overlap, 2)), x = "MDS1", y = "MDS2")
Code
ss_type_us <- space_similarity(country.region ~ MDS1 + MDS2, data = as.data.frame(mpa_calls[mpa_calls$country ==
"United States", ]), method = "density.overlap")
ss_type_Uruguay <- space_similarity(country.region ~ MDS1 + MDS2,
data = as.data.frame(mpa_calls[mpa_calls$country == "Uruguay",
]), method = "density.overlap")
mean_ovlp <- mean(c(ss_type_us$mean.overlap, ss_type_Uruguay$mean.overlap))
ggplot(mpa_calls, aes(x = MDS1, y = MDS2, color = country.region)) +
geom_point() + scale_colour_viridis_d(option = "G", begin = 0.2,
end = 0.8, name = "country.region") + theme_classic(base_size = bs) +
facet_grid(~country, scales = "free") + labs(title = paste0("Mean density overlap:",
round(mean_ovlp, 2)), x = "MDS1", y = "MDS2")
2.2 MFCCs
Code
mpa_mfcc <- readRDS("./data/processed/mfcc_mpa_calls.rds")
# pca
pca <- prcomp(mpa_mfcc[, -c(1:2)], center = TRUE, scale. = TRUE)
mpa_calls$PC1.mfcc <- pca$x[, 1]
mpa_calls$PC2.mfcc <- pca$x[, 2]
ss_type <- space_similarity(country ~ PC1.mfcc + PC2.mfcc, data = as.data.frame(mpa_calls),
method = "density.overlap")
# graph bidimensional space with gpplot coloring by type
ggplot(mpa_calls, aes(x = PC1.mfcc, y = PC2.mfcc, color = country)) +
geom_point() + scale_colour_viridis_d(option = "G", begin = 0.2,
end = 0.8, name = "Country") + theme_classic(base_size = bs) +
labs(title = paste0("Mean density overlap:", round(ss_type$mean.overlap,
2)), x = "PC1", y = "PC2")
Code
ss_type_us <- space_similarity(country.region ~ PC1.mfcc + PC2.mfcc,
data = as.data.frame(mpa_calls[mpa_calls$country == "United States",
]), method = "density.overlap")
ss_type_Uruguay <- space_similarity(country.region ~ PC1.mfcc + PC2.mfcc,
data = as.data.frame(mpa_calls[mpa_calls$country == "Uruguay",
]), method = "density.overlap")
mean_ovlp <- mean(c(ss_type_us$mean.overlap, ss_type_Uruguay$mean.overlap))
ggplot(mpa_calls, aes(x = PC1.mfcc, y = PC2.mfcc, color = country.region)) +
geom_point() + scale_colour_viridis_d(option = "G", begin = 0.2,
end = 0.8, name = "country.region") + theme_classic(base_size = bs) +
facet_grid(~country, scales = "free") + labs(title = paste0("Mean density overlap:",
round(mean_ovlp, 2)), x = "PC1", y = "PC2")
2.3 Spectral features
Code
mpa_spft <- readRDS("./data/processed/spectral_features_mpa_calls.rds")
# pca
pca <- prcomp(mpa_spft[, -c(1:2)], center = TRUE, scale. = TRUE)
mpa_calls$PC1.spft <- pca$x[, 1]
mpa_calls$PC2.spft <- pca$x[, 2]
ss_type <- space_similarity(country ~ PC1.spft + PC2.spft, data = as.data.frame(mpa_calls),
method = "density.overlap")
# graph bidimensional space with gpplot coloring by type
ggplot(mpa_calls, aes(x = PC1.spft, y = PC2.spft, color = country)) +
geom_point() + scale_colour_viridis_d(option = "G", begin = 0.2,
end = 0.8, name = "Country") + theme_classic(base_size = bs) +
labs(title = paste0("Mean density overlap:", round(ss_type$mean.overlap,
2)), x = "PC1", y = "PC2")
Code
ss_type_us <- space_similarity(country.region ~ PC1.spft + PC2.spft,
data = as.data.frame(mpa_calls[mpa_calls$country == "United States",
]), method = "density.overlap")
ss_type_Uruguay <- space_similarity(country.region ~ PC1.spft + PC2.spft,
data = as.data.frame(mpa_calls[mpa_calls$country == "Uruguay",
]), method = "density.overlap")
mean_ovlp <- mean(c(ss_type_us$mean.overlap, ss_type_Uruguay$mean.overlap))
ggplot(mpa_calls, aes(x = PC1.spft, y = PC2.spft, color = country.region)) +
geom_point() + scale_colour_viridis_d(option = "G", begin = 0.2,
end = 0.8, name = "country.region") + theme_classic(base_size = bs) +
facet_grid(~country, scales = "free") + labs(title = paste0("Mean density overlap:",
round(mean_ovlp, 2)), x = "PC1", y = "PC2")
2.4 Spectral + harmonic features
Code
mpa_spft_harm <- readRDS("./data/processed/spectral_features_harmonics_mpa_calls.rds")
sub_mpa_calls <- mpa_calls[complete.cases(mpa_spft_harm), ]
# remove NAs
mpa_spft_harm <- mpa_spft_harm[complete.cases(mpa_spft_harm), ]
# pca
pca <- prcomp(mpa_spft_harm[, -c(1:2)], center = TRUE, scale. = TRUE)
sub_mpa_calls$PC1.spftharmft <- pca$x[, 1]
sub_mpa_calls$PC2.spftharmft <- pca$x[, 2]
ss_type <- space_similarity(country ~ PC1.spftharmft + PC2.spftharmft,
data = as.data.frame(sub_mpa_calls), method = "density.overlap")
# graph bidimensional space with gpplot coloring by type
ggplot(sub_mpa_calls, aes(x = PC1.spftharmft, y = PC2.spftharmft,
color = country)) + geom_point() + scale_colour_viridis_d(option = "G",
begin = 0.2, end = 0.8, name = "Country") + theme_classic(base_size = bs) +
labs(title = paste0("Mean density overlap:", round(ss_type$mean.overlap,
2)), x = "PC1", y = "PC2")
Code
ss_type_us <- space_similarity(country.region ~ PC1.spftharmft + PC2.spftharmft,
data = as.data.frame(sub_mpa_calls[sub_mpa_calls$country == "United States",
]), method = "density.overlap")
ss_type_Uruguay <- space_similarity(country.region ~ PC1.spftharmft +
PC2.spftharmft, data = as.data.frame(sub_mpa_calls[sub_mpa_calls$country ==
"Uruguay", ]), method = "density.overlap")
mean_ovlp <- mean(c(ss_type_us$mean.overlap, ss_type_Uruguay$mean.overlap))
ggplot(sub_mpa_calls, aes(x = PC1.spftharmft, y = PC2.spftharmft,
color = country.region)) + geom_point() + scale_colour_viridis_d(option = "G",
begin = 0.2, end = 0.8, name = "country.region") + theme_classic(base_size = bs) +
facet_grid(~country, scales = "free") + labs(title = paste0("Mean density overlap:",
round(mean_ovlp, 2)), x = "PC1", y = "PC2")
2.5 Wavelet features
Code
mpa_wavelet <- readRDS("./data/processed/wavelets_mpa_calls.rds")
# pca
pca <- prcomp(mpa_wavelet[, -c(1:2)], center = TRUE, scale. = TRUE)
mpa_calls$PC1.wpd <- pca$x[, 1]
mpa_calls$PC2.wpd <- pca$x[, 2]
ss_type <- space_similarity(country ~ PC1.wpd + PC2.wpd, data = as.data.frame(mpa_calls),
method = "density.overlap")
# graph bidimensional space with gpplot coloring by type
ggplot(mpa_calls, aes(x = PC1.wpd, y = PC2.wpd, color = country)) +
geom_point() + scale_colour_viridis_d(option = "G", begin = 0.2,
end = 0.8, name = "Country") + theme_classic(base_size = bs) +
labs(title = paste0("Mean density overlap:", round(ss_type$mean.overlap,
2)), x = "PC1", y = "PC2")
Code
ss_type_us <- space_similarity(country.region ~ PC1.wpd + PC2.wpd,
data = as.data.frame(mpa_calls[mpa_calls$country == "United States",
]), method = "density.overlap")
ss_type_Uruguay <- space_similarity(country.region ~ PC1.wpd + PC2.wpd,
data = as.data.frame(mpa_calls[mpa_calls$country == "Uruguay",
]), method = "density.overlap")
mean_ovlp <- mean(c(ss_type_us$mean.overlap, ss_type_Uruguay$mean.overlap))
ggplot(mpa_calls, aes(x = PC1.wpd, y = PC2.wpd, color = country.region)) +
geom_point() + scale_colour_viridis_d(option = "G", begin = 0.2,
end = 0.8, name = "country.region") + theme_classic(base_size = bs) +
facet_grid(~country, scales = "free") + labs(title = paste0("Mean density overlap:",
round(mean_ovlp, 2)), x = "PC1", y = "PC2")
Takeaways
- MFCCs seems to do a better job at capturing the acoustic variation in thick-billed parrots calls.
Session information
─ Session info ───────────────────────────────────────────────────────────────
setting value
version R version 4.5.0 (2025-04-11)
os Ubuntu 22.04.5 LTS
system x86_64, linux-gnu
ui X11
language (EN)
collate en_US.UTF-8
ctype en_US.UTF-8
tz America/Costa_Rica
date 2025-07-23
pandoc 3.2 @ /usr/lib/rstudio/resources/app/bin/quarto/bin/tools/x86_64/ (via rmarkdown)
quarto 1.7.31 @ /usr/local/bin/quarto
─ Packages ───────────────────────────────────────────────────────────────────
package * version date (UTC) lib source
abind 1.4-8 2024-09-12 [1] CRAN (R 4.5.0)
bitops 1.0-9 2024-10-03 [3] CRAN (R 4.5.0)
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devtools 2.4.5 2022-10-11 [1] CRAN (R 4.5.0)
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gridExtra 2.3 2017-09-09 [1] CRAN (R 4.5.0)
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httr 1.4.7 2023-08-15 [3] CRAN (R 4.5.0)
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MASS 7.3-65 2025-02-28 [4] CRAN (R 4.4.3)
Matrix 1.7-3 2025-03-11 [4] CRAN (R 4.4.3)
memoise 2.0.1 2021-11-26 [3] CRAN (R 4.1.2)
mgcv 1.9-3 2025-04-04 [4] CRAN (R 4.5.0)
mime 0.13 2025-03-17 [1] CRAN (R 4.5.0)
miniUI 0.1.2 2025-04-17 [3] CRAN (R 4.5.0)
NatureSounds * 1.0.5 2025-01-17 [1] CRAN (R 4.5.0)
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permute 0.9-8 2025-06-25 [1] CRAN (R 4.5.0)
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[2] /usr/local/lib/R/site-library
[3] /usr/lib/R/site-library
[4] /usr/lib/R/library
* ── Packages attached to the search path.
──────────────────────────────────────────────────────────────────────────────