Code
# set working directory as project directory or one directory above,
knitr::opts_knit$set(root.dir = "..")Acoustic analysis
Cape parrot vocal dialects
Source code and data found at https://github.com/maRce10/cape_parrot_dialects
1 Purpose
Measure acoustic structure of cape parrot contact calls
Compare acoustic dissimilarity between individuals from different localities and regions
Load packages
Code
# knitr is require for creating html/pdf/word reports formatR is
# used for soft-wrapping code
# install/ load packages
sketchy::load_packages(packages = c("knitr", "formatR", "viridis",
"warbleR", github = "maRce10/PhenotypeSpace", "ggplot2", "randomForest",
"mlbench", "caret", "pbapply", "vegan", "umap", "brms", "brmsish",
"maRce10/ohun"))Warning: replacing previous import 'brms::rstudent_t' by 'ggdist::rstudent_t'
when loading 'brmsish'Warning: replacing previous import 'brms::dstudent_t' by 'ggdist::dstudent_t'
when loading 'brmsish'Warning: replacing previous import 'brms::qstudent_t' by 'ggdist::qstudent_t'
when loading 'brmsish'Warning: replacing previous import 'brms::pstudent_t' by 'ggdist::pstudent_t'
when loading 'brmsish'2 Acoustic analysis
Code
## Format data dat <-
## read.csv('./data/raw/consolidated_sound_files_CPV_contact_calls_USEaug2026
## - UPDATED_USE for analyses.csv')
## names(dat)[grep('Regions..4.', names(dat))] <- 'Regions4'
## nrow(dat)
# all(dat$New_Name %in% st$sound.files) table(dat$Sorted)
# dat <- dat[dat$Sorted != 'delete', ] unique(dat$New_Name)
# ohun::feature_acoustic_data(path =
# './data/raw/consolidated_files') warbleR_options(path =
# './data/raw/consolidated_files') st
# <-selection_table(whole.recs = TRUE) st <- st[st$sound.files
# %in% dat$New_Name, ] nrow(st) nrow(dat) st$sorted <-
# sapply(st$sound.files, function(x) dat$Sorted[dat$New_Name ==
# x][1]) table(st$sorted)
# spectrograms(st, wl = 512, flim = c(0, 10), dest.path =
# './data/processed/spectrograms', pal = viridis, collevels =
# seq(-100, 0, 5)) spectrograms(st[st$sorted == 'unsorted', ],
# wl = 512, flim = c(0, 10), dest.path =
# './data/processed/unsorted_spectrograms', pal = viridis,
# collevels = seq(-100, 0, 5)) tailor_sels(st, auto.next = TRUE,
# flim = c(0, 8), collevels = seq(-100, 0, 5))2.1 Make selection table
Code
sel_tab <- selection_table(path = "./data/raw/consolidated_files/",
whole.recs = TRUE)
tailored <- read.csv("./data/raw/consolidated_files/seltailor_output.csv")
tailored <- tailored[tailored$tailored == "y", ]
non_tailored <- sel_tab[!sel_tab$sound.files %in% tailored$sound.files,
]
non_tailored$tailored <- "n"
tailored$top.freq[is.na(tailored$bottom.freq)] <- non_tailored$bottom.freq <- min(tailored$bottom.freq,
na.rm = TRUE)
tailored$top.freq[is.na(tailored$top.freq)] <- non_tailored$top.freq <- max(tailored$top.freq,
na.rm = TRUE)
comm_names <- intersect(names(tailored), names(non_tailored))
all_sels <- rbind(tailored[, comm_names], non_tailored[, comm_names])
write.csv(all_sels, "./data/processed/selection_table_entire_sound_files.csv",
row.names = FALSE)2.2 Run cross-correlation
Code
sel_tab <- read.csv("./data/processed/selection_table_entire_sound_files.csv")
xcorr <- cross_correlation(X = sel_tab, path = "./data/raw/consolidated_files/",
method = 2, parallel = 1)
rownames(xcorr) <- gsub("-1$", "", rownames(xcorr))
colnames(xcorr) <- gsub("-1$", "", colnames(xcorr))
saveRDS(xcorr, "./data/processed/cross_correlation_matrix.RDS")
# less than 0.1% were undefined
sum(is.infinite(xcorr))/length(xcorr)
# convert infinite to mean xcorr
xcorr[is.infinite(xcorr)] <- mean(xcorr[!is.infinite(xcorr) & xcorr <
1])
xcorr_mds <- cmdscale(d = as.dist(xcorr), k = 2)
rownames(xcorr_mds) <- gsub("-1$", "", rownames(xcorr_mds))
saveRDS(xcorr_mds, "./data/processed/cross_correlation_MDS.RDS")3 Data description
Code
# add data from second location
dat <- read.csv("./data/raw/consolidated_sound_files_CPV_contact_calls_USEaug2026 - UPDATED_USE for analyses.csv")
names(dat)[grep("Regions..4.", names(dat))] <- "Regions4"
names(dat) <- gsub("..cluster.", ".for.cluster", names(dat))
dat <- dat[!is.na(dat$Location.for.cluster) & !is.na(dat$Longitude.for.cluster) &
!is.na(dat$Latitude.for.cluster), ]- 6287 calls
- 14 localities
- 4 regions
- Number of localities per region:
Code
agg <- aggregate(Location.for.cluster ~ Regions4, dat, function(x) length(unique(x)))
names(agg) <- c("region", "localities")
agg$calls <- aggregate(Location.for.cluster ~ Regions4, dat, length)[,
2]
agg$localities <- aggregate(Location.for.cluster ~ Regions4, dat,
function(x) paste(unique(x), collapse = "-"))[, 2]
agg| region | localities | calls |
|---|---|---|
| central subA | Jetty River Lodge-Ntafufu Ecolodge | 687 |
| central subB | Polela Sawmill-iGxalingenwa Nature Reserve-Marutswa Forest-Salt Spring Farm-Hoha Forest | 1082 |
| northern | Amorentia | 717 |
| southern | Hogsback-Schwarzwald Forest-Alice Pecan Orchard-Stutterheim-King William’s Town-Baddaford Farm | 3801 |
- Region of each locality:
Code
agg <- aggregate(Regions4 ~ Location.for.cluster, dat, function(x) paste(unique(x),
collapse = "-"))
names(agg) <- c("localities", "region")
agg[order(agg$region), 2:1]| region | localities | |
|---|---|---|
| 7 | central subA | Jetty River Lodge |
| 10 | central subA | Ntafufu Ecolodge |
| 5 | central subB | Hoha Forest |
| 6 | central subB | iGxalingenwa Nature Reserve |
| 9 | central subB | Marutswa Forest |
| 11 | central subB | Polela Sawmill |
| 12 | central subB | Salt Spring Farm |
| 2 | northern | Amorentia |
| 1 | southern | Alice Pecan Orchard |
| 3 | southern | Baddaford Farm |
| 4 | southern | Hogsback |
| 8 | southern | King William’s Town |
| 13 | southern | Schwarzwald Forest |
| 14 | southern | Stutterheim |
4 Statistical analysis
To evaluate whether Cape Parrot contact call structure is better explained by region or by locality, we fitted three competing Bayesian mixed-effects models on pairwise acoustic dissimilarities between recordings, accounting for the non-independence inherent to pairwise distance data.
Model specifications
Three competing models were specified, each sharing the same multi-membership random-effect structure but differing in which fixed effect(s) they include, to test via leave-one-out cross-validation (LOO) whether region, locality, or both best explain acoustic dissimilarity between recordings:
\[ \text{mod\_region}: \quad \text{acoustic dissimilarity}_{ij} \sim \text{same region}_{ij} + (1 \mid \text{mm(recording}_i, \text{recording}_j)) \]
\[ \text{mod\_locality}: \quad \text{acoustic dissimilarity}_{ij} \sim \text{same locality}_{ij} + (1 \mid \text{mm(recording}_i, \text{recording}_j)) \]
\[ \text{mod\_both}: \quad \text{acoustic dissimilarity}_{ij} \sim \text{same region}_{ij} + \text{same locality}_{ij} + (1 \mid \text{mm(recording}_i, \text{recording}_j)) \]
where:
\(\text{acoustic dissimilarity}_{ij}\) is the mean pairwise cross-correlation-based dissimilarity (\(1 - r\)) between all calls of recording \(i\) and all calls of recording \(j\).
\(\text{same region}_{ij}\) is a binary predictor indicating whether recordings \(i\) and \(j\) come from the same region (1) or different regions (0).
\(\text{same locality}_{ij}\) is a binary predictor indicating whether recordings \(i\) and \(j\) come from the same locality (1) or different localities (0).
mm(recording\(_i\), recording\(_j\)) is a multi-membership random intercept accounting for the repeated use of the same recordings across pairwise comparisons.
The response variable was mean pairwise acoustic dissimilarity (1 - cross-correlation) between recordings, rescaled to the open (0, 1) interval and modeled with a Beta error distribution.
The unit of analysis is the recording pair, not the individual call pair: region and locality are properties of the recording, and acoustic dissimilarity was averaged across all call pairs belonging to each pair of recordings.
Recording identity was modeled as a multi-membership random intercept,
(1 | mm(rec1, rec2)), because each recording contributes to multiple pairwise comparisons and its pairs are not independent.Three fixed-effect specifications, sharing the same multi-membership structure, were compared via leave-one-out cross-validation (LOO) to test whether region, locality, or both best explain acoustic dissimilarity:
mod_region: same region only.mod_locality: same locality only.mod_both: same region and same locality together.
same_regionandsame_localityare binary predictors (same vs. different); a negative coefficient indicates that pairs from the same region/locality are acoustically more similar than pairs from different ones.Locality is nested within region, so
same_localityimpliessame_region; inmod_both, the region effect is expected to shrink if locality is doing most of the explanatory work.Mildly regularizing priors were used for all fixed effects and the intercept: Normal(0, 1) and Normal(0, 2), respectively.
Models were fitted using Hamiltonian Monte Carlo as implemented in Stan through the
cmdstanrbackend, with within-chain thread parallelization, 4 chains, 4 cores, and 4,000 iterations per chain.
Code
xcorr <- readRDS("./data/processed/cross_correlation_matrix.RDS")
dat$recording <- substr(dat$Old_Name, 0, 4)
## ---------------------------------------------------------------
## Region vs. locality as predictors of acoustic (dis)similarity
## brms multi-membership model, recording as the mm() grouping
## factor every pairwise combination of the 116 recordings,
## filled with mean acoustic distance across their calls
## ---------------------------------------------------------------
## 2. similarity -> dissimilarity (full matrix, keep symmetric,
## no NAs)
## -----------------------------------------------------------------------
dissim <- 1 - xcorr
## -----------------------------------------------------------------------
## 3. all pairwise combinations of the 116 recordings, filled
## with the mean call-level dissimilarity between the two
## recordings
## -----------------------------------------------------------------------
rec_ids <- dat$recording # recording id per call, same order as dissim
recordings <- sort(unique(rec_ids)) # 116 unique recordings
n_rec <- length(recordings)
pairs_idx <- t(combn(n_rec, 2)) # 6670 combinations, 116 choose 2
dist_dat <- data.frame(recording.1 = recordings[pairs_idx[, 1]], recording.2 = recordings[pairs_idx[,
2]], stringsAsFactors = FALSE)
dist_dat$mean_dissim <- sapply(seq_len(nrow(pairs_idx)), function(p) {
f1 <- dat$New_Name[dat$recording == dist_dat$recording.1[p]]
f2 <- dat$New_Name[dat$recording == dist_dat$recording.2[p]]
dists <- as.vector(dissim[rownames(dissim) %in% f1, colnames(dissim) %in%
f2])
dists <- dists[!is.infinite(dists)]
mean(dists, na.rm = TRUE)
})
## -----------------------------------------------------------------------
## 4. attach one locality / region per recording (assumes a
## recording was made at a single site - check this holds in
## your data)
## -----------------------------------------------------------------------
dist_dat$locality.1 <- sapply(dist_dat$recording.1, function(x) unique(dat$Location.for.cluster[dat$recording ==
x])[1])
dist_dat$locality.2 <- sapply(dist_dat$recording.2, function(x) unique(dat$Location.for.cluster[dat$recording ==
x])[1])
dist_dat$region.1 <- sapply(dist_dat$recording.1, function(x) unique(dat$Regions4[dat$recording ==
x])[1])
dist_dat$region.2 <- sapply(dist_dat$recording.2, function(x) unique(dat$Regions4[dat$recording ==
x])[1])
dist_dat$same_locality <- factor(ifelse(dist_dat$locality.1 == dist_dat$locality.2,
"same", "different"), levels = c("different", "same"))
dist_dat$same_region <- factor(ifelse(dist_dat$region.1 == dist_dat$region.2,
"same", "different"), levels = c("different", "same"))
names(dist_dat)[names(dist_dat) %in% c("recording.1", "recording.2")] <- c("rec1",
"rec2")
# dist_dat$rec1 <- factor(dist_dat$rec1) dist_dat$rec2 <-
# factor(dist_dat$rec2)
## -----------------------------------------------------------------------
## 5. dissimilarity to open (0,1) interval for beta family
## -----------------------------------------------------------------------
rescale01 <- function(x, eps = 1e-04) (x * (1 - 2 * eps)) + eps
dist_dat$dissim_beta <- rescale01(dist_dat$mean_dissim)
## -----------------------------------------------------------------------
## 6. priors
## -----------------------------------------------------------------------
priors <- c(prior(normal(0, 1), class = "b"), prior(normal(0, 2),
class = "Intercept"))
## -----------------------------------------------------------------------
## 7. competing models: locality vs region, same mm() structure
## -----------------------------------------------------------------------
mod_region <- brm(bf(dissim_beta ~ same_region + (1 | mm(rec1, rec2))),
data = dist_dat, family = Beta(), prior = priors, chains = 4,
cores = 4, iter = 4000, warmup = 1000, backend = "cmdstanr", threads = threading(8),
control = list(adapt_delta = 0.95), seed = 123, file = "./data/processed/brms_model_region")
mod_locality <- brm(bf(dissim_beta ~ same_locality + (1 | mm(rec1,
rec2))), data = dist_dat, family = Beta(), prior = priors, backend = "cmdstanr",
threads = threading(8), chains = 4, cores = 4, iter = 4000, warmup = 1000,
control = list(adapt_delta = 0.95), seed = 123, file = "./data/processed/brms_model_locality")
# full model with both terms - check for
# collinearity/confounding between region and locality before
# trusting this one
mod_both <- brm(bf(dissim_beta ~ same_region + same_locality + (1 |
mm(rec1, rec2))), data = dist_dat, family = Beta(), prior = priors,
backend = "cmdstanr", threads = threading(8), chains = 4, cores = 4,
iter = 4000, warmup = 1000, control = list(adapt_delta = 0.95),
seed = 123, file = "./data/processed/brms_model_both")
## -----------------------------------------------------------------------
## 8. model comparison
## -----------------------------------------------------------------------
mod_region <- add_criterion(mod_region, "loo")
mod_locality <- add_criterion(mod_locality, "loo")
mod_both <- add_criterion(mod_both, "loo")The following table ranks models by out-of-sample predictive fit (elpd_loo), with the top model set to elpd_diff = 0 and all others shown as the gap below it, plus a standard error (se_diff) for that gap. mod_both’s difference from mod_region (-0.32, SE 1.35) is well within noise, so locality adds nothing once region is included. mod_locality’s difference (-51.50, SE 12.28) is over 4 SEs from zero — a real, not chance, gap — so region alone predicts acoustic dissimilarity substantially better than locality alone. p_loo (effective parameters, ~118 across models, driven by the recording-level multi-membership term) is comparable throughout, so the fit differences reflect predictive power, not overfitting. mod_region is the best-supported model.
Code
mod_region <- readRDS("./data/processed/brms_model_region.rds")
mod_locality <- readRDS("./data/processed/brms_model_locality.rds")
mod_both <- readRDS("./data/processed/brms_model_both.rds")
loo_comp <- loo_compare(mod_region, mod_locality, mod_both)
kable(as.data.frame(loo_comp), digits = 2)| elpd_diff | se_diff | elpd_loo | se_elpd_loo | p_loo | se_p_loo | looic | se_looic | |
|---|---|---|---|---|---|---|---|---|
| mod_region | 0.00 | 0.00 | 11107.32 | 92.08 | 117.75 | 3.51 | -22214.65 | 184.16 |
| mod_both | -0.32 | 1.35 | 11107.01 | 91.98 | 118.83 | 3.52 | -22214.01 | 183.95 |
| mod_locality | -51.50 | 12.28 | 11055.82 | 92.22 | 118.18 | 3.53 | -22111.65 | 184.44 |
Code
extended_summary(mod_both, highlight = TRUE)4.1 mod_both
| priors | formula | iterations | chains | thinning | warmup | diverg_transitions | rhats > 1.05 | min_bulk_ESS | min_tail_ESS | seed | |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 1 | b-normal(0, 1) Intercept-normal(0, 2) phi-gamma(0.01, 0.01) sd-student_t(3, 0, 2.5) | dissim_beta ~ same_region + same_locality + (1 | mm(rec1, rec2)) | 4000 | 4 | 1 | 1000 | 0 (0%) | 0 | 215.76 | 463.087 | 123 |
| Estimate | l-95% CI | u-95% CI | Rhat | Bulk_ESS | Tail_ESS | |
|---|---|---|---|---|---|---|
| b_Intercept | 0.262 | 0.193 | 0.331 | 1.041 | 215.760 | 463.087 |
| b_same_regionsame | -0.104 | -0.123 | -0.085 | 1 | 5266.276 | 7306.824 |
| b_same_localitysame | -0.008 | -0.022 | 0.007 | 1 | 10168.787 | 8880.007 |
Code
# effect on the response scale
conditional_effects(mod_region, effects = "same_region")
Code
conditional_effects(mod_locality, effects = "same_locality")
Takeaways
Region is doing essentially all the work.
mod_regionhas the best expected out-of-sample fit;mod_localityis dramatically worse (Δelpd = -51.5, SE = 12.3 — a difference roughly 4 SEs from zero, so not noise).Locality alone is a poor predictor of acoustic dissimilarity compared to region — the “same locality vs. different” split by itself doesn’t capture much of the structure in the data.
Adding locality on top of region doesn’t help.
mod_both(region + locality) is statistically indistinguishable frommod_regionalone (Δelpd = -0.3, SE = 1.3 — well within noise). Locality contributes no additional explanatory power once region is already in the model.Practical conclusion: region is the better (and sufficient) grouping variable for acoustic dissimilarity in this dataset; locality’s effect, to the extent it exists, appears to be entirely absorbed by region rather than adding anything on top.
mod_regionis the model to report/interpret going forward.
NoteSession information
─ Session info ───────────────────────────────────────────────────────────────
setting value
version R version 4.5.2 (2025-10-31)
os Ubuntu 22.04.4 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 2026-08-24
pandoc 3.6.3 @ /usr/lib/rstudio/resources/app/bin/quarto/bin/tools/x86_64/ (via rmarkdown)
quarto 1.8.25 @ /usr/lib/rstudio/resources/app/bin/quarto/bin/quarto
─ Packages ───────────────────────────────────────────────────────────────────
package * version date (UTC) lib source
abind 1.4-8 2024-09-12 [1] CRAN (R 4.5.2)
ape 5.8-1 2024-12-16 [1] CRAN (R 4.5.2)
arrayhelpers 1.1-0 2020-02-04 [1] CRAN (R 4.5.2)
askpass 1.2.1 2024-10-04 [1] CRAN (R 4.5.2)
backports 1.5.1 2026-04-03 [1] CRAN (R 4.5.2)
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brms * 2.23.0 2025-09-09 [1] CRAN (R 4.5.2)
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* ── Packages attached to the search path.
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