Objetive

  • Demonstrate how to articulate functions used during the course to obtain, explore and quantify acoustic data

 

1 Download Xeno-Canto data

The warbleR function query_xc() queries for avian vocalization recordings in the open-access online repository Xeno-Canto. It can return recordings metadata or download the associated sound files.

Get recording metadata for green hermits (Phaethornis guy):

Code 
library(warbleR)

pg <- query_xc(qword = 'Phaethornis guy', download = FALSE)

 

Keep only song vocalizations of high quality:

Code 
song_pg <- pg[grepl("song", ignore.case = TRUE, pg$Vocalization_type) & pg$Quality == "A", ]

# remove 1 site from Colombia to have a few samples per country
song_pg <- song_pg[song_pg$Locality != "Suaita, Santander", ]

Map locations using map_xc():

Code 
map_xc(song_pg, leaflet.map = TRUE)

 

Once you feel fine with the subset of data you can go ahead and download the sound files and save the metadata as a .csv file:

Code 
query_xc(X = song_pg, path = "./examples/p_guy", parallel = 3)

write.csv(song_pg, file = "./examples/p_guy/metadata_p_guy_XC.csv", row.names = FALSE)

 

2 Preparing sound files for analysis (optional)

Now convert all to .wav format (mp3_2_wav) and homogenizing sampling rate and bit depth (fix_wavs):

Code 
mp3_2_wav(samp.rate = 22.05, path =  "./examples/p_guy")

fix_wavs(path =  "./examples/p_guy", samp.rate = 44.1, bit.depth = 16)

 

3 Annotating sound files in Raven

Now songs should be manually annotated and all the selection in the .txt files should be pooled together in a single spreadsheet.

 

4 Importing annotations into R

Once that is done we can read the spreadsheet with the package ‘readxl’ as follows:

Code 
# install.packages("readxl") # install if needed

# load package
library(readxl)

# read data
annotations <- read_excel(path = "./examples/p_guy/annotations_p_guy.xlsx")

# check data
head(annotations)
selec Channel start end bottom.freq top.freq selec.file
1 1 0.7737 0.9939384 2.0962 7.7252 Phaethornis-guy-2022.Table.1.selections.txt
2 1 1.6837 1.9068363 2.0726 7.6074 Phaethornis-guy-2022.Table.1.selections.txt
3 1 10.1657 10.3917342 1.8371 8.0078 Phaethornis-guy-2022.Table.1.selections.txt
4 1 16.3237 16.5468363 2.0726 7.3248 Phaethornis-guy-2022.Table.1.selections.txt
5 1 1.6069 1.7517937 1.7193 8.7615 Phaethornis-guy-2022.Table.1.selections.txt
6 1 1.0129 1.1548958 1.7193 8.9264 Phaethornis-guy-2022.Table.1.selections.txt

Note that the column names should be: “start”, “end”, “bottom.freq”, “top.freq” and “sound.files”. In addition frequency columns (“bottom.freq” and “top.freq”) must be in kHz, not in Hz. We can check if the annotations are in the right format using warbleR’s check_sels():

Code 
sound_file_path <- "./examples/p_guy/converted_sound_files/"

cs <- check_sels(annotations, path = sound_file_path)
all selections are OK

 

5 Measure acoustic structure

We can measured several parameters of acoustic structure with the warbleR function spectro_analysis():

Code 
sp <- spectro_analysis(X = annotations, path = sound_file_path)

 

Then we summarize those parameters with a Principal Component Analysis (PCA):

Code 
# run excluding sound file and selec columns
pca <- prcomp(sp[, -c(1, 2)])


# add first 2 PCs to sound file and selec columns
pca_data <- cbind(sp[, c(1, 2)], pca$x[, 1:2])

 

At this point should should get someting like this:

Code 
head(pca_data)
sound.files selec PC1 PC2
Phaethornis-guy-227574.wav 1 22.5940413 13.148139
Phaethornis-guy-227574.wav 2 -0.1051713 17.152528
Phaethornis-guy-227574.wav 3 -8.2102916 -1.806982
Phaethornis-guy-227574.wav 4 6.9500021 -4.559926
Phaethornis-guy-238804.wav 5 -11.0761462 -7.156525
Phaethornis-guy-238804.wav 6 -4.5189993 -8.129007

 

‘PC1’ and ‘PC2’ are the 2 new dimensions that will be used to represent the acoustic space.

 

6 Adding metadata

Now we just need to add any metadata we considered important to try to explain acoustic similarities shown in the acoustic space scatterplot:

Code 
# read XC metadata
song_pg <- read.csv("./examples/p_guy/metadata_p_guy_XC.csv")

# create a column with the file name in the metadata
song_pg$sound.files <- paste0(song_pg$Genus, "-", song_pg$Specific_epithet, "-", song_pg$Recording_ID, ".wav")

# and merge based on sound files and any metadata column we need
pca_data_md <- merge(pca_data, song_pg[, c("sound.files", "Country", "Latitude", "Longitude")])

 

7 Assessing geographic patterns of variation

We are ready to plot the acoustic space scatterplot. For this we will use the package ‘ggplot2’:

Code 
# install.packages("ggplot2")
library(ggplot2)

# install.packages("viridis")
library(viridis)
Loading required package: viridisLite
Code 
# plot
ggplot(data = pca_data_md, aes(x = PC1, y = PC2, color = Country, shape = Country)) +
  geom_point(size = 3) + 
  scale_color_viridis_d()

 

You can also add information about their geographic location (in this case longitude) to the plot as follows:

Code 
# plot
ggplot(data = pca_data_md, aes(x = PC1, y = PC2, color = Longitude, shape = Country)) +
  geom_point(size = 3) +
  scale_color_viridis_c()

 

We can even test if geographic distance is associated to acoustic distance (i.e. if individuals geographically closer produce more similar songs) using a mantel test (mantel function from the package vegan):

Code 
# create geographic and acoustic distance matrices
geo_dist <- dist(pca_data_md[, c("Latitude", "Longitude")])
acoust_dist <- dist(pca_data_md[, c("PC1", "PC2")])

# install.packages("vegan")
library(vegan)

# run test
mantel(geo_dist, acoust_dist)

Mantel statistic based on Pearson's product-moment correlation 

Call:
mantel(xdis = geo_dist, ydis = acoust_dist) 

Mantel statistic r: 0.02618 
      Significance: 0.267 

Upper quantiles of permutations (null model):
   90%    95%  97.5%    99% 
0.0774 0.1072 0.1395 0.1795 
Permutation: free
Number of permutations: 999

 

In this example no association between geographic and acoustic distance was detected (p value > 0.05).

Session information

R version 4.3.2 (2023-10-31)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Ubuntu 22.04.2 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

locale:
 [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              
 [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    
 [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   
 [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 
 [9] LC_ADDRESS=C               LC_TELEPHONE=C            
[11] LC_MEASUREMENT=en_US.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] vegan_2.6-4        lattice_0.20-45    permute_0.9-7      viridis_0.6.5     
 [5] viridisLite_0.4.2  ggplot2_3.5.1      readxl_1.4.3       warbleR_1.1.30    
 [9] NatureSounds_1.0.4 knitr_1.46         seewave_2.2.3      tuneR_1.4.6       

loaded via a namespace (and not attached):
 [1] gtable_0.3.4      rjson_0.2.21      xfun_0.43         htmlwidgets_1.6.4
 [5] vctrs_0.6.5       tools_4.3.2       crosstalk_1.2.1   bitops_1.0-7     
 [9] generics_0.1.3    parallel_4.3.2    tibble_3.2.1      proxy_0.4-27     
[13] fansi_1.0.6       cluster_2.1.2     pkgconfig_2.0.3   Matrix_1.6-5     
[17] leaflet_2.2.1     lifecycle_1.0.4   compiler_4.3.2    farver_2.1.1     
[21] brio_1.1.4        munsell_0.5.0     codetools_0.2-18  htmltools_0.5.8.1
[25] maps_3.4.2        RCurl_1.98-1.14   yaml_2.3.8        pillar_1.9.0     
[29] jquerylib_0.1.4   MASS_7.3-55       iterators_1.0.14  foreach_1.5.2    
[33] nlme_3.1-155      tidyselect_1.2.0  digest_0.6.35     dplyr_1.1.4      
[37] labeling_0.4.3    splines_4.3.2     fastmap_1.1.1     grid_4.3.2       
[41] colorspace_2.1-0  cli_3.6.2         magrittr_2.0.3    utf8_1.2.4       
[45] withr_3.0.0       scales_1.3.0      shinyBS_0.61.1    rmarkdown_2.26   
[49] signal_1.8-0      gridExtra_2.3     cellranger_1.1.0  pbapply_1.7-2    
[53] evaluate_0.23     dtw_1.23-1        fftw_1.0-8        testthat_3.2.1   
[57] mgcv_1.8-39       rlang_1.1.3       Rcpp_1.0.12       glue_1.7.0       
[61] rstudioapi_0.15.0 jsonlite_1.8.8    R6_2.5.1          soundgen_2.6.2