DOI: 10.5061/dryad.69p8cz99k
title: Global analysis of acoustic frequency characteristics in birds

description: Animal communication plays a crucial role in biology, yet the wide
 variability in vocalizations is not fully understood. Previous studies in
 birds have been limited in taxonomic and analytical breadth. Here we
 analyse an extensive dataset of &gt;140,000 recordings of
 vocalisations from 10,124 bird species, representing nearly every avian
 order and family, under a structural causal model framework, to explore
 the influence of eco-evolutionary traits on acoustic frequency
 characteristics. We find that body mass, beak size, habitat associations,
 and geography influence acoustic frequency characteristics, with varying
 degrees of interaction with song acquisition type. We find no evidence for
 the influence of vegetation density, sexual dimorphism, range size and
 competition on our measures of acoustic frequency characteristics. Our
 results, built on decades of researchers’ empirical observations collected
 across the globe, provide a new breadth of evidence about how
 eco-evolutionary processes shape bird communication.
description: Acoustic signal characteristics of bird
 sounds We accessed the recordings of bird
 vocalisations (both songs and calls) of all the species from the online
 repository xeno-canto. First, we downloaded the meta-data of all the bird
 recordings available on xeno-canto in December 2019, using the R package
 warbleR. We then cleaned the meta-data file by removing unidentified
 species, such as ‘<em>Mystery mystery</em>’ and non-avian
 sounds. For species that have a distinct song and call, we limited our
 analysis to songs only, based on the existing distinctions within
 Xeno-canto. For species without a clear distinction between songs and
 calls, such as raptors, we included all vocalizations in our analysis. For
 each species we then hierarchically downloaded the best available
 recording (refer Fig. S9 in manuscript), using the R package warbleR. The
 quality of the recordings in xeno-canto is ranked from A to E, with A
 representing the highest and E the lowest quality, with a few recordings
 having “no score”. We selected the highest available rank, without
 requiring a minimum number of recordings for each species. For example, if
 <em>Copsychus malabaricus</em> (White-rumped Shama) had
 recordings of quality A, B and C, we selected only and all those ranked as
 A. If another species, <em>Copsychus saularis</em> (Oriental
 Magpie-robin) had no recording of quality A, but only of quality from B to
 E, we chose B recordings. We then analysed individual
 recordings for each species to detect and measure acoustic frequency
 values (refer Fig. S2 in manuscript). For each recording, we first
 automatically detected all acoustic signals therein, each between a
 duration of 1 – 5 sec - acoustic note, (refer Fig. S2b in manuscript). We
 used 0 and 22 kHz as our lower and upper limits of a frequency bandpass
 filter, to capture the entire range of the acoustic frequencies, with a
 Hanning window of default length of 512. We did not use any proportional
 reduction of amplitude envelopes through thinning. We only used converted
 Waveform Audio File Format (.wav) recordings that had a sampling rate of
 44.2 kHz, after decompressing them from mp3 format. While this has been
 shown not to significantly bias any of the acoustic or similarity
 measurements, it has been shown to affect the precision of acoustic
 parameters, such as peak frequency. However, the negative effect of
 compression is assumed to be less problematic when comparing acoustic
 frequency values across species, as the differences between species are
 usually stronger, than within species. For each
 acoustic note from a recording, we calculated the Signal-to-Noise Ratio
 (SNR) to discard low-quality (e.g., high background noise) selections, by
 following the recommendation from Araya-Salas et. al 2019, because
 background noise has been shown to bias most energy distribution-related
 parameters such as spectral entropy and affect the precision of most
 acoustic parameters. Here we used the default value of not using any lower
 and upper limits of a frequency bandpass filter, or a window length, as
 they were previously used to automatically detect the acoustic note in the
 previous step. We used a very small margin value of 0.04, adjacent to the
 start and end points of selection over which to measure noise. For each
 recording, we then selected the top 100 non-overlapping acoustic notes,
 i.e., those with the highest SNR, to measure acoustic characteristics of
 each species (refer Fig. S1c-d in manuscript ): (i) minimum frequency
 (ƒ<sub>min</sub>), (ii) maximum frequency
 (<em>ƒ</em><sub>max</sub>), (iii) dominant
 frequency (ƒ<sub>dom</sub>), (iv) standard deviation of
 ƒ<sub>min</sub>
 (σ<em>f</em><sub>min</sub>), (v) standard
 deviation of ƒ<sub>max</sub>
 (σ<em>f</em><sub>max</sub>), and (vi)
 interquartile range of acoustic notes (Δƒ). We define
 ƒ<sub>min</sub> as the start frequency of the amplitude peak
 containing the highest amplitude, ƒ<sub>max</sub> as the end
 frequency of the amplitude peak containing the highest amplitude;
 ƒ<sub>dom</sub> as the average dominant frequency measured
 across the spectrogram of the acoustic note; and Δƒ as the frequency range
 between the first quartile (frequency at 25% energy of the spectrum) and
 the third quartile (frequency at 75% energy of the spectrum).
 ƒ<sub>min</sub>, ƒ<sub>max</sub>, and
 ƒ<sub>dom</sub>, forms the fundamental frequency measures,
 σ<em>f</em><sub>min </sub>and
 σ<em>f</em><sub>max</sub> characterize the
 variability found within minimum and maximum fundamental frequencies
 respectively, and Δƒ characterizes the frequency bandwidth. We also
 modelled Δƒ, measured as the difference between
 ƒ<sub>min</sub> and ƒ<sub>max</sub>, which we
 elaborate in Supplementary Information in the manuscript. Assuming the
 recordist manipulated their equipment and recording strategy such that the
 acoustic signal of the target species would be the clearest and loudest in
 the recording, we repeatedly estimated each of these six response
 variables under a series of thresholds - amplitude of every 5 dB between 5
 and 50 dB (refer to Fig. S1e in manuscript). Median values of the
 estimates of the six response variables, from each of the threshold
 percentages (refer Fig. S1e, in manuscript) were used to estimate acoustic
 characteristics for each species, across all recordings (refer Fig. S1f,
 in manuscript). We used the R package warbleR to calculate the acoustic
 characteristics. We specifically chose these measures of bird vocalisation
 because they have all been previously linked to the evolutionary forces of
 physiology, inter-specific competition, distribution, and environmental
 variables (Fig. 1 & Table S1 in manuscript ). Additionally, they
 were also the easiest acoustic measures that could be consistently
 extracted from thousands of recordings of our focus bird
 species. Zoogeographic distribution of
 species We divided the zoogeographic
 distribution of our study species into 12 major zoogeographic realms
 (refer Fig S9 in manuscript). In addition to the 11 zoogeographic realms
 identified by Holt et al. 2013, namely, Afrotropical, Australian,
 Madagascan, Nearctic, Neotropical, Oceanian, Oriental, Palearctic,
 Panamanian, Saharo-Arabian, and Sino-Japanese, Oceanian, we also included
 species from Antarctica and surrounding islands under a new realm, South
 Polar, based on our data from xeno-canto. We listed a species under
 multiple zoogeographic realms if its distribution spanned multiple realms,
 either due to its natural or introduced distribution. We calculated the
 frequency values separately for species that occurred in more than one
 zoogeographic realm. Species
 traits We collated information on the
 morphology, behaviour, ecology, and geographical data of birds from AVONET
 and BirdBase. We used log<sub>(e) </sub>transformed body mass
 (g) as a measure of body mass, results are presented as back-transformed
 values in the main text. We calculated beak size
 (log<sub>(e)</sub> transformed) by multiplying beak length,
 width, and depth (mm<sup>3</sup>). As a measure of vegetation
 density, we used two categorical variables: (i) habitat density and (ii)
 primary habitat, both from AVONET. We estimated overall species richness
 as a proxy for competition for acoustic frequency space, by calculating
 the mean number of terrestrial vertebrates: amphibians and birds using
 global richness data from BiodiversityMapping.org. We extracted these
 values as means within a species’ breeding range, which we calculated as a
 circular area, equivalent to the species range size, centred around its
 centroid latitude and longitude. To account for plasticity in song
 acquisition, we divided the bird species into two major groups, (i) song
 learners – species that learn songs from adult tutors to use in mate
 choice and territorial displays, i.e., hummingbirds, parrots, and oscine
 passerines and (ii) innate songsters – species that do not learn their
 vocalisation. i.e., suboscine passerines and all remaining birds. Even
 though there are exceptions (e.g., a few species of Ant wrens and Wood
 creepers), in general, song learners tend to have more complex
 vocalisation. Additionally, song learners, show a slower rate of evolution
 and diversification due to their significantly greater variability in
 acoustic traits (by 54 – 79%) compared to innate songsters.
description: # Global analysis of acoustic frequency characteristics in birds
 [https://doi.org/10.5061/dryad.69p8cz99k](https://doi.org/10.5061/dryad.69p8cz99k) The folder contains the data used to model the relationship between the acoustic frequency characteristics of 8450 bird species, globally, under a backdoor criteria. ## Description of the data and file structure The data is kept under one folder named, *Data_RCodes*. The folder has three main objects: (1) *data*, (2) *output*, (3) *R_scrips* and (4) *Bird_acoustics-R.Project*.  1. **data:** This is a folder that consists of all the files needed to model and visualise the relationships and hypotheses tested within our manuscript. The folder contains three files: (i) *all_bird_tree.tre* - this is an ultrametric dichotomous phylogenetic tree dataset for a subset of 7916 species within our study. We used this to test for the residual variance in our models due to phylogenetic relationships using the “phylosig” function from the phytools R package. We created this using the following steps. We first dowloaded a 1000-tree phylogenetic subset for all the species from [www.vertlife.org](http://www.vertlife.org). This gave us a nexus (.nex) file. We then converted the 1000 trees into a single majority-rules concenses tree using phytools R packace. We then converted our concensus tree into an Ultrametric and Dichotomous tree using APE function in R.  An ultrametric tree has all tips (representing species or taxa) equidistant from the root, which means it is calibrated to represent time or evolutionary distance consistently. This ensures that all species in the dataset share a common timeline, which is important for models that assume lineage similarity decreases over time or is related to shared ancestry. A dichotomous tree is necessary because each node (branch point) in the tree represents a single evolutionary split, which simplifies calculations and assumptions about evolutionary relationships. Mixed models often assume that every split creates exactly two lineages, enabling straightforward interpretations of divergence patterns. (ii) *Bird_acoustics_dataframe -* This contains various song frequency characteristics for 8450 species of birds, which we extracted using &gt;140,000 song recordings from xeno-canto, along with their eco-evolutionary traits (refer to the methods section below). (iii) *global_centroid_0.25 -* This is a csv file with latitude and longitude that divides the global terrestrial surface into 0.25 degrees. Using QGIS version 3.22.13-Białowieża, we generated a 0.25-degree global grid covering terrestrial areas. First, the project’s coordinate reference system (CRS) was set to EPSG:4326 (WGS 84) for consistency in latitude and longitude alignment. The extent was defined globally, from -180 to 180 degrees longitude and -90 to 90 degrees latitude, but with land boundaries limited by a terrestrial mask layer to exclude oceanic regions. Using the QGIS we specified a 0.25 x 0.25-degree rectangular grid resolution, generating cells with approximately 111 km² per cell at the equator. Next, we used *Centroids* tool to calculate the geographic centre for each land grid cell, creating a new point layer representing centroids with unique cell identifiers. This layer was then exported in CSV format with each of the centroid coordinates. 2. **output:** This is an empty folder to save all our outputs from R project. 3. **R_scripts:** This is a folder containing an R_script, *Code_model-visualisation.R*, to run the model and visualise the causal relationships. 4. **Bird_acoustics-R.Project:** This is the R project to open the above script and run the model.  ## Sharing/Access information We downoaded most of our bird aocustic data from xeno-canto (described in details within the methods section, and manuscript)  * [https://xeno-canto.org/](https://xeno-canto.org/) We collated information of bird traits from AVONET dataset * [https://doi.org/10.1111/ele.13898](https://doi.org/10.1111/ele.13898) ## Code/Software Within the "R_script" subfolder, we have uploaded the R script that you can open using the *Bird_acoustics-R.Project*, and you can use the codes there to analyse the dataset and create figures that are simialr to ones used in the manuscript.