DOI: 10.5061/dryad.wm37pvmqg
title: Evolution of species recognition when ecology and sexual selection favor signal stasis

description: These data were generated over an 11-year period in a study of song
 recognition among four related bird species in the Himalaya (Cyanoderma
 ambiguum, C. ruficeps, C. chrysaeum, C. pyrrhops), as well as close
 relatives. The data primarily consists of measurements of songs and notes
 of all species, and the strength of responses to playback of songs, some
 of which were experimentally manipulated for frequency.
description: <em>Abstract</em> These data were generated over an
 11-year period in a study of song recognition among four related bird
 species in the Himalaya <em>(Cyanoderma ambiguum, C. ruficeps, C.
 chrysaeum, C. pyrrhops),</em> as well as close relatives. The data
 primarily consists of measurements of songs and notes of all species, and
 the strength of responses to playback of songs, some of which were
 experimentally manipulated for frequency.
 <em>Methods </em>During the breeding seasons
 2011-2021, we visited localities across the Himalaya between one and ten
 times. We recorded songs using Telinga Twin Science microphones with a
 Sony PCM-D50 solid state recorder, or later with a Sound Devices Mixpre3
 digital audio tape recorder. All the recordings were made in .wav file
 format with 16-bit resolution and 44.1-kHz sampling rate, or later with
 24-bit resolution and 48 kHz sampling rate. For each recording, we noted
 altitude, date and locality. For the experimental analysis of frequency,
 we manipulated songs using the “Change Pitch” function in Audacity (https://audacityteam.org/). Tapes for playback were made from clean recordings, with any noisy portions removed if needed. Amplitudes were standardized to similar levels, just below the maximum possible. We located a singing male and conducted a playback for 5 minutes. We noted responses on a four-point scale, based on distance from speaker and directional movement (we also recorded flyovers, which correlate with the metric used, and are available on request). Some males were tested sequentially with different tapes, usually the playing of conspecific song after a trial with no response to the first tape, but on other occasions three or four tapes were also played, with a maximum of 4 sequential trials (to 10 males). From the recordings, we compiled four sets of measurements. First, we measured characteristics of entire songs. For each song, we measured minimum and maximum frequency, center frequency (the frequency that divides the selected sound spectrum into two frequency intervals of equal energy), bandwidth 90%—the difference in frequencies separating the spectrogram into 5% and 95% energy quantiles—and peak frequency (the frequency with highest energy), with a Hann Window of size of 1024 samples. We measured song length, with a window size of 256 samples. We also counted the number of notes and number of distinct notes in each song. We scored songs for whether they contained an introductory note (or on occasion two introductory notes) which we define as at least 50% greater space between this note (or pair of notes) than the other notes. Second, taking a single clear song from each bout, we measured all notes in the song for frequency parameters, note length and qualitatively recorded note shape, as upsweep, downsweep, dome, saucer or flat, according to whether frequency monotonically increased, monotonically decreased, showed an intermediate peak, an intermediate minimum, or did not change. We split each note into four equal time intervals and recorded the center frequency of each interval. We then took the deviation of each center frequency from the average of the four frequencies (for example for a flat note, the deviations would be 0,0,0,0). We extracted principal components from the correlation matrix derived from the 4 columns of deviations x 1921 rows (notes). Correlations of the first principal component with the four center frequency residuals are: 0.94, 0.41, -0.84, -0.9, implying notes with large positive scores are downsweeps, and those with large negative scores upsweeps. Finally, we computed a measure of amplitude from the waveform, dividing a single note into 0.0002 second intervals, and computing the root mean square of amplitudes within each interval.  
description: <em>List of files</em>  
 The excel file ‘metadata’ explains all the columns in the
 associated files.   Data file 1.
 This file gives all the original measurements (duration, frequency, number
 of notes and number of distinct notes) for all songs separately from each
 individual analyzed in the dataset. Altitude is not in this file, but in
 Data file 5. (wholesongs.csv) Data file 2. Measurements
 of notes for south Asian species and populations. This file gives
 frequency, duration and note shape, including center frequency
 measurements for the four note quartiles, and the principal component
 scores extracted from it, as well as qualitative descriptions of shape
 (#notemaster.csv) Data file 3. Amplitude. Mean
 amplitudes in quarter note intervals, for <strong>Fig.
 S6</strong>. (#eastHimwaves.csv) Data File 4. For
 <strong>Figs. 2, 6, 7, S3.</strong> Results from the 550
 playback experiments, including date, elevation, response on a 4-point
 scale, and whether it was a frequency-manipulated song.
 #Playbacks.xlsx Data file 5. This is taken from Data
 file 1, but here we have averaged songs within individuals.
 #wholesongsaveraged.csv Data file 6. <strong>For
 Fig. 4.</strong>  Gives note shapes (PC1 score) and frequencies
 averaged across all measurements for an individual and elevation of
 recording for south Asian species (from data file 2).
 #ShapeFrequency.csv Data file 7. <strong>For Fig.
 S8</strong>. Gives note shapes and frequency for the allopatric
 populations (Taiwan, eastern ghats, <em>C. pyrrhops</em> from
 the west, as well as the 3 sympatric species). X1-X4 are the quartile
 measurements with mean subtracted, from which the principal components are
 computed. #indsStachyris.csv Data file 8.
 <strong>For Figs. 5, S7. </strong>Biogeography and mean
 species values for ancestral inferences, including mass, frequency and
 note shape, as extracted from a separate principal component analysis from
 the data for the 4 Himalayan species and outgroup species, as listed in
 Data File 9. #biogeography.xlsx Data file 9.
 <strong>For Fig. S2.</strong> Original song data for outgroup
 species and Himalayan species used for ancestral reconstruction.
 (#speciesdata.csv) Data File 10. <strong>For Fig.
 S5.</strong> Original recordings of the spectrograms illustrated
 (songs.zip) Data File 11. Sources of all recordings,
 including links to Xeno cantor records. For any song in Pratap Singh’s
 personal collection, please contact PS (pratapsingh6019@gmail.com).
 #Recordings_Source Data File 12. <strong>For Fig.
 S4:</strong> Frequencies of note quartiles for the ancestral
 reconstructions. Note that here the frequencies of the four quartiles are
 listed one above the other in one column, rather than in separate columns.
 #fournotes.csv