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Gradual transitions in genetics and songs between coastal and inland populations of Setophaga townsendi

Cite this dataset

Ore, Madelyn; Wang, Silu; Irwin, Darren E. (2024). Gradual transitions in genetics and songs between coastal and inland populations of Setophaga townsendi [Dataset]. Dryad. https://doi.org/10.5061/dryad.bg79cnpf4

Abstract

Setophaga townsendi is a species of wood-warbler (family Parulidae) in northwestern North America that has a geographic structure in the mitochondrial and nuclear genomes: while interior populations have differentiated mitonuclear ancestry from the sister species S. occidentalis, coastal populations have a mix of inland and S. occidentalis mitonuclear ancestries. This coastal-to-inland transition in genomic ancestry raises the possibility of similar geographic structure in phenotypic traits, especially those involved in mate choice. Using qualitative and multivariate approaches, we investigated whether there is a sharp transition between coastal and inland populations in both songs and nuclear DNA. We find there is a shallow geographic cline in the Type I song but not in the Type II song. Nuclear DNA shows a gradient between the coast and inland. There is little correlation between variation in song and the isolation-by-distance pattern in the nuclear DNA. The learned songbird song is shaped by both genetic and cultural processes. There has been a debate on whether song learning promotes or slows down population differentiation. By comparing the within-species variation in song and genetic structures, we can expand our understanding of the dynamic interplay between mating signals and population differentiation.

README: Gradual transitions in genetics and songs between coastal and inland populations of Setophaga townsendi


This dataset consists of song recordings that were collected at 30 locations across British Columbia from May to July of 2017, using a Marantz PMD660 digital recorder and an Audio-Technica 815a Shotgun microphone.

Three songs of each song type were analyzed using Raven Pro 1.4 for an analysis of song variation across the range of Setophaga townsendi.

These song variables were used in a PCA to quantify song variation.

Description of the Data and file structure

This dataset consists of songs of 249 birds (180 from field recordings, 39 from Xeno-Canto, and 30 from Macaulay Library). For each bird, songs were characterized into types based on visual similarity and the results of Janes and Ryker (2016) and Janes (2017). We classified the clear song as the Type I song (i.e., used more in female attraction), and the buzzy song as the Type II song (i.e., used more in territorial defense).

We randomly selected three numbers from the total in each recording. These three songs were analyzed as follows: Spectrograms were visualized in Raven Pro 1.4 using Hann spectrogram windows with 512 samples, discrete Fourier transform (DFT) size of 512 samples, hop size of 5.6 ms, sampling frequency of 44.1 kHz, and a time resolution of 11.6 ms. The boundaries of each selection were conducted using the power spectrum as outlined in Zollinger et al. (2012).

This dataset shows the following for 3 songs per bird per song type (Type I song n = 195; Type II song n =74):

  • Twenty-one variables were measured from each song using Raven Pro 1.4: total number of notes, number of unique notes, duration, minimum and maximum frequencies, bandwidth, and aggregate entropy of the whole song and parts 1 and 2 of the song.
  • Part 1 of the song is a collection of repeated notes that starts the song - referred to in the dataset as syl_1
  • Part 2 is the collection of notes that ends the song - referred to in the dataset at syl_2

Description of data contained in each column. * denotes columns that were used in PCA analysis in Ore et al. 2022. Not all measurements were used in the analysis because they measured the same information differently as other measurements used. For example, Freq 95% is a different method to measure Maximum Frequency. These measurements are described in the Raven Pro manual as 'more robust measurements' (see manual for more detail). Though these were not used for the analysis published, they are included if future studies wish to use these measurements. These descriptions are based on the Raven Pro 1.4 manual:

sound.files - the sound file associated with the song.
selec - the selection on Raven Pro that this song was on. This is used to identify that each song is unique from other songs.
song.ID - the ID of the bird that this song is associated with
num.unique.notes - * the total number of unique notes in the entire song. Counted using a spectrogram image.
num.notes - * the total number of notes in a song. Counted using a spectrogram image.
start - where in the sound file this song started
end - where in the sound file this song ended
Max.Freq..Hz. - the frequency where the Maximum Power occurs in a song. Measured in Hertz
Freq.95...Hz. - the frequency that contains 95% of the energy in a song. Measured in Hertz
Freq.5...Hz. - the frequency that contains 5% of the energy in a song. Measured in Hertz
Delta.Time..s. - * the duration of the song. Measured in seconds
Center.Freq..Hz. - the frequency that divided the song into two even intervals of energy. Measured in Hertz
BW.90...Hz. - * =(Freq 95% - Freq 5%). The span of frequencies in a song. Measured in hertz.
Avg.Entropy..bits. - the average amount of energy in a song - this measurement attempts to measure the variation of frequencies in a selection. Measured in bits.
Agg.Entropy..bits. - * the total amount of energy in a song - this measurement attempts to measure the variation of frequencies in a selection. Measured in bits.
High.Freq..Hz. - * the highest frequency in the song. Measured in hertz.
Low.Freq..Hz. - * the lowest frequency in the song. Measured in Hertz.
buzzy.or.clear - the song type of the song. clear = Type I song, buzzy = Type II song
syl_1_unique_notes - * the number of unique notes in syllable 1 of the song. A syllable is a repeated collection of notes. Counted using a spectrogram image.
syl_1_num_notes - * the number of unique notes in syllable 1 of the song. A syllable is a repeated collection of notes. Counted using a spectrogram image.
syl_1_begin - where in the sound file syllable 1 for a given song begins
syl_1_end - where in the sound file syllable 1 for a given song ends
syl_1_max_freq - the frequency where the Maximum Power occurs in syllable 1. Measured in Hertz
syl_1_freq_95 - the frequency that contains 95% of the energy in a song. Measured in Hertz.
syl_1_freq_5 - the frequency that contains 5% of the energy in a song. Measured in Hertz.
syl_1_delta_time - the duration of syllable 1. measured in seconds.
syl_1_center_freq - the frequency that divides syllable 1 into two even intervals of energy. Measured in Hertz.
syl_1_BW_90 - * =(Freq 95% - Freq 5%). The span of frequencies in syllable 1. Measured in hertz.
syl_1_avg_entropy - the average amount of energy in syllable 1- this measurement attempts to measure the variation of frequencies in a selection. Measured in bits.
syl_1_agg_entropy - * the total amount of energy in syllable 1 - this measurement attempts to measure the variation of frequencies in a selection. Measured in bits.
syl_1_high_freq - * the highest frequency in syllable 1. Measured in Hertz
syl_1_low_freq- * the lowest frequency in syllable 1. Measured in Hertz
syl_2_unique_notes - * the number of unique notes in syllable 2. Counted using a spectrogram image.
syl_2_num_notes - * the total number of notes in syllable 2. Counted using a spectrogram image.
syl_2_begin - where in the sound file syllable 2 begins
syl_2_end - where in the sound file syllable 2 ends
syl_2_max_freq - the frequency at where Maximum Power occurs in syllable 2. Measured in Hertz
syl_2_freq_95 - the frequency that contains 85% of the energy in syllable 2. Measured in Hertz.
syl_2_freq_5 - the frequency that contains 5% of the energy in syllable 2. Measured in Hertz.
syl_2_delta_time - * the duration of syllable 2. Measured in seconds
syl_2_center_freq - the frequency that divides syllable 2 into two even intervals of energy. Measured in Hertz
syl_2_BW_90 - * =(Freq 95% - Freq 5%). The span of frequencies in syllable 2. Measured in hertz.
syl_2_avg_entropy - the average amount of energy in syllable 2- this measurement attempts to measure the variation of frequencies in a selection. Measured in bits.
syl_2_agg_entropy - * the total amount of energy in syllable 2- this measurement attempts to measure the variation of frequencies in a selection. Measured in bits.
syl_2_high_freq - * the highest frequency of syllable 2. Measured in Hertz
syl_2_low_freq - * the lowest frequency of syllable 2. Measured in Hertz
location - the locality where the song was recorded
region - the general region where the song was recorded.Regions in British Columbia based on map here
WA: Washington (state)
OR: Oregon (state)
ID: Idaho (state)
MT: Montana (state)
Islands: Vancouver Island and surrounding islands
Van CM: Vancouver Coast and Mountains in SW British Columbia
Okanagan: Thompson-Okanagan region in southern interior BC Kootenay:
Cariboo Chil: Cariboo-Chilcotin region in central BC
N BC: Northern British Columbia
AK: Alaska (state)
Source - where the song recording is derived from:
Field Season: collected by MJO in May-July 2017
Macauley Library: downloaded from Macauley Library
Xento-Canto: downloaded from xeno-canto website

Sharing/Access Information

Data was derived from the following sources, as indicated in the Source column:

  • Macauley Library
  • Xeno canto

Methods

Data collection

Song recordings were collected at 30 locations across British Columbia from May to July of 2017, using a Marantz PMD660 digital recorder and an Audio-Technica 815a Shotgun microphone. Recordings were typically eight to ten minutes long and consisted of ten to forty songs. For songs recorded after June 25th, a playback of song recordings was used to encourage birds to sing. We designed playbacks to consist of three song variants from different regions of the S. townsendi range, to avoid playback matching. 

This dataset consists of songs of 249 birds (180 from field recordings, 39 from Xeno-Canto, and 30 from Macaulay Library). For each bird, songs were characterized into types based on visual similarity and the results of Janes and Ryker (2016) and Janes (2017). We classified the clear song as the Type I song (i.e., used more in female attraction), and the buzzy song as the Type II song (i.e., used more in territorial defense).

We randomly selected three numbers from the total in each recording. These three songs were analyzed as follows: Spectrograms were visualized in Raven Pro 1.4 using Hann spectrogram windows with 512 samples, discrete Fourier transform (DFT) size of 512 samples, hop size of 5.6 ms, sampling frequency of 44.1 kHz, and a time resolution of 11.6 ms. The boundaries of each selection were conducted using the power spectrum as outlined in Zollinger et al. (2012). 

This dataset shows the following for 3 songs per bird per song type (Type I song n = 195; Type II song n =74):

  • Twenty-one variables were measured from each song using Raven Pro 1.4(Figure 1): total number of notes, number of unique notes, duration, minimum and maximum frequencies, bandwidth, and aggregate entropy of the whole song and parts 1 and 2 of the song.

The scores for these songs were analyzed in a PCA and then the mean and standard deviation of each variable were calculated for the three songs of each type.

Funding

Natural Sciences and Engineering Research Council, Award: RGPIN 311931-12

Natural Sciences and Engineering Research Council, Award: RGPIN-2017-03919

Natural Sciences and Engineering Research Council, Award: RGPAS-2017-507830