DOI: 10.5061/dryad.bg79cnpf4
title: Gradual transitions in genetics and songs between coastal and inland populations of Setophaga townsendi

description: 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.
description: <strong>Data collection</strong>
 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 <em>S. townsendi</em>
 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 <em>et al.</em> (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.
description: # 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](https://britishcolumbia.com/plan-your-trip/maps-of-bc/british-columbia-regions/#:~:text=The%20province%20of%20British%20Columbia,Cariboo%2C%20Chilcotin%2C%20Coast%20region) 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](https://search.macaulaylibrary.org/catalog) Xento-Canto: downloaded from [xeno-canto website](https://xeno-canto.org/) ## Sharing/Access Information Data was derived from the following sources, as indicated in the **Source** column: * Macauley Library * Xeno canto