Data from: Singing behaviour of Ruby-crowned Kinglets (Regulus calendula) in relation to time-of-day, time-of-year, and social context
Data files
May 01, 2020 version files 67.45 MB
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element_level_data_v02.csv
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Fahmy_Wilson_RCKI_song_structure_analysis.R
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peak_frequency_data_v02.csv
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song_level_data_v02.csv
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summary_stats_from_models.csv
Abstract
Methods
Songs from the solo singing and counter-singing contexts were derived from the annotated songs from microphone array recordings. A song was considered 'solo' if it was not preceded or followed by the song of another male for 10 s (as determined by the time stamps of all annotated songs, not just those meeting the inclusion criteria), and 'countersinging' if it was. A song was considered 'intrusion' if it was produced by a focal male during the 5-min playback period or within one minute after the end of the playback period. We retained songs that were not masked by the playback for the ‘intrusion’ context. For ‘contest’ and ‘solo’ contexts, we retained only high-quality songs for our structural analysis, which included songs with no distortion or overlapping sounds, and that were produced within 15 m from an array microphone (as determined by the localization process).
We measured the acoustic structure of each song using Luscinia software (http://rflachlan.github.io/Luscinia/). Each song was viewed as a spectrogram (512-point fast Fourier transform, Hamming window, 88% overlap, 28 dB dynamic range, 100% dynamic compression), and the automatic signal detection algorithm was used to identify the elements composing the song and the silent intervals between them. We measured the following four structural features from each song:
- Song duration: the length of time between the beginning of the first element of the song and the end of the last, including the silent intervals between each element. The end of a song was determined when there were no elements produced within 4 seconds after the last element.
- Duty cycle: the sum of the durations of each element divided by song duration.
- Frequency modulation: the frequency of maximum amplitude (i.e. peak frequency) was determined for each spectrum within each element of the song. The cumulative absolute change in peak frequency from one spectrum to the next was then divided by the cumulative duration of all signal elements within the song to yield a rate of peak frequency change (Hz/s).
- Bandwidth: the difference between the 95th percentile and 5th percentile of all peak frequency values in a song.
Usage notes
'element level data v02.csv' - contains the duration of each single element (i.e. a continuous note) in each song
'song level data v02.csv' - contains information about the number of elements comprising a song as well as the duration of each song.
'peak frequency data v02.csv' - contains peak frequency measurements for the time bins compriseing each element in a song.
'summary stats from models.csv' - contains the mean and standard error of the song structure variables derived from the models run in the R script.
Fahmy Wilson RCKI song structure analysis R script:
This script calculates several structural variables from raw data created by Luscinia software (http://rflachlan.github.io/Luscinia/) and runs mixed effects models to determine the relation of the song structure variables to social context.
line 67 - used to relevel the treatments (i.e. social context) to obtain the mean and st error of the response variable from each model using the summary() function on the model result (e.g. line 73) and then document the mean and standard error of the intercept in an excel spreadsheat to create 'summary stats from models.csv', which is used to generate Figure 4. To relevel the dataframe, change the 'ref =' to the different contexts (solo, context, intrusion) and rerun the models.