DOI: 10.5061/dryad.g1jwstqnh
title: Singing behaviour of Ruby-crowned Kinglets (Regulus calendula) in relation to time-of-day, time-of-year, and social context

description: Observational field studies provide insight on the multifunctional nature
 of birdsong. For example, if song production were limited to
 pre-fertilization, then that would suggest a mate attraction function. If
 it were used throughout the breeding season and in response to intruding
 males, then that would suggest a territorial defence function. In the
 present study, we determined the daily and seasonal singing patterns of
 male Ruby-crowned Kinglets (Regulus calendula) in Labrador, Canada, using
 microphone arrays in two breeding seasons. Using a playback experiment, we
 simulated a territorial intrusion to compare the structure of songs
 produced while defending a territory to the structure of songs produced
 during solo and contest singing. Singing peaked in the early part of the
 breeding season and then declined continuously for the remainder of the
 season, which suggests that the songs function in mate attraction. Singing
 peaked 2-3 h after dawn, and then declined steadily until it stopped at
 2200 h. Some nocturnal singing was observed, but no dawn singing was
 observed. A high probability of signal overlap by heterospecific songs at
 dawn would hinder signal recognition and explain the observed delay in
 peak singing activity. Vocal responses to playback suggested a function in
 territory defence. However, there were no significant differences in the
 duty cycle, frequency modulation, and bandwidth of songs in relation to
 the context of song production, though songs were shorter in the intrusion
 context than during solo singing. Overall, the study provides the first
 quantitative description of the effects of time of day, time of year, and
 social context on singing behaviour in this understudied species.
description: We assessed diel and seasonal patterns of song production by
 setting up 112 microphone arrays for a minimum of 24 hours each from 16
 May to 14 July 2016 (n = 70), and from 16 May to 28 June 2017 (n = 42).
 Each microphone array comprised three (n=13) or four (n=99) autonomous
 digital audio recorders (Song Meter SM3; Wildlife Acoustics Inc., Concord,
 MA, USA) arranged in either an equilateral triangle or square with sides
 approximately 40m in length. Recorders were programmed to record
 continuously until stopped, and to create a new stereo audio file every 2
 h (WAVE format, 24 kHz sampling rate, 16-bit amplitude encoding). Each
 array was set up in the morning or early afternoon (0800 – 1400 h), and
 then left to record in the absence of human observers until at least 17:00
 h the following day; therefore, recordings sampled the evening chorus on
 the day of set-up plus the dawn chorus the following day.
 For each set of array recordings, we examined the first 5 min of
 every 20-min period over a 24-h period that began at 17:00 h on the day
 the array was set up. Recordings were viewed as multichannel (6 channels
 for arrays with 3 recorders; 8 channels for arrays with 4 recorders)
 spectrograms (512-point FFT; Hamming window; gain = 25 dB; range = 78 dB)
 in Audacity, and songs were annotated when they were visible on one or
 both channels of at least 3 of the audio recorders composing the
 array. Annotated songs were localized automatically
 using a customized program in MATLAB (MATLAB 6.1; The MathWorks Inc.,
 Natick, MA, 2000). Prior to localization, the program bandpass filtered
 (2400 – 3000 Hz) songs to minimize background noise created by wind,
 traffic, or other species. The selected frequency range excluded the faint
 introductory notes of the song, but included the song’s louder third unit,
 which was always present (Figure 1). The localization process estimates
 the GPS coordinates of the origin of a sound and provides a unitless
 measure of localization error that reflects the confidence of the
 estimate. The procedure included two steps. First, it identified the
 channel in which the song was the loudest (i.e. the reference channel),
 and then used waveform cross-correlation to measure the song's
 time-of-arrival differences between the reference channel and the other
 seven channels in the array. This step produced a vector containing seven
 observed time-of-arrival differences. Second, the program superimposed a
 three-dimensional lattice, with 2-m resolution, over the entire study
 site, which was defined as the area bounded by the microphone array, plus
 a 100-m buffer around the array and a 10-m buffer above and below the
 array. At each vertex in the lattice, the program used the temperature at
 the time of recording to calculate the speed of sound and the time it
 would take for a sound to travel from the vertex to each microphone. Those
 times were then used to produce a vector containing the seven theoretical
 time-of-arrival differences between the reference channel and the other
 seven channels. The vertex that minimized the sum of absolute differences
 between the observed and theoretical time-of-arrival vectors (i.e.
 localization error) was selected by the program as the most likely origin
 of the song. This second step was repeated 100 times, with each iteration
 using a finer-resolution lattice and a smaller study area (equal to the
 spatial resolution of the previous iteration) centred on the estimated
 origin of the song from the previous iteration. The result of the
 localization process is 'RCKI_Masterfile.csv', which is used in
 R script for further analysis to determine the diel and seasonal singing
 patterns. 
description: <strong>In R script 'Fahmy Wilson RCKI song
 localization':</strong><br> Line 5 - set working
 directory to the file location containing the required csv files:
 'RCKI_MasterFile.csv'
 and 'Microphone_Coordinates.csv' Lines 185
 and 208 - change to your desired file location to export diel and seasonal
 plots in pdf format <strong>Required packages to
 run R script</strong>: sp, rgeos, dplyr, ggplot2, ggpubr,
 plotrix
 <strong>Sunrise.GooseBay.csv</strong>: the start and
 end times of civil twilight at the study site during the sampling period,
 as determined by National Research Council Canada; http://www.nrc-cnrc.gc.ca/eng/services/sunrise/. Infromation from this dataset was used to create the dashed vertical lines in the diel pattern plot