DOI: 10.5061/dryad.5mkkwh77q
title: Female choice scores and Peak Frequency and Duration in calls from Wood frog chorus recordings

description: A limitation in bioacoustic studies has been the inability to
 differentiate individual sonic contributions from group-level dynamics. We
 present a novel application of acoustic-camera technology to investigate
 how individual wood frogs calls influence chorus properties, and how
 variation influences mating opportunities. We recorded mating calls and
 used playback trials to gauge preference for different chorus types in the
 laboratory. Males and females preferred chorus playbacks with low variance
 in dominant frequency. Females preferred choruses with low mean peak
 frequency. Field studies revealed more egg masses laid in ponds where
 males chorused with low variance in dominant frequency. We also noted a
 trend towards more egg masses laid in ponds where males called with low
 mean frequency. Nearest neighbor distances influenced call timing
 (neighbors called in succession) and distances increased with variance in
 chorus frequency. Results highlight the potential fitness implications of
 individual-level contributions to a bioacoustic signal produced by groups.
description: To evaluate female choice between choruses,
 we combined recordings of individual males to construct three chorus
 types: 1) LFLV choruses had low peak frequency (e.g., PF ~1542 Hz) with
 low variation in peak frequency (measured as the interval above and below
 PF before the amplitude dropped 20dB below the peak e.g., 1329-1937 Hz).
 2) HFLV choruses had high peak frequency (e.g., 1808 Hz) with low
 variation (e.g., ±20
 dB range= 1413-2037 Hz) and 3) HV choruses had both high and low frequency
 calls (PF 1807 and 1542 Hz) and thus high variance in call frequency. Two
 versions of each chorus-type were produced and were used in alternating
 order between trials. Each frog heard the HFLV and LFLV choruses, LFLV and
 HV choruses, or HFLV and HV choruses from two of four speakers. Choruses
 were played through alternate pairs of speakers in each consecutive
 trial.  The other two speakers served as silent controls. Trials lasted
 ten minutes or until the focal frog had moved to within 5cm of a speaker
 and remained at that speaker for at least one minute. Trials in which the
 focal frog did not move towards a chorus or did not make a definitive
 choice (14, 10, and 13 trials in the three treatments respectively) were
 recorded ‘NA’.<br><br> We recorded natural choruses at each of 11
 ponds using an acoustic camera mounted to a tripod. All recordings were
 made between 8:00 a.m. and 1:00 p.m.  Acoustic cameras integrate a digital
 video-camera with a microphone array to map sound onto acoustic still or
 video images. Frog choruses were recorded using a Ring48 AC Pro Polytech
 acoustic camera. This configuration consisted of a 0.75-meter rigid carbon
 fiber ring with 48 calibrated microphones (+/- 0.5 dB sensitivity) and a
 centrally located video camera. Data were aggregated in a Polytech Multi
 Channel Data Recorder (mcdRec). Data were extracted using the NoiseImage
 software v. 4.11 (Gfai tech). The NoiseImage software uses time of arrival
 differences among microphones to reconstruct sound sources on the video of
 the scene, producing a video with an overlaid heatmap of sound sources.
 All videos were reconstructed with a framerate of 25 frames/second and an
 overlap of 3 frames. This process resulted in one WAV file per frog that
 contained all calls of that individual. To extract acoustic measurements
 from these recordings, we compiled all of the WAV files into a
 multichannel file, with one channel per frog. We then converted the video
 frame numbers into time stamps using the frame rate of the video and used
 a custom script to build a selection table for the Raven Pro sound
 analysis software. To maximize the precision of the measurements, we used
 two different sets of spectrogram parameters. We exported peak frequency
 values using an FFT (Hanning) size of 4096 samples to facilitate frequency
 resolution, giving a 3 dB filter bandwidth of 33.7 Hz. We exported 90%
 duration values using an FFT of 256 samples, resulting in FFT bins of
 0.0027 sec. All spectrograms were computed with 50%
 overlap.
description: Missing values are coded NA Readme file
 contains all relevant information