DOI: 10.5061/dryad.mw6m905z2
title: Geographic variation in the matching between call characteristics and tympanic sensitivity in the Weeping lizard

description: Effective communication requires a match among signal characteristics,
 environmental conditions, and receptor tuning and decoding. The degree of
 matching, however, can vary, among others due to different selective
 pressures affecting the communication components. For evolutionary
 novelties, strong selective pressures are likely to act upon the signal
 and receptor to promote a tight match among them. We test this prediction
 by exploring the coupling between the acoustic signals and auditory
 sensitivity in Liolaemus chiliensis, the Weeping lizard, the only one of
 more than 285 Liolaemus species that vocalizes. Individuals emit distress
 calls that convey information of predation risk to conspecifics, which may
 respond with antipredator behaviors upon hearing calls. Specifically, we
 explored the match between spectral characteristics of the distress calls
 and the tympanic sensitivities of two populations separated by more than
 700 km, for which previous data suggested variation in their distress
 calls. We found that populations differed in signal and receptor
 characteristics and that this signal variation was explained by population
 differences in body size. No precise match occurred between the
 communication components studied, and populations differed in the degree
 of such correspondence. We suggest that this difference in matching
 between populations relates to evolutionary processes affecting the
 Weeping lizard distress calls.
description: Calls: We recorded the vocalizations between 11:00 and 16:00 h in
 a sound-attenuated booth in which walls and the ceiling were covered with
 50-cm-height foam wedges. Before a recording, and to avoid variations in
 body temperature that could affect sound production, lizards were exposed
 to a heat source to allow them to thermoregulate and achieve the
 species' preferred value. After vocal recordings, we measured the
 cloacal temperatures and excluded vocalizations from individuals with
 temperatures beyond 35 ± 2°C (mean ± SE). Additional recordings from a
 given individual were obtained after a minimum of 48 h. We evoked distress
 calls by gently grasping the lizard with the thumb and forefinger, and
 softly touching its snout with a finger for two minutes. The lizard was
 positioned 10 cm in front of a directional microphone (Sennheiser ME 66;
 frequency response: 40 Hz-22 kHz) connected to a digital recorder (Tascam
 DR-100). For the southern recordings, we also obtained the sound levels
 (in dB SPL) by positioning at 10 cm in front of the focal lizard, a
 precision integrating sound level meter (Brüel & Kjær 2230),
 previously calibrated with a sound level calibrator (Brüel
 & Kjær 4230); the SPL values were dictated to the
 recorder. For
 each individual, we averaged all its recorded sound levels
 independently of the
 emitted call type (see below). The generated .WAV files (44.1
 kHz, 16 bits) were high-pass filtered (cutoff: 200 Hz) and analyzed using
 Raven Pro 1.3 (Cornell Laboratory of Ornithology, Ithaca, NY).
 We identified two types of distress calls: (i)
 harmonic: calls with a complete or partial clear harmonic structure, and
 (ii) noisy or non-harmonic: calls not having any clear harmonic structure.
 We further classified harmonic calls as simple or complex, based on the
 absence or presence of nonlinear phenomena, respectively. We measured the
 duration (ms) of all call types, and for the harmonic calls, we also
 determined the number of harmonics recognizable in the spectrograms (fast
 Fourier transform length = 1024, Hamming Window = 87.5% overlap,
 resolution: frequency = 488 Hz; time = 0.256 ms), as this variable may
 modulate the responses to distress calls and might help to discriminate
 between populations. In addition, from the oscillograms, we also obtained
 the time to the maximum amplitude (ms) measured from the start of the
 call, while from the fast Fourier transform, we obtained the fundamental
 and dominant frequencies. These frequencies, and the number of harmonics,
 were measured in a segment free from nonlinear phenomena, preferably at
 the beginning of the signal. Although calls of this lizard species contain
 ultrasonic components, we did not detect them, as our microphone was
 nominally sensitive up to 22 kHz. However, since the energy in these calls
 decreases gradually toward the higher frequencies without energy gaps, we
 considered that calls with frequencies between 20 to 22 kHz contained
 ultrasonic components, which provides an estimate of the occurrence of
 ultrasound in these calls. 
 Tympanic sensitivities the focal lizard
 was lightly anesthetized (i.e., motionless, but with normal lung
 respiration) via an intramuscular injection of Virbac Zoletil® 50 (0.4
 µl/g body mass) in a forearm. This dosage was typically effective for 2-3
 h, though some individuals required an additional half dose to complete
 the recordings. Experiments were done in the sound-attenuated booth
 previously described, where the anesthetized lizard was placed on a
 temperature-controlled (~ 35°C) thermal plate (ReptiTherm®) located on an
 anti-vibration table (TMC 63-500). The response of the left eardrum, or
 tympanic membrane, to various acoustic stimuli, was measured with a laser
 Doppler vibrometer (Polytec CLV-2534). The compact sensor head of the
 laser was positioned 30 cm from the lizard’s eardrum, and the laser beam
 was aimed perpendicular to the tympanic surface, aimed at the tip of the
 extracolumellar attachment, close to the center of the eardrum. We
 enhanced beam reflection by placing a ~1 mm<sup>2</sup> flake
 of highly-reflecting white correction tape at the target point of the
 laser beam with the aid of a binocular light microscope (PZO OP-1, PZO,
 Warsaw, Poland). The vibrometer sensitivity was set at 5 mm/s, and the
 incoming signal was amplified by 20 dB with a custom-made amplifier.
 Automated custom software recorded the vibrometer output signal and
 controlled stimulus generation and production. For this, we used a data
 acquisition card (National Instruments NI-6071E), a programmable
 attenuator (PA5, System 3, Tucker-Davis Technologies, Alachua, FL, USA),
 and an amplifier (SKP Pro Audio Max 710X). Acoustic stimuli were broadcast
 for frequencies up to 20 kHz and above this limit, using an audio
 loudspeaker (Dynaudio BM 6, Skanderborg, Denmark) and an ultrasonic
 loudspeaker (Fostex
 Company, Tokyo, Japan), respectively, both
 placed at 50 cm in front of the focal lizard. We measured the
 response to ultrasonic frequencies to explore whether these frequencies
 would be involved in the species communication. Before the recordings, we
 calibrated the sound pressure using an ultrasonic ¼” free-field microphone
 (GRAS 40BE) powered by a preamplifier (GRAS 26CB), placed above the head
 of a realistic silicone lizard model (~ 5 cm), positioned where the focal
 lizard would be placed later. The GRAS microphone was calibrated within
 the audible frequency range with a sound level meter (Brüel & Kjær
 2238) by broadcasting pure tones of the same frequencies that were used
 later in the trials. The microphone output was stored, and the SPL
 obtained was used to automatically adjust the programmable attenuator to
 the SPL to be used during the recordings. Stimulus generation and signal
 acquisition were performed at a 200 kHz-sample rate using 16-bit
 resolution. All lizards were
 exposed individually to stimuli consisting of pure tones and synthetic
 distress calls of each population; for logistic reasons, however,
 only a subset of eight adults (4 ♀, 4 ♂; SVL 86.61 ± 2.59 mm)
 from the central population was analyzed for the response to distress
 calls. We synthesized tones with a custom program, and their duration was
 100 ms, with rise and fall ramps of 10 ms.  A sequence of tones was
 presented, starting at 0.1 kHz, and in frequency steps of 0.2 kHz from 0.2
 to 9.0 kHz. Then, from 9.0 to 20 kHz and 20 to 40 kHz the frequency steps
 were 0.50 and 2 kHz, respectively. After each tone, there was a period of
 silence of the same duration as the tone. We controlled the
 intrapopulation variation in the call characteristics by creating one call
 for each population using
 Adobe Audition 3.0 (Adobe System Inc.), based on the average
 spectro-temporal characteristics of each population harmonic calls. The
 synthetic calls had a downward frequency modulation pattern, the most
 frequently found in these populations (see Results). The values of the variables for the
 call of the central and southern population are, respectively: number of
 harmonics: six and three, duration: 71 and 42 ms, time to maximum
 amplitude: 26 and 19 ms, fundamental frequency (which was also the
 dominant frequency): 2.7 and 6.3 kHz, and a downward sweep from 2.7 to 2.1
 kHz and from 6.3 to 5.6 kHz. Acoustic stimuli were broadcast
 at 55, 60, 70, and 80 dB SPL. The order of presentation of the stimulus
 types and the sound levels followed a counterbalanced design to avoid
 potential effects of order presentation. The signal output of the laser
 was obtained simultaneously with the stimulus presentation. The
 acquisition window included the stimulus and its silence interval. For
 each acoustic stimulus, we recorded 20 response replicates.
 The acquired
 signals were analyzed with a custom-made script in the R environment,
 using the Seewave package. For each of the 20 response replicates by tone,
 we obtained the RMS (root-mean-square) of a segment of 80 ms in the middle
 of the stimulus and in the silence period. We determined the ratio between
 these RMS values, discarding the values in the first quartile, i.e., those
 with the lowest signal-to-noise ratio, to reduce the noise and obtain
 better responses. The remaining replicates were averaged for further
 analysis. A fast Fourier transform (window length = 8192 points; frequency
 resolution = 24.41 Hz) was applied at the mid-point of the average
 response to obtain the vibration velocity of the eardrum. Subsequently, we
 used these measurements to get the velocity transfer function for the
 different frequencies and sound levels. From these curves, we obtained the
 maximum velocity and the frequency at which it was measured, i.e., the
 best frequency. Additionally, we characterized the sensitivity of the
 tympanic response by considering the: (1) sensitivity range: the frequency
 range over which the eardrum vibrated at least at half of the velocity
 recorded at the best frequency, and (2) the lower and upper-frequency
 limits of this range. To analyze the
 matching between signals and tympanic sensitivities, we recorded the
 tympanic response to the synthetic distress calls, obtaining the RMS of 20
 replicates by call. The values that fell in the first quartile were
 discarded, and the remaining values were averaged for further analyses. In
 contrast to the tone analyses, in this case, we used the RMS of the whole
 stimulus because it showed different temporal characteristics. Mean power
 spectra of the synthetic distress calls and the tympanic response were
 obtained with a fast Fourier transform (window length = 2048 points;
 frequency resolution = 97.66 Hz). This lower frequency resolution, as
 compared to the one used in the tone analyses, allowed smoother spectra.
 Finally, we estimated the matching between the spectra of the synthetic
 distress calls and the tympanic sensitivities, following a method similar
 to the one used by Moreno-Gómez et al.
 (2013), acquiring the spectral cross-correlations at zero-lag
 between these spectra using the function “ccf” from the R environment.