Las Madres Comunitarias del Sector Santa Lucia una lectura desde la realidad.

We simultaneously deployed five direct high-speed bat detectors for recording both synthetic playback and free-flying bats: AnaBat SD2 (Titley Scientific, Ballina, NSW, Australia), Avisoft UltraSoundGate 116 CM16/CMPA (Avisoft Bioacoustics, Berlin, Germany), Batcorder 2.0 (ecoObs, Nuremberg, Germany), Batlogger (Elekon AG, Luzern, Switzerland), and Song Meter SM2BAT (Wildlife Acoustics, Inc, Concord, MA). There are a several other commercially available detectors that we were unable to include in this study, for example D500X and D1000X (Pettersson Elektronik) and AR125 (Binary Acoustic Technology). During all trials, microphones were within 10 cm of each other, on a parallel plane. Microphone order and position were rearranged

randomly for each trial to change microphone position, but maintain consistent

microphone spacing. We avoided variation by recording with only one detector of each model and recording with all detectors at the same time.

2.2.1

Optimizing detector recording settings

We used playback of synthetic signals to optimize detection settings for each system. Our synthetic signal file was 1478 ms in duration, and consisted of 20, 57 ms long, constant frequency (CF) signals, five signals at each of four frequencies: 25, 55, 85, and 115 kHz. For playback, we used a laptop running Avisoft RECORDER-NiDAQmx software connected to an ultrasonic playback interface with an integrated D/A power amplifier (UltraSoundGate Player 116). The interface was connected to an UltraSoundGate Dynamic Speaker ScanSpeak (hardware and software: Avisoft Bioacoustics, Berlin, Germany), which we did not calibrate. When possible, we recorded with all combinations of setting configurations for each detector. When combinations were prohibitively large (>100) we recorded in intervals spanning the full range of configurations. For each configuration, we played synthetic signals 5 m from each device. We analyzed each recording visually to find the optimum settings for recording conditions. In cases where multiple configurations were equal, we chose the settings closest to the default settings for the detector. These settings were used for the remainder of our experiments (Table 2.2).

Table 2.2. Detector settings used in this study.

AnaBat SD2 Avisoft UltraSoundGate 116 Batcorder 2.0 Batlogger Song Meter SM2BAT

Gain: 7 Data Div - 16 Gain: 7 Trigger: permanent (continuous) Sampling rate: 500 kHz Format: 16 bit Buffer: 0.050 No. Buffers: 4 Critical frequency: 14 kHz Threshold: -36 dB Post trigger: 800 ms Quality: 40 Crest minCrest: 5 minRMS: 2 minPeak: 5 HighPass: 6 Sampling rate: 192 kHz Compression: WAC0 Gain: 36 dB Dig HPF: fs/16 Dig LPF: Off Trigger Level: 15 SNR Trigger Win Right: 1 s Div Ratio: 16

2.2.2

Synthetic call playback

We played the synthetic CF signals three times at 5 m intervals (5 – 40 m) and three angles (0°, 45°, 90°) in an open field. This resulted in 15 calls of each frequency played at each distance and angle (24 combinations). We used the automated detection feature (Table 2.3) of callViewer (v. 18, Skowronski and Fenton 2008), to count the number of calls detected by each system and manually inspected each recording to ensure that there were no false positives. CallViewer is a custom echolocation sound analysis program written with MATLAB software (The MathWorks, Natick, Massachusetts). Because AnaBat file formats are not compatible with callViewer software, we visually inspected these recordings in AnaLook (v. 3.8, Titley Electronics, Ballina, Australia). We used general linear models to analyze the number of signals detected (considering each frequency separately) with angle, detector, distance and all two-way interactions. To compare among detectors we generated pair-wise comparisons of the estimated marginal means, controlling for the effect of distance and angle. We used a similar approach to compare the effect among the three angles. We estimated the detection range by modeling the probability of detection of each signal frequency at each angle by all detectors with a logistic regression in PASW18 (SPSS Inc., Chicago, IL). From the fitted logistic regression we determined the distance corresponding to a detection probability of 0.50 as our estimate of detection range (i.e., beyond this distance there is less than a 50% chance that the signal would be detected).

Table 2.3. Automated detection parameter settings used for call analysis in callViewer.

Parameter Setting

Minimum link length 10

Window length (ms) 0.3

Frame rate (fps) 10000

Chunk size (sec) 1

Minimum energy (dB) 14

Echo filter threshold (dB) 10

UPPER cutoff freq. (kHz) Inf

LOWER cutoff freq. (kHz) 15

Window type Blackman

2.2.3

Recording free-flying bats

Free-flying bats produce complex, frequency-modulated calls that vary in intensity in contrast to the simple, constant-frequency signals we used for the synthetic playback experiment. To introduce the variability that is present in natural settings we recorded free-flying bats. We deployed the detectors for two hours per night on three separate nights in a suburban area in London, Ontario, Canada. The Avisoft system detected more bat echolocation calls than any of the other detectors so we used the data from it as a baseline. We chose 26 easily identifiable passes (minimum seven consecutive calls), from hoary bats (Lasiurus cinereus), and counted the number of calls in each pass. We

manually counted all calls recorded regardless of call quality or completeness. We used callViewer to analyze the full spectrum system calls and AnaLook to analyze calls from AnaBat. We calculated the proportion of calls detected per pass relative to Avisoft, arcsine-square root transformed the data, and compared detector performance with

ANOVA and Tukey’s post hoc test in PASW18 after finding no effect of recording night.