Sound level measurements from audio recordings provide objective distance estimates for distance sampling wildlife populations

• Published in: Remote sensing in ecology and conservation Vol. 6; no. 3; pp. 301 - 315
• Main Authors: Yip, Daniel A., Knight, Elly C., Haave‐Audet, Elène, Wilson, Scott J., Charchuk, Connor, Scott, Chris D., Sólymos, Péter, Bayne, Erin M., Rowcliffe, Marcus, Quick, Nicola
• Format: Journal Article
• Language: English
• Published: Oxford John Wiley & Sons, Inc 01-09-2020; Wiley
• Subjects: Acoustics; Animal populations; Automated signal recognition; autonomous recording unit; Bias; bioacoustics; Calibration; Chordeiles minor; Computer programs; Datasets; distance estimation; distance sampling; Error analysis; Estimates; Population density; Predictions; recognizer; Sampling; Seiurus aurocapilla; Software; Sound; Violations; Wildlife
• ISSN: 2056-3485; 2056-3485
• DOI: 10.1002/rse2.118

Distance sampling is widely used to estimate animal population densities by accounting for imperfect detection of individuals with increasing distance from an observer. Distance sampling assumes that distances are measured without error; however, it is often applied to human estimated distances, which are known to be inconsistent, inaccurate, and biased. We present an objective technique for estimating distance to vocalizing individuals that relies on the relative sound level (RSL) of the vocalization extracted from autonomous recording unit (ARU) recordings and show the error is less than human estimated error extracted from a literature case study. RSL predicted distances can be obtained by manual measurement in sound viewing software, or automatically with automated signal recognition software. We built calibration datasets of Ovenbirds (Seiurus aurocapilla) and Common Nighthawks (Chordeiles minor) recorded at known distances and used regression of RSL from those recordings to predict distance. There was no error bias of RSL predicted distances when compared to known distances for Common Nighthawk, minimal error bias for Ovenbird, and error from all RSL predicted distances was less than human estimated error extracted from the literature. We then simulated ARU point count surveys with a known density and estimated that density with distance sampling to test whether RSL distance prediction does not violate the assumption that distances are measured without error. There was no difference in density estimates from known distance and density estimates obtained from RSL predicted distance, while density estimates contaminated with human estimated error were significantly lower than density estimates from known distance. We found that a calibration dataset of approximately 300 vocalizations was suitable to minimize error for both species, and so conclude that RSL distance prediction is an accessible method of improving distance estimates relative to human estimation. We provide general recommendations on how to collect calibration recordings for the application of RSL distance prediction to other species and areas.
We present an objective technique for estimating distance to vocalizing individuals that relies on the relative sound level (RSL) of the vocalization extracted from autonomous recording unit (ARU) recordings and show it has less error than human estimated distances. We found there was no error bias of RSL predicted distances when compared to known distances for Common Nighthawk, minimal error bias for Ovenbird, and all RSL predicted distances had less error than human estimated distances extracted from the literature. We conclude that RSL distance prediction improves distance estimates relative to human estimation. RSL can be retrieved through automatic signal recognition, which increases processing efficiency and can increase the number of observations available for distance sampling wildlife populations.