Experimentally derived detection distances from audio recordings and human observers enable integrated analysis of point count data

• Published in: Avian conservation and ecology Vol. 12; no. 1; p. 11
• Main Authors: Yip, Daniel A., Leston, Lionel, Bayne, Erin M., Sólymos, Péter, Grover, Alison
• Format: Journal Article
• Language: English
• Published: Waterloo Resilience Alliance 01-06-2017
• Subjects: Abundance; Acoustics; Amplitude; Amplitudes; Audio data; autonomous recording unit; bioacoustics; Calibration; Data; Data processing; Detection; Distance; effective detection radius; Evaluation; maximum detection distance; Methods; Observers; Recording; Song; Species richness; survey bias; Terrain; Wildlife
• ISSN: 1712-6568; 1712-6568
• DOI: 10.5751/ACE-00997-120111

Point counts are one of the most commonly used methods for assessing bird abundance. Autonomous recording units (ARUs) are increasingly being used as a replacement for human-based point counts. Previous studies have compared the relative benefits of human versus ARU-based point count methods, primarily with the goal of understanding differences in species richness and the abundance of individuals over an unlimited distance. What has not been done is an evaluation of how to standardize these two types of data so that they can be compared in the same analysis, especially when there are differences in the area sampled. We compared detection distances between human observers in the field and four commercially available recording devices (Wildlife Acoustics SM2, SM3, RiverForks, and Zoom H1) by simulating vocalizations of various avian species at different distances and amplitudes. We also investigated the relationship between sound amplitude and detection to simplify ARU calibration. We used these data to calculate correction factors that can be used to standardize detection distances of ARUs relative to each other and human observers. In general, humans in the field could detect sounds at greater distances than an ARU although detectability varied depending on species song characteristics. We provide correction factors for four commonly used ARUs and propose methods for calibrating ARUs relative to each other and human observers.