A comparison of acoustic montoring methods for common anurans of the northeastern United States

Wildlife Society Bulletin
By: , and 



Many anuran monitoring programs now include autonomous recording units (ARUs). These devices collect audio data for extended periods of time with little maintenance and at sites where traditional call surveys might be difficult. Additionally, computer software programs have grown increasingly accurate at automatically identifying the calls of species. However, increased automation may cause increased error. We collected 435 min of audio data with 2 types of ARUs at 10 wetland sites in Vermont and New York, USA, from 1 May to 1 July 2010. For each minute, we determined presence or absence of 4 anuran species (Hyla versicolorPseudacris cruciferAnaxyrus americanus, and Lithobates clamitans) using 1) traditional human identification versus 2) computer-mediated identification with software package, Song Scope® (Wildlife Acoustics, Concord, MA). Detections were compared with a data set consisting of verified calls in order to quantify false positive, false negative, true positive, and true negative rates. Multinomial logistic regression analysis revealed a strong (P < 0.001) 3-way interaction between the ARU recorder type, identification method, and focal species, as well as a trend in the main effect of rain (P = 0.059). Overall, human surveyors had the lowest total error rate (<2%) compared with 18–31% total errors with automated methods. Total error rates varied by species, ranging from 4% for A. americanus to 26% for L. clamitans. The presence of rain may reduce false negative rates. For survey minutes where anurans were known to be calling, the odds of a false negative were increased when fewer individuals of the same species were calling.

Publication type Article
Publication Subtype Journal Article
Title A comparison of acoustic montoring methods for common anurans of the northeastern United States
Series title Wildlife Society Bulletin
DOI 10.1002/wsb.619
Volume 40
Issue 1
Year Published 2016
Language English
Publisher Wiley
Contributing office(s) Coop Res Unit Leetown
Description 10 p.
First page 140
Last page 149
Online Only (Y/N) N
Additional Online Files (Y/N) N
Google Analytic Metrics Metrics page
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