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

Abstract

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&thinsp;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 versicolor , Pseudacris crucifer , Anaxyrus americanus , and Lithobates clamitans ) using 1) traditional human identification versus 2) computer-mediated identification with software package, Song Scope&reg; (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 &thinsp;<&thinsp;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 &thinsp;=&thinsp;0.059). Overall, human surveyors had the lowest total error rate (<2%) compared with 18&ndash;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.