Acoustic Identification of Bats in the Eastern United States: A Comparison of Parametric and Nonparametric Methods

• Published in: The Journal of wildlife management Vol. 75; no. 3; pp. 660 - 667
• Main Authors: Britzke, Eric R, Duchamp, Joseph E, Murray, Kevin L, Swihart, Robert K, Robbins, Lynn W
• Format: Journal Article
• Language: English
• Published: Hoboken, USA The Wildlife Society 01.04.2011; John Wiley & Sons, Inc; Blackwell Publishing Ltd
• Subjects: Acoustic echoes; acoustic identification; Acoustics; Anabat; Animal communication; Bats; Biological taxonomies; Classification; Comparative studies; Discriminant analysis; discriminant function analysis; echolocation; Habitat conservation; Habitat utilization; Libraries; Mammals; Neural networks; quantitative identification; Regression analysis; Species; ultrasonic detectors; Ultrasonics; Vocalization behavior; Wildlife conservation; Wildlife management; Eastern states
• ISSN: 0022-541X; 1937-2817
• DOI: 10.1002/jwmg.68

Ultrasonic detectors are widely used to survey bats in ecological studies. To evaluate efficacy of acoustic identification, we compiled a library of search phase calls from across the eastern United States using the Anabat system. The call library included 1,846 call sequences of 12 species recorded from 14 states. We determined accuracy rates using 3 parametric and 4 nonparametric classification functions for acoustic identification. The 2 most flexible classification functions also were the most accurate: neural networks (overall classification accuracy = 0.94) and mixture discriminant analysis incorporating an adaptive regression model (overall classification accuracy = 0.93). Flexible nonparametric methods offer substantial benefits when discriminating among closely related species and may preclude the need to group species with similar calls. We demonstrate that quantitative methods provide an effective technique to acoustically identify bats in the eastern United States with known accuracy rates.