期刊
JOURNAL OF WILDLIFE MANAGEMENT
卷 75, 期 3, 页码 660-667出版社
WILEY
DOI: 10.1002/jwmg.68
关键词
acoustic identification; Anabat; discriminant function analysis; echolocation; neural networks; quantitative identification; ultrasonic detectors
资金
- United States Fish and Wildlife Service
- United States Forest Service
- Missouri Department of Conservation
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. (C) 2011 The Wildlife Society.
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