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Comparison of Full-Spectrum and Zero-Crossing Automated Bat Call Classifiers

Comparison of Full-Spectrum and Zero-Crossing Automated Bat Call Classifiers. Donald Solick , Matthew Clement, Kevin Murray, Christopher Nations, and Jeffery Gruver Western EcoSystems Technology (WEST), Inc. Full-Spectrum (FS) Time and Frequency Amplitude Multiple frequency content

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Comparison of Full-Spectrum and Zero-Crossing Automated Bat Call Classifiers

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  1. Comparison of Full-Spectrum and Zero-Crossing Automated Bat Call Classifiers Donald Solick, Matthew Clement, Kevin Murray, Christopher Nations, and Jeffery Gruver Western EcoSystems Technology (WEST), Inc.

  2. Full-Spectrum (FS) Time and Frequency Amplitude Multiple frequency content -harmonics, multiple bats, calls against background noise Zero-Crossing (ZC) Time and Frequency Dominant frequency content -loudest sound gets recorded

  3. Assumption More information = better species discrimination ? Objective To determine which type of classifier is better at species discrimination given the same set of known calls

  4. FS Reference Calls

  5. SonoBat 3.04 Northeast Mean Classification: average of all calls in file By Vote: majority of calls in file Consensus: when Class and Vote agree

  6. SonoBat 3.04 Northeast x = Lano Lano 0.9991 Mean Classification: average of all calls in file By Vote: majority of calls in file Consensus: when Class and Vote agree

  7. SonoBat 3.04 Northeast 4 of 5 Lano Lano Lano Epfu Lano Lano Mean Classification: average of all calls in file By Vote: majority of calls in file Consensus: when Class and Vote agree

  8. SonoBat 3.04 Northeast 4 of 5 Lano Lano Lano 0.9991 Mean Classification: average of all calls in file By Vote: majority of calls in file Consensus: when Class and Vote agree

  9. ZC Classifiers Bat Classification and Identification (BCID) East v2.4mAC www.batcallid.com EchoClass 64 v1 www.fws.gov/midwest/Endangered/mammals/inba/inbasummersurveyguidance.html Discriminant Function Analysis for New York Developed by Eric Britzke for use by NY Dept.of Environmental Conservation

  10. Converting FS to ZC Anabat Converter 0.8 (http://bertrik.sikken.nl/anabat/) AnalookW 3.8e Applied filter and extracted parameters Except EchoClass

  11. Overall Classification Rates (%)

  12. Overall Classification Rates (%)

  13. Correct Classification Rates (% Correct)

  14. Correct Classification Rates (% Correct)

  15. Correct Classification Rates (% Correct)

  16. Summary • None of the classifiers performed well overall • SonoBat better for non-Myotis, ZC better for Myotis • SonoBat more conservative • Caution when using automated classification for Indiana bat surveys

  17. Caveats • Low sample size for some species • Lost in translation? • Different Analook filters could improve or worsen ZC classifier performance • Focus on trends, not absolute comparisons

  18. Conclusion • Illustrates limitations of automated classification of bat acoustic data • Species presence/probable absence should be based on multiple lines of evidence

  19. Thank You! • Ryan Allen, Bat Call Identification, Inc. • Eric Britzke, US Army Engineer Research & Development Center • John Chenger, Bat Conservation and Management • Carl Herzog, New York Dept. of Environmental Conservation • Amie Shovlain, Montana Natural Heritage Program • Craig Stihler, West Virginia Dept. of Natural Resources • Joe Szewczak, Humboldt State University

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