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Testing the performances of automated identification of bat echolocation calls: A request for prudence

Research output: Contribution to journalArticle

Original languageEnglish
Pages (from-to)416-420
Number of pages5
JournalEcological Indicators
Volume78
Early online date31 Mar 2017
DOIs
DateAccepted/In press - 13 Mar 2017
DateE-pub ahead of print - 31 Mar 2017
DatePublished (current) - Jul 2017

Abstract

Echolocating bats are surveyed and studied acoustically with bat detectors routinely and worldwide, yet identification of species from calls often remains ambiguous or impossible due to intraspecific call variation and/or interspecific overlap in call design. To overcome such difficulties and to reduce workload, automated classifiers of echolocation calls have become popular, but their performance has not been tested sufficiently in the field. We examined the absolute performance of two commercially available programs (SonoChiro and Kaleidoscope) and one freeware package (BatClassify). We recorded noise from rain and calls of seven common bat species with Pettersson real-time full spectrum detectors in Sweden. The programs could always (100%) distinguish rain from bat calls, usually (68–100%) identify bats to group (Nyctalus/Vespertilio/Eptesicus, Pipistrellus, Myotis, Plecotus, Barbastella) and usually (83–99%) recognize typical calls of some species whose echolocation pulses are structurally distinct (Pipistrellus pygmaeus, Barbastella barbastellus). Species with less characteristic echolocation calls were not identified reliably, including Vespertilio murinus (16–26%), Myotis spp. (4–93%) and Plecotus auritus (0–89%). All programs showed major although different shortcomings and the often poor performance raising serious concerns about the use of automated classifiers for identification to species level in research and surveys. We highlight the importance of validating output from automated classifiers, and restricting their use to specific situations where identification can be made with high confidence. For comparison we also present the result of a manual identification test on a random subset of the files used to test the programs. It showed a higher classification success but performances were still low for more problematic taxa.

    Research areas

  • Biosonar, Methodology, Software, Species identification, Ultrasound

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  • Full-text PDF (accepted author manuscript)

    Rights statement: This is the author accepted manuscript (AAM). The final published version (version of record) is available online via Elsevier at http://www.sciencedirect.com/science/article/pii/S1470160X17301401. Please refer to any applicable terms of use of the publisher.

    Accepted author manuscript, 216 KB, PDF-document

    Licence: CC BY-NC-ND

  • Full-text PDF (accepted author manuscript)

    Rights statement: This is the author accepted manuscript (AAM). The final published version (version of record) is available online via Elsevier at http://www.sciencedirect.com/science/article/pii/S1470160X17301401. Please refer to any applicable terms of use of the publisher.

    Accepted author manuscript, 24 KB, PDF-document

    Licence: CC BY-NC-ND

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