VenierMazerolleRodgersEtAl2017

Référence

Venier, L.A., Mazerolle, M.J., Rodgers, A., McIlwrick, K.A., Holmes, S., Thompson, D. (2017) Comparison of semiautomated bird song recognition with manual detection of recorded bird song samples [Comparaison de la reconnaissance semi-automatisée de chants d'oiseaux avec des détections manuelles d'échantillons de chants d'oiseaux enregistrés]. Avian Conservation and Ecology, 12(2). (Scopus )

Résumé

Automated recording units are increasingly being used to sample wildlife populations. These devices can produce large amounts of data that are difficult to process manually. However, the information in the recordings can be summarized with semiautomated sound recognition software. Our objective was to assess the utility of the semiautomated bird song recognizers to produce data useful for conservation and sustainable forest management applications. We compared detection data generated from expert-interpreted recordings of bird songs collected with automated recording units and data derived from a semiautomated recognition process. We recorded bird songs at 109 sites in boreal forest in 2013 and 2014 using automated recording units. We developed bird-song recognizers for 10 species using Song Scope software (Wildlife Acoustics) and each recognizer was used to scan a set of recordings that was also interpreted manually by an expert in birdsong identification. We used occupancy models to estimate the detection probability associated with each method. Based on these detection probability estimates we produced cumulative detection probability curves. In a second analysis we estimated detection probability of bird song recognizers using multiple 10-minute recordings for a single station and visit (35–63, 10-minute recordings in each of four one-week periods). Results show that the detection probability of most species from single 10-min recordings is substantially higher using expert-interpreted bird song recordings than using the song recognizer software. However, our results also indicate that detection probabilities for song recognizers can be significantly improved by using more than a single 10-minute recording, which can be easily done with little additional cost with the automate procedure. Based on these results we suggest that automated recording units and song recognizer software can be valuable tools to estimate detection probability and occupancy of boreal forest birds, when sampling for sufficiently long periods. © 2017 by the author(s).

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@ARTICLE { VenierMazerolleRodgersEtAl2017,
    AUTHOR = { Venier, L.A. and Mazerolle, M.J. and Rodgers, A. and McIlwrick, K.A. and Holmes, S. and Thompson, D. },
    TITLE = { Comparison of semiautomated bird song recognition with manual detection of recorded bird song samples [Comparaison de la reconnaissance semi-automatisée de chants d'oiseaux avec des détections manuelles d'échantillons de chants d'oiseaux enregistrés] },
    JOURNAL = { Avian Conservation and Ecology },
    YEAR = { 2017 },
    VOLUME = { 12 },
    NUMBER = { 2 },
    NOTE = { cited By 1 },
    ABSTRACT = { Automated recording units are increasingly being used to sample wildlife populations. These devices can produce large amounts of data that are difficult to process manually. However, the information in the recordings can be summarized with semiautomated sound recognition software. Our objective was to assess the utility of the semiautomated bird song recognizers to produce data useful for conservation and sustainable forest management applications. We compared detection data generated from expert-interpreted recordings of bird songs collected with automated recording units and data derived from a semiautomated recognition process. We recorded bird songs at 109 sites in boreal forest in 2013 and 2014 using automated recording units. We developed bird-song recognizers for 10 species using Song Scope software (Wildlife Acoustics) and each recognizer was used to scan a set of recordings that was also interpreted manually by an expert in birdsong identification. We used occupancy models to estimate the detection probability associated with each method. Based on these detection probability estimates we produced cumulative detection probability curves. In a second analysis we estimated detection probability of bird song recognizers using multiple 10-minute recordings for a single station and visit (35–63, 10-minute recordings in each of four one-week periods). Results show that the detection probability of most species from single 10-min recordings is substantially higher using expert-interpreted bird song recordings than using the song recognizer software. However, our results also indicate that detection probabilities for song recognizers can be significantly improved by using more than a single 10-minute recording, which can be easily done with little additional cost with the automate procedure. Based on these results we suggest that automated recording units and song recognizer software can be valuable tools to estimate detection probability and occupancy of boreal forest birds, when sampling for sufficiently long periods. © 2017 by the author(s). },
    AFFILIATION = { Canadian Forest Service, Natural Resources Canada, Sault Ste. Marie, ON, Canada; Centre d'étude de la forêt, Département des sciences du bois et de la forêt, Université Laval, Québec, QC, Canada },
    ART_NUMBER = { 2 },
    AUTHOR_KEYWORDS = { Automated recording units; Boreal forest birds; Detection probability; Point counts; Song recognition; Song recognizer software },
    DOCUMENT_TYPE = { Article },
    DOI = { 10.5751/ACE-01029-120202 },
    PAGE_COUNT = { 25 },
    SOURCE = { Scopus },
    URL = { https://www.scopus.com/inward/record.uri?eid=2-s2.0-85039898682&doi=10.5751%2fACE-01029-120202&partnerID=40&md5=76d2e42c1788f968795a99020867084e },
}

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