Living Together, Singing Together: Revealing Similar Patterns of Vocal Activity in Two Tropical Songbirds Applying BirdNET

In recent years, several automated and noninvasive methods for wildlife monitoring, such as passive acoustic monitoring (PAM), have emerged. PAM consists of the use of acoustic sensors followed by sound interpretation to obtain ecological information about certain species. One challenge associated with PAM is the generation of a significant amount of data, which often requires the use of machine learning tools for automated recognition. Here, we couple PAM with BirdNET, a free-to-use sound algorithm to assess, for the first time, the precision of BirdNET in predicting three tropical songbirds and to describe their patterns of vocal activity over a year in the Brazilian Pantanal. The precision of the BirdNET method was high for all three species (ranging from 72 to 84%). We were able to describe the vocal activity patterns of two of the species, the Buff-breasted Wren (<i>Cantorchilus leucotis</i>) and Thrush-like Wren (<i>Campylorhynchus turdinus</i>). Both species presented very similar vocal activity patterns during the day, with a maximum around sunrise, and throughout the year, with peak vocal activity occurring between April and June, when food availability for insectivorous species may be high. Further research should improve our knowledge regarding the ability of coupling PAM with BirdNET for monitoring a wider range of tropical species.

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Publication Details

Journal
Sensors
Published
2024-09-05
DOI
https://doi.org/10.3390/s24175780
Citations
11
Primary Topic
Animal Vocal Communication and Behavior
Type
article
Field-Weighted Citation Impact
5.61

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article

Living Together, Singing Together: Revealing Similar Patterns of Vocal Activity in Two Tropical Songbirds Applying BirdNET

Cristian Pérez‐Granados, Karl‐Ludwig Schuchmann, Marinêz Isaac Marques, David Amorós-Ausina
11 citations
Sensors
Animal Vocal Communication and Behavior
5.61
article

Living Together, Singing Together: Revealing Similar Patterns of Vocal Activity in Two Tropical Songbirds Applying BirdNET

Cristian Pérez‐Granados, Karl‐Ludwig Schuchmann, Marinêz Isaac Marques, David Amorós-Ausina
article en
11 citations

Abstract

In recent years, several automated and noninvasive methods for wildlife monitoring, such as passive acoustic monitoring (PAM), have emerged. PAM consists of the use of acoustic sensors followed by sound interpretation to obtain ecological information about certain species. One challenge associated with PAM is the generation of a significant amount of data, which often requires the use of machine learning tools for automated recognition. Here, we couple PAM with BirdNET, a free-to-use sound algorithm to assess, for the first time, the precision of BirdNET in predicting three tropical songbirds and to describe their patterns of vocal activity over a year in the Brazilian Pantanal. The precision of the BirdNET method was high for all three species (ranging from 72 to 84%). We were able to describe the vocal activity patterns of two of the species, the Buff-breasted Wren (<i>Cantorchilus leucotis</i>) and Thrush-like Wren (<i>Campylorhynchus turdinus</i>). Both species presented very similar vocal activity patterns during the day, with a maximum around sunrise, and throughout the year, with peak vocal activity occurring between April and June, when food availability for insectivorous species may be high. Further research should improve our knowledge regarding the ability of coupling PAM with BirdNET for monitoring a wider range of tropical species.

SensorsVol. 24(17)
University of Alicante (ES), Universidade Federal de Mato Grosso (BR), Zoological Research Museum Alexander Koenig (DE), Forest Science and Technology Centre of Catalonia (ES)
Instituto Nacional de Ciência e Tecnologia em Áreas Umidas, Coordenação de Aperfeiçoamento de Pessoal de Nível Superior, Conselho Nacional de Desenvolvimento Científico e Tecnológico, Centro de Pesquisa do Pantanal, Ministerio de Educación y Formación Profesional
Life in Land
Openalex Percentile: Top 4%
Animal Vocal Communication and Behavior
5.61
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