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Cited by 138Animal Vocal Communication and BehaviorWildlife Ecology and ConservationMarine animal studies overview

Bioacoustic monitoring of animal vocal behavior for conservation

Open access
Teixeira, Daniella, Maron, Martine, van Rensburg, Berndt J.·2019·Conservation Science and Practice
DOI 10.1111/csp2.72
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Abstract

The popularity of bioacoustics for threatened species monitoring has surged. Large volumes of acoustic data can be collected autonomously and remotely with minimal human effort. The approach is commonly used to detect cryptic species and, more recently, to estimate abundance or density. However, the potential for conservation‐relevant information to be derived from acoustic signatures associated with particular behavior is less well‐exploited. Animal vocal behavior can reveal important information about critical life history events. In this study, we argue that the overlap of the disciplines of bioacoustics, vocal communication, and conservation behavior—thus, “acoustic conservation behavior”—has much to offer threatened species monitoring. In particular, vocalizations can serve as indicators of behavioral states and contexts that provide insight into populations as it relates to their conservation. We explore the information available from monitoring species' vocalizations that relate to reproduction and recruitment, alarm and defense, and social behavior, and how this information could translate into potential conservation benefits. While there are still challenges to processing acoustic data, we conclude that acoustic conservation behavior may improve threatened species monitoring where vocalizations reveal behaviors that are informative for management and decision‐making.

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  • Machine learning techniques are revolutionising acoustic data processing through the development of automatic detection, measurement, and species-specific call recognisers.

    “However, machine learning techniques to process acoustic data are revolutionising such studies, through the development of techniques including automatic detection, measurement, and the application of species‐specific call recognisers (Bianco et al. 2019; Teixeira et al. 2019; Eichinski et al. 2022; Stowell 2022).”

    From Roars, Rumbles, and Resonance: A Systematic Review and Meta‐Analysis of Crocodylian Acoustic Signals (2026) · cites this paper as Teixeira et al. 2019

    Section 3 (page unavailable) describes algorithms that automatically detect vocalizations and gives species-specific examples of call recognizers. It does not establish that machine learning is revolutionising acoustic-data processing or describe automatic measurement; it also cautions that recognizer reliability is mixed. The conclusion (section 4, page unavailable) says sound-data processing remains a limitation. (LLM, conf=0.86)

    Experimental — accuracy not yet measured

  • Many taxonomic groups, including amphibians, birds, mammals, and insects include a considerable proportion of species that vocalize or otherwise use sound to communicate, making acoustic monitoring of these groups a particularly promising tool for biodiversity responses.

    “Many taxonomic groups, including amphibians, birds, mammals, and insects include a considerable proportion of species that vocalize or otherwise use sound to communicate, making acoustic monitoring of these groups a particularly promising tool for biodiversity responses17,18.”

    From Soundscapes and deep learning enable tracking biodiversity recovery in tropical forests (2023) · cites this paper as [17]

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Research works printed but not linked: 3

  1. McGregor, P. K., Peake, T. M., & Gilbert, G. (2000). Communication behaviour and conservation. In L. M. Gosling & W. J. Sutherland (Eds.), Behaviour and conservation. Cambridge, UK: Cambridge University Press.
  2. In C. Moss, H. Croze, & P. C. Lee (Eds.), The Amboseli elephants: A long-term perspective on a long-lived mammal (pp. 125-161). Chi- cago, IL/London, England: Univeristy of Chicago Press.
  3. Sharpe, D. L., Castellote, M., Wade, P. R., & Cornick, L. A. (2017). Call types of Bigg's killer whales (Orcinus orca) in western Alaska: Using vocal dialects to assess population structure. Bioacoustics, 28(1), 1-26.

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Authors
Teixeira, D., Maron, M., & van Rensburg, B. J.
Affiliations
  • The University of Queensland (AU)
  • School of Biological Sciences The University of Queensland Brisbane Queensland Australia
Corresponding author
Teixeira, Daniella
ORCID
Venue
Conservation Science and Practice
Publisher
Wiley
Published
2019-06-11
Volume
1
Issue
8
ISSN
2578-4854 (ISSN-L)
DOI
10.1111/csp2.72
OpenAlex
W2950845555
Cited by
138
References
154
Citations by year
2026: 30 · 2025: 20 · 2024: 28 · 2023: 18 · 2022: 16 · 2021: 17 · 2020: 7 · 2019: 2
FWCI
10.00
Type
journal article
Language
en
Open access
Licence
Locations
  • Conservation Science and Practice (publishedVersion, cc-by)
  • QUT ePrints (Queensland University of Technology) (submittedVersion, cc-by)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Funders
  • Queensland University of Technology
  • Cornell Lab of Ornithology
Topics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Developmental Biology › Animal Vocal Communication and Behavior
  • Physical Sciences › Environmental Science › Ecology › Wildlife Ecology and Conservation
  • Physical Sciences › Environmental Science › Ecology › Marine animal studies overview
Keywords
bioacoustics, animal vocal behavior, endangered species monitoring, social behavior, acoustic data processing, wildlife conservation management

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