Acoustic Monitoring Enables Multi‐Taxa Conservation Assessment and Prioritisation Over Large Scales and for Rare and Cryptic Species

ABSTRACT Aim To combat the global biodiversity crisis, robust and scalable data are needed to target, monitor and evaluate conservation efforts, particularly in data‐poor regions and for cryptic taxa. Passive acoustic monitoring (PAM) has the potential to provide solutions, but real‐world examples are still rare. We demonstrate how PAM data can be used to rapidly and effectively map distributions of multiple taxa over large scales, in data‐poor regions. We show how these data can be used to assess the importance of existing protected areas and prioritise future conservation efforts, including for rare and cryptic species often neglected in such assessments. Location Global with a case study from Polesia, Eastern Europe. Time Period Present. Major Taxa Studied Bats, Birds, Small mammals and Bush crickets. Methods Using machine‐learning and manual verification, we identified bats, nocturnally active birds, small mammals and bush crickets from over 34,000 monitoring hours at 506 sites in the Polesia region of Belarus and Ukraine. Using multi‐species generalised mixed models in a Bayesian framework, we then predicted occupancy and acoustic activity for these species and their associations with protected areas, over a 151,000 km 2 project area. We identified areas of high conservation priority as measured by species richness and/or importance for globally or regionally threatened species. Main Conclusions Our approach provides a roadmap for collecting and processing large‐scale, multi‐taxa biodiversity data using passive acoustic monitoring. In our case study region, we show that although existing protected areas contain a relatively large proportion of high conservation priority areas, there are significant gaps in the protected area network. We also show low surrogacy of areas of high conservation priority between taxa at fine scales, but did at larger scales, showing the importance of multi‐taxa monitoring to prioritise protected areas that conserve a wide variety of species.

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Publication Details

Journal
Global Ecology and Biogeography
Published
2025-12-01
DOI
https://doi.org/10.1111/geb.70175
Primary Topic
Animal Vocal Communication and Behavior
Type
article
Field-Weighted Citation Impact
0.00

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article

Acoustic Monitoring Enables Multi‐Taxa Conservation Assessment and Prioritisation Over Large Scales and for Rare and Cryptic Species

Stuart E. Newson, Simon Gillings, Adham Ashton‐Butt, Chris Scott et al.
Global Ecology and Biogeography
Animal Vocal Communication and Behavior
article

Acoustic Monitoring Enables Multi‐Taxa Conservation Assessment and Prioritisation Over Large Scales and for Rare and Cryptic Species

Stuart E. Newson, Simon Gillings, Adham Ashton‐Butt, Chris Scott, Tatiana Kornienko, Philip W. Atkinson, James W. Pearce‐Higgins, Jeffrey W. Doser, Viktar Fenchuk
article en

Abstract

ABSTRACT Aim To combat the global biodiversity crisis, robust and scalable data are needed to target, monitor and evaluate conservation efforts, particularly in data‐poor regions and for cryptic taxa. Passive acoustic monitoring (PAM) has the potential to provide solutions, but real‐world examples are still rare. We demonstrate how PAM data can be used to rapidly and effectively map distributions of multiple taxa over large scales, in data‐poor regions. We show how these data can be used to assess the importance of existing protected areas and prioritise future conservation efforts, including for rare and cryptic species often neglected in such assessments. Location Global with a case study from Polesia, Eastern Europe. Time Period Present. Major Taxa Studied Bats, Birds, Small mammals and Bush crickets. Methods Using machine‐learning and manual verification, we identified bats, nocturnally active birds, small mammals and bush crickets from over 34,000 monitoring hours at 506 sites in the Polesia region of Belarus and Ukraine. Using multi‐species generalised mixed models in a Bayesian framework, we then predicted occupancy and acoustic activity for these species and their associations with protected areas, over a 151,000 km 2 project area. We identified areas of high conservation priority as measured by species richness and/or importance for globally or regionally threatened species. Main Conclusions Our approach provides a roadmap for collecting and processing large‐scale, multi‐taxa biodiversity data using passive acoustic monitoring. In our case study region, we show that although existing protected areas contain a relatively large proportion of high conservation priority areas, there are significant gaps in the protected area network. We also show low surrogacy of areas of high conservation priority between taxa at fine scales, but did at larger scales, showing the importance of multi‐taxa monitoring to prioritise protected areas that conserve a wide variety of species.

Global Ecology and BiogeographyVol. 34(12)
University of East Anglia (GB), University of Leeds (GB), North Carolina State University (US), University of Cambridge (GB), Swiss Ornithological Institute (CH), County Hospital (GB), Conservation Leadership Programme (GB), Oldham Council (GB), Frankfurt Zoological Society (DE), British Trust for Ornithology (GB), Michigan State University (US)
Arcadia Fund, Natural England, Esmée Fairbairn Foundation, Zoologische Gesellschaft Frankfurt, Cambridge Conservation Initiative
Openalex Percentile: Top 62%
Animal Vocal Communication and Behavior
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