Soundscapes predict species occurrence in tropical forests
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Abstract
Accurate occurrence data is necessary for the conservation of keystone or endangered species, but acquiring it is usually slow, laborious and costly. Automated acoustic monitoring offers a scalable alternative to manual surveys but identifying species vocalisations requires large manually annotated training datasets, and is not always possible (e.g. for lesser studied or silent species). A new approach is needed that rapidly predicts species occurrence using smaller and more coarsely labelled audio datasets. We investigated whether local soundscapes could be used to infer the presence of 32 avifaunal and seven herpetofaunal species in 20 min recordings across a tropical forest degradation gradient in Sabah, Malaysia. Using acoustic features derived from a convolutional neural network (CNN), we characterised species indicative soundscapes by training our models on a temporally coarse labelled point‐count dataset. Soundscapes successfully predicted the occurrence of 34 out of the 39 species across the two taxonomic groups, with area under the curve (AUC) metrics from 0.53 up to 0.87. The highest accuracies were achieved for species with strong temporal occurrence patterns. Soundscapes were a better predictor of species occurrence than above‐ground carbon density – a metric often used to quantify habitat quality across forest degradation gradients. Our results demonstrate that soundscapes can be used to efficiently predict the occurrence of a wide variety of species and provide a new direction for data driven large‐scale assessments of habitat suitability.
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All conservation measures require cost-efficient and robust biodiversity monitoring, which is lagging behind carbon monitoring due in part to the lack of scalable, reproducible and cost-effective sampling methodologies.
“To be effective, all conservation measures require cost-efficient and robust biodiversity monitoring, which is lagging behind carbon monitoring due in part to the lack of scalable, reproducible and cost-effective sampling methodologies13,15.”
From Soundscapes and deep learning enable tracking biodiversity recovery in tropical forests (2023) · cites this paper as [13]
The recovery of tropical forest fauna varies widely, and depending on taxa, it is contentious and less predictable.
“However, the recovery of tropical forest fauna varies widely, and depending on taxa, it is contentious and less predictable9–13.”
From Soundscapes and deep learning enable tracking biodiversity recovery in tropical forests (2023) · cites this paper as [13]
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- Authors
- Sethi, S. S., Ewers, R. M., Jones, N. S., Sleutel, J., Shabrani, A., Zulkifli, N., & Picinali, L.
- Affiliations
- Norwegian Institute for Nature Research (NO)
- Imperial College London (GB)
- Vrije Universiteit Brussel (BE)
- WWF‐Malaysia, Sabah Office Kota Kinabalu Malaysia
- Southeast Asia Rainforest Research Partnership Lahad Datu Malaysia
- Corresponding author
- Sethi, Sarab S.
- Venue
- Oikos
- Publisher
- Wiley
- Published
- 2021-12-08
- Volume
- 2022
- Issue
- 3
- ISSN
- 0030-1299 (ISSN-L)
- 1600-0706
- OpenAlex
- W4225625984
- Cited by
- 34
- References
- 43
- Citations by year
- 2026: 3 · 2025: 4 · 2024: 6 · 2023: 14 · 2022: 7
- FWCI
- 6.38
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Oikos (publishedVersion, cc-by)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by-nc)
- NORA - Norwegian Open Research Archives (submittedVersion, cc-by)
- Spiral (Imperial College London) (submittedVersion)
- Spiral (Imperial College London) (submittedVersion, cc-by)
- Funders
- Yayasan Sime Darby
- Engineering and Physical Sciences Research Council (EP/R511547/1, EP/N014529/1, EP/N014529/)
- Natural Environment Research Council (NE/K007270/1, NE/L012456/1)
- 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 › Ecological Modeling › Species Distribution and Climate Change
- Keywords
- soundscape, species occurrence prediction, tropical forest degradation, avifauna, herpetofauna, convolutional neural networks, habitat suitability, point counts, Sabah
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