Imperial College London
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ProfessorRobertEwers

Faculty of Natural Sciences, Department of Life Sciences (Silwood Park)

Professor of Ecology
 
 
 
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Contact

 

+44 (0)20 7594 2223r.ewers

 
 
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Location

 

1.4Centre for Population BiologySilwood Park

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Summary

 

Publications

Citation

BibTex format

@unpublished{Sethi:2020:10.1101/2020.09.24.311381,
author = {Sethi, SS and Ewers, RM and Jones, NS and Sleutel, J and Shabrani, A and Zulkifli, N and Picinali, L},
doi = {10.1101/2020.09.24.311381},
publisher = {Cold Spring Harbor Laboratory},
title = {Soundscapes predict species occurrence in tropical forests},
url = {http://dx.doi.org/10.1101/2020.09.24.311381},
year = {2020}
}
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RIS format (EndNote, RefMan)

TY  - UNPB
AB  - 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 silent species). A new, intermediate approach is needed that rapidly predicts species occurrence without requiring extensive labelled data.We investigated whether local soundscapes could be used to infer the presence of 32 avifaunal and seven herpetofaunal species across a tropical forest degradation gradient in Sabah, Malaysia. We developed a machine-learning based approach to characterise species indicative soundscapes, training our models on a coarsely labelled manual 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 of up to 0.87 (Bold-striped Tit-babbler Macronus bornensis). The highest accuracies were achieved for common species with strong temporal occurrence patterns.Soundscapes were a better predictor of species occurrence than above-ground biomass – a metric often used to quantify habitat quality across forest degradation gradients.Synthesis and applications: Our results demonstrate that soundscapes can be used to efficiently predict the occurrence of a wide variety of species. This provides a new direction for audio data to deliver large-scale, accurate assessments of habitat suitability using cheap and easily obtained field datasets.
AU  - Sethi,SS
AU  - Ewers,RM
AU  - Jones,NS
AU  - Sleutel,J
AU  - Shabrani,A
AU  - Zulkifli,N
AU  - Picinali,L
DO  - 10.1101/2020.09.24.311381
PB  - Cold Spring Harbor Laboratory
PY  - 2020///
TI  - Soundscapes predict species occurrence in tropical forests
UR  - http://dx.doi.org/10.1101/2020.09.24.311381
UR  - https://www.biorxiv.org/content/10.1101/2020.09.24.311381v1
UR  - http://hdl.handle.net/10044/1/83241
ER  -
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