Soundscapes predict species occurrence in tropical forests

Title: Soundscapes predict species occurrence in tropical forests
Authors: Sethi, SS
Ewers, RM
Jones, NS
Sleutel, J
Shabrani, A
Zulkifli, N
Picinali, L
Item Type: Working Paper
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 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.
Issue Date: 25-Sep-2020
URI: http://hdl.handle.net/10044/1/83241
DOI: 10.1101/2020.09.24.311381
Publisher: Cold Spring Harbor Laboratory
Sponsor/Funder: Rainforest Research Sdn Bhd
World Wide Fund for Nature (WWF)
Funder's Grant Number: LBEE_P34395
UCL Ref: 542177
Publication Status: Published
Open Access location: https://www.biorxiv.org/content/10.1101/2020.09.24.311381v1
Appears in Collections:Mathematics
Applied Mathematics and Mathematical Physics
Dyson School of Design Engineering
Faculty of Natural Sciences



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