Environmental and ecological signals predict food shortages for subtropical populations of Australian flying foxes, reservoirs of Hendra virus

Food availability determines where and how animals use space across a landscape and, therefore, affects the risk of encounters leading to zoonotic spillover. This relationship is evident in Australian flying foxes (Pteropus spp.; fruit bats), where acute food shortages precede clusters of Hendra virus spillovers. Using machine learning, we predicted months of food shortages from climatological and ecological covariates (1996-2022) in subtropical Australia. Overall accuracy in predicting months of low food availability on a test set from 2018 up to 2022 reached 93.33 and 92.59% based on climatological and bat-level features, respectively. Seasonality and the Oceanic El Niño Index were the most important environmental features, while the number of bats in rescue centres and their body weights were the most important bat-level features. These models support predictive signals up to nine months in advance, facilitating action to mitigate spillover risk.

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Publication Details

Journal
Biology Letters
Published
2025-12-01
DOI
https://doi.org/10.1098/rsbl.2025.0371
Primary Topic
Virology and Viral Diseases
Type
article
Field-Weighted Citation Impact
0.00

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article

Environmental and ecological signals predict food shortages for subtropical populations of Australian flying foxes, reservoirs of Hendra virus

Daniel J. Becker, Raina K. Plowright, Barbara A. Han, Peggy Eby et al.
Biology Letters
Virology and Viral Diseases
article

Environmental and ecological signals predict food shortages for subtropical populations of Australian flying foxes, reservoirs of Hendra virus

Daniel J. Becker, Raina K. Plowright, Barbara A. Han, Peggy Eby, Daniel Jacobson, Andrew Hoegh, Wyatt Madden, John Lagergren, Alison J. Peel, Lib Ruytenberg, Manuel Ruiz‐Aravena
article en

Abstract

Food availability determines where and how animals use space across a landscape and, therefore, affects the risk of encounters leading to zoonotic spillover. This relationship is evident in Australian flying foxes (Pteropus spp.; fruit bats), where acute food shortages precede clusters of Hendra virus spillovers. Using machine learning, we predicted months of food shortages from climatological and ecological covariates (1996-2022) in subtropical Australia. Overall accuracy in predicting months of low food availability on a test set from 2018 up to 2022 reached 93.33 and 92.59% based on climatological and bat-level features, respectively. Seasonality and the Oceanic El Niño Index were the most important environmental features, while the number of bats in rescue centres and their body weights were the most important bat-level features. These models support predictive signals up to nine months in advance, facilitating action to mitigate spillover risk.

Biology LettersVol. 21(12)
Griffith University (AU), Oak Ridge National Laboratory (US), The University of Sydney (AU), United States Fish and Wildlife Service (US), Emory University (US), Cornell University (US), Montana State University (US), Cary Institute of Ecosystem Studies (US), University of Oklahoma (US), Mississippi State University (US)
U.S. Department of Energy, Battelle, UT-Battelle, National Oceanic and Atmospheric Administration
Openalex Percentile: Top 49%
Virology and Viral Diseases
DOIOpen Access PDFOpenAlex
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