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Estimating animal density for a community of species using information obtained only from camera‐traps

Wearn, Oliver R., Bell, Thomas E. M., Bolitho, Adam, Durrant, James, Haysom, Jessica K., Nijhawan, Sahil, Thorley, Jack, Rowcliffe, J. Marcus (2022) Estimating animal density for a community of species using information obtained only from camera‐traps. Methods in Ecology and Evolution, 13 (10). pp. 2248-2261. ISSN 2041-210X. (doi:10.1111/2041-210x.13930) (The full text of this publication is not currently available from this repository. You may be able to access a copy if URLs are provided) (KAR id:102083)

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Official URL:
https://doi.org/10.1111/2041-210x.13930

Abstract

1. Animal density is a fundamental parameter in ecology and conservation, and yet it has remained difficult to measure. For terrestrial mammals and birds, camera-traps have dramatically improved our ability to collect systematic data across a large number of species, but density estimation (except for species with natural marks) is still faced with statistical and logistical hurdles, including the requirement for auxiliary data and large sample sizes, and an inability to incorporate covariates.

2. To fill this gap in the camera-trapper's statistical toolbox, we extended the ex-isting Random Encounter Model (REM) to the multi-species case in a Bayesian framework. This multi-species REM can incorporate covariates and provides pa-rameter estimates for even the rarest species. As input to the model, we used information directly available in the camera-trap data. The model outputs poste-rior distributions for the REM parameters—movement speed, activity level, the effective angle and radius of the camera-trap detection zone, and density—for each species. We applied this model to an existing dataset for 35 species in Borneo, collected across old- growth and logged forest. Here, we added animal position data derived from the image sequences in order to estimate the speed and detection zone parameters.

3. The model revealed a decrease in movement speeds, and therefore day- range, across the species community in logged compared to old- growth forest, whilst activity levels showed no consistent trend. Detection zones were shorter, but of similar width, in logged compared to old- growth forest. Overall, animal density was lower in logged forest, even though most species individually occurred at higher density in logged forest. However, the biomass per unit area was sub-stantially higher in logged compared to old- growth forest, particularly among herbivores and omnivores, likely because of increased resource availability at ground level. We also included body mass as a variable in the model, revealing that larger- bodied species were more active, had more variable speeds, and had larger detection zones.

Item Type: Article
DOI/Identification number: 10.1111/2041-210x.13930
Uncontrolled keywords: Ecological Modeling, Ecology, Evolution, Behavior and Systematics
Subjects: Q Science > QH Natural history > QH75 Conservation (Biology)
Divisions: Divisions > Division of Human and Social Sciences > School of Anthropology and Conservation > DICE (Durrell Institute of Conservation and Ecology)
Funders: AXA Research Fund (https://ror.org/02zxqxw53)
SWORD Depositor: JISC Publications Router
Depositing User: JISC Publications Router
Date Deposited: 14 Jul 2023 14:18 UTC
Last Modified: 30 Aug 2023 15:01 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/102083 (The current URI for this page, for reference purposes)
Wearn, Oliver R.: https://orcid.org/0000-0001-8258-3534
Nijhawan, Sahil: https://orcid.org/0000-0001-9195-9863
Thorley, Jack: https://orcid.org/0000-0002-8426-610X
Rowcliffe, J. Marcus: https://orcid.org/0000-0002-4286-6887
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