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Consequences of modelling procedures on detecting environmental effects on species distribution from camera-trap data: implications for wildlife conservation

Published online by Cambridge University Press:  15 September 2025

Clément Harmange Open the ORCID record for Clément Harmange [Opens in a new window] ,
Thiago Silva Teles ,
Danilo Bandini Ribeiro ,
Anny M. Costa ,
Mauricio N. Godoi  and
Olivier Pays
Clément Harmange*
Affiliation:
Univ Angers, BiodivAG, Angers, France Current address: Institute of Advanced Studies, University of São Paulo, São Paulo, Brazil
Thiago Silva Teles
Affiliation:
Univ Angers, BiodivAG, Angers, France Bioscience Institute, Federal University of Mato Grosso Do Sul, Cidade Universitaria, Campo Grande, Brazil
Danilo Bandini Ribeiro
Affiliation:
Bioscience Institute, Federal University of Mato Grosso Do Sul, Cidade Universitaria, Campo Grande, Brazil
Anny M. Costa
Affiliation:
Bioscience Institute, Federal University of Mato Grosso Do Sul, Cidade Universitaria, Campo Grande, Brazil
Mauricio N. Godoi
Affiliation:
eeCoo Sustainability and Environmental Consulting, Goiânia, Brazil
Olivier Pays
Affiliation:
Univ Angers, BiodivAG, Angers, France REHABS International Research Laboratory, CNRS–Université Lyon 1–Nelson Mandela University, George, South Africa
*
Corresponding author: Clément Harmange; Email: c.harmange.pro@gmail.com
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Abstract

Camera traps have revolutionised wildlife monitoring. However, no consensus method exists for analysing these data. We investigated how commonly used modelling procedures affect the detection of environmental effects and quantified how this affected species distribution maps, which are essential tools for conservation planning. We used the tapeti Sylvilagus brasiliensis sensu lato, monitored using camera traps in a Brazilian indigenous reserve. We compared the ability of two commonly used modelling procedures (occurrence- vs abundance-based models, controlling or not for imperfect detection, using or not time-to-independence thresholds) to detect species responses to environmental variables. We then compared the species distribution predicted from each modelling procedure. Abundance models detected additional effects compared with occurrence models. Occurrence models detected the same environmental effects whether or not they accounted for imperfect detection. In contrast, abundance models were sensitive to imperfect detection. N-mixture models that controlled for detection provided consistent results regarding the nature, sign, and magnitude of effects, whether no time-to-independence, 30-min or 60-min thresholds were applied. Ignoring imperfect detection should not be an option for analysing camera-trap data of unmarked individuals. Hierarchical modelling, allowing detection and ecological processes to be modelled separately, should be preferred. We advocate for developing guidelines for analysing camera-trap data.

Keywords

Camera-trap technologycerrado hotspothierarchical modellingimperfect detectionindigenous reservemanagement and conservation planningoccupancyrelative abundancetropical savannaunmarked animals

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Research Article
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Journal of Tropical Ecology , Volume 41 , 2025 , e26
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© The Author(s), 2025. Published by Cambridge University Press

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