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Cited by 1,258

The DOI matches its Crossref record; 60 of the 60 references it cites were found in a registry or an index.

REVIEW: Wildlife camera trapping: a review and recommendations for linking surveys to ecological processes

Burton, A. Cole, Neilson, Eric, Moreira, Dario, Ladle, Andrew, Steenweg, Robin, Fisher, Jason T., Bayne, Erin, Boutin, Stan·2015·Journal of Applied Ecology

Abstract

Summary: Reliable assessment of animal populations is a long‐standing challenge in wildlife ecology. Technological advances have led to widespread adoption of camera traps (CT s) to survey wildlife distribution, abundance and behaviour. As for any wildlife survey method, camera trapping must contend with sources of sampling error such as imperfect detection. Early applications focused on density estimation of naturally marked species, but there is growing interest in broad‐scale CT surveys of unmarked populations and communities. Nevertheless, inferences based on detection indices are controversial, and the suitability of alternatives such as occupancy estimation is debatable. We reviewed 266 CT studies published between 2008 and 2013. We recorded study objectives and methodologies, evaluating the consistency of CT protocols and sampling designs, the extent to which CT surveys considered sampling error, and the linkages between analytical assumptions and species ecology. Nearly two‐thirds of studies surveyed more than one species, and a majority used response variables that ignored imperfect detection (e.g. presence–absence, relative abundance). Many studies used opportunistic sampling and did not explicitly report details of sampling design and camera deployment that could affect conclusions. Most studies estimating density used capture–recapture methods on marked species, with spatially explicit methods becoming more prominent. Few studies estimated density for unmarked species, focusing instead on occupancy modelling or measures of relative abundance. While occupancy studies estimated detectability, most did not explicitly define key components of the modelling framework (e.g. a site) or discuss potential violations of model assumptions (e.g. site closure). Studies using relative abundance relied on assumptions of equal detectability, and most did not explicitly define expected relationships between measured responses and underlying ecological processes (e.g. animal abundance and movement). Synthesis and applications. The rapid adoption of camera traps represents an exciting transition in wildlife survey methodology. We remain optimistic about the technology's promise, but call for more explicit consideration of underlying processes of animal abundance, movement and detection by cameras, including more thorough reporting of methodological details and assumptions. Such transparency will facilitate efforts to evaluate and improve the reliability of camera trap surveys, ultimately leading to stronger inferences and helping to meet modern needs for effective ecological inquiry and biodiversity monitoring.

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Metadata

Authors
Burton, A. C., Neilson, E., Moreira, D., Ladle, A., Steenweg, R., Fisher, J. T., Bayne, E., & Boutin, S.
Venue
Journal of Applied Ecology
Publisher
Wiley
Published
2015-04-11
Volume
52
Issue
3
Pages
675-685
ISSN
  • 0021-8901 (ISSN-L)
  • 1365-2664
OpenAlex
W2055032654
Cited by
1,258
References
60
FWCI
51.29
Type
journal article
Language
en
Open access
closed

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CoChat. (2026). Citation check: REVIEW: Wildlife camera trapping: a review and recommendations for linking surveys to ecological processes. https://app.cochat.ai/papers/10.1111/1365-2664.12432
The paper
Burton, A. C., Neilson, E., Moreira, D., Ladle, A., Steenweg, R., Fisher, J. T., Bayne, E., & Boutin, S. (2015). REVIEW: Wildlife camera trapping: a review and recommendations for linking surveys to ecological processes. Journal of Applied Ecology. https://doi.org/10.1111/1365-2664.12432

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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.