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Cited by 284

The DOI matches its Crossref record; 40 of the 40 references it cites were found in a registry or an index.

Distance sampling with camera traps

Open access
Howe, Eric J., Buckland, Stephen T., Després‐Einspenner, Marie‐Lyne, Kühl, Hjalmar S.·2017·Methods in Ecology and Evolution

Abstract

Summary: Reliable estimates of animal density and abundance are essential for effective wildlife conservation and management. Camera trapping has proven efficient for sampling multiple species, but statistical estimators of density from camera trapping data for species that cannot be individually identified are still in development. We extend point‐transect methods for estimating animal density to accommodate data from camera traps, allowing researchers to exploit existing distance sampling theory and software for designing studies and analysing data. We tested it by simulation, and used it to estimate densities of Maxwell's duikers (Philantomba maxwellii) in Taï National Park, Côte d'Ivoire. Densities estimated from simulated data were unbiased when we assumed animals were not available for detection during long periods of rest. Estimated duiker densities were higher than recent estimates from line transect surveys, which are believed to underestimate densities of forest ungulates. We expect these methods to provide an effective means to estimate animal density from camera trapping data and to be applicable in a variety of settings.

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Metadata

Authors
Howe, E. J., Buckland, S. T., Després‐Einspenner, M., & Kühl, H. S.
Venue
Methods in Ecology and Evolution
Publisher
Wiley
Published
2017-05-10
Volume
8
Issue
11
Pages
1558-1565
ISSN
2041-210X (ISSN-L)
OpenAlex
W2611997463
Cited by
284
References
40
FWCI
10.82
Type
journal article
Language
en
Open access
bronze

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CoChat. (2026). Citation check: Distance sampling with camera traps. https://app.cochat.ai/papers/10.1111/2041-210x.12790
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Howe, E. J., Buckland, S. T., Després‐Einspenner, M., & Kühl, H. S. (2017). Distance sampling with camera traps. Methods in Ecology and Evolution. https://doi.org/10.1111/2041-210x.12790

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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.