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Empirical evaluation of the spatial scale and detection process of camera trap surveys
BACKGROUND: Camera traps present a valuable tool for monitoring animals but detect species imperfectly. Occupancy models are frequently used to address this, but it is unclear what spatial scale the data represent. Although individual cameras monitor animal activity within a small target window in f...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8364038/ https://www.ncbi.nlm.nih.gov/pubmed/34391486 http://dx.doi.org/10.1186/s40462-021-00277-3 |
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author | Kays, Roland Hody, Allison Jachowski, David S. Parsons, Arielle W. |
author_facet | Kays, Roland Hody, Allison Jachowski, David S. Parsons, Arielle W. |
author_sort | Kays, Roland |
collection | PubMed |
description | BACKGROUND: Camera traps present a valuable tool for monitoring animals but detect species imperfectly. Occupancy models are frequently used to address this, but it is unclear what spatial scale the data represent. Although individual cameras monitor animal activity within a small target window in front of the device, many practitioners use these data to infer animal presence over larger, vaguely-defined areas. Animal movement is generally presumed to link these scales, but fine-scale heterogeneity in animal space use could disrupt this relationship. METHODS: We deployed cameras at 10 m intervals across a 0.6 ha forest plot to create an unprecedentedly dense sensor array that allows us to compare animal detections at these two scales. Using time-stamped camera detections we reconstructed fine-scale movement paths of four mammal species and characterized (a) how well animal use of a single camera represented use of the surrounding plot, (b) how well cameras detected animals, and (c) how these processes affected overall detection probability, p. We used these observations to parameterize simulations that test the performance of occupancy models in realistic scenarios. RESULTS: We document two important aspects of animal movement and how it affects sampling with passive detectors. First, animal space use is heterogeneous at the camera-trap scale, and data from a single camera may poorly represent activity in its surroundings. Second, cameras frequently (14–71%) fail to record passing animals. Our simulations show how this heterogeneity can introduce unmodeled variation into detection probability, biasing occupancy estimates for species with low p. CONCLUSIONS: Occupancy or population estimates with camera traps could be improved by increasing camera reliability to reduce missed detections, adding covariates to model heterogeneity in p, or increasing the area sampled by each camera through different sampling designs or technologies. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s40462-021-00277-3. |
format | Online Article Text |
id | pubmed-8364038 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-83640382021-08-17 Empirical evaluation of the spatial scale and detection process of camera trap surveys Kays, Roland Hody, Allison Jachowski, David S. Parsons, Arielle W. Mov Ecol Research BACKGROUND: Camera traps present a valuable tool for monitoring animals but detect species imperfectly. Occupancy models are frequently used to address this, but it is unclear what spatial scale the data represent. Although individual cameras monitor animal activity within a small target window in front of the device, many practitioners use these data to infer animal presence over larger, vaguely-defined areas. Animal movement is generally presumed to link these scales, but fine-scale heterogeneity in animal space use could disrupt this relationship. METHODS: We deployed cameras at 10 m intervals across a 0.6 ha forest plot to create an unprecedentedly dense sensor array that allows us to compare animal detections at these two scales. Using time-stamped camera detections we reconstructed fine-scale movement paths of four mammal species and characterized (a) how well animal use of a single camera represented use of the surrounding plot, (b) how well cameras detected animals, and (c) how these processes affected overall detection probability, p. We used these observations to parameterize simulations that test the performance of occupancy models in realistic scenarios. RESULTS: We document two important aspects of animal movement and how it affects sampling with passive detectors. First, animal space use is heterogeneous at the camera-trap scale, and data from a single camera may poorly represent activity in its surroundings. Second, cameras frequently (14–71%) fail to record passing animals. Our simulations show how this heterogeneity can introduce unmodeled variation into detection probability, biasing occupancy estimates for species with low p. CONCLUSIONS: Occupancy or population estimates with camera traps could be improved by increasing camera reliability to reduce missed detections, adding covariates to model heterogeneity in p, or increasing the area sampled by each camera through different sampling designs or technologies. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s40462-021-00277-3. BioMed Central 2021-08-14 /pmc/articles/PMC8364038/ /pubmed/34391486 http://dx.doi.org/10.1186/s40462-021-00277-3 Text en © The Author(s) 2021 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data. |
spellingShingle | Research Kays, Roland Hody, Allison Jachowski, David S. Parsons, Arielle W. Empirical evaluation of the spatial scale and detection process of camera trap surveys |
title | Empirical evaluation of the spatial scale and detection process of camera trap surveys |
title_full | Empirical evaluation of the spatial scale and detection process of camera trap surveys |
title_fullStr | Empirical evaluation of the spatial scale and detection process of camera trap surveys |
title_full_unstemmed | Empirical evaluation of the spatial scale and detection process of camera trap surveys |
title_short | Empirical evaluation of the spatial scale and detection process of camera trap surveys |
title_sort | empirical evaluation of the spatial scale and detection process of camera trap surveys |
topic | Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8364038/ https://www.ncbi.nlm.nih.gov/pubmed/34391486 http://dx.doi.org/10.1186/s40462-021-00277-3 |
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