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A simple framework for maximizing camera trap detections using experimental trials

Camera trap data are biased when an animal passes through a camera’s field of view but is not recorded. Cameras that operate using passive infrared sensors rely on their ability to detect thermal energy from the surface of an object. Optimal camera deployment consequently depends on the relationship...

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Autores principales: DeWitt, Philip D., Cocksedge, Amy G.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer International Publishing 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10611648/
https://www.ncbi.nlm.nih.gov/pubmed/37889358
http://dx.doi.org/10.1007/s10661-023-11945-9
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author DeWitt, Philip D.
Cocksedge, Amy G.
author_facet DeWitt, Philip D.
Cocksedge, Amy G.
author_sort DeWitt, Philip D.
collection PubMed
description Camera trap data are biased when an animal passes through a camera’s field of view but is not recorded. Cameras that operate using passive infrared sensors rely on their ability to detect thermal energy from the surface of an object. Optimal camera deployment consequently depends on the relationship between a sensor array and an animal. Here, we describe a general, experimental approach to evaluate detection errors that arise from the interaction between cameras and animals. We adapted distance sampling models and estimated the combined effects of distance, camera model, lens height, and vertical angle on the probability of detecting three different body sizes representing mammals that inhabit temperate, boreal, and arctic ecosystems. Detection probabilities were best explained by a half-normal-logistic mixture and were influenced by all experimental covariates. Detection monotonically declined when proxies were ≥6 m from the camera; however, models show that body size and camera model mediated the effect of distance on detection. Although not a focus of our study, we found that unmodeled heterogeneity arising from solar position has the potential to bias inferences where animal movements vary over time. Understanding heterogeneous detection probabilities is valuable when designing and analyzing camera trap studies. We provide a general experimental and analytical framework that ecologists, citizen scientists, and others can use and adapt to optimize camera protocols for various wildlife species and communities. Applying our framework can help ecologists assess trade-offs that arise from interactions among distance, cameras, and body sizes before committing resources to field data collection. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s10661-023-11945-9.
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spelling pubmed-106116482023-10-29 A simple framework for maximizing camera trap detections using experimental trials DeWitt, Philip D. Cocksedge, Amy G. Environ Monit Assess Research Camera trap data are biased when an animal passes through a camera’s field of view but is not recorded. Cameras that operate using passive infrared sensors rely on their ability to detect thermal energy from the surface of an object. Optimal camera deployment consequently depends on the relationship between a sensor array and an animal. Here, we describe a general, experimental approach to evaluate detection errors that arise from the interaction between cameras and animals. We adapted distance sampling models and estimated the combined effects of distance, camera model, lens height, and vertical angle on the probability of detecting three different body sizes representing mammals that inhabit temperate, boreal, and arctic ecosystems. Detection probabilities were best explained by a half-normal-logistic mixture and were influenced by all experimental covariates. Detection monotonically declined when proxies were ≥6 m from the camera; however, models show that body size and camera model mediated the effect of distance on detection. Although not a focus of our study, we found that unmodeled heterogeneity arising from solar position has the potential to bias inferences where animal movements vary over time. Understanding heterogeneous detection probabilities is valuable when designing and analyzing camera trap studies. We provide a general experimental and analytical framework that ecologists, citizen scientists, and others can use and adapt to optimize camera protocols for various wildlife species and communities. Applying our framework can help ecologists assess trade-offs that arise from interactions among distance, cameras, and body sizes before committing resources to field data collection. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s10661-023-11945-9. Springer International Publishing 2023-10-27 2023 /pmc/articles/PMC10611648/ /pubmed/37889358 http://dx.doi.org/10.1007/s10661-023-11945-9 Text en © Crown 2023 https://creativecommons.org/licenses/by/4.0/Open Access This 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/) .
spellingShingle Research
DeWitt, Philip D.
Cocksedge, Amy G.
A simple framework for maximizing camera trap detections using experimental trials
title A simple framework for maximizing camera trap detections using experimental trials
title_full A simple framework for maximizing camera trap detections using experimental trials
title_fullStr A simple framework for maximizing camera trap detections using experimental trials
title_full_unstemmed A simple framework for maximizing camera trap detections using experimental trials
title_short A simple framework for maximizing camera trap detections using experimental trials
title_sort simple framework for maximizing camera trap detections using experimental trials
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10611648/
https://www.ncbi.nlm.nih.gov/pubmed/37889358
http://dx.doi.org/10.1007/s10661-023-11945-9
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