How many sequences should I track when applying the random encounter model to camera trap data?

[EN]The random encounter model (REM) is a camera trapping method to estimate population density (i.e. number of individuals per unit area) without the need for individual recognition. The REM can be applied considering camera trap data only by tracking the passages of animals in front of the camera...

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Detalles Bibliográficos
Autores: Palencia Mayordomo, Pablo, Barroso Seano, Patricia
Tipo de recurso: artículo
Estado:Versión publicada
Fecha de publicación:2024
País:España
Institución:Universidad de León
Repositorio:BULERIA. Repositorio Institucional de la Universidad de León
OAI Identifier:oai:buleria.unileon.es:10612/25967
Acceso en línea:https://zslpublications.onlinelibrary.wiley.com/doi/10.1111/jzo.13204
https://hdl.handle.net/10612/25967
Access Level:acceso abierto
Palabra clave:Ecología. Medio ambiente
Sanidad animal
Veterinaria
Zoología
Abundance
Camera trapping
Population density
Unmarked
Wildlife
2401 Biología Animal (Zoología)
2401.06 Ecología Animal
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oai_identifier_str oai:buleria.unileon.es:10612/25967
network_acronym_str ES
network_name_str España
repository_id_str
spelling How many sequences should I track when applying the random encounter model to camera trap data?Palencia Mayordomo, PabloBarroso Seano, PatriciaEcología. Medio ambienteSanidad animalVeterinariaZoologíaAbundanceCamera trappingPopulation densityUnmarkedWildlife2401 Biología Animal (Zoología)2401.06 Ecología Animal[EN]The random encounter model (REM) is a camera trapping method to estimate population density (i.e. number of individuals per unit area) without the need for individual recognition. The REM can be applied considering camera trap data only by tracking the passages of animals in front of the camera (i.e. sequences). However, it has not been assessed how the number of sequences tracked (i.e. trajectory of the animal reconstructed) influences the REM estimates. In this context, we aimed to gain further insights into the relationship between the number of sequences tracked and reliability in REM estimates to optimize its applicability. We monitored multiple species using camera traps, and we applied REM to estimate population density. We considered red fox Vulpes vulpes, roe deer Capreolus capreolus, fallow deer Dama dama, red deer Cervus elaphus and wild boar Sus scrofa as model species. We tracked from a minimum of 154 (red fox) to a maximum of 527 (red deer) sequences per species, and we then sampled the dataset to simulate different scenarios in which a lower number of sequences were tracked (20, 40, 80 and 160). We also assessed the effect of adjusting the survey period to the minimum necessary to record the desired number of sequences. Our results suggest that tracking around 100 sequences returns a precision level equivalent to the one obtained by tracking a considerably higher number of sequences and reduced and optimized the human effort necessary to apply REM. Tracking less than 40 sequences could result in low precise density estimates. Our results also highlighted the relevance of considering study periods of ca. 2 months to increase the number of sequences recorded and tracking a random sample of them. Our results contribute to the optimization and harmonization of REM as a reference method to estimate wildlife population density without the need for individual identification. We make clear recommendations on the cost-effective sample size for estimating REM parameters, optimizing the human effort when applying REM, and discouraging REM applications based on low sample sizes.SIThis study was partially supported by the Alt Pirineo Natural Park Research Observatory, the Institute for the Development and Promotion of the High Pyrenees and Aran, the Center d'Art i Natura de Farrera and the Fundació Catalunya La Pedrera, the Servei de Fauna I Flora i del Parc natural De l'Alt Pirineu del Departament d'Accio Climàtica, Alimentació I Agenda Rural. The Salvador Grau i Tort scholarship partially supports the research. We also would like to thank the Spanish Association of Terrestrial Ecology (AEET) and the project-call ‘Ganando independencia’ which partially supported this research. Pablo Palencia received support from the University of Castilla-La Mancha through a contract Margarita Salas (2022-NACIONAL-110053), and from University of Oviedo through a Juan de la Cierva contract JDC2022-048567-I supported by ‘Ministerio de Ciencia e Innovación’, ‘Agencia Estatal de Investigacion’ and ‘NextGeneration EU (MCIN/AEI/10.13039/501100011033)’. Patricia Barroso received support from the University of Castilla-La Mancha through a contract Margarita Salas (2022-NACIONAL-110053).WileySanidad AnimalFacultad de Veterinaria2024info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttps://zslpublications.onlinelibrary.wiley.com/doi/10.1111/jzo.13204https://hdl.handle.net/10612/25967reponame:BULERIA. Repositorio Institucional de la Universidad de Leóninstname:Universidad de LeónIngléshttp://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/openAccessoai:buleria.unileon.es:10612/259672026-06-24T12:43:27Z
dc.title.none.fl_str_mv How many sequences should I track when applying the random encounter model to camera trap data?
title How many sequences should I track when applying the random encounter model to camera trap data?
spellingShingle How many sequences should I track when applying the random encounter model to camera trap data?
Palencia Mayordomo, Pablo
Ecología. Medio ambiente
Sanidad animal
Veterinaria
Zoología
Abundance
Camera trapping
Population density
Unmarked
Wildlife
2401 Biología Animal (Zoología)
2401.06 Ecología Animal
title_short How many sequences should I track when applying the random encounter model to camera trap data?
title_full How many sequences should I track when applying the random encounter model to camera trap data?
title_fullStr How many sequences should I track when applying the random encounter model to camera trap data?
title_full_unstemmed How many sequences should I track when applying the random encounter model to camera trap data?
title_sort How many sequences should I track when applying the random encounter model to camera trap data?
dc.creator.none.fl_str_mv Palencia Mayordomo, Pablo
Barroso Seano, Patricia
author Palencia Mayordomo, Pablo
author_facet Palencia Mayordomo, Pablo
Barroso Seano, Patricia
author_role author
author2 Barroso Seano, Patricia
author2_role author
dc.contributor.none.fl_str_mv Sanidad Animal
Facultad de Veterinaria
dc.subject.none.fl_str_mv Ecología. Medio ambiente
Sanidad animal
Veterinaria
Zoología
Abundance
Camera trapping
Population density
Unmarked
Wildlife
2401 Biología Animal (Zoología)
2401.06 Ecología Animal
topic Ecología. Medio ambiente
Sanidad animal
Veterinaria
Zoología
Abundance
Camera trapping
Population density
Unmarked
Wildlife
2401 Biología Animal (Zoología)
2401.06 Ecología Animal
description [EN]The random encounter model (REM) is a camera trapping method to estimate population density (i.e. number of individuals per unit area) without the need for individual recognition. The REM can be applied considering camera trap data only by tracking the passages of animals in front of the camera (i.e. sequences). However, it has not been assessed how the number of sequences tracked (i.e. trajectory of the animal reconstructed) influences the REM estimates. In this context, we aimed to gain further insights into the relationship between the number of sequences tracked and reliability in REM estimates to optimize its applicability. We monitored multiple species using camera traps, and we applied REM to estimate population density. We considered red fox Vulpes vulpes, roe deer Capreolus capreolus, fallow deer Dama dama, red deer Cervus elaphus and wild boar Sus scrofa as model species. We tracked from a minimum of 154 (red fox) to a maximum of 527 (red deer) sequences per species, and we then sampled the dataset to simulate different scenarios in which a lower number of sequences were tracked (20, 40, 80 and 160). We also assessed the effect of adjusting the survey period to the minimum necessary to record the desired number of sequences. Our results suggest that tracking around 100 sequences returns a precision level equivalent to the one obtained by tracking a considerably higher number of sequences and reduced and optimized the human effort necessary to apply REM. Tracking less than 40 sequences could result in low precise density estimates. Our results also highlighted the relevance of considering study periods of ca. 2 months to increase the number of sequences recorded and tracking a random sample of them. Our results contribute to the optimization and harmonization of REM as a reference method to estimate wildlife population density without the need for individual identification. We make clear recommendations on the cost-effective sample size for estimating REM parameters, optimizing the human effort when applying REM, and discouraging REM applications based on low sample sizes.
publishDate 2024
dc.date.none.fl_str_mv 2024
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv https://zslpublications.onlinelibrary.wiley.com/doi/10.1111/jzo.13204
https://hdl.handle.net/10612/25967
url https://zslpublications.onlinelibrary.wiley.com/doi/10.1111/jzo.13204
https://hdl.handle.net/10612/25967
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.rights.none.fl_str_mv http://creativecommons.org/licenses/by-nc-nd/4.0/
info:eu-repo/semantics/openAccess
rights_invalid_str_mv http://creativecommons.org/licenses/by-nc-nd/4.0/
eu_rights_str_mv openAccess
dc.publisher.none.fl_str_mv Wiley
publisher.none.fl_str_mv Wiley
dc.source.none.fl_str_mv reponame:BULERIA. Repositorio Institucional de la Universidad de León
instname:Universidad de León
instname_str Universidad de León
reponame_str BULERIA. Repositorio Institucional de la Universidad de León
collection BULERIA. Repositorio Institucional de la Universidad de León
repository.name.fl_str_mv
repository.mail.fl_str_mv
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score 15.81155

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