Sampling effort required for fitting spatially explicit models of species distribution dynamics

Understanding and accurately predicting species distribution dynamics is essential for effective biodiversity conservation and management. Spatial dynamic occupancy models (SpDynOcc models) provide a valuable framework for analyzing temporal changes in species occurrence but require substantial data...

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Autores: Solà, Oriol, Kéry, Marc, Aquilué, Núria, Brotons, Lluís
Tipo de recurso: artículo
Estado:Versión publicada
Fecha de publicación:2025
País:España
Institución:Universitat de Lleida (UdL)
Repositorio:Repositori Obert UdL
OAI Identifier:oai:repositori.udl.cat:10459.1/469446
Acceso en línea:https://doi.org/10.1016/j.ecolmodel.2024.110998
https://hdl.handle.net/10459.1/469446
Access Level:acceso abierto
Palabra clave:Colonization
Detection
Distribution change
Dynamic occupancy models
Extinction
Model bias
Neighborhood connectivity
Sampling coverage
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spelling Sampling effort required for fitting spatially explicit models of species distribution dynamicsSolà, OriolKéry, MarcAquilué, NúriaBrotons, LluísColonizationDetectionDistribution changeDynamic occupancy modelsExtinctionModel biasNeighborhood connectivitySampling coverageUnderstanding and accurately predicting species distribution dynamics is essential for effective biodiversity conservation and management. Spatial dynamic occupancy models (SpDynOcc models) provide a valuable framework for analyzing temporal changes in species occurrence but require substantial data, making it critical to understand their data needs for reliable estimates. In this study, we use a simulation approach to investigate the role of survey effort, both in terms of study duration and spatial coverage, in obtaining accurate predictions from a generic SpDynOcc model. We also test the efficacy of two alternative sampling designs compared to random sampling. We varied multiple factors influencing species occurrence and detection (initial occupancy, occupancy dynamics, and probability of detection) to study the way in which they affect the data requirements for accurate parameter estimation. Models performed best with longer study durations, higher spatial coverage, and higher effective probability of detection (i.e., over all survey occasions). Nevertheless, the specific minimum sampling coverage needed notably varied based on initial occupancy and on occupancy dynamics scenarios. Preferential habitat sampling performed particularly well for low initial occupancy and high-decrease scenarios. These results indicate that tailored survey strategies are essential and must be informed by the specific ecological context. Our findings provide guidance on the survey designs needed to obtain accurate SpDynOcc model predictions, aiding researchers in the effective application of these models for studying species spatial occupancy dynamics.This study was partially funded by MCIN/AEI through the projects GREENRISK (PID2020-119933RB-C22) and CEX-2018-000828-S “Cen tro de Excelencia Severo Ochoa”. O.S. was supported by a pre-doctoral grant PRE2020-092082, by the Spanish Ministry of Science, Innova tion and Universities. N.A. was supported by a Juan de la Cierva fellowship of the Spanish Ministry of Science, Innovation and Univer sities (FCJ2020-046387-I).Elsevier2025info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttps://doi.org/10.1016/j.ecolmodel.2024.110998https://hdl.handle.net/10459.1/469446reponame:Repositori Obert UdL instname:Universitat de Lleida (UdL)Inglésinfo:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/PID2020-119933RB-C22Reproducció del document publicat a https://doi.org/10.1016/j.ecolmodel.2024.110998Ecological Modelling, 2025, vol. 501, art. 110998, p. 1-9© 2024 The AuthorsAttribution 4.0 Internationalinfo:eu-repo/semantics/openAccesshttp://creativecommons.org/licenses/by/4.0/oai:repositori.udl.cat:10459.1/4694462026-06-24T12:42:17Z
dc.title.none.fl_str_mv Sampling effort required for fitting spatially explicit models of species distribution dynamics
title Sampling effort required for fitting spatially explicit models of species distribution dynamics
spellingShingle Sampling effort required for fitting spatially explicit models of species distribution dynamics
Solà, Oriol
Colonization
Detection
Distribution change
Dynamic occupancy models
Extinction
Model bias
Neighborhood connectivity
Sampling coverage
title_short Sampling effort required for fitting spatially explicit models of species distribution dynamics
title_full Sampling effort required for fitting spatially explicit models of species distribution dynamics
title_fullStr Sampling effort required for fitting spatially explicit models of species distribution dynamics
title_full_unstemmed Sampling effort required for fitting spatially explicit models of species distribution dynamics
title_sort Sampling effort required for fitting spatially explicit models of species distribution dynamics
dc.creator.none.fl_str_mv Solà, Oriol
Kéry, Marc
Aquilué, Núria
Brotons, Lluís
author Solà, Oriol
author_facet Solà, Oriol
Kéry, Marc
Aquilué, Núria
Brotons, Lluís
author_role author
author2 Kéry, Marc
Aquilué, Núria
Brotons, Lluís
author2_role author
author
author
dc.subject.none.fl_str_mv Colonization
Detection
Distribution change
Dynamic occupancy models
Extinction
Model bias
Neighborhood connectivity
Sampling coverage
topic Colonization
Detection
Distribution change
Dynamic occupancy models
Extinction
Model bias
Neighborhood connectivity
Sampling coverage
description Understanding and accurately predicting species distribution dynamics is essential for effective biodiversity conservation and management. Spatial dynamic occupancy models (SpDynOcc models) provide a valuable framework for analyzing temporal changes in species occurrence but require substantial data, making it critical to understand their data needs for reliable estimates. In this study, we use a simulation approach to investigate the role of survey effort, both in terms of study duration and spatial coverage, in obtaining accurate predictions from a generic SpDynOcc model. We also test the efficacy of two alternative sampling designs compared to random sampling. We varied multiple factors influencing species occurrence and detection (initial occupancy, occupancy dynamics, and probability of detection) to study the way in which they affect the data requirements for accurate parameter estimation. Models performed best with longer study durations, higher spatial coverage, and higher effective probability of detection (i.e., over all survey occasions). Nevertheless, the specific minimum sampling coverage needed notably varied based on initial occupancy and on occupancy dynamics scenarios. Preferential habitat sampling performed particularly well for low initial occupancy and high-decrease scenarios. These results indicate that tailored survey strategies are essential and must be informed by the specific ecological context. Our findings provide guidance on the survey designs needed to obtain accurate SpDynOcc model predictions, aiding researchers in the effective application of these models for studying species spatial occupancy dynamics.
publishDate 2025
dc.date.none.fl_str_mv 2025
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://doi.org/10.1016/j.ecolmodel.2024.110998
https://hdl.handle.net/10459.1/469446
url https://doi.org/10.1016/j.ecolmodel.2024.110998
https://hdl.handle.net/10459.1/469446
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/PID2020-119933RB-C22
Reproducció del document publicat a https://doi.org/10.1016/j.ecolmodel.2024.110998
Ecological Modelling, 2025, vol. 501, art. 110998, p. 1-9
dc.rights.none.fl_str_mv © 2024 The Authors
Attribution 4.0 International
info:eu-repo/semantics/openAccess
http://creativecommons.org/licenses/by/4.0/
rights_invalid_str_mv © 2024 The Authors
Attribution 4.0 International
http://creativecommons.org/licenses/by/4.0/
eu_rights_str_mv openAccess
dc.publisher.none.fl_str_mv Elsevier
publisher.none.fl_str_mv Elsevier
dc.source.none.fl_str_mv reponame:Repositori Obert UdL
instname:Universitat de Lleida (UdL)
instname_str Universitat de Lleida (UdL)
reponame_str Repositori Obert UdL
collection Repositori Obert UdL
repository.name.fl_str_mv
repository.mail.fl_str_mv
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