Methods to detect spatial biases in tracking studies caused by differential representativeness of individuals, populations and time
Autor(a) principal: | |
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Data de Publicação: | 2022 |
Outros Autores: | , , , , , , , , , , , , , , , , , , , , , , , , , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
Texto Completo: | http://hdl.handle.net/10400.12/8871 |
Resumo: | Aim Over the last decades, the study of movement through tracking data has grown exceeding the expectations of movement ecologists. This has posed new challenges, specifically when using individual tracking data to infer higher-level distributions (e.g. population and species). Sources of variability such as individual site fidelity (ISF), environmental stochasticity over time, and space-use variability across species ranges must be considered, and their effects identified and corrected, to produce accurate estimates of spatial distribution using tracking data. Innovation We developed R functions to detect the effect of these sources of variability in the distribution of animal groups when inferred from individual tracking data. These procedures can be adapted for their use in most tracking datasets and tracking techniques. We demonstrated our procedures with simulated datasets and showed their applicability on a real-world dataset containing 1346 year-round migratory trips from 805 individuals of three closely related seabird species breeding in 34 colonies in the Mediterranean Sea and the Atlantic Ocean, spanning 10 years. We detected an effect of ISF in one of the colonies, but no effect of the environmental stochasticity on the distribution of birds for any of the species. We also identified among-colony variability in nonbreeding space use for one species, with significant effects of population size and longitude. Main conclusions This work provides a useful, much-needed tool for researchers using animal tracking data to model species distributions or establish conservation measures. This methodology may be applied in studies using individual tracking data to accurately infer the distribution of a population or species and support the delineation of important areas for conservation based on tracking data. This step, designed to precede any analysis, has become increasingly relevant with the proliferation of studies using large tracking datasets that has accompanied the globalization process in science driving collaborations and tracking data sharing initiatives. |
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Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
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Methods to detect spatial biases in tracking studies caused by differential representativeness of individuals, populations and timeAnimal movementEnvironmental stochasticityMetapopulation studySite fidelitySpecies distributionAim Over the last decades, the study of movement through tracking data has grown exceeding the expectations of movement ecologists. This has posed new challenges, specifically when using individual tracking data to infer higher-level distributions (e.g. population and species). Sources of variability such as individual site fidelity (ISF), environmental stochasticity over time, and space-use variability across species ranges must be considered, and their effects identified and corrected, to produce accurate estimates of spatial distribution using tracking data. Innovation We developed R functions to detect the effect of these sources of variability in the distribution of animal groups when inferred from individual tracking data. These procedures can be adapted for their use in most tracking datasets and tracking techniques. We demonstrated our procedures with simulated datasets and showed their applicability on a real-world dataset containing 1346 year-round migratory trips from 805 individuals of three closely related seabird species breeding in 34 colonies in the Mediterranean Sea and the Atlantic Ocean, spanning 10 years. We detected an effect of ISF in one of the colonies, but no effect of the environmental stochasticity on the distribution of birds for any of the species. We also identified among-colony variability in nonbreeding space use for one species, with significant effects of population size and longitude. Main conclusions This work provides a useful, much-needed tool for researchers using animal tracking data to model species distributions or establish conservation measures. This methodology may be applied in studies using individual tracking data to accurately infer the distribution of a population or species and support the delineation of important areas for conservation based on tracking data. This step, designed to precede any analysis, has become increasingly relevant with the proliferation of studies using large tracking datasets that has accompanied the globalization process in science driving collaborations and tracking data sharing initiatives.Fundação para a Ciência e Tecnologia - FCTWiley-Blackwell Publishing LtdRepositório do ISPAPujol, Virginia MoreraCatry, PauloMagalhães, MariaPeron, ClaraReyes‐González, José ManuelGranadeiro, José P.Militão, TeresaDias, Maria P.Oro, DanielDell'Omo, GiacomoMüller, MartinaPaiva, Vitor H.Metzger, BenjaminNeves, V CNavarro, JoanKarris, GeorgiosXirouchakis, StavrosCecere, Jacopo G.Zamora‐López, AntonioForero, Manuel G.Ouni, RidhaRomdhane, Mohamed SalahDe Felipe, FernandaZajková, ZuzanaCruz‐Flores, MartaGrémillet, DavidGonzález‐Solís, JacobRamos, Raül2022-12-20T20:19:52Z20222022-01-01T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttp://hdl.handle.net/10400.12/8871engMorera, P. V., Catry, P., Magalhães, M., Péron, C., Reyes, G. J. M., Granadeiro, J. P., Militão, T., Dias, M. P., Oro, D., Dell’Omo, G., Müller, M., Paiva, V. H., Metzger, B., Neves, V., Navarro, J., Karris, G., Xirouchakis, S., Cecere, J. G., Zamora, L. A., & Forero, M. G. (2022). Methods to detect spatial biases in tracking studies caused by differential representativeness of individuals, populations and time. Diversity & Distributions, 1. https://doi.org/10.1111/ddi.136421366951610.1111/ddi.13642info:eu-repo/semantics/openAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2022-12-25T02:15:27Zoai:repositorio.ispa.pt:10400.12/8871Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-19T16:28:52.815924Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse |
dc.title.none.fl_str_mv |
Methods to detect spatial biases in tracking studies caused by differential representativeness of individuals, populations and time |
title |
Methods to detect spatial biases in tracking studies caused by differential representativeness of individuals, populations and time |
spellingShingle |
Methods to detect spatial biases in tracking studies caused by differential representativeness of individuals, populations and time Pujol, Virginia Morera Animal movement Environmental stochasticity Metapopulation study Site fidelity Species distribution |
title_short |
Methods to detect spatial biases in tracking studies caused by differential representativeness of individuals, populations and time |
title_full |
Methods to detect spatial biases in tracking studies caused by differential representativeness of individuals, populations and time |
title_fullStr |
Methods to detect spatial biases in tracking studies caused by differential representativeness of individuals, populations and time |
title_full_unstemmed |
Methods to detect spatial biases in tracking studies caused by differential representativeness of individuals, populations and time |
title_sort |
Methods to detect spatial biases in tracking studies caused by differential representativeness of individuals, populations and time |
author |
Pujol, Virginia Morera |
author_facet |
Pujol, Virginia Morera Catry, Paulo Magalhães, Maria Peron, Clara Reyes‐González, José Manuel Granadeiro, José P. Militão, Teresa Dias, Maria P. Oro, Daniel Dell'Omo, Giacomo Müller, Martina Paiva, Vitor H. Metzger, Benjamin Neves, V C Navarro, Joan Karris, Georgios Xirouchakis, Stavros Cecere, Jacopo G. Zamora‐López, Antonio Forero, Manuel G. Ouni, Ridha Romdhane, Mohamed Salah De Felipe, Fernanda Zajková, Zuzana Cruz‐Flores, Marta Grémillet, David González‐Solís, Jacob Ramos, Raül |
author_role |
author |
author2 |
Catry, Paulo Magalhães, Maria Peron, Clara Reyes‐González, José Manuel Granadeiro, José P. Militão, Teresa Dias, Maria P. Oro, Daniel Dell'Omo, Giacomo Müller, Martina Paiva, Vitor H. Metzger, Benjamin Neves, V C Navarro, Joan Karris, Georgios Xirouchakis, Stavros Cecere, Jacopo G. Zamora‐López, Antonio Forero, Manuel G. Ouni, Ridha Romdhane, Mohamed Salah De Felipe, Fernanda Zajková, Zuzana Cruz‐Flores, Marta Grémillet, David González‐Solís, Jacob Ramos, Raül |
author2_role |
author author author author author author author author author author author author author author author author author author author author author author author author author author author |
dc.contributor.none.fl_str_mv |
Repositório do ISPA |
dc.contributor.author.fl_str_mv |
Pujol, Virginia Morera Catry, Paulo Magalhães, Maria Peron, Clara Reyes‐González, José Manuel Granadeiro, José P. Militão, Teresa Dias, Maria P. Oro, Daniel Dell'Omo, Giacomo Müller, Martina Paiva, Vitor H. Metzger, Benjamin Neves, V C Navarro, Joan Karris, Georgios Xirouchakis, Stavros Cecere, Jacopo G. Zamora‐López, Antonio Forero, Manuel G. Ouni, Ridha Romdhane, Mohamed Salah De Felipe, Fernanda Zajková, Zuzana Cruz‐Flores, Marta Grémillet, David González‐Solís, Jacob Ramos, Raül |
dc.subject.por.fl_str_mv |
Animal movement Environmental stochasticity Metapopulation study Site fidelity Species distribution |
topic |
Animal movement Environmental stochasticity Metapopulation study Site fidelity Species distribution |
description |
Aim Over the last decades, the study of movement through tracking data has grown exceeding the expectations of movement ecologists. This has posed new challenges, specifically when using individual tracking data to infer higher-level distributions (e.g. population and species). Sources of variability such as individual site fidelity (ISF), environmental stochasticity over time, and space-use variability across species ranges must be considered, and their effects identified and corrected, to produce accurate estimates of spatial distribution using tracking data. Innovation We developed R functions to detect the effect of these sources of variability in the distribution of animal groups when inferred from individual tracking data. These procedures can be adapted for their use in most tracking datasets and tracking techniques. We demonstrated our procedures with simulated datasets and showed their applicability on a real-world dataset containing 1346 year-round migratory trips from 805 individuals of three closely related seabird species breeding in 34 colonies in the Mediterranean Sea and the Atlantic Ocean, spanning 10 years. We detected an effect of ISF in one of the colonies, but no effect of the environmental stochasticity on the distribution of birds for any of the species. We also identified among-colony variability in nonbreeding space use for one species, with significant effects of population size and longitude. Main conclusions This work provides a useful, much-needed tool for researchers using animal tracking data to model species distributions or establish conservation measures. This methodology may be applied in studies using individual tracking data to accurately infer the distribution of a population or species and support the delineation of important areas for conservation based on tracking data. This step, designed to precede any analysis, has become increasingly relevant with the proliferation of studies using large tracking datasets that has accompanied the globalization process in science driving collaborations and tracking data sharing initiatives. |
publishDate |
2022 |
dc.date.none.fl_str_mv |
2022-12-20T20:19:52Z 2022 2022-01-01T00:00:00Z |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://hdl.handle.net/10400.12/8871 |
url |
http://hdl.handle.net/10400.12/8871 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
Morera, P. V., Catry, P., Magalhães, M., Péron, C., Reyes, G. J. M., Granadeiro, J. P., Militão, T., Dias, M. P., Oro, D., Dell’Omo, G., Müller, M., Paiva, V. H., Metzger, B., Neves, V., Navarro, J., Karris, G., Xirouchakis, S., Cecere, J. G., Zamora, L. A., & Forero, M. G. (2022). Methods to detect spatial biases in tracking studies caused by differential representativeness of individuals, populations and time. Diversity & Distributions, 1. https://doi.org/10.1111/ddi.13642 13669516 10.1111/ddi.13642 |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
application/pdf |
dc.publisher.none.fl_str_mv |
Wiley-Blackwell Publishing Ltd |
publisher.none.fl_str_mv |
Wiley-Blackwell Publishing Ltd |
dc.source.none.fl_str_mv |
reponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação instacron:RCAAP |
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Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
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RCAAP |
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RCAAP |
reponame_str |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
collection |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
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Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
repository.mail.fl_str_mv |
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1817553555589955584 |