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Methods to detect spatial biases in tracking studies caused by differential representativeness of individuals, populations and time

dc.contributor.authorPujol, Virginia Morera
dc.contributor.authorCatry, Paulo
dc.contributor.authorMagalhães, Maria
dc.contributor.authorPeron, Clara
dc.contributor.authorReyes‐González, José Manuel
dc.contributor.authorGranadeiro, José P.
dc.contributor.authorMilitão, Teresa
dc.contributor.authorDias, Maria P.
dc.contributor.authorOro, Daniel
dc.contributor.authorDell'Omo, Giacomo
dc.contributor.authorMüller, Martina
dc.contributor.authorPaiva, Vitor H.
dc.contributor.authorMetzger, Benjamin
dc.contributor.authorNeves, V C
dc.contributor.authorNavarro, Joan
dc.contributor.authorKarris, Georgios
dc.contributor.authorXirouchakis, Stavros
dc.contributor.authorCecere, Jacopo G.
dc.contributor.authorZamora‐López, Antonio
dc.contributor.authorForero, Manuel G.
dc.contributor.authorOuni, Ridha
dc.contributor.authorRomdhane, Mohamed Salah
dc.contributor.authorDe Felipe, Fernanda
dc.contributor.authorZajková, Zuzana
dc.contributor.authorCruz‐Flores, Marta
dc.contributor.authorGrémillet, David
dc.contributor.authorGonzález‐Solís, Jacob
dc.contributor.authorRamos, Raül
dc.date.accessioned2022-12-20T20:19:52Z
dc.date.available2022-12-20T20:19:52Z
dc.date.issued2022
dc.description.abstractAim 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.pt_PT
dc.description.sponsorshipFundação para a Ciência e Tecnologia - FCTpt_PT
dc.description.versioninfo:eu-repo/semantics/publishedVersionpt_PT
dc.identifier.citationMorera, 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.13642pt_PT
dc.identifier.doi10.1111/ddi.13642pt_PT
dc.identifier.issn13669516
dc.identifier.urihttp://hdl.handle.net/10400.12/8871
dc.language.isoengpt_PT
dc.peerreviewedyespt_PT
dc.publisherWiley-Blackwell Publishing Ltdpt_PT
dc.relationMARE - Marine and Environmental Sciences Centre
dc.relationCentre for Environmental and Marine Studies
dc.relationTrophic links in open oceans: combining spatial data, dietary information and biomarkers from top predators to unveil structure and functioning of subtropical pelagic ecosystems
dc.relationCentre for Environmental and Marine Studies
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/pt_PT
dc.subjectAnimal movementpt_PT
dc.subjectEnvironmental stochasticitypt_PT
dc.subjectMetapopulation studypt_PT
dc.subjectSite fidelitypt_PT
dc.subjectSpecies distributionpt_PT
dc.titleMethods to detect spatial biases in tracking studies caused by differential representativeness of individuals, populations and timept_PT
dc.typejournal article
dspace.entity.typePublication
oaire.awardTitleMARE - Marine and Environmental Sciences Centre
oaire.awardTitleCentre for Environmental and Marine Studies
oaire.awardTitleTrophic links in open oceans: combining spatial data, dietary information and biomarkers from top predators to unveil structure and functioning of subtropical pelagic ecosystems
oaire.awardTitleCentre for Environmental and Marine Studies
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UID%2FMAR%2F04292%2F2019/PT
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oaire.citation.conferencePlaceUnited Kingdompt_PT
oaire.citation.endPage20pt_PT
oaire.citation.startPage1pt_PT
oaire.citation.titleDiversity and Distributionspt_PT
oaire.fundingStream6817 - DCRRNI ID
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