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dc.contributor.authorShimada, Takahiro
dc.contributor.authorThums, Michele
dc.contributor.authorHamann, Mark
dc.contributor.authorLimpus, Colin J.
dc.contributor.authorHays, Graeme C.
dc.contributor.authorFitzSimmons, Nancy
dc.contributor.authorWildermann, Natalie E.
dc.contributor.authorDuarte, Carlos M.
dc.contributor.authorMeekan, Mark G.
dc.date.accessioned2020-10-15T11:14:31Z
dc.date.available2020-10-15T11:14:31Z
dc.date.issued2020-10-28
dc.identifier.citationShimada, T., Thums, M., Hamann, M., Limpus, C. J., Hays, G. C., FitzSimmons, N., … Meekan, M. G. (2020). Optimising sample sizes for animal distribution analysis using tracking data. Methods in Ecology and Evolution. doi:10.1111/2041-210x.13506
dc.identifier.issn2041-210X
dc.identifier.issn2041-210X
dc.identifier.doi10.1111/2041-210x.13506
dc.identifier.urihttp://hdl.handle.net/10754/665593
dc.description.abstract1. Knowledge of the spatial distribution of populations is fundamental to management plans for any species. When tracking data are used to describe distributions, it is sometimes assumed that the reported locations of individuals delineate the spatial extent of areas used by the target population. 2. Here, we examine existing approaches to validate this assumption, highlight caveats, and propose a new method for a more informative assessment of the number of tracked animals (i.e. sample size) necessary to identify distribution patterns. We show how this assessment can be achieved by considering the heterogeneous use of habitats by a target species using the probabilistic property of a utilisation distribution. Our methods are compiled in the R package SDLfilter. 3. We illustrate and compare the protocols underlying existing and new methods using conceptual models and demonstrate an application of our approach using a large satellite tracking data-set of flatback turtles, Natator depressus, tagged with accurate Fastloc-GPS tags (n = 69). 4. Our approach has applicability for the post-hoc validation of sample sizes required for the robust estimation of distribution patterns across a wide range of taxa, populations and life history stages of animals.
dc.description.sponsorshipWe thank staff and field team leaders of the Queensland Turtle Conservation Project within Queensland Parks and Wildlife Service, Hector Barrios Garrido, Miles Yeates, Rebecca Hide, Renee Whitchurch and numerous volunteers for their support of research.
dc.description.sponsorshipThis research was funded by Gladstone Port Corporation (partially funded by the Ecosystem Research and Monitoring Program), Shell’s QGC Business, Australia Pacific LNG, Santos GLNG, James Cook University and DES.
dc.publisherWiley
dc.relation.urlhttps://onlinelibrary.wiley.com/doi/10.1111/2041-210X.13506
dc.rightsArchived with thanks to Methods in Ecology and Evolution
dc.titleOptimising sample sizes for animal distribution analysis using tracking data
dc.typeArticle
dc.contributor.departmentRed Sea Research Center (RSRC)
dc.contributor.departmentBiological and Environmental Sciences and Engineering (BESE) Division
dc.contributor.departmentMarine Science Program
dc.identifier.journalMethods in Ecology and Evolution
dc.rights.embargodate2021-10-09
dc.eprint.versionPost-print
dc.contributor.institutionAustralian Institute of Marine Science Crawley Western Australia 6009 Australia
dc.contributor.institutionCollege of Science and Engineering James Cook University Townsville Queensland 4811 Australia
dc.contributor.institutionDepartment of Environment and Science Brisbane Queensland 4102 Australia
dc.contributor.institutionDeakin University Geelong Victoria Australia
dc.contributor.institutionTexas Sea Grant at Texas A&M University College Station Texas 77843 USA
dc.contributor.institutionHarte Research Institute for Gulf of Mexico Studies Corpus Christi Texas 78412 USA
kaust.personShimada, Takahiro
kaust.personDuarte, Carlos M.
dc.date.accepted2020-10-09
refterms.dateFOA2020-10-15T11:18:43Z


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