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    Optimising sample sizes for animal distribution analysis using tracking data

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    2041-210X.13506.pdf
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    Description:
    Accepted Article
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    Type
    Article
    Authors
    Shimada, Takahiro cc
    Thums, Michele
    Hamann, Mark
    Limpus, Colin J.
    Hays, Graeme C. cc
    FitzSimmons, Nancy
    Wildermann, Natalie E.
    Duarte, Carlos M. cc
    Meekan, Mark G.
    KAUST Department
    Red Sea Research Center (RSRC)
    Biological and Environmental Sciences and Engineering (BESE) Division
    Marine Science Program
    Date
    2020-10-28
    Online Publication Date
    2020-10-28
    Print Publication Date
    2021-02
    Embargo End Date
    2021-10-09
    Permanent link to this record
    http://hdl.handle.net/10754/665593
    
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    Abstract
    1. 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.
    Citation
    Shimada, 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
    Sponsors
    We 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.
    This 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.
    Publisher
    Wiley
    Journal
    Methods in Ecology and Evolution
    DOI
    10.1111/2041-210x.13506
    Additional Links
    https://onlinelibrary.wiley.com/doi/10.1111/2041-210X.13506
    Relations
    Is Supplemented By:
    • [Dataset]
      Shimada, T., Thums, M., Hamann, M., Limpus, C., Hays, G., FitzSimmons, N., Wildermann, N., Duarte, C., & Meekan, M. (2020). Data from: Optimising sample sizes for animal distribution analysis using tracking data (Version 2) [Data set]. Dryad. https://doi.org/10.5061/DRYAD.X69P8CZGH. DOI: 10.5061/dryad.x69p8czgh Handle: 10754/667709
    ae974a485f413a2113503eed53cd6c53
    10.1111/2041-210x.13506
    Scopus Count
    Collections
    Articles; Biological and Environmental Science and Engineering (BESE) Division; Red Sea Research Center (RSRC); Marine Science Program

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