Optimising sample sizes for animal distribution analysis using tracking data

Shimada, Takahiro, Thums, Michele, Hamann, Mark, Limpus, Colin J., Hays, Graeme C., Fitzsimmons, Nancy N., Wildermann, Natalie E., Duarte, Carlos M., Meekan, Mark G., and UNSPECIFIED (2021) Optimising sample sizes for animal distribution analysis using tracking data. Methods in Ecology and Evolution, 12 (2). pp. 288-297.

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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 dataset 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.

Item ID: 65064
Item Type: Article (Research - C1)
ISSN: 2041-210X
Keywords: continuous-time Markov chain, habitat use, kernel density, overlap probability, power analysis, step-selection functions, telemetry, time spent analysis
Copyright Information: © 2020 British Ecological Society
Funders: Gladstone Ports Corporation (GPC), Shell's QGC Business, Australia Pacific LNG, Santos GLNG, Coupled Animal and Artificial Sensing for Sustainable Ecosystems, James Cook University, Queensland Department of Environment and Science
Date Deposited: 18 Nov 2020 23:53
FoR Codes: 31 BIOLOGICAL SCIENCES > 3109 Zoology > 310901 Animal behaviour @ 100%
SEO Codes: 96 ENVIRONMENT > 9608 Flora, Fauna and Biodiversity > 960808 Marine Flora, Fauna and Biodiversity @ 100%
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