Optimising sample sizes for animal distribution analysis using tracking data

Takahiro Shimada, Michele Thums, Mark Hamann, Colin J. Limpus, Graeme C. Hays, Nancy N. FitzSimmons, Natalie E. Wildermann, Carlos M. Duarte, Mark G. Meekan

Research output: Contribution to journalArticlepeer-review

8 Citations (Web of Science)

Abstract

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. 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. 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). 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.

Original languageEnglish
Pages (from-to)288-297
Number of pages10
JournalMethods in Ecology and Evolution
Volume12
Issue number2
Early online date9 Oct 2020
DOIs
Publication statusPublished - Feb 2021

Fingerprint

Dive into the research topics of 'Optimising sample sizes for animal distribution analysis using tracking data'. Together they form a unique fingerprint.

Cite this