Optimising sample sizes for animal distribution analysis using tracking data

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.

Multiple satellite tracking datasets inform green turtle conservation at a regional scale

“…Please see Shimada et al. (2020) for details but to summarize, we took the cell values of each turtle raster layer (i.e. relative proportion of time spent per cell) and for increasing sample size from 2 to the maximum number of individuals, we merged all areas identified by existing data (e.g.…”

“…We assessed the effect of sample size on the spatial distribution extent for each behaviour using the 95% distribution using the R package SDLfilter (Shimada, 2019). Please see Shimada et al (2020) for details but to summarize, we took the cell values of each turtle raster layer (i.e. relative proportion of time spent per cell) and for increasing sample size from 2 to the maximum number of individuals, we merged all areas identified by existing data (e.g.…”

Satellite Tracking Reveals Nesting Patterns, Site Fidelity, and Potential Impacts of Warming on Major Green Turtle Rookeries in the Red Sea

“…Every few years, adult females return to the same natal nesting areas to lay several clutches of eggs on a beach, although foraging grounds of each turtle can be separated by thousands of kilometers (Miller, 1997;Jensen et al, 2013;Shimada et al, 2020). Between nesting events, females rest in nearshore waters (inter-nesting habitats) to prepare their eggs for the next clutch (Houghton et al, 2002;Ferreira et al, 2021;Shimada et al, 2021). This aggregation and fidelity to specific breeding areas makes turtles extremely vulnerable to sudden alteration or loss of nesting and inter-nesting habitats, due to both natural (e.g., coastal erosion) and anthropogenic (e.g., development, climate change) threats (Lutcavage et al, 1997;Hamann et al, 2013).…”

The Use of Animal-Borne Biologging and Telemetry Data to Quantify Spatial Overlap of Wildlife with Marine Renewables

“…While small tracking sample sizes may be unavoidable due to ethical or logistical considerations [78,79], assessing representativeness can be used as a measure of uncertainty. A test for representativeness was therefore performed using the SDLfilter package [80]. Following methods outlined in [80], the overlap probabilities of the Muckle Skerry utilization distributions, grouped by individual (n = 5) and trip (n = 23), were quantified, as per the recommendations of [81].…”

“…A test for representativeness was therefore performed using the SDLfilter package [80]. Following methods outlined in [80], the overlap probabilities of the Muckle Skerry utilization distributions, grouped by individual (n = 5) and trip (n = 23), were quantified, as per the recommendations of [81]. For each group, the function "boot_overlap()" was applied, with 10,000 iterations and method "PHR"(the probability distribution) specified.…”