DOI: 10.5683/sp3/bsqxyg
title: Are trapping data suited for home-range estimation?

description: &lt;b&gt;Abstract&lt;/b&gt;&lt;br/&gt;&lt;p&gt;Modern home-range estimation typically relies on data derived from expensive radio- or GPS-tracking. Although trapping represents a low-cost alternative to telemetry, there lacks an evaluation of the performance of home-range estimators on trap-derived data. Using simulated data, we evaluate three variables reflecting the key trade-offs ecologists face when designing a trapping study: 1) the number of observations obtained per individual, 2) the trap density, and 3) the proportion of the home range falling inside the trapping area. We compare the performance of five home-range estimators (MCP, LoCoH, KDE, AKDE, bicubic interpolation). We further explore the potential benefits of combining these estimators with asymptotic models, which leverage the saturating behavior of changes in the estimated home-range area as the number of observations increases to improve accuracy, as well as different data ordering procedures. We then quantified the bias in home-range size under the different scenarios investigated. The number of observations and the proportion of the home range within the trapping grid were the most important predictors of the accuracy and the precision of home-range estimates. The use of asymptotic models helped obtain accurate estimates at smaller sample sizes, while distance-ordering improved the precision and asymptotic consistency of estimates. While AKDE was the best-performing estimator under most conditions evaluated, bicubic interpolation was a viable alternative under common real-world conditions of low trap density and area covered. A case study using empirical data from white-tailed deer in Florida and another from jaguars in Belize demonstrated support for the findings of our simulation results. Although researchers with trap data often overlook home-range estimation, our results indicate that these data have the capacity to yield accurate estimates of home-range size. Trapping data can therefore lower the economic costs of home-range analysis, potentially enlarging the span of species, researchers and questions studied in ecology and conservation.&lt;/p&gt;
description: &lt;b&gt;Methods&lt;/b&gt;&lt;br /&gt;&lt;p&gt;Using an I.I.D. movement model, we simulated captures in different trapping conditions and compared the home range size obtained with different methods. We analyzed real-world trapping data for white-tailed deer (Florida) and jaguars (Belize) from publicly available repositories and compared the home range sizes obtained with those predicted based on our simulations. &lt;/p&gt;
description: &lt;b&gt;Usage notes&lt;/b&gt;&lt;br /&gt;&lt;p&gt;All data can be opened with R. All R scripts to create and analyze the data can be found at &lt;a href="https://github.com/llsociasmartinez/home-range-trapping-data"&gt;https://github.com/llsociasmartinez/home-range-trapping-data&lt;/a&gt;.&lt;/p&gt;
description: <b>Dryad version number:</b> 3
<b>Version status:</b> submitted
<b>Dryad curation status:</b> Published
<b>Sharing link:</b> https://datadryad.org/stash/share/KkOSoDdV82fdNuFGZHbd0U1L2i4N4iJUXRCtmjInbno
<b>Storage size:</b> 5466370104
<b>Visibility:</b> public