Data from: A comprehensive analysis of autocorrelation and bias in home range estimation

• Main Authors: Noonan, Michael J., Tucker, Marlee A., Fleming, Christen H., Akre, Tom S., Alberts, Susan C., Ali, Abdullahi H., Altmann, Jeanne, Antunes, Pamela C., Belant, Jerrold L., Beyer, Dean, Blaum, Niels, Böhning-Gaese, Katrin, Cullen Jr., Laury, De Paula Cunha, Rogerio, Dekker, Jasja, Drescher-Lehman, Jonathan, Farwig, Nina, Fichtel, Claudia, Fischer, Christina, Ford, Adam T., Goheen, Jacob R., Janssen, René, Jeltsch, Florian, Kauffman, Matthew, Kappeler, Peter M., Koch, Flávia, LaPoint, Scott, Markham, A. Catherine, Medici, Emilia Patricia, Morato, Ronaldo G., Nathan, Ran, Oliveira-Santos, Luiz Gustavo R., Olson, Kirk A., Patterson, Bruce D., Paviolo, Agustin, Ramalho, Emiliano E., Rosner, Sascha, Schabo, Dana G., Selva, Nuria, Sergiel, Agnieszka, Da Silva, Marina X., Spiegel, Orr, Thompson, Peter, Ullmann, Wiebke, Zięba, Filip, Zwijacz-Kozica, Tomasz, Fagan, William F., Mueller, Thomas, Calabrese, Justin M.
• Other Authors: Federated Research Data Repository, Dépôt fédéré de données de recherche
• Format: Dataset
• Language: unknown
• Published: The University of British Columbia 2018
• Subjects: Aepyceros melampus; Anthropocene; Eulemur rufifrons; Madoqua guentheri; Beatragus hunteri; Bycanistes bucinator; GPS; Ursus arctos; Gyps coprotheres; Glyptemys insculpta; Cerdocyon thous; Ovis canadensis; Sus scrofa; Propithecus verreauxi; Life sciences; medicine and health care; Other; envir; stat; Thumb
• Online Access: https://doi.org/10.14288/1.0397835; https://doi.org/10.5061/dryad.v5051j2; https://doi.org/10.5683/SP2/OAJTAO

Dryad version number: 1 Version status: submitted Dryad curation status: Published Sharing link: https://datadryad.org/stash/share/wFYV98M7IdYpyA4nBl6nKWqB4kAj5c79puIOUUDkbMY Storage size: 24175161 Visibility: public Usage notes Empirical GPS tracking data Anonymised, empirical tracking data used to estimate home range areas based on various home range estimators. Anonymised_Data.zip Abstract Home range estimation is routine practice in ecological research. While advances in animal tracking technology have increased our capacity to collect data to support home range analysis, these same advances have also resulted in increasingly autocorrelated data. Consequently, the question of which home range estimator to use on modern, highly autocorrelated tracking data remains open. This question is particularly relevant given that most estimators assume independently sampled data. Here, we provide a comprehensive evaluation of the effects of autocorrelation on home range estimation. We base our study on an extensive dataset of GPS locations from 369 individuals representing 27 species distributed across 5 continents. We first assemble a broad array of home range estimators, including Kernel Density Estimation (KDE) with four bandwidth optimizers (Gaussian reference function, autocorrelated-Gaussian reference function (AKDE), Silverman's rule of thumb, and least squares cross-validation), Minimum Convex Polygon, and Local Convex Hull methods. Notably, all of these estimators except AKDE assume independent and identically distributed (IID) data. We then employ half-sample cross-validation to objectively quantify estimator performance, and the recently introduced effective sample size for home range area estimation ($\hat{N}_\mathrm{area}$) to quantify the information content of each dataset. We found that AKDE 95\% area estimates were larger than conventional IID-based estimates by a mean factor of 2. The median number of cross-validated locations included in the holdout sets by AKDE 95\% (or 50\%) estimates was 95.3\% (or ...