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.
• Format: Dataset
• Language: English
• Published: Dryad 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; Thumb
• Online Access: https://dx.doi.org/10.5061/dryad.v5051j2; https://datadryad.org/stash/dataset/doi:10.5061/dryad.v5051j2

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 ... : Empirical GPS tracking dataAnonymised, empirical tracking data used to estimate home range areas based on various home range estimators.Anonymised_Data.zip ...