Clarifying space use concepts in ecology: range vs. occurrence distributions
Authors: Alston Jesse M. , Fleming Christen H. , Noonan Michael J. , Tucker Marlee A. , Silva Inês , Folta Cody , Akre Thomas S.B. , Ali Abdullahi H. , Belant Jerrold L. , Beyer Dean , Blaum Niels , Böhning-Gaese Katrin , de Paula Rogerio Cunha , Dekker Jasja , Drescher-Lehman Jonathan , Farwig Nina , Fichtel Claudia , Fischer Christina , Ford Adam T. , Janssen René , Jeltsch Florian , Kappeler Peter M. , LaPoint Scott D. , Markham A. Catherine , Medici E. Patricia , Morato Ronaldo Gonçalves , Nathan Ran , Olson Kirk A. , Patterson Bruce D. , Petroelje Tyler R. , Ramalho Emiliano Esterci , Rösner Sascha , Oliveira Santos Luiz Gustavo , Schabo Dana G. , Selva Nuria , Sergiel Agnieszka , Spiegel Orr , Ullmann Wiebke , Zieba Filip , Zwijacz-Kozica Tomasz , Wittemyer George , Fagan William F. , Müller Thomas , Calabrese Justin M. 
Published: October 2022

Quantifying animal movements is necessary for answering a wide array of research questions in ecology and conservation biology. Consequently, ecologists have made considerable efforts to identify the best way to estimate an animal’s home range, and many methods of estimating home ranges have arisen over the past half century. Most of these methods fall into two distinct categories of estimators that have only recently been described in statistical detail: those that measure range distributions (methods such as Kernel Density Estimation that quantify the long-run behavior of a movement process that features restricted space use) and those that measure occurrence distributions (methods such as Brownian Bridge Movement Models and the Correlated Random Walk Library that quantify uncertainty in an animal movement path during a specific period of observation). In this paper, we use theory, simulations, and empirical analysis to demonstrate the importance of applying these two classes of space use estimators appropriately and distinctly. Conflating range and occurrence distributions can have serious consequences for ecological inference and conservation practice. For example, in most situations, home-range estimates quantified using occurrence estimators are too small, and this problem is exacerbated by ongoing improvements in tracking technology that enable more frequent and more accurate data on animal movements. We encourage researchers to use range estimators to estimate the area of home ranges and occurrence estimators to answer other questions in movement ecology, such as when and where an animal crosses a linear feature, visits a location of interest, or interacts with other animals.

Open Research Statement

Tracking data onAepyceros melampus, Beatragus hunteri, Bycanistes bucinator, Cerdocyon thous, Eulemur rufifrons, Glyptemys insculpta, Gyps coprotheres, Madoqua guentheri, Ovis canadensis, Propithecus verreauxi, Sus scrofa, andUrsus arctosare publicly archived in the Dryad repository (Noonan et al. 2018;https://doi.org/10.5061/dryad.v5051j2), as are data fromProcapra gutturosa(Fleming et al. 2014a;https://doi.org/10.5061/dryad.45157). Data onPanthera oncawere taken from (Morato et al. 2018). Additional data are publicly archived in the Movebank repository under the following identifiers:Canis latrans, 8159699;Canis lupus, 8159399;Chrysocyon brachyurus, 18156143;Felis silvestris, 40386102;Gyps africanus, 2919708;Lepus europaeus, 25727477;Martes pennanti, 2964494;Panthera leo, 220229;Papio cynocephalus, 222027;Syncerus caffer, 1764627;Tapirus terrestris, 443607536;Torgos tracheliotus, 2919708; andUrsus americanus, 8170674.