Population-level inference for home-range areas

Fleming, Christen H., Deznabi, Iman, Alavi, Shauhin, Crofoot, Margaret C., Hirsch, Ben T., Medici, E. Patricia, Noonan, Michael J., Kays, Roland, Fagan, William F., Sheldon, Daniel, and Calabrese, Justin M. (2022) Population-level inference for home-range areas. Methods in Ecology and Evolution, 13 (5). pp. 1027-1041.

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Home-range estimates are a common product of animal tracking data, as each range represents the area needed by a given individual. Population-level inference of home-range areas—where multiple individual home ranges are considered to be sampled from a population—is also important to evaluate changes over time, space or covariates such as habitat quality or fragmentation, and for comparative analyses of species averages. Population-level home-range parameters have traditionally been estimated by first assuming that the input tracking data were sampled independently when calculating home ranges via conventional kernel density estimation (KDE) or minimal convex polygon (MCP) methods, and then assuming that those individual home ranges were measured exactly when calculating the population-level estimates. This conventional approach does not account for the temporal autocorrelation that is inherent in modern tracking data, nor for the uncertainties of each individual home-range estimate, which are often large and heterogeneous.

Here, we introduce a statistically and computationally efficient framework for the population-level analysis of home-range areas, based on autocorrelated kernel density estimation (AKDE), that can account for variable temporal autocorrelation and estimation uncertainty.

We apply our method to empirical examples on lowland tapir Tapirus terrestris, kinkajou Potos flavus, white-nosed coati Nasua narica, white-faced capuchin monkey Cebus capucinus and spider monkey Ateles geoffroyi, and quantify differences between species, environments and sexes.

Our approach allows researchers to more accurately compare different populations with different movement behaviours or sampling schedules while retaining statistical precision and power when individual home-range uncertainties vary. Finally, we emphasize the estimation of effect sizes when comparing populations, rather than mere significance tests.

Item ID: 73292
Item Type: Article (Research - C1)
ISSN: 2041-210X
Keywords: animal movement, autocorrelation, home range, population ecology
Copyright Information: © 2022 The Authors. Methods in Ecology and Evolution published by John Wiley & Sons Ltd on behalf of British Ecological Society. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.
Research Data: https://doi.org/10.5441/001/1.03ck4s52, https://doi.org/10.5061/dryad. k3j9kd58t, https://cran.r-project.org/package=ctmm
Date Deposited: 30 Mar 2022 08:41
FoR Codes: 31 BIOLOGICAL SCIENCES > 3103 Ecology > 310307 Population ecology @ 100%
SEO Codes: 18 ENVIRONMENTAL MANAGEMENT > 1899 Other environmental management > 189999 Other environmental management not elsewhere classified @ 100%
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