Publication Details |
| Category | Text Publication |
| Reference Category | Journals |
| DOI | 10.1111/2041-210X.13815 |
Licence  |
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| Title (Primary) | Population-level inference for home-range areas |
| Author | Fleming, C.H.; Deznabi, I.; Alavi, S.; Crofoot, M.C.; Hirsch, B.T.; Medici, E.P.; Noonan, M.J.; Kays, R.; Fagan, W.F.; Sheldon, D.R.; Calabrese, J.M. |
| Source Titel | Methods in Ecology and Evolution |
| Year | 2022 |
| Department | OESA |
| Volume | 13 |
| Issue | 5 |
| Page From | 1027 |
| Page To | 1041 |
| Language | englisch |
| Topic | T5 Future Landscapes |
| Data and Software links | https://doi.org/10.5061/dryad.k3j9kd58t https://doi.org/10.5441/001/1.03ck4s52 |
| Supplements | https://besjournals.onlinelibrary.wiley.com/action/downloadSupplement?doi=10.1111%2F2041-210X.13815&file=mee313815-sup-0001-AppendixA.pdf https://besjournals.onlinelibrary.wiley.com/action/downloadSupplement?doi=10.1111%2F2041-210X.13815&file=mee313815-sup-0002-AppendixB.pdf |
| Keywords | highlight; Home range; animal movement; population ecology; autocorrelation |
| Abstract | 1. Home-range estimates are a common product of animal
tracking data, as each range informs on the area needed by a given
individual. Population-level inference on 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. 2. 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. 3. 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. 4. Our approach allows researchers to more accurately compare different populations with different movement behaviors 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. |
| Persistent UFZ Identifier | https://www.ufz.de/index.php?en=20939&ufzPublicationIdentifier=25757 |
| Fleming, C.H., Deznabi, I., Alavi, S., Crofoot, M.C., Hirsch, B.T., Medici, E.P., Noonan, M.J., Kays, R., Fagan, W.F., Sheldon, D.R., Calabrese, J.M. (2022): Population-level inference for home-range areas Methods Ecol. Evol. 13 (5), 1027 - 1041 10.1111/2041-210X.13815 |
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