A rich set of statistical techniques has been developed over the last several decades to estimate the spatial extent of animal home ranges fromtelemetry data, and new methods to estimate home ranges continue to be developed. Here we investigate home-range estimation from a computational point of view and aim to provide a general framework for computing home ranges, independent of specific estimators. We show how such a workflow can help to make home-range estimation easier and more intuitive, and we provide a series of examples illustrating how different estimators can be compared easily. This allows one to perform a sensitivity analysis to determine the degree to which the choice of estimator influences qualitative and quantitative conclusions. By providing a standardized implementation of home-range estimators, we hope to equip researchers with the tools needed to explore how estimator choice influences answers to biologically meaningful questions.
Bibliographical noteFunding Information:
We are grateful to comments on earlier version of the manuscript from the associate editor, Scott LaPoint, Chris Fleming, Michael Noonan and an anonymous reviewer. We acknowledge support by the Open Access Publication Funds of Göttingen University.
John R. Fieberg received salary support from the Minnesota Agricultural Experimental Station and the McKnight Foundation. There was no additional external funding received for this study. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Copyright 2021 Signer and Fieberg.
- Animal tracking
- Home range
- Occurrence distribution
- Range distribution
- Space use
PubMed: MeSH publication types
- Journal Article