Abstract
A rich set of statistical techniques has been developed over the last several decades to estimate the spatial extent of animal home ranges from telemetry 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.
Highlights
The biological concept of an animal’s home range has served as a useful construct for organizing our thinking about how animals use and interact with space since the time of Darwin (Kie et al 2010; Horne et al 2020).Today, most people associate the term home range with Burt (1943)’s definition, “that area traversed by the individual in its normal activities of food gathering, mating and caring for young
Horne et al (2020) have argued for classifying statistical home-range methods by whether they estimate one of two estimation targets: the range distribution or long-term distribution that would result from an animal continuing to move in a consistent manner and an occurrence distribution that captures the path of movement an animal takes during a specific observation window, along with its uncertainty
This dichotomy is appealing from a theoretical point of view, and several new statistical estimators have been developed for targeting these quantities while addressing issues related to autocorrelation, a prominent feature of modern day Global Positioning System (GPS) data (Fleming et al 2014, 2015, 2016)
Summary
The biological concept of an animal’s home range has served as a useful construct for organizing our thinking about how animals use and interact with space since the time of Darwin (Kie et al 2010; Horne et al 2020). Horne et al (2020) have argued for classifying statistical home-range methods by whether they estimate one of two estimation targets: the range distribution or long-term (equilibrium) distribution that would result from an animal continuing to move in a consistent manner and an occurrence distribution that captures the path of movement an animal takes during a specific observation window, along with its uncertainty This dichotomy is appealing from a theoretical point of view, and several new statistical estimators have been developed for targeting these quantities while addressing issues related to autocorrelation, a prominent feature of modern day Global Positioning System (GPS) data (Fleming et al 2014, 2015, 2016). The function hr_isopleth() returns a tibble with a simple feature column of class sfc_POLYGON from the sf package (Pebesma 2018), which we can use to conduct further spatial analyses, visually inspect the home range, or export it to a GIS. Is there a correlation between environmental covariates and estimates of home-range size?
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