Simulation analyses of space use: Home range estimates, variability, and sample size

Abstract

Simulations of space use by animals were run to determine the relationship among home range area estimates, variability, and sample size (number of locations). As sample size increased, home range size increased asymptotically, whereas variability decreased among mean home range area estimates generated by multiple simulations for the same sample size. Our results suggest that field workers should ascertain between 100 and 200 locations in order to estimate reliably home range area. In some cases, this suggested guideline is higher than values found in the few published studies in which the relationship between home range area and number of locations is addressed. Sampling differences for small species occupying relatively small home ranges indicate that fewer locations may be sufficient to allow for a reliable estimate of home range. Intraspecific variability in social status (group member, loner, resident, transient), age, sex, reproductive condition, and food resources also have to be considered, as do season, habitat, and differences in sampling and analytical methods. Comparative data still are needed.The way animals use space is of practical and theoretical interest to a wide variety of scientists, including those studying (1) relationships between behavior, age, sex, and spacing patterns, (2) resource distribution as it affects space use, (3) the relationship between metabolic requirements and home range size, and (4) space requirements for purposes of control or reintroduction of "problem" or endangered species

Rigorous home range estimation with movement data: a new autocorrelated kernel density estimator

“…Animal tracking data increasingly constitute the key inputs into home range estimation procedures. Conventional methods of home range estimation largely fall into two camps: geometric techniques, such as the minimum convex polygon (MCP; Bekoff andMech 1984, Fieberg andBo¨rger 2012), that lack an underlying probabilistic model, and statistical techniques that were not developed for use with animal tracking data, such as kernel density estimators (KDEs; Worton 1989). While KDEs are the most efficient nonparametric estimators of probability density functions (PDFs), they are derived under the assumption of independent and identically distributed (IID) data, an assumption violated by autocorrelation and nonstationarity (Silverman 1986).…”

Home range and seasonality of Yunnan snub‐nosed monkeys

“…Home range studies are essential for understanding an animal's behavioral ecology and for making wildlife conservation efficient (Bekoff & Mech 1984). Home range size and home range shape are core parameters in the expression of the home range of a given animal species located in a particular area.…”

Location analysis and strontium-90 concentrations in deer antlers on the Hanford Site

“…computer program developed at the University of Idaho (Ackerman et al 1990). Home ranges were obtained using three techniques: 100 % and 95 % minimum convex poiygons (Michener 1979;Bowen 1982;Bekoff and Mech 1984), 95% bivariate normal and 95% bivariate weighted ellipse (Samuel and Garton 1985), and 95% harmonic mean utilization (Dixon and Chapman 1980). Minimum convex polygons are concentric convex polygons whose boundaries encompass a specified innermost percentage of all the observations.…”