QUANTIFYING HOME-RANGE OVERLAP: THE IMPORTANCE OF THE UTILIZATION DISTRIBUTION

By J. Fieberg, C. O. Kochanny. 2005

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View Paper: https://www.semanticscholar.org/paper/df197300468e85bb41f6ab6eafc9eb823b944b5a

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In Ecology

In Small Animals

Citations in the corpus

Prevalence and Mechanisms of Partial Migration in Ungulates (2019, background): “Migrants are often distinguished from residents based on criteria such as the amount of seasonal home range overlap (Mysterud, 1999; Ball et al., 2001; Fieberg and Kochanny, 2005), trajectory segmentation (Buchin et al.”
Generalized affiliation indices extract affiliations from social network data (2015, background): “We are interested in the extent to which the utilization distributions of different individuals overlap, for which there are several potential statistics, including the utilization distribution overlap index and Bhattacharyya’s affinity (Fieberg & Kochanny 2005).”
Estimating Utilization Distributions with Kernel Versus Local Convex Hull Methods (2011, methodology): “We compared each estimated UD to its corresponding parent distribution using the VI index (Seidel 1992, Millspaugh et al. 2004, Fieberg and Kochanny 2005).”
Population-specific home ranges and migration timing of Pacific Arctic beluga whales (Delphinapterus leucas) (2014, methodology): “The proportion of home range overlap (Feiberg and Kochanny 2005) was also calculated as: HRi;j ¼ Ai;j=Ai where HRi,j is the proportion of population i’s (or sexpopulation group) home range that is overlapped by population j’s home range, such that Ai is the total home range area of population i,…”
Spatial scales of marine conservation management for breeding seabirds (2018, methodology): “The overlap in core areas of individuals was quantified using Bhattacharyya’s Affinity index (BA), a non-directional measure of home-range overlap that ranges between 0 (complete separation) and 1 (completely matching probability distributions), and is considered the most appropriate index for quant”
Utilization Distribution Estimation Using Weighted Kernel Density Estimators (2007, methodology): “I used Bhattacharyya’s affinity (BA), a measure of similarity between 2 probability distributions (Bhattacharyya 1943, Fieberg and Kochanny 2005), to examine the reliability of the UD estimators.”
Responding to spatial and temporal variations in predation risk: space use of a game species in a changing landscape of fear (2009, methodology): “Mean Inter-individual Overlaps (MIO: percentage) were estimated by averaging for each individual, and “Volumes of Intersections” (the VI in Fieberg and Kochanny 2005) with all the other individuals tracked during the same year.”
Statistical inference for home range overlap (2018, methodology): “As noted by Fieberg and Kochanny (2005), however, the BC is prone to exhibiting negative, small-sample-size bias (Djouadi & Snorrason, 1990).”
Contacts and foot and mouth disease transmission from wild to domestic bovines in Africa (2013, methodology): “The extent to which the collared individuals shared space was quantified using the volume of intersection statistics (Fieberg and Kochanny 2005).”
Estimating utilization distributions from fitted step-selection functions (2017, methodology): “We compared the estimated SSUD/TUD to the true SSUD/TUD using Bhattacharyya’s affinity (BA; Bhattacharyya 1943, Fieberg and Kochanny 2005).”
Multispecies tracking reveals a major seabird hotspot in the North Atlantic (2021, methodology): “…(ANOSIM, a measure of relative within-group dissimilarity based on a bootstrap randomization; Oksanen et al., 2013) to test for differences in the distribution within and between-years using two metrics of distribution overlap (Bhattacharyya’s affinity and HR indexes; Fieberg & Kochanny, 2005).”