Measuring the Degree of Overlap and Segregation among Multiple Probabilistic Home Ranges: A New Index with Illustrative Application to the Lesser Kestrel Falco naumanni.

Abstract

Simple SummaryIntraspecific and interspecific interactions regulate the extent and spatial patterns of animal home ranges. If we are able to estimate the home range overlap for a large number of individuals, populations and/or species, then we can readily identify important ecological properties, such as social network structure, competition during the breeding season, contact rates with implications for disease transmission, change in space use over time, interactions among different age classes and site fidelity for a particular individual, population or species. We can also evaluate the robustness of probabilistic home range assessment through the degree of overlap of several estimators. Accordingly, in this study, we first solve the issue of measuring the degree of overlap/segregation among a large number of probabilistic animal home ranges and provide a demonstrative case study.Home range overlap/segregation has several important applications to wildlife conservation and management. In this work, we first address the issue of measuring the degree of overlap/segregation among an arbitrarily large number (i.e., n ≥ 2) of probabilistic animal home ranges (i.e., utilization distributions). This subject matter has recently been solved for home ranges measured as polygons (e.g., percent minimum convex polygons and multinuclear cores) but not yet for probabilistic ones. Accordingly, we introduce a novel index named the PGOI (probabilistic general overlap index), and its complement, the PGSI (probabilistic general segregation index), an index for computation of probabilistic home range overlap/segregation at individual, population and species levels. Whatever the number of probabilistic home ranges, the PGOI returns a single score ranging in the [0, 100] interval. We applied the PGOI to five lesser kestrels (Falco naumanni) at Santeramo in Colle (Apulia region; Southern Italy) as a case study. Our new index can be applied to any animal species and to home ranges derived from any type of probabilistic home range estimator.