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Abstract Background There is increasing interest in evaluating home-range overlap (or, otherwise, segregation) between bird species, and between or within bird populations, to inform spatial planning. So far, studies of home-range overlap typically make use of comparisons between pairs of individuals, populations or species, and return a matrix of pairwise overlaps (e.g., percent overlaps). However, when the number of individuals, populations or species to be compared is elevated, an overlarge overlap matrix is difficult to interpret from an ecological viewpoint. Methods We propose here a new, conceptually simple and computationally efficient index (general overlap index; GOI) for the ready computation within GIS of home range overlap of an arbitrarily large number (i.e., n ≥ 2) of individuals, populations or species. Whatever the number of home ranges to be compared, GOI always returns a single score between 0 and 100. As a case study, we applied our index to 24,074 GPS points of 10 Lesser Kestrels (Falco naumanni) in order to estimate within-colony and between-colony overlaps in two neighboring colonies in Southern Italy. Results Within-colony overlap was elevated for both colonies (96.41% at Cassano delle Murge, n = 5 individuals; 81.38% at Santeramo in Colle, n = 5 individuals), while between-colony overlap was low (19.12%; n = 2 colonies) and, after a randomization procedure, more spatially-segregated than expected by chance. Conclusions Modern biotelemetry offers huge amounts of data describing the space use of animal species. The use of intuitive and straightforward indices, like GOI, can be useful to promptly extract ecological information from such an amount of data (e.g. detecting change in space use over successive years, evaluating the reliability of various home-range estimators).

In this study we solved the issue of measuring the degree of overlap/segregation among an arbitrarily large number (n ≥ 2) of 3D volumetric home ranges (i.e., x, y, and hg; where hg is height above ground level) for the first time. For this purpose, we introduced the novel non-pairwise index MVOI (Multiple Volumetric Overlap Index) and its complement to 100 MVSI (Multiple Volumetric Segregation Index). Regardless of the number of 3D volumetric home ranges, the MVOI and MVSI generate a single score of overlap/segregation between 0 and 100, making ecological interpretation much easier and more meaningful when compared to n × n pairwise overlap indices. As a case study, we applied the MVOI and MVSI to 12,081 GPS points of five lesser kestrels (Falco naumanni) during the nesting period at Santeramo in Colle (Apulia region; Italy) in an area with the most elevated density of lesser kestrels in urban colonies worldwide. The 3D volumetric home ranges ranged between 1.79 km3 and 8.19 km3. We found that the tracked birds had different vertical profiles, possibly to limit intraspecific competition, resulting in a 3D home range overlap that was only 61.1% of the 2D overlap and 52.8% of the probabilistic one.

The conservation of threatened species is prevalently oriented towards two management strategies, i.e., habitat-level and species-level approaches. The former is focused on improving the conditions of the habitat of a certain species, whereas the latter is aimed at directly strengthening the species of interest. In this work, we adopted a different solution based on a community-level approach. Firstly, we identified the species (predators, competitors, prey) that interact with the species of interest (the lesser kestrel, Falco naumanni) in Southern Italy and mapped all of the ecological interactions among these species. Secondly, we built a simulation framework of the entire ecological network of the lesser kestrel. Thirdly, we simulated different management strategies that could increase the lesser kestrel population stock by targeting the species that interact with it. We found that the lesser kestrel in Southern Italy can be effectively protected by targeting the species interacting with the lesser kestrel, and that natural changes in the abundance of the interacting species could be used to pro-actively predict the dynamics of the lesser kestrel population. Our study demonstrates that a community-level approach to species conservation is highly appropriate on a local scale. Our methodological framework, based on qualitative modeling and what-if scenarios, can be applied in the absence of quantitative estimations of population stocks and interaction strengths.

Abstract Background So far, studies of avian space use are mostly realized in 2D, with the vertical dimension ignored. We propose here a new, relatively simple and computationally reasonable method for the estimation of volumetric (i.e. 3D) avian home ranges. Methods Through accurate GPS data-loggers, we collected 25,405 GPS points on Lesser Kestrels’ (Falco naumanni) space use during the nestling period in one main colony in Italy. We applied our 3D home range estimator to the whole GPS dataset, and also separately to diurnal and nocturnal GPS points. Results The 3D colony home range resulted equal to 28.12 km3. By considering daytime and night-time separately, the volumetric home ranges resulted considerably different. Conclusions Our 3D home range estimator, because of its intuitive and straightforward properties, can easily capitalize on the datasets offered by modern biotelemetry (data-loggers, light detection and LIDAR sensors) and enhance conservation strategies for mitigating anthropogenic impacts on bird species. Its applications embrace, but are not limited to, more accurate estimates of collision risk with power lines, aircrafts and wind farms, and increased knowledge of birds’ space requirements in order to persist in their distribution areas.

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.