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Aim Home range ( HR) metrics are widely used in ecology and conservation, but the quantitative basis for choosing and parameterizing metrics is weak. Home range estimates are ecological and statistical hypotheses that must balance type I and type II errors. Here, we present and test a new approach to fine-tuning and comparing HR estimates using the area under the curve ( AUC) statistic. Location Test data are taken from telemetry studies of 44 individual ducks in southern Africa and nine buffaloes in southern and western Africa. Methods We use a meta-analysis of AUC statistics to compare the performance of four standard HR metrics on data from 44 ducks (two species) and nine African buffaloes. Results The AUC method emerges as a useful and accessible statistical tool. It captures clear differences between HR estimators as well as providing a way of fine-tuning parameters for an individual HR estimate. Code to run the HR AUC analyses in R is provided. As argued by others, we found that kernel density estimators offer the best combination of ecological and statistical validity, while estimators that use minimum convex polygons at any stage of the algorithm perform poorly and should be avoided. Main conclusions The AUC statistic provides a readily implementable and straightforward approach to comparing different HR metrics and to selecting parameters for individual metrics. It thus offers a valuable tool for conservation efforts that seek to define HRs for species or populations. The use of the AUC in this new context further contributes to solidifying the interface between species occurrence models and HR estimators. [ABSTRACT FROM AUTHOR]

We investigated space-use patterns of the West African savanna buffalo (Syncerus caffer brachyceros), a little-studied subspecies occurring at the northern limit of the African buffalo's geographical range. This buffalo generally ranges in small herds (about 45 individuals) and has a low body mass (approximately 400 kg) relative to the Cape buffalo (S. c. caffer). We monitored the movements of 7 breeding herds in W Regional Park (Burkina Faso, Benin, Niger) using global positioning system collars and activity data loggers. Habitat selection was analyzed at both large (interseasonal) and small (intraseasonal) scales in a context where resources are segregated spatially at some times of year. Both biotic (primary production and vegetation types) and abiotic (timing of rainfall and surface water) covariates, and the extent to which neighboring herds shared space, were considered. In the dry season buffalo herds ranged close (within 5.3 ± 2.0 km, mean ± SD) to segments of permanent rivers. At the onset of the monsoon all herds but 1 (which had year-round access to suitable resources) performed a large (35 ± 10 km) directional movement in response to a large-scale gradient of primary production. Spatiotemporal dynamics of forage and water resources thus jointly stimulated interseasonal directional movements and shaped large (335 ± 167 km² ) annual home ranges. Furthermore, the establishment of home ranges in the wet season appears to be conditioned by a threshold (about 10%) in the availability of perennial grasses. Habitat-selection analysis at intraseasonal scale also underlines the key role played by perennial grasses for buffaloes. The spatial arrangements of home ranges of neighboring herds also suggest that interherd behavioral avoidance is a high-level constraint on foraging processes. The ability of the African buffalo to cope with contrasting environmental conditions throughout most sub-Saharan ecosystems highlights the high behavioral plasticity of this species. [ABSTRACT FROM AUTHOR]

Kernel-based utilization distribution (UD) estimates are powerful tools to investigate home range space use and resource selection in many vertebrate species. By ignoring local movement information provided by the serial correlation between successive locations and the constraints to movement imposed by obvious boundaries, the classical kernel method results in loosely estimated UDs that tend to overflow into never-visited areas and eventually in possibly biased estimates of space use and habitat selection. We improved biological relevance of kernel home range space use estimates by incorporating both movement (and activity) information and boundary constraints. [ABSTRACT FROM AUTHOR]