Effective conservation solutions for small and isolated wildlife populations depend on identifying and preserving critical biological corridors and dispersal routes. With a worldwide population of ≤70 individuals, the critically endangered Asiatic cheetah Acinonyx jubatus venaticus persists in several fragmented nuclei in Iran. Connectivity between nuclei is crucial for the survival of this subspecies, but detailed information to guide conservation actions is lacking. We developed a resistance surface that predicted cost of cheetah movement as functions of topographical complexity, human development, surface water and landscape protection level. We predicted alternative models for the landscape connectivity of Asiatic cheetahs, considering the combination of relative landscape resistance and different dispersal ability scenarios. We predicted that core connected habitat patches are concentrated in three sub-regions, and within these sub-regions, populations were predicted to be broken up into two to eight isolated patches, depending on the dispersal ability scenario. Despite the achievements of recent conservation initiatives, long-term survival of the Asiatic cheetah in Iran is threatened by the combination of its small population size and fragmented distribution. We propose that conservation of the Asiatic cheetah urgently requires integrated landscape-level management to reduce mortality risk, protect core areas and corridors, and ultimately establish steppingstone populations to integrate this fragmented population.
In the Caucasus the Endangered Persian leopard Panthera pardus saxicolor has been persecuted to the verge of extinction, primarily as a result of conflict with people over livestock predation. The socio-economic factors that influence this interaction have received little attention and the attitudes of local people towards leopards remain unknown. Here we assess the extent of cattle predation by leopards and how this influences people's attitudes towards leopards among village residents around the Dorfak NoHunting Area, a priority reserve in the Iranian Caucasus. In a survey of households, % of interviewees reported losing cattle to leopards during -. A mean of c. . head of cattle per interviewed household was reportedly killed by leopards over the -year survey period. Cattle predation peaked during warm seasons, when most family members were busy with rice farming-related activities, thus leaving their cattle grazing unguarded in the forest. Regardless of the intensity of cattle predation or socioeconomic status, % of respondents perceived leopards as a pest, with % of interviewees expressing support for either licensed hunting or culling of the Dorfak leopards. We recommend that the Iranian government considers the financial consequences of livestock loss for poor rural communities across the leopard's range. In addition, a combination of different livestock husbandry practices, with the direct involvement of local residents, is essential to ensure the long-term survival of the regional leopard population of the Caucasus.
ContextSpatial capture-recapture (SCR) models are increasingly popular for analyzing wildlife monitoring data. SCR can account for spatial heterogeneity in detection that arises from individual space use (detection kernel), variation in the sampling process, and the distribution of individuals (density). However, unexplained and unmodeled spatial heterogeneity in detectability may remain due to cryptic factors, intrinsic and extrinsic to the study system.ObjectivesWe identify how the magnitude and configuration of unmodeled, spatially variable detection probability influence SCR parameter estimates.MethodsWe simulated realistic SCR data with spatially variable and autocorrelated detection probability. We then fitted a single-session SCR model ignoring this variation to the simulated data and assessed the impact of model misspecification on inferences.ResultsHighly autocorrelated spatial heterogeneity in detection probability (Moran’s I = 0.85 - 0.96), modulated by the magnitude of that variation, can lead to pronounced negative bias (up to 75%), reduction in precision (249%), and decreasing coverage probability of the 95% credible intervals associated with abundance estimates to 0. Conversely, at low levels of spatial autocorrelation (median Moran’s I = 0), even severe unmodeled heterogeneity in detection probability did not lead to pronounced bias and only caused slight reductions in precision and coverage of abundance estimates.ConclusionsUnknown and unmodeled variation in detection probability is liable to be the norm, rather than the exception, in SCR studies. We encourage practitioners to consider the impact that spatial autocorrelation in detectability has on their inferences and urge the development of SCR methods that can take structured unknown or partially unknown spatial variability in detection probability into account.
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