Spatio‐temporal partitioning is a viable mechanism for minimizing resource competition among sympatric species. The occurrence of sympatric large carnivores – tiger Panthera tigris, leopard Panthera pardus and dhole Cuon alpinus – in forests of the Indian subcontinent is complemented with high dietary overlap. We characterized temporal and spatial patterns of large carnivores with major prey species using photo‐captures from 50 camera trap stations in Mudumalai Tiger Reserve, Western Ghats during 2008–2010. We tested whether major prey species' activity and spatial use acted as drivers for coexistence among large carnivores. Tiger exhibited cathemeral activity in the night and is spatially correlated with sambar and gaur, supporting hypotheses related to large‐sized prey. Leopard was active throughout the day and is spatially correlated with almost all prey species with no active separation from tiger. Dhole exhibited diurnal activity and spatial use in relation to chital and avoided felids to a certain extent. Leopard exhibited spatial correlation with tiger and dhole, while tiger did not correlate with dhole. Leopard exhibited relatively broader temporal and spatial tolerance due to its generalist nature, which permits opportunistic exploitation of resources. This supports the hypothesis that predators actively used areas at the same time as their principal prey species depending upon their body size and morphological adaptation. We conclude that resource partitioning in large carnivores by activity and spatial use of their principal prey governs spatio‐temporal separation in large carnivores.

Density of tiger Panthera tigris and leopard Panthera pardus was estimated using photographic capturerecapture sampling in a tropical deciduous forest of Mudumalai Tiger Reserve, southern India, from November 2008 to February 2009. A total of 2,000 camera trap nights for 100 days yielded 19 tigers and 29 leopards within an intensive sampling area of 107 km 2 . Population size of tiger from closed population estimator model M b Zippin was 19 tigers (SE=±0.9) and for leopards M h Jackknife estimated 53 (SE=±11) individuals. Spatially explicit maximum likelihood and Bayesian model estimates were 8.31 (SE=±2.73) and 8.9 (SE=±2.56) per 100 km 2 for tigers and 13.17 (SE=±3.15) and 13.01 (SE=±2.31) per 100 km 2 for leopards, respectively. Tiger density for MMDM models ranged from 6.07 (SE=±1.74) to 9.72 (SE=±2.94) per 100 km 2 and leopard density ranged from 13.41 (SE=±2.67) to 28.91 (SE=±7.22) per 100 km 2 . Spatially explicit models were more appropriate as they handle information at capture locations in a more specific manner than some generalizations assumed in the classical approach. Results revealed high density of tiger and leopard in Mudumalai which is unusual for other high density tiger areas. The tiger population in Mudumalai is a part of the largest population at present in India and a source for the surrounding Reserved Forest.

Due to their secretive habits, predicting the pattern of spatial distribution of small carnivores has been typically challenging, yet for conservation management it is essential to understand the association between this group of animals and environmental factors. We applied maximum entropy modeling (MaxEnt) to build distribution models and identify environmental predictors including bioclimatic variables, forest and land cover type, topography, vegetation index and anthropogenic variables for six small carnivore species in Mudumalai Tiger Reserve. Species occurrence records were collated from camera-traps and vehicle transects during the years 2010 and 2011. We used the average training gain from forty model runs for each species to select the best set of predictors. The area under the curve (AUC) of the receiver operating characteristic plot (ROC) ranged from 0.81 to 0.93 for the training data and 0.72 to 0.87 for the test data. In habitat models for F. chaus, P. hermaphroditus, and H. smithii “distance to village” and precipitation of the warmest quarter emerged as some of the most important variables. “Distance to village” and aspect were important for V. indica while “distance to village” and precipitation of the coldest quarter were significant for H. vitticollis. “Distance to village”, precipitation of the warmest quarter and land cover were influential variables in the distribution of H. edwardsii. The map of predicted probabilities of occurrence showed potentially suitable habitats accounting for 46 km2 of the reserve for F. chaus, 62 km2 for V. indica, 30 km2 for P. hermaphroditus, 63 km2 for H. vitticollis, 45 km2 for H. smithii and 28 km2 for H. edwardsii. Habitat heterogeneity driven by the east-west climatic gradient was correlated with the spatial distribution of small carnivores. This study exemplifies the usefulness of modeling small carnivore distribution to prioritize and direct conservation planning for habitat specialists in southern India.

Identifying the primary causes affecting population densities and distribution of flagship species are necessary in developing sustainable management strategies for large carnivore conservation. We modeled drivers of spatial density of the common leopard (Panthera pardus) using a spatially explicit capture–recapture—Bayesian approach to understand their population dynamics in the Maputaland Conservation Unit, South Africa. We camera‐trapped leopards in four protected areas (PAs) of varying sizes and disturbance levels covering 198 camera stations. Ours is the first study to explore the effects of poaching level, abundance of prey species (small, medium, and large), competitors (lion Panthera leo and spotted hyenas Crocuta crocuta), and habitat on the spatial distribution of common leopard density. Twenty‐six male and 41 female leopards were individually identified and estimated leopard density ranged from 1.6 ± 0.62/100 km2 (smallest PA—Ndumo) to 8.4 ± 1.03/100 km2 (largest PA—western shores). Although dry forest thickets and plantation habitats largely represented the western shores, the plantation areas had extremely low leopard density compared to native forest. We found that leopard density increased in areas when low poaching levels/no poaching was recorded in dry forest thickets and with high abundance of medium‐sized prey, but decreased with increasing abundance of lion. Because local leopard populations are vulnerable to extinction, particularly in smaller PAs, the long‐term sustainability of leopard populations depend on developing appropriate management strategies that consider a combination of multiple factors to maintain their optimal habitats.