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Species within a guild vary their use of time, space and resources, thereby enabling sympatry. As intra-guild competition intensifies, such behavioural adaptations may become prominent. We assessed mechanisms of facilitating sympatry among dhole (Cuon alpinus), leopard (Panthera pardus) and tiger (Panthera tigris) in tropical forests of India using camera-trap surveys. We examined population-level temporal, spatial and spatio-temporal segregation among them across four reserves representing a gradient of carnivore and prey densities. Temporal and spatial overlaps were higher at lower prey densities. Combined spatio-temporal overlap was minimal, possibly due to chance. We found fine-scale avoidance behaviours at one high-density reserve. Our results suggest that: (i) patterns of spatial, temporal and spatio-temporal segregation in sympatric carnivores do not necessarily mirror each other; (ii) carnivores are likely to adopt temporal, spatial, and spatio-temporal segregation as alternative mechanisms to facilitate sympatry; and (iii) carnivores show adaptability across a gradient of resource availability, a driver of inter-species competition. We discuss behavioural mechanisms that permit carnivores to co-occupy rather than dominate functional niches, and adaptations to varying intensities of competition that are likely to shape structure and dynamics of carnivore guilds.

Most large carnivore populations currently occur in heterogeneous landscapes, with source populations embedded in a matrix of human-dominated habitats. Understanding changes in distribution of endangered carnivores is critical for prioritizing and implementing conservation strategies. We examined distribution and dynamics of a dhole Cuon alpinus metapopulation, first in 2007 and subsequently in 2015, based on indirect sign surveys across 37, 000sq. km of India’s Western Ghats. Predicted dhole occupancy declined from 0.62 (95% CI: 0.58–0.66) in 2007 to 0.54 (95% CI: 0.50–0.58) in 2015. Occupancy was associated with abundance of primary prey species and anthropogenic disturbance. Local extinction appeared to be influenced by forest cover loss, and offset by protected reserves; colonization was influenced by occupancy in neighbouring sites. Perturbation analysis indicated that occupancy was more sensitive to local extinction within reserves and to colonization in sites abutting reserves. The Western Ghats could serve as a stronghold for the endangered dhole, provided future colonizations are facilitated through habitat consolidation beyond reserve boundaries, and local extinctions are prevented by increasing protection efforts within select reserves. We advocate for wildlife managers to adopt a landscape-based approach and periodic monitoring to ensure persistence of the dhole metapopulation in Western Ghats, and in other critical conservation regions across the species’ geographic range.

We develop a class of models for inference about abundance or density using spatial capture-recapture data from studies based on camera trapping and related methods. The model is a hierarchical model composed of two components: a point process model describing the distribution of individuals in space (or their home range centers) and a model describing the observation of individuals in traps. We suppose that trap- and individual-specific capture probabilities are a function of distance between individual home range centers and trap locations. We show that the models can be regarded as generalized linear mixed models, where the individual home range centers are random effects. We adopt a Bayesian framework for inference under these models using a formulation based on data augmentation. We apply the models to camera trapping data on tigers from the Nagarahole Reserve, India, collected over 48 nights in 2006. For this study, 120 camera locations were used, but cameras were only operational at 30 locations during any given sample occasion. Movement of traps is common in many camera-trapping studies and represents an important feature of the observation model that we address explicitly in our application.

There is increasing evidence of large carnivore presence outside protected areas, globally. Although this spells conservation success through population recoveries, it makes carnivore persistence in human-use landscapes tenuous. The widespread distribution of leopards in certain regions of India typifies this problem. We obtained information on leopard-human interactions at a regional scale in Karnataka State, India, based on systematic surveys of local media reports. We applied an innovative occupancy modelling approach to map their distribution patterns and identify hotspots of livestock/human depredation. We also evaluated management responses like removals of 'problem' leopards through capture and translocations. Leopards occupied around 84,000 km2 or 47% of the State's geographic area, outside designated national parks and wildlife sanctuaries. Their presence was facilitated by extent of vegetative cover- including irrigated croplands, rocky escarpments, and prey base in the form of feral and free-ranging dogs. Higher probabilities of livestock/human attacks by leopards were associated with similar ecological features as well as with capture/removals of leopards. Of the 56 cases of leopard removals reported, 91% did not involve human attacks, but followed livestock predation or only leopard sightings. The lack of knowledge on leopard ecology in human-use areas has resulted in unscientific interventions, which could aggravate the problem rather than mitigating it. Our results establish the presence of resident, breeding leopards in human-use areas. We therefore propose a shift in management focus, from current reactive practices like removal and translocation of leopards, to proactive measures that ensure safety of human lives and livelihoods.

Although they play a critical role in shaping ecological communities, many threatened predator species are data-deficient. The Dhole Cuon alpinus is one such rare canid with a global population thought to be <2500 wild individuals. We assessed habitat occupancy patterns of dholes in the Western Ghats of Karnataka, India, to understand ecological and anthropogenic determinants of their distribution and habitat-use. We conducted spatially replicated detection/non-detection surveys of dhole signs along forest trails at two appropriate scales: the entire landscape and a single wildlife reserve. Landscape-scale habitat occupancy was assessed across 38,728 km(2) surveying 206 grid cells of 188-km(2) each. Finer scale habitat-use within 935 km2 Bandipur Reserve was studied surveying 92 grid cells of 13-km(2) km each. We analyzed the resulting data of dhole signs using likelihood-based habitat occupancy models. The models explicitly addressed the problematic issue of imperfect detection of dhole signs during field surveys as well as potential spatial auto-correlation between sign detections made on adjacent trail segments. We show that traditional 'presence versus absence' analyses underestimated dhole habitat occupancy by 60% or 8682 km2 [naïve = 0.27; ψL(SE)  = 0.68 (0.08)] in the landscape. Addressing imperfect sign detections by estimating detection probabilities [p(t)(L) (SE) = 0.12 (0.11)] was critical for reliable estimation. Similar underestimation occurred while estimating habitat-use probability at reserve-scale [naïve = 0.39; Ψs(SE) = 0.71 (0.06)]. At landscape scale, relative abundance of principal ungulate prey primarily influenced dhole habitat occupancy. Habitat-use within a reserve, however, was predominantly and negatively influenced by anthropogenic disturbance. Our results are the first rigorous assessment of dhole occupancy at multiple spatial scales with potential conservation value. The approach used in this study has potential utility for cost-effectively assessing spatial distribution and habitat-use in other species, landscapes and reserves.

Occupancy modeling focuses on inference about the distribution of organisms over space, using temporal or spatial replication to allow inference about the detection process. Inference based on spatial replication strictly requires that replicates be selected randomly and with replacement, but the importance of these design requirements is not well understood. This paper focuses on an increasingly popular sampling design based on spatial replicates that are not selected randomly and that are expected to exhibit Markovian dependence. We develop two new occupancy models for data collected under this sort of design, one based on an underlying Markov model for spatial dependence and the other based on a trap response model with Markovian detections. We then simulated data under the model for Markovian spatial dependence and fit the data to standard occupancy models and to the two new models. Bias of occupancy estimates was substantial for the standard models, smaller for the new trap response model, and negligible for the new spatial process model. We also fit these models to data from a large-scale tiger occupancy survey recently conducted in Karnataka State, southwestern India. In addition to providing evidence of a positive relationship between tiger occupancy and habitat, model selection statistics and estimates strongly supported the use of the model with Markovian spatial dependence. This new model provides another tool for the decomposition of the detection process, which is sometimes needed for proper estimation and which may also permit interesting biological inferences. In addition to designs employing spatial replication, we note the likely existence of temporal Markovian dependence in many designs using temporal replication. The models developed here will be useful either directly, or with minor extensions, for these designs as well. We believe that these new models represent important additions to the suite of modeling tools now available for occupancy estimation in conservation monitoring. More generally, this work represents a contribution to the topic of cluster sampling for situations in which there is a need for specific modeling (e.g., reflecting dependence) for the distribution of the variable(s) of interest among subunits.

Although wide-ranging, elusive, large carnivore species, such as the tiger, are of scientific and conservation interest, rigorous inferences about their population dynamics are scarce because of methodological problems of sampling populations at the required spatial and temporal scales. We report the application of a rigorous, noninvasive method for assessing tiger population dynamics to test model-based predictions about population viability. We obtained photographic capture histories for 74 individual tigers during a nine-year study involving 5725 trap-nights of effort. These data were modeled under a likelihood-based, \"robust design\" capture-recapture analytic framework. We explicitly modeled and estimated ecological parameters such as time-specific abundance, density, survival, recruitment, temporary emigration, and transience, using models that incorporated effects of factors such as individual heterogeneity, trap-response, and time on probabilities of photo-capturing tigers. The model estimated a random temporary emigration parameter of$\\hat{\\gamma}^{\\prime \\prime}=\\hat{\\gamma}^{\\prime}=0.10\\pm 0.069$(values are estimated mean ± SE). When scaled to an annual basis, tiger survival rates were estimated at Ŝ = 0.77 ± 0.051, and the estimated probability that a newly caught animal was a transient was$\\hat{\\tau}=0.18\\pm 0.11$. During the period when the sampled area was of constant size, the estimated population size$\\hat{N}_{t}$varied from 17 ± 1.7 to 31 ± 2.1 tigers, with a geometric mean rate of annual population change estimated as$\\hat{\\overline{\\lambda}}=1.03\\pm 0.020$, representing a 3% annual increase. The estimated recruitment of new animals,$\\hat{B}_{t}$, varied from 0 ± 3.0 to 14 ± 2.9 tigers. Population density estimates, D̂, ranged from 7.33 ± 0.8 tigers/100 km⁲ to 21.73 ± 1.7 tigers/100 km⁲ during the study. Thus, despite substantial annual losses and temporal variation in recruitment, the tiger density remained at relatively high levels in Nagarahole. Our results are consistent with the hypothesis that protected wild tiger populations can remain healthy despite heavy mortalities because of their inherently high reproductive potential. The ability to model the entire photographic capture history data set and incorporate reduced-parameter models led to estimates of mean annual population change that were sufficiently precise to be useful. This efficient, noninvasive sampling approach can be used to rigorously investigate the population dynamics of tigers and other elusive, rare, wide-ranging animal species in which individuals can be identified from photographs or other means.

Understanding species distribution patterns has direct ramifications for the conservation of endangered species, such as the Asian elephant Elephas maximus. However, reliable assessment of elephant distribution is handicapped by factors such as the large spatial scales of field studies, survey expertise required, the paucity of analytical approaches that explicitly account for confounding observation processes such as imperfect and variable detectability, unequal sampling probability and spatial dependence among animal detections. We addressed these problems by carrying out 'detection--non-detection' surveys of elephant signs across a c. 38,000-km(2) landscape in the Western Ghats of Karnataka, India. We analyzed the resulting sign encounter data using a recently developed modeling approach that explicitly addresses variable detectability across space and spatially dependent non-closure of occupancy, across sampling replicates. We estimated overall occupancy, a parameter useful to monitoring elephant populations, and examined key ecological and anthropogenic drivers of elephant presence. Our results showed elephants occupied 13,483 km(2) (SE = 847 km(2)) corresponding to 64% of the available 21,167 km(2) of elephant habitat in the study landscape, a useful baseline to monitor future changes. Replicate-level detection probability ranged between 0.56 and 0.88, and ignoring it would have underestimated elephant distribution by 2116 km(2) or 16%. We found that anthropogenic factors predominated over natural habitat attributes in determining elephant occupancy, underscoring the conservation need to regulate them. Human disturbances affected elephant habitat occupancy as well as site-level detectability. Rainfall is not an important limiting factor in this relatively humid bioclimate. Finally, we discuss cost-effective monitoring of Asian elephant populations and the specific spatial scales at which different population parameters can be estimated. We emphasize the need to model the observation and sampling processes that often obscure the ecological process of interest, in this case relationship between elephants to their habitat.

The goal of ecology is to understand interactions that determine the distribution and abundance of organisms. In principle, ecologists should be able to identify a small number of limiting resources for a species of interest, estimate densities of these resources at different locations across the landscape, and then use these estimates to predict the density of the focal species at these locations. In practice, however, development of functional relationships between abundances of species and their resources has proven extremely difficult, and examples of such predictive ability are very rare. Ecological studies of prey requirements of tigers Panthera tigris led us to develop a simple mechanistic model for predicting tiger density as a function of prey density. We tested our model using data from a landscape-scale long-term (1995-2003) field study that estimated tiger and prey densities in 11 ecologically diverse sites across India. We used field techniques and analytical methods that specifically addressed sampling and detectability, two issues that frequently present problems in macroecological studies of animal populations. Estimated densities of ungulate prey ranged between 5.3 and 63.8 animals per km2. Estimated tiger densities (3.2-16.8 tigers per 100 km2) were reasonably consistent with model predictions. The results provide evidence of a functional relationship between abundances of large carnivores and their prey under a wide range of ecological conditions. In addition to generating important insights into carnivore ecology and conservation, the study provides a potentially useful model for the rigorous conduct of macroecological science.

AIM: Information on patterns and determinants of spatial distributions remains poorly available for many widespread species of conservation importance. The sloth bear Melursus ursinus in the Indian subcontinent exemplifies this requirement. We aimed at assessing (1) distribution patterns of sloth bears at two spatial scales, (2) ecological and anthropogenic factors that determine bear occupancy. LOCATION: We estimated sloth bear habitat occupancy at a nationwide scale across India and at the landscape scale (38, 540 km²) in the Western Ghats of Karnataka, India. METHODS: We used a grid‐based occupancy approach to determine sloth bear distribution patterns. At the nationwide scale, we used data from questionnaire surveys of field experts (grid‐cell size ~2818 km²; 1326 cells). At the landscape scale, we conducted field surveys of bear signs (grid‐cell size = 188 km²; 205 cells). Detection/non‐detection data from both surveys were analyzed using occupancy modelling methods that account for imperfect detection. We examined the influence of scale‐specific ecological and social covariates on patterns of occupancy. RESULTS: Nationwide, sloth bears occupied an estimated 67% of plausible bear habitat in contrast to 46% derived from methods that disregard detectability. Bear distribution was positively influenced by deciduous forests, scrub and barren areas, regions with high human densities and cultural tolerance. At the landscape scale, bears occupied 61% of the area versus 54% estimated from methods ignoring detectability. Occupancy probabilities increased with forest cover and topographic heterogeneity, whereas annual precipitation and human disturbance showed negative effects. MAIN CONCLUSIONS: Our study underlines the need to integrate human‐modified areas with existing conservation landscapes. Given its widespread nature, functional role, conservation status and relatively benign interactions with humans, we propose recognizing sloth bear as an umbrella species for securing unprotected habitats in India. Protection of widespread species like the sloth bear in other landscapes may complement current conservation strategies for large mammalian communities.