Tiger (Panthera tigris) conservation efforts in Asia are focused on protected areas embedded in human-dominated landscapes. A system of protected areas is an effective conservation strategy for many endangered species if the network is large enough to support stable metapopulations. The long-term conservation of tigers requires that the species be able to meet some of its life-history needs beyond the boundaries of small protected areas and within the working landscape, including multiple-use forests with logging and high human use. However, understanding of factors that promote or limit the occurrence of tigers in working landscapes is incomplete. We assessed the relative influence of protection status, prey occurrence, extent of grasslands, intensity of human use, and patch connectivity on tiger occurrence in the 5400 km(2) Central Terai Landscape of India, adjacent to Nepal. Two observer teams independently surveyed 1009 km of forest trails and water courses distributed across 60 166-km(2) cells. In each cell, the teams recorded detection of tiger signs along evenly spaced trail segments. We used occupancy models that permitted multiscale analysis of spatially correlated data to estimate cell-scale occupancy and segment-scale habitat use by tigers as a function of management and environmental covariates. Prey availability and habitat quality, rather than protected-area designation, influenced tiger occupancy. Tiger occupancy was low in some protected areas in India that were connected to extensive areas of tiger habitat in Nepal, which brings into question the efficacy of current protection and management strategies in both India and Nepal. At a finer spatial scale, tiger habitat use was high in trail segments associated with abundant prey and large grasslands, but it declined as human and livestock use increased. We speculate that riparian grasslands may provide tigers with critical refugia from human activity in the daytime and thereby promote tiger occurrence in some multiple-use forests. Restrictions on human-use in high-quality tiger habitat in multiple-use forests may complement existing protected areas and collectively promote the persistence of tiger populations in working landscapes.

Conserving wide‐ranging large carnivores in human‐dominated landscapes is contingent on acknowledging the conservation value of human‐modified lands. This is particularly true for tigers (Panthera tigris), now largely dependent on small and fragmented habitats, embedded within densely populated agroecosystems in India. Devising a comprehensive conservation strategy for the species requires an understanding of the temporal patterns of space use by tiger within these human‐modified areas. These areas are often characterized by altered prey communities, novel risks resulting from high human densities and seasonally dynamic vegetative cover. Understanding space use within these areas is vital to devising human‐tiger conflict prevention measures and for conserving landscape elements critical to maintain functional connectivity between populations. We documented seasonal space‐use patterns of tigers in agricultural lands surrounding protected areas in the Central Terai Landscape (CTL) in northern India. We estimated the probability of space use and its drivers by applying dynamic occupancy models that correct for false‐positive and false‐negative errors to tiger detection\non‐detection data within agricultural areas. These data were generated by conducting local interviews, sign surveys, and camera trapping within 94 randomly selected 2.5‐km2 grid cells. We found that agricultural areas were used with high probability in the winter (0.64; standard error [SE] 0.08), a period of high vegetative cover availability. The use of agricultural lands was lower in the summer (0.56; SE 0.09) and was lowest in the monsoon season (0.21; SE 0.07), tracking a decline in vegetative cover and available habitat across the landscape. Availability of vegetative cover and drainage features positively influenced space use, whereas use declined with increasing distance to protected areas and the extent of human settlements. These findings highlight the role of agricultural areas in providing seasonal habitats for tigers and offer a basis for understanding where tigers and humans co‐occur in these landscapes. These findings help expand our current understanding of what constitutes large carnivore habitats to include human‐dominated agricultural areas. They underscore the need for greater integration of land‐sharing and land‐sparing initiatives to conserve large carnivores within human‐dominated agroecosystems.

Grassland ecosystems have declined in their extent globally, driving declines in wild ungulate populations. Even within the remnant grasslands, ungulate distribution is highly heterogeneous for reasons that are not well understood. This in turn undermines both local and landscape level conservation efforts for these often‐neglected ecosystems and the herbivores they support. We investigated grassland‐ungulate relationships in the Terai region of North India, where wild ungulates are patchily distributed across alluvial grasslands, a besieged ecosystem. Specifically, we posited that spatial variation in grassland habitat use by swamp deer (Rucervus duvaucelii duvaucelii), hog deer (Axis porcinus) and spotted deer (Axis axis) would be explained by community composition, palatability of key grasses, and fire and flooding regimes in grasslands. Hierarchical clustering and multivariate ordination revealed that three dominant tall‐grass communities and the narrowly distributed and more palatable short grass Cynodon‐Oryza community were only marginally influenced by fire and flood histories. Ungulate habitat use, assessed using occupancy models for spatially correlated data, indicated that hog deer were ubiquitously distributed across the grasslands in the park (habitat use probability, ᴪ = 0.92, se = 0.05) utilizing a diverse range of grassland types, while spotted deer (ᴪ = 0.80, se = 0.17) and swamp deer (ᴪ = 0.45, se = 0.10) occurrence within grasslands was relatively lower. Grass height and grassland extent positively influenced habitat use of swamp deer and spotted deer, respectively. However, grassland community composition, fire history, flood history and palatability were relatively uninformative predictors of fine‐scale habitat use. Our study provides a robust baseline for future monitoring of grasslands and ungulates as well as insights for the design and implementation of grassland management interventions.

Managing human-wildlife conflicts (HWCs) is an important conservation objective for the many terrestrial landscapes dominated by humans. Forecasting where future conflicts are likely to occur and assessing risks to lives and livelihoods posed by wildlife are central to informing HWC management strategies. Existing assessments of the spatial occurrence patterns of HWC are based on either understanding spatial patterns of past conflicts or patterns of species distribution. In the former case, the absence of conflicts at a site cannot be attributed to the absence of the species. In the latter case, the presence of a species may not be an accurate measure of the probability of conflict occurrence. We present a Bayesian hierarchical modeling framework that integrates conflict reporting data and species distribution data, thus allowing the estimation of the probability that conflicts with a species are reported from a site, conditional on the species being present. In doing so, our model corrects for both false-positive and false-negative conflict reporting errors. We provide study design recommendations using simulations that explore the performance of the model under a range of conflict reporting probabilities. We applied the model to data on wild boar (Sus scrofa) space use and conflicts collected from the Central Terai Landscape (CTL), an important tiger conservation landscape in India. We found that tolerance for wildlife was a predictor of the probability with which farmers report conflict with wild boars from sites not used by the species. We also discuss useful extensions of the model when conflict data are verified for potential false-positive errors and when landscapes are monitored over multiple seasons.