Land‐sharing potential of large carnivores in human‐modified landscapes of western India

Can Bengal Tiger (Panthera tigris tigris) endure the future climate and land use change scenario in the East Himalayan Region? Perspective from a multiple model framework

Large mammals are susceptible to land use and climate change, unless they are safeguarded within large, protected areas. It is crucial to comprehend the effects of these changes on mammals to develop a conservation plan. We identified ecological hotspots that can sustain an ecosystem for the endangered Bengal tiger (Panthera tigris tigris), an umbrella species. We developed three distinct ensemble species distribution models (SDMs) for the Bengal tiger in the Indian East Himalayan Region (IEHR). The first model served as the baseline and considered habitat type, climate, land cover, and anthropogenic threats. The second model focused on climate, land use, and anthropogenic threats, the third model focused on climate variables. We projected the second and third models onto two future climate scenarios: RCP 4.5 and RCP 8.5. We evaluated the threats possess to protected areas within eco-sensitive zone based on the potential tiger habitat. Finally, we compared the potential habitat with the IUCN tiger range. Our study revealed that the Brahmaputra valley will serve as the primary habitat for tigers in the future. However, considering the projected severe climate scenarios, it is anticipated that tigers will undergo a range shift towards the north and east, especially in high-altitude regions. Very high conservation priority areas, which make up 3.4% of the total area, are predominantly located in the riverine corridor of Assam. High conservation priority areas, which make up 5.5% of total area are located in Assam and Arunachal Pradesh. It is important to note that conservation priority areas outside of protected areas pose a greater threat to tigers. We recommend reassessing the IUCN Red List's assigned range map for tigers in the IEHR, as it is over-predicted. Our study has led us to conclude both land use and climate change possess threats to the future habitat of tigers. The outcomes of our study will provide crucial information on identifying habitat hotspots and facilitate appropriate conservation planning efforts.

Spatial Determinants of Livestock Depredation and Human Attitude Toward Wolves in Kailadevi Wildlife Sanctuary, Rajasthan, India

Gray wolves are capable of adapting to human-dominated landscapes by utilizing domestic prey as a source of food. Livestock depredation by wolves incurs a heavy economic loss to the villagers, resulting in negative attitudes toward the species and leading to increased conservation conflict. We used multi-state occupancy modeling on the interview data to assess the ecological factors governing livestock depredation by wolves. We also assessed the socio-demographic factors that may govern the attitude of villagers toward the wolf using ordinal regression. Over the past year, 64% of respondents reported a loss of livestock, in which goats (63%) comprised the major share, followed by sheep (22%) and cattle calves (15%). Wolves tend to hunt medium-sized domestic prey (sheep and goats) that commonly graze in open agricultural areas. The estimated livestock depredation probability of wolves was 0.84 (SD = ± 0.23). Depredation probability was influenced by habitat use by wolves, the extent of agricultural areas, scrubland area, and settlement size. Respondents with prior experience of livestock loss held more negative attitudes. Shepherds held more negative attitudes than other occupations. Increases in the respondent’s age and education level reflected a positive shift in attitudes toward the wolf. High economic loss caused by livestock depredation by wolves can lead to retaliatory persecution of wolves. Adequate compensation for livestock loss, along with better education and awareness can help lead to coexistence between wolves and humans in multi-use landscape of Kailadevi Wildlife Sanctuary, Rajasthan, India.

Assessing Carnivore Occurrence and Community Attitudes Towards Wildlife in a Multi-Use Arid Landscape Corridor

Small population sizes, low densities, and large area requirements make large carnivores particularly sensitive to habitat degradation and land-use change. In fragmented landscapes, many protected areas cannot accommodate viable wildlife populations in themselves, which brings the surrounding human-dominated matrix that may extend wildlife habitats or serve as corridors into focus. Such areas are typically excluded from the conservation portfolio and are subject to rapid land -use change in many areas. This study investigates the occurrence of tigers, sloth bears, leopards and striped hyenas and assesses community use of natural resources and attitudes towards wildlife in a 3,384 km2 portion of semi-arid multiple-use landscape in Western India that also serves as an important wildlife corridor. This area abuts Ranthambore Tiger Reserve, a preeminent protected area in Western India. Sign surveys spanning 1,039.22 km of trails were conducted in 94, 36 km2 grids spanning agricultural land, forests and other land use types to collate information on wildlife occurrence and associated environmental and human factors. Analysis using occupancy models revealed that tiger and sloth bear occurrence probabilities (0.093 ± 0.05), and (0.13 ± 0.02) were considerably lower than those for leopards (0.72 ± 0.22) and striped hyenas (0.91 ± 0.08). Lack of sufficient cover and limited food availability renders these multiple-use habitats poorly suited for tigers and sloth bears, while leopards and hyenas are able to adapt better to multi-use areas. Concurrently, 66 villages were surveyed across the study landscape, where data on broad socio-economic attributes of communities and their attitudes towards wildlife were assessed through questionnaire surveys. More respondents expressed negative attitudes than positive attitudes which vary as a function of education levels, occupation and land holding sizes. Ongoing landscape transformation through mining, agricultural expansion, infrastructure development, and negative attitudes towards wildlife conservation among people living in the agricultural matrix threatens the long-term functionality of these corridors. Therefore, immediate measures are needed to develop and implement corridor conservation strategies and plans, with a focus on land use planning and human-wildlife conflict mitigation. In the absence of decisive and timely action, wildlife populations may increasingly get relegated to fragmented patches, jeopardising their persistence.

Wild Predators, Livestock, and Free Ranging Dogs: Patterns of Livestock Mortality and Attitudes of People Toward Predators in an Urbanizing Trans-Himalayan Landscape

Livestock depredation by large carnivores is a significant source of conflicts over predators and an important conservation and economic concern. Preventing livestock loss to wild predators is a substantial focus of human-carnivore conflict mitigation programs. A key assumption of the preventive strategy is reduction in the livestock losses leading to a positive shift in the attitudes toward predators. Therefore, it is important to quantify the true extent of livestock mortality caused by wild predators and its influence on attitudes of the affected communities. We examined seasonal and spatial patterns of livestock mortality and factors influencing people's attitudes toward wild predators i.e., snow leopards (Panthera uncia) and wolves (Canis lupus chanco) and free-ranging dogs (Canis lupus familiaris) in a Trans-Himalayan urbanizing landscape in India. We used systematic sampling to select the survey households and implemented a semi-structured questionnaire to respondents. The sampled villages (n = 16) represent a mosaic of urban and agricultural ecosystems within a radius of 40 km of Leh town. In 2016–2017, 93% of the sampled households lost livestock to predators, accounting for 0.93 animals per household per year. However, of the total events of livestock mortality, 33% were because of weather/natural events, 24% by snow leopards, 20% because of disease, 15% because of free-ranging dogs and 9% because of wolves. The annual economic loss per household because of livestock mortality was USD 371, a substantial loss given the average per capita income of USD 270 in the region. Of the total loss, weather/natural events caused highest loss of USD 131 (35%), followed by snow leopards USD 91 (25%), disease USD 87 (24%), free ranging dogs USD 48 (13%), and wolves USD 14 (4%). Despite losing a considerable proportion of livestock (33 %) to wild predators, respondents showed a positive attitude toward them but exhibited neutral attitudes toward free-ranging dogs. Gender emerged as the most important determinant of attitudes toward wild predators, with men showing higher positive attitude score toward wild predators than women. Our findings highlight the context specific variation in human-wildlife interactions and emphasize that generalizations must be avoided in the absence of site specific evidence.

Identifying unknown Indian wolves by their distinctive howls: its potential as a non-invasive survey method

Previous studies have posited the use of acoustics-based surveys to monitor population size and estimate their density. However, decreasing the bias in population estimations, such as by using Capture-Mark-Recapture, requires the identification of individuals using supervised classification methods, especially for sparsely populated species like the wolf which may otherwise be counted repeatedly. The cryptic behaviour of Indian wolf (Canis lupus pallipes) poses serious challenges to survey efforts, and thus, there is no reliable estimate of their population despite a prominent role in the ecosystem. Like other wolves, Indian wolves produce howls that can be detected over distances of more than 6 km, making them ideal candidates for acoustic surveys. Here, we explore the use of a supervised classifier to identify unknown individuals. We trained a supervised Agglomerative Nesting hierarchical clustering (AGNES) model using 49 howls from five Indian wolves and achieved 98% individual identification accuracy. We tested our model's predictive power using 20 novel howls from a further four individuals (test dataset) and resulted in 75% accuracy in classifying howls to individuals. The model can reduce bias in population estimations using Capture-Mark-Recapture and track individual wolves non-invasively by their howls. This has potential for studies of wolves' territory use, pack composition, and reproductive behaviour. Our method can potentially be adapted for other species with individually distinctive vocalisations, representing an advanced tool for individual-level monitoring.