Abstract: People's views and values for wild animals are often a result of their experiences and traditional knowledge. Local folklore represents a resource that can enable an understanding of the nature of human-wildlife interactions, especially the underlying cultural values. Using archival searches and semi-structured interviews, we collected narratives about the ibex (Capra sibirica) (n = 69), and its predators, the wolf (Canis lupus) (n = 52) and the snow leopard (Panthera uncia) (n = 43), in Ladakh, India. We compared these stories to those of a mythical carnivore called seng ge or snow lion (n = 19), frequently referenced in local Tibetan Buddhist folklore and believed to share many of the traits commonly associated with snow leopards (except for livestock depredation). We then categorized the values along social-cultural, ecological and psychological dimensions. We found that the ibex was predominantly associated with utilitarianism and positive symbolism. Both snow leopard and wolf narratives referenced negative affective and negative symbolic values, though more frequently in the case of wolves. Snow leopard narratives largely focused on utilitarian and ecologistic values. In contrast, snow lion narratives were mostly associated with positive symbolism. Our results suggest that especially for snow leopards and wolves, any potentially positive symbolic associations appeared to be overwhelmed by negative sentiments because of their tendency to prey on livestock, unlike in the case of the snow lion. Since these values reflect people's real and multifarious interactions with wildlife, we recommend paying greater attention to understanding the overlaps between natural and cultural heritage conservation to facilitate human-wildlife coexistence.

Abstract: The complexities of multi-use landscapes require sophisticated approaches to addressing disease transmission risks. We explored gastro-intestinal nematode (GINs) infections in the North India Trans-Himalayas through a socio-ecological lens, integrating parasite transmission modelling with field surveys and local knowledge, and evaluated the likely effectiveness of potential interventions. Bharal (blue sheep; Pseudois nayaur), a native wild herbivore, and livestock share pasture year-round and livestock commonly show signs of GINs infection. While both wild and domestic ungulates had GINs infections, egg counts indicated significantly higher parasite burdens in bharal than livestock. However, due to higher livestock densities, they contributed more to the total count of eggs and infective larvae on pasture. Herders also reported health issues in their sheep and goats consistent with parasite infections. Model simulations suggested that pasture infectivity in this system is governed by historical pasture use and gradually accumulated larval development during the summer, with no distinct short-term flashpoints for transmission. The most effective intervention was consequently predicted to be early-season parasite suppression in livestock using temperature in spring as a cue. A 1-month pause in egg output from livestock could lead to a reduction in total annual availability of infective larvae on pasture of 76%, potentially benefitting the health of both livestock and bharal. Modelling suggested that climate change over the past 33 years has led to no overall change in GINs transmission potential, but an increase in the relative influence of temperature over precipitation in driving pasture infectivity. Our study provides a transferable multi-pronged approach to investigating disease transmission, in order to support herders’ livelihoods and conserve wild ungulates.

Abstract: The snow leopard (Panthera uncia) and blue sheep (Pseudois nayaur) are the inhabitants of remote areas at higher altitudes with extreme geographic and climatic conditions. The habitats of these least-studied species are crucial for sustaining the Himalayan ecosystem. We employed the Maximum Entropy (MaxEnt) species distribution model to predict the potential habitat suitability of snow leopards and blue sheep and extracted common overlapped niches. For this, we utilised presence location, bio-climatic and environmental variables, and correlation analysis was applied to reduce the negative impact of multicollinearity. A total of 134 presence locations of snow leopards and 64 for blue sheep were selected from the Global Biodiversity Information Facility (GBIF). The annual mean temperature (Bio1) was found to be the most useful and highly influential factor to predict the potential habitat suitability of snow leopards. Annual mean temperature, annual precipitation and isothermality were the major influencing factors for blue sheep habitat suitability. Highly influential bio-climatic, topographic and environmental variables were integrated to construct the model for predicting habitat suitability. The area under the curve (AUC) values for snow leopard (0.87) and blue sheep (0.82) showed that the models are under good representation. Of the total area investigated, 47% was suitable for the blue sheep and 38% for the snow leopards. Spatial habitat assessment revealed that nearly 11% area from the predicted suitable habitat class of both species was spatially matched (overlapped), 48.6% area was unsuitable under niche overlap and 40.5% area was spatially mismatched niche. The presence of snow leopards and blue sheep in some highly suitable areas was not observed, yet such areas have the potential to sustain these elusive species. The other geographical regions interested in exploring habitat suitability may find the methodological framework adopted in this study useful for formulating an effective conservation policy and management strategy.

Abstract: Camera trapping is the most widely used data collection method for estimating snow leopard (Panthera uncia) abundance; however, the accuracy of this method is limited by human observer errors from misclassifying individuals in camera trap images. We evaluated the extent Whiskerbook (www.whiskerbook.org), an artificial intelligence (AI) software, could reduce this error rate and enhance the accuracy of capture-recapture abundance estimates. Using 439 images of 34 captive snow leopard individuals, classification was performed by five observers with prior experience in individual snow leopard ID (“experts”) and five observers with no such experience (“novices”). The “expert” observers classified 35 out of 34 snow leopard individuals, on average erroneously splitting one individual into two, thus resulting in a higher number than true individuals. The success rate of experts was 90 %, with less than a 3 % error in estimating the population size in capture-recapture modeling. However, the “novice” observers successfully matched 71 % of encounters, recognizing 25 out of 34 individuals, underestimating the population by 25 %. It was found that expert observers significantly outperformed novice observers, making statistically fewer errors (Mann Whitney U test P = 0.01) and finding the true number of individuals (P = 0.01). These differences were contrasted with a previous study by Johansson et al. 2020, using the same subset of 16 individuals from European zoos. With the help of AI and the Whiskerbook platform, “experts” were able to match 87 % of encounters and identify 15 out of 16 individuals, with modeled estimates of 16 ± 1 individuals. In contrast, “novices” were 63 % accurate in matching encounters and identified 12 out of 16 individuals, modeling 12 ± 1 individuals that underestimated the population size by 12 %. When comparing the performance of observers using AI and the Whiskerbook platform to observers performing the tasks manually, we found that observers using Whiskerbook made significantly fewer errors in splitting one individual into two (P = 0.04). However, there were also a significantly higher number of combination errors, where two individuals were combined into one (P = 0.01). Specifically, combination errors were found to be made by “novices” (P = 0.04). Although AI benefited both expert and novice observers, expert observers outperformed novices. Our results suggest that AI effectively reduced the misclassification of individual snow leopards in camera trap studies, improving abundance estimates. However, even with AI support, expert observers were needed to obtain the most accurate estimates.