Effects of herbivore species richness on the niche dynamics and distribution of blue sheep in the Trans-Himalaya

Aim To understand the community structure of mountain ungulates by exploring their niche dynamics in response to sympatric species richness. Location Ladakh and Spiti Regions of the Western Indian Trans‐Himalaya. Methods We used the blue sheep Pseudois nayaur, a relatively widely distributed mountain ungulate, as a model species to address the issue. We selected three discrete valleys in three protected areas with similar environmental features but varying wild ungulate species richness, and studied blue sheep’s diet and habitat utilization in them. Habitat variables such as slope angle, distance to cliff and elevation at blue sheep locations were recorded to determine the habitat width of the species. Faecal pellets were collected and microhistological faecal analysis was carried out to determine the diet width of blue sheep in the three areas with different ungulate species richness. Blue sheep’s niche width in terms of habitat and diet was determined using the Shannon’s Index. Results The habitat width of blue sheep had a negative relationship with the number of sympatric species. However, contrary to our expectation, there was a hump‐shaped relationship between blue sheep’s diet width and the sympatric species richness, with the diet width being narrower in areas of allopatry as well as in areas with high herbivore species richness, and the greatest in areas with moderate species richness. Main conclusions We suspect that the narrow diet width in allopatry is out of choice, whereas it is out of necessity in areas with high herbivore species richness because of resource partitioning that enables coexistence. We suggest that interactions with sympatric species lead to niche adjustment of mountain ungulates, implying that competition may play a role in structuring Trans‐Himalayan mountain ungulate assemblages. Given these results, we underscore the importance of including biotic interactions in species distribution models, which have often been neglected.