Dietary habits and competitive interactions of scavenging raptors in the Thar desert

Abstract

Scavenging raptors, especially vultures, play a crucial role in recycling nutrients and curbing pathogen prevalence in an ecosystem. Their populations in the Indian subcontinent have declined substantially in the past few decades, primarily due to the presence of toxic non-steroidal anti-inflammatory drugs (NSAIDs) in livestock carcasses. This problem worsens for vultures in the Thar desert landscapes, where large numbers of migratory vultures visit in the winter months and increase competition for resident vultures, reducing their access to resources in their critical breeding months. Therefore, understanding their dietary dependence on livestock and their interactions with other scavengers is essential for implementing targeted conservation measures. This study examines the dietary and competitive ecology of White-rumped vulture (Gyps bengalensis), other sympatric vultures, and mammalian scavengers in the Thar Desert, with additional focus on assessing risk from NSAID poisoned livestock carcasses and identifying ecological pressures from carcass use dynamics. For dietary analysis, fecal samples of White-rumped vulture (n = 10) and other migratory vultures (n = 22) of the same foraging guild collected from their identified nesting and roosting sites respectively, and were analysed using DNA metabarcoding to assess dietary composition and overlap. Questionnaires (n = 128) with pastoralists and pharmacy surveys (n = 4) were used to evaluate NSAID usage patterns, along with a geographic hotspot analysis of toxic NSAIDs. Camera traps (n = 34) were opportunistically placed at carcasses to record scavenger presence, feeding time, and aggressive interspecific interactions. Interference and exploitative competition was quantified by developing dominance and efficiency indices, and constructing hierarchies of aggressive dominance and foraging efficiency, followed by the analysis of their ecological predictors using generalised linear models.