Trace DNA from kill sites identifies predating tigers
Predation ecology and evidence-based conflict management strategies require reliable and accurate identification of individual predators. Identifying predators is, however, complex, as they are secretive and individual identification is difficult. Trace DNA that predators leave behind at kill sites might provide an effective strategy to identify them but remains poorly evaluated at scale. We use non-invasive genetic samples from kill sites to assess their utility for predator identification. We systematically investigated 198 livestock kills in two critical source tiger populations in central India: Kanha and Bandhavgarh Tiger Reserves. We collected 342 salivary swabs from carcasses, 33 scat and 395 shed hair samples as potential sources of predator DNA, and individual tigers were identified using up to 123 SNP markers. All three sample sources identified predator species with high success (>95%). We identified individuals (with at least one sample per kill site, based on >40 SNPs) at 86% of all kill sites where tigers were detected. Shed hair samples were most effective for individual identification, followed by saliva and scat. Sample source and sampling season were the primary determinants of the number of SNPs typed per sample and the success of individual identification. Based on the site and type of sample collection, we classify species and individuals into three categories: true predator (high confidence as predator), circumstantial predator (medium confidence) and predator uncertain (low confidence). Individuals were classified as a true predator at 72 sites, circumstantial predator at 34 sites and predator uncertain at 49 sites. Our protocol allowed us to differentiate between predators and scavengers, even when multiple tigers were detected at the same kill site. Surprisingly, [~]40% of Bandhavgarhs tigers were identified at at least one kill site. We suggest that when paired with systematic kill site investigation and sample collection, these methods can be effectively used to understand predation ecology better and facilitate evidence-based conflict management.