A major challenge in conservation is developing scientific theory into practical tools that stakeholders can use for living sustainably with wildlife in their backyards. Coexistence is a fragile state, often imbalanced by opposing pressures from human development and species protection. Keystone predators play important roles within ecosystems, especially by affecting prey spatial movement and behavior, but can also threaten human well being. My research aims to quantify patterns of species interactions to identify ecological drivers useful for guiding management and stakeholder decision-making.
Tiger and leopard predation risk on livestock
Paper: Miller, Jhala, Jena and Schmitz. Ecology and Evolution.
Despite the importance of predators in driving ecosystems, large carnivores pose threats to human livelihoods that create challenges for conservation. Innovative tools are greatly needed to reduce carnivore attacks on livestock and resulting retaliations against carnivores. Spatial risk modeling is an emerging method for assessing the spatial patterns of predator–prey interactions which can have implications for mitigating human-carnivore conflict.
To further develop risk modeling for understanding carnivore attacks on livestock, we explored the best spatial resolution for predicting where tigers access livestock in Kanha Tiger Reserve, India. We generated risk models at 20, 100, and 200-m spatial grains and analyzed land-use, human presence, and vegetation structure variables at kill sites and random sites to identify key landscape attributes where livestock were vulnerable to tigers.
We found that land-use and human presence variables contributed strongly to predation risk models. The risk of a tiger killing livestock increased near dense forests and near the boundary of the park core zone where human presence is restricted. Kill sites were characterized by denser, patchier, and more complex vegetation with lower visibility than random sites. Risk maps revealed high-risk hot spots inside of the core zone boundary and in several patches in the human-dominated buffer zone. Validation against known kills revealed predictive accuracy for only the 20 m model, the resolution best representing the kill stage of hunting for large carnivores that ambush prey, like the tiger.
Results demonstrate that risk models developed at fine spatial grains can offer accurate guidance on landscape attributes livestock should avoid to minimize human–carnivore conflict.
Related popular articles
- Tiger vs. Cow: Risk Models Help Beat the Odds (Conservation India).
- Mapping tiger attack hotspots to reduce conflict (Conservation Corridor).
- Tools for saving tigers (Yale F&ES blog).
- Study could help reduce tiger killing (Yale Daily News).
- Using technology to help wild cats and people coexist (Yale FES News)
- Live and let live (Frontline). Link to original article and photos (make a free account to access) or download PDF.
- Reading the bones: In the field with a depredation detective (Sage Magazine).
- Following the hunt: Tracking livestock kills to reduce villager's losses in India (Scientists Without Borders).
Framework for predicting predator effects on prey
Paper: Miller, Ament and Schmitz. 2013. Journal of Animal Ecology.
Ecologists have long searched for a framework of a priori species traits to help predict predator–prey interactions in food webs. Empirical evidence has shown that predator hunting mode and predator and prey habitat domain are useful traits for explaining predator–prey interactions. Yet, individual experiments have yet to replicate predator hunting mode, calling into question whether predator impacts can be attributed to hunting mode or merely species identity.
We conducted research in the Connecticut fields of Yale Myers Forest to investigate how predator hunting mode and habitat domain influence prey anti-predator behavior. We carried out behavioral observations in field microcosms to test how six spider species displaying a gradient of hunting modes - from sit-&-wait to sit-&-pursue to active - influence grasshopper mortality, habitat domain and activity levels. We found that predator hunting mode predicted both density- and trait-mediated effects on prey, generating further evidence that hunting mode is a strong predictor of prey anti-predator behavior.