This year, our Year of Coexistence has led you through stories about Defenders’ work to support the sharing of landscapes between people and wildlife. When these landscapes contain cattle or sheep, Defenders’ efforts center around using non-lethal management interventions (like range riders, guard dogs, and fladry) to keep both livestock and imperiled predators (like wolves or grizzly bears) separate and safe. But how do we decide where and when we use these interventions so they are most effective? This is an open question that managers often answer through trial and error. To assist managers with these decisions, Defenders is developing science-based tools that build on our understanding of animal behavior and ecology to figure out how to put human-wildlife coexistence into practice.
Predation Risk Maps
One of the tools we’re advancing is called a ‘predation risk map’. This tool is based on an approach that identifies the landscape variables associated with where predators and livestock meet, like certain types of vegetation that can enable predators to hunt more easily or natural barriers that limit livestock movement. The technique uses these landscape variables and known kill sites to calculate the likeliness that a predator will attack livestock. The resulting map shows hotspots where a livestock manager, conflict specialist or range rider can use non-lethal management tools to prevent predators from accessing livestock.
Creating these maps involves three main steps. First, we collect GPS locations where a predator species (like wolves) has killed livestock (let’s say cattle). We also gather spatial data layers of landscape variables that we suspect predators may use to hunt. Second, we use statistical models to find the top landscape features associated with kill sites. For example, research suggests that variables like cattle and wolf densities, distance to road, pasture size, and vegetation type increase or decrease the risk of interactions. Third, we use our model and data on landscape features to predict the probability of predation risk across the landscape. This means we’re able to predict which areas have the combination of landscape features that are the most likely to lead to predation. We can then map risk as a gradient of color representing the risk probability.
Voilà – we have a predation risk map! These maps can be – and often are – used as a decision support tool to guide management. Risk maps have been created to anticipate how recolonizing wolves in Washington and California might affect livestock in future years. In fact, a review found that 80% of research teams used their predation risk maps to help livestock managers identify priority areas for implementing predator deterrents. Some of these management efforts achieved as much as 90% reductions in livestock losses. Now that’s science-based success and for ranchers who rely on livestock for their livelihoods, a sense of relief that their cattle are safe!
The Center for Conservation Innovation at Defenders is working to make the mapping process as accessible as possible to decision-makers, ranchers, and all stakeholders. We’re improving the science and envisioning next steps for how to put this approach directly in the hands of the user (you!). This could enable more frequent predictions so managers can quickly adapt management approaches. Since nimble management means more secure animals, these enhancements could protect both livestock and our imperiled predators.
Food Web Framework
Another tool we recently published is a framework for considering how animals’ use of space and time affects how they interact. Ecologists long ago discovered that the way a predator (like a cougar) and prey (like a deer) use space can predict how that prey responds to that predator. For example, if a cougar and a deer roam over the same types of habitats, the deer will likely adjust its activity pattern (time) to avoid the cougar (think of deer foraging during broad daylight when predation risk is lower because cougars are less active). But if the cougar is more limited in its habitats than the deer, the deer will move to a different habitat (space) to avoid the cougar (think of deer feeding on suburban lawns where cougars may avoid people). These types of characteristics can also help predict whether a predator will primarily affect a prey through fear (the worry of being eaten, shaping a popular predator effect known as the ‘landscape of fear’) or mortality (the reality of being eaten).
To consider how these guidelines about animal behavior could guide prevention of conflict, we explored this idea in a broader food web that included people and livestock. We envisioned scenarios in which fear of cougar predation could cause deer to shift habitat or activity patterns to avoid predation, leading to predation on livestock instead. So, if a deer adjusts its activity pattern to avoid a cougar, livestock constrained by pasture fencing could be protected using predator-deterring stimulants that scare away the cougar. Similarly, if the cougar is more limited in its habitat than the deer, livestock originally grazing in similar habitat as the cougar could be moved to grazing locations outside of cougar habitat to protect the livestock and reduce conflict. Given this, the framework predicts that people can respond in context-dependent ways to protect the livestock and create a ‘human-predator coexistence food web’. Although these interventions may seem obvious, the framework helps explain why certain approaches work with animal behavior and may illuminate opportunities for new or more effective interventions.
These types of simple scenarios use animal behavior to predict situations in which the landscape of fear from people can scare off predators, thus preventing them from depredating livestock and stimulating conflict with people. For now, this framework remains theoretical, and the science needs to be field tested. But we’ll be sure to keep you updated as coexistence tools and science improve and we continue to protect imperiled wildlife!