Modeling prey flight decisions during predator-prey encounters
Predator-prey interactions represent one of the most fundamental species interactions, and play an important role in understanding and managing wildlife populations. As such there is a growing need for predictive models to inform management decisions. Most existing predator-prey models focus on the behavior of populations, predicting changes in population size and structure while assuming that interaction strengths and frequencies are determined by population densities. The classic example is the Lotka-Volterra predator-prey equations which parameterize the strength of interactions between predator and prey populations to predict rates of population change. In this model every predator-prey encounter has the same probability of resulting in the death of prey, and only the population densities affect the frequencies and strength of interactions. In reality, differences in information available to prey within encounters can result in variable probabilities of predation. The Lotka-Volterra models are unable to account for this due to their parameterization of interaction strength, ignoring the context of and information available during a given predator-prey encounter.
Rather than focus on the behavior and characteristics of the population, we sought to use the behavior of the individual in considering predator-prey interactions. Using an optimal foraging theory approach, we created a theoretical model to predict prey assessment and response to predator direction, distance, and velocity. We fitted our model using data from a white-tailed deer study on flight responses. The results of our model show that the optimal flight initiation distance depends on the frequency of encounters with predators, consistent with our empirical results that white-tailed deer have lower flight initiation distances when faced with a high density predator population versus a low density one. More generally, our model could be applied to a broad range of systems where prey gather information from predator behavior to make decisions concerning flight distance, patch use, home range size, etc. Our framework will therefore contribute to a better understanding of complex predator-prey interactions, leading to better predictions of prey decision making and its effect on prey density, land use, reproductive success, and other population characteristics affected by behavior.