Born to Run? Quantifying the Balance of Prior Bias and New Information in Prey Escape Decisions

Animal behaviors can often be challenging to model and predict, though optimality theory has improved our ability to do so. While many qualitative predictions of behavior exist, accurate quantitative models, tested by empirical data, are often lacking. This is likely due to variation in biases across individuals and variation in the way new information is gathered and used. We propose a modeling framework based on a novel interpretation of Bayes's theorem to integrate optimization of energetic constraints with both prior biases and specific sources of new information gathered by individuals. We present methods for inferring distributions of prior biases within populations rather than assuming known priors, as is common in Bayesian approaches to modeling behavior, and for evaluating the goodness of fit of overall model descriptions. We apply this framework to predict optimal escape during predator-prey encounters, based on prior biases and variation in what information prey use. Using this approach, we collected and analyzed data characterizing white-tailed deer (Odocoileus virginianus) escape behavior in response to human approaches. We found that distance to predator alone was not sufficient to predict deer flight response and show that the inclusion of additional information is necessary. We also compared differences in the inferred distributions of prior biases across different populations and discuss the possible role of human activity in influencing these distributions.

Fifty years of chasing lizards: new insights advance optimal escape theory

Systematic reviews and meta-analyses often examine data from diverse taxa to identify general patterns of effect sizes. Meta-analyses that focus on identifying generalisations in a single taxon are also valuable because species in a taxon are more likely to share similar unique constraints. We conducted a comprehensive phylogenetic meta-analysis of flight initiation distance in lizards. Flight initiation distance (FID) is a common metric used to quantify risk-taking and has previously been shown to reflect adaptive decision-making. The past decade has seen an explosion of studies focused on quantifying FID in lizards, and, because lizards occur in a wide range of habitats, are ecologically diverse, and are typically smaller and differ physiologically from the better studied mammals and birds, they are worthy of detailed examination. We found that variables that reflect the costs or benefits of flight (being engaged in social interactions, having food available) as well as certain predator effects (predator size and approach speed) had large effects on FID in the directions predicted by optimal escape theory. Variables that were associated with morphology (with the exception of crypsis) and physiology had relatively small effects, whereas habitat selection factors typically had moderate to large effect sizes. Lizards, like other taxa, are very sensitive to the costs of flight.