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

Probabilistic inference and Bayesian-like estimation in animals: Empirical evidence

Animals often make decisions without perfect knowledge of environmental parameters like the quality of an encountered food patch or a potential mate. Theoreticians often assume animals make such decisions using a Bayesian updating process that combines prior information about the frequency distribution of resources in the environment with sample information from an encountered resource; such a process leads to decisions that maximize fitness, given the available information. I examine three aspects of empirical work that shed light on the idea that animals can make such decisions in a Bayesian-like manner. First, many animals are sensitive to variance differences in behavioral options, one metric used to characterize frequency distributions. Second, several species use information about the relative frequency of preferred versus nonpreferred items in different populations to make probabilistic inferences about samples taken from populations in a manner that results in maximizing the likelihood of obtaining a preferred reward. Third, the predictions of Bayesian models often match the behavior of individuals in two main approaches. One approach compares behavior to models that make different assumptions about how individuals estimate the quality of an environmental parameter. The patch exploitation behavior of nine species of birds and mammals has matched the predictions of Bayesian models. The other approach compares the behavior of individuals who learn, through experience, different frequency distributions of resources in their environment. The behavior of three bird species and bumblebees exploiting food patches and fruit flies selecting mates is influenced by their experience learning different frequency distributions of food and mates, respectively, in ways consistent with Bayesian models. These studies lend support to the idea that animals may combine prior and sample information in a Bayesian-like manner to make decisions under uncertainty, but additional work on a greater diversity of species is required to better understand the generality of this ability.

Integrating sensory ecology and predator-prey theory to understand animal responses to fire

Fire regimes are changing dramatically worldwide due to climate change, habitat conversion, and the suppression of Indigenous landscape management. Although there has been extensive work on plant responses to fire, including their adaptations to withstand fire and long-term effects of fire on plant communities, less is known about animal responses to fire. Ecologists lack a conceptual framework for understanding behavioural responses to fire, which can hinder wildlife conservation and management. Here, we integrate cue-response sensory ecology and predator-prey theory to predict and explain variation in if, when and how animals react to approaching fire. Inspired by the literature on prey responses to predation risk, this framework considers both fire-naïve and fire-adapted animals and follows three key steps: vigilance, cue detection and response. We draw from theory on vigilance tradeoffs, signal detection, speed-accuracy tradeoffs, fear generalization, neophobia and adaptive dispersal. We discuss how evolutionary history with fire, but also other selective pressures, such as predation risk, should influence animal behavioural responses to fire. We conclude by providing guidance for empiricists and outlining potential conservation applications.

Can we use antipredator behavior theory to predict wildlife responses to high-speed vehicles?

Animals seem to rely on antipredator behavior to avoid vehicle collisions. There is an extensive body of antipredator behavior theory that have been used to predict the distance/time animals should escape from predators. These models have also been used to guide empirical research on escape behavior from vehicles. However, little is known as to whether antipredator behavior models are appropriate to apply to an approaching high-speed vehicle scenario. We addressed this gap by (a) providing an overview of the main hypotheses and predictions of different antipredator behavior models via a literature review, (b) exploring whether these models can generate quantitative predictions on escape distance when parameterized with empirical data from the literature, and (c) evaluating their sensitivity to vehicle approach speed using a simulation approach wherein we assessed model performance based on changes in effect size with variations in the slope of the flight initiation distance (FID) vs. approach speed relationship. The slope of the FID vs. approach speed relationship was then related back to three different behavioral rules animals may rely on to avoid approaching threats: the spatial, temporal, or delayed margin of safety. We used literature on birds for goals (b) and (c). Our review considered the following eight models: the economic escape model, Blumstein's economic escape model, the optimal escape model, the perceptual limit hypothesis, the visual cue model, the flush early and avoid the rush (FEAR) hypothesis, the looming stimulus hypothesis, and the Bayesian model of escape behavior. We were able to generate quantitative predictions about escape distance with the last five models. However, we were only able to assess sensitivity to vehicle approach speed for the last three models. The FEAR hypothesis is most sensitive to high-speed vehicles when the species follows the spatial (FID remains constant as speed increases) and the temporal margin of safety (FID increases with an increase in speed) rules of escape. The looming stimulus effect hypothesis reached small to intermediate levels of sensitivity to high-speed vehicles when a species follows the delayed margin of safety (FID decreases with an increase in speed). The Bayesian optimal escape model reached intermediate levels of sensitivity to approach speed across all escape rules (spatial, temporal, delayed margins of safety) but only for larger (> 1 kg) species, but was not sensitive to speed for smaller species. Overall, no single antipredator behavior model could characterize all different types of escape responses relative to vehicle approach speed but some models showed some levels of sensitivity for certain rules of escape behavior. We derive some applied applications of our findings by suggesting the estimation of critical vehicle approach speeds for managing populations that are especially susceptible to road mortality. Overall, we recommend that new escape behavior models specifically tailored to high-speeds vehicles should be developed to better predict quantitatively the responses of animals to an increase in the frequency of cars, airplanes, drones, etc. they will face in the next decade.

A Bayesian optimal escape model reveals bird species differ in their capacity to habituate to humans

The capacity to habituate to, or tolerate, the close proximity of humans varies among wildlife species and may mediate population and species viability. Some species readily habituate to human proximity while others remain sensitive. These differences are important for predicting human impact on wildlife, but can be difficult to quantify given wildlife responses are highly idiosyncratic and are often context dependent. A general method for assimilating multiple sources of information and variation in individual responses is needed to extract meaningful descriptors of population- and species-level behavior. We apply a previously verified Bayesian optimal escape model to quantify differences in the perceived risk of seven species of waterbird, and generate a metric for species-specific sensitivity by estimating the relative change in risk perception of each species across an environmental gradient from low to high prevailing human activity level. We found that, in general, birds are the least habituated (evidently perceived the highest level of risk from humans) in sites with low human activity and the most habituated (evidently perceived lower levels of risk from humans) in sites with high human activity. Species varied in the degree of these differences, with some insensitive to human activity level, while others were highly sensitive. Aside from improving our ability to study the habituation of wild populations, this method for quantifying risk perception at population and species scales has broad applications in the management and monitoring of wildlife, and may aid in environmental impact assessments and identifying populations/species susceptible to disturbance.

An escape theory model for directionally moving prey and an experimental test in juvenile Chinook salmon

Prey evaluate risk and make decisions based on the balance between the costs of predation and those of engaging in antipredator behaviour. Economic escape theory has been valuable in understanding the responses of stationary prey under predation risk; however, current models are not applicable for directionally moving prey.

Here we present an extension of existing escape theory that predicts how much predation risk is perceived by directionally moving prey. Perceived risk is measured by the extent antipredator behaviour causes a change in travel speed (the distance to a destination divided by the total time to reach that destination). Cryptic or cautious antipredator behaviour slows travel speed, while prey may also speed up to reduce predator–prey overlap. Next, we applied the sensitization hypothesis to our model, which predicts that prey with more predator experience should engage in more antipredator behaviour, which leads to a larger change in travel speed under predation risk. We then compared the qualitative predictions of our model to the results of a behavioural assay with juvenile Chinook salmon Oncorhynchus tshawytscha that varied in their past predator experience.

We timed salmon swimming downstream through a mesh enclosure in the river with and without predator cues present to measure their reaction to a predator. Hatchery salmon had the least predator experience, followed by wild salmon captured upstream (wild‐upstream) and wild‐salmon captured downstream (wild‐downstream).

Both wild salmon groups slowed down in response to predator cues, whereas hatchery salmon did not change travel speed. The magnitude of reaction to predator cues by salmon group followed the gradient of previous predator experience, supporting the sensitization hypothesis.

Moving animals are conspicuous and vulnerable to predators. Here we provide a novel conceptual framework for understanding how directionally moving prey perceive risk and make antipredator decisions. Our study extends the scope of economic escape theory and improves general understanding of non‐lethal effects of predators on moving prey.

Experimental subjects do not know what we think they know

Many biological, psychological and economic experiments have been designed where an organism or individual must choose between two options that have the same expected reward but differ in the variance of reward received. In this way, designed empirical approaches have been developed for evaluating risk preferences. Here, however, we show that if the experimental subject is inferring the reward distribution (to optimize some process), they will rarely agree in finite time that the expected rewards are equal. In turn, we argue that this makes discussions of risk preferences, and indeed the motivations of behaviour, not so simple or straightforward to interpret. We use this particular experiment to highlight the serious need to consider the frame of reference of the experimental subject in studies of behaviour.

Should I stay, or should I go: Modeling optimal flight initiation distance in nesting birds

Flight initiation distance (FID)-the distance at which an individual leaves in response to the approach of a perceived threat-provides a measurement of risk-taking behavior. If individuals optimize their FID, this distance should reflect the point at which the fitness resulting from leaving exceeds the fitness resulting from all other possible decisions. Previous theory of FID has often been aimed at explaining this behavior in foraging individuals. Yet flight initiation in response to approaching threats occurs in a range of contexts that might influence optimal behavior. In breeding individuals, risk-taking decisions that are made at a location of offspring care (e.g., a nest or den) can have significant effects on fitness. Here, we develop a theoretical model of distances at which a parent bird flushes from a nest in response to an approaching threat. We estimate parent fitness with regards to characteristics of the parent (reproductive values, detection distance, and cost of lost parental care cost), the nest (concealment and accessibility), and the approaching predator (detection capability and predation success), developing a dichotomous scenario between staying at the nest or leaving at varying distances. Using a generalized comparison of the benefits of leaving versus staying, we find that increasing costs of lost parental care, probability of predation of the parent due to fleeing, or current reproductive value lead to more instances of staying at the nest. In a complementary approach with specified parameters based on biologically-informed factors that likely influence a predator-prey encounter, we find that increasing the current reproductive value, concealment of the nest, or costs of lost parental care decrease optimal FID and can lead to the parent staying at the nest. Other factors, such as increasing residual reproductive value, predation success, and predator capability of detecting the nest, increase optimal FID with some instances of costs of fleeing being so great that staying becomes an optimal strategy. Our theory provides a framework to explain variation in FID among nesting species and individuals and could provide a foundation for future empirical investigations of risk-taking behavior.