Daily rhythms occur in numerous physiological and behavioral processes across an immense diversity of taxa, but mechanistic links between rhythms of trait expression and organismal fitness remain poorly understood. We constructed a dynamic optimization model to determine whether risk allocation provides an adaptive explanation for the daily foraging rhythm observed in many species using the orb-weaving spider Cyclosa turbinata as a case study. Specifically, our model determines for each point in the day and level of energetic reserves the foraging decision (to forage or rest) that maximizes expected fecundity at the end of the day. We used published data on C. turbinata to parameterize the model, and we further explored the model predictions over a wide range of parameter values to determine whether risk allocation generally predicts a daily rhythm of behavior and how the rhythm responds to external and internal environmental conditions. Thus, we examine how daily patterns of behavior depend on temporal variation in predator and prey encounters, the average daily rate of predator and prey encounters, and individual foraging efficacy (the success probability of prey capture attempts).
Results/Conclusions
Our model predicts that C. turbinata should generally begin foraging at lower levels of energy reserves during midday when predators are most abundant. We find that a daily change in optimal behavior depends strongly on temporal variation in predator, but not prey, encounters, while both the average daily encounter rates with predators and with prey affect the level of reserves at which foraging should begin during high-risk periods. We find that the effect of individuals’ foraging efficacy on the behavioral rhythm depends on average rates of encounters with prey, such that correlations between individual foraging efficacy and foraging behavior may either be positive or negative depending on overall prey availability. We also find that the interaction between foraging efficacy and prey availability in determining optimal behavior depends on the average rate of predator encounters. Our analysis suggests these patterns arise because individual’s foraging efficacy determines whether the average rate of encounters with predators or with prey more strongly influences optimal behavior. The robustness of the qualitative model predictions to variation in our parameter estimates suggests that risk allocation could explain foraging rhythms in a wide range of taxa.