sOAR: a tool for modelling optimal animal life‐history strategies in cyclic environments

Abstract

Periodic environments determine the life cycle of many animals across the globe and the timing of important life history events, such as reproduction and migration. These adaptive behavioural strategies are complex and can only be fully understood (and predicted) within the framework of natural selection in which species adopt evolutionary stable strategies. We present sOAR, a powerful and user-friendly implementation of the well-established framework of optimal annual routine modelling. It allows determining optimal animal life history strategies under cyclic environmental conditions using stochastic dynamic programming. It further includes the simulation of population dynamics under the optimal strategy. sOAR provides an important tool for theoretical studies on the behavioural and evolutionary ecology of animals. It is especially suited for studying bird migration. In particular, we integrated options to differentiate between costs of active and passive flight into the optimal annual routine modelling framework, as well as options to consider periodic wind conditions affecting flight energetics. We provide an illustrative example of sOAR where food supply in the wintering habitat of migratory birds significantly alters the optimal timing of migration. sOAR helps improving our understanding of how complex behaviours evolve and how behavioural decisions are constrained by internal and external factors experienced by the animal. Such knowledge is crucial for anticipating potential species' response to global environmental change.

Movement-mediated community assembly and coexistence

“…However, early arrival also increases direct interference competition with residents (Ahola et al, 2007). As arrival times are furthermore strongly linked to food availability, this creates a complex optimization problem, in which movementrelated decisions are one means to secure competitiveness (Schaefer et al, 2018).…”

“…Over the last two decades, however, the interaction of movement and habitat features is increasingly taken into account (Kramer-Schadt et al, 2004), while explicitly linking movement decisions to established energy budget theories is a very recent development (Malishev, Bull, & Kearney, 2018). Here, IBMs might profit from mechanistic optimal annual routine modelling that determines the behavioural decision rules underlying movement based on energy and health budgets, taking evolutionary considerations into account (Schaefer et al, 2018).…”

Stochastic optimal switching model for migrating population dynamics

“…Evolution of optimal gut size of birds has been analysed in Ez-zizi et al [42] based on a time-periodic SDP model. A unified SDP-based numerical tool for modelling animal life histories under temporally cyclic environment has been developed in Schaefer et al [43]. Satterthwaite et al [44] developed an SDP model for understanding linkages between migration timing of anadromous salmonids and multiple environmental factors.…”

Long-distance migratory birds threatened by multiple independent risks from global change

“…Nevertheless, optimal migration theory predicts that any increase in migration distance will cause the species to expend more energy26, and refuelling will necessitate longer overall stopover duration27. Such extra time costs may not be easy to accommodate in the annual cycle of many migrants given the complex trade-offs in the timing of migration, breeding and moult and its synchronisation with food resources1,28. For example, a prolonged spring migration would require earlier departure at the risk of not finding enough food resources en route , or late arrival at the risk of reduced breeding success.…”