Abstract
Dynamic state variable models of individual behaviour have been developed to describe and study this highly flexible and dynamic process. In addition to external environmental factors such as predation or food availability, these models account for the internal physiological state of an individual. They explicitly incorporate state variables such as hunger level, energy reserve or body size as the internal driver behind behavioural adjustments. Dynamic state variable models thus yield predictions about the origins and consequences of flexible behaviour and offer a framework in which to quantify the shapes of trade-offs. Such quantification provides the detail necessary to parameterise a variety of hugely insightful and predictive models where individual behaviour and life history are explicitly linked to population dynamics. Studies of predation risk provide some of the best examples of this framework. Here, state-dependent models have shown how prey individuals balance the need of energy acquisition with predation risk1-6. Prey frequently decrease their activity or use refuge habitats in response to predation, but a higher survival probability is paid for with lower feeding rates. At some point, hungry individuals, caught between the fitness consequences of starvation or predation, will just take the risk. The exact feeding effort is a function of numerous internal and external states including hunger and food availability predation vulnerability and risk1-6. The quantitative detail derived in this context is the detail required to link individual behaviour to community dynamics. While empirical studies have confirmed several qualitative aspects of these models, no experiments have comprehensively evaluated and quantified trade-off surfaces. In this context, our project will address three unresolved questions: (1) Can we experimentally derive precise empirical forms of the relationships between behaviour and external and internal drivers? 2) Can we expand behaviour models and experiments to consider more than one state variable?(3) Can we better accommodate feedbacks between foraging behaviour and resource availability? Together, our analyses will reveal the strategies among multiple internal and external state variables that optimize fitness and provide some of the most reliable detail about behaviour, state and life history required to link individual behaviour to community dynamics
United Kingdom