State-dependent behaviour: confronting models with high resolution data

个体行为的动态状态变量模型已经被开发来描述和研究这一高度灵活和动态的过程。除了捕食或食物供应等外部环境因素外，这些模型还解释了个体的内部生理状态。他们明确地将饥饿水平、能量储备或体型等状态变量纳入行为调整背后的内在驱动因素。因此，动态状态变量模型可以预测灵活行为的起源和后果，并提供一个框架来量化权衡的形状。这种量化为参数化各种极具洞察力和预测性的模型提供了必要的细节，在这些模型中，个体行为和生活史与种群动态明确地联系在一起。对捕食风险的研究为这一框架提供了一些最好的例子。在这里，状态依赖模型显示了猎物个体如何平衡能量获取需求和捕食风险[1-6]。作为对捕食者的回应，猎物经常减少它们的活动或使用避难栖息地，但较低的摄食率换来了较高的生存概率。在某种程度上，饥饿的个体，在饥饿和捕食的适应度之间，会冒险。确切的进食努力是许多内部和外部状态的函数，包括饥饿和食物供应，捕食脆弱性和风险1-6。在这种情况下产生的定量细节是将个人行为与社区动态联系起来所需的细节。虽然实证研究已经证实了这些模型的几个定性方面，但没有实验全面评估和量化权衡面。在此背景下，我们的项目将解决三个尚未解决的问题：(1)我们能否通过实验得出行为与外部和内部驱动因素之间关系的精确经验形式？2)我们可以扩展行为模型和实验来考虑多个状态变量吗？(3)能否更好地适应觅食行为与资源可用性之间的反馈？总之，我们的分析将揭示优化适应度的多个内部和外部状态变量之间的策略，并提供一些最可靠的关于行为、状态和生活史的细节，这些细节将个体行为与社区动态联系起来

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