State-dependent behaviour: confronting models with high resolution data

状态相关行为：用高分辨率数据对抗模型

• 批准号: NE/J013315/1
• 负责人: Andrew Beckerman
• 金额: $ 6.58万
• 依托单位: University of Sheffield
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2012
• 资助国家: 英国
• 起止时间: 2012 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=NE%2FJ013315%2F1
• 关键词: State; dependent; behaviour; confronting; models

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

人们开发了个人行为的动态状态变量模型来描述和研究这个高度灵活和动态的过程。除了外部环境因素，如捕食或食物的可用性，这些模型占一个人的内部生理状态。它们明确地将饥饿水平、能量储备或身体大小等状态变量作为行为调整背后的内部驱动因素。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。这种量化提供了必要的细节，以参数化各种非常有洞察力和预测性的模型，其中个人行为和生活史明确地与人口动态联系在一起。对捕食风险的研究提供了这一框架的一些最好的例子。在这里，依赖于状态的模型已经显示了猎物个体如何平衡能量获取与捕食风险的需求1 -6。猎物经常减少他们的活动或使用避难所栖息地，以应对捕食，但较高的生存概率与较低的摄食率。在某种程度上，饥饿的个体，夹在饥饿或捕食的健康后果之间，只会冒这个险。确切的喂养努力是许多内部和外部状态的函数，包括饥饿和粮食供应、捕食、脆弱性和风险1 -6。在这种情况下得出的定量细节是连接个人行为的社区dynamics.While实证研究证实了这些模型的几个定性方面的细节，没有实验全面评估和量化的权衡表面。在此背景下，我们的项目将解决三个悬而未决的问题：（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)我们能扩展行为模型和实验来考虑一个以上的状态变量吗？(3)我们能更好地适应觅食行为和资源可用性之间的反馈吗？总之，我们的分析将揭示多个内部和外部状态变量之间的策略，这些变量优化了适应性，并提供了将个人行为与社区动态联系起来所需的行为，状态和生活史的一些最可靠的细节