Integrating individual search and navigation behaviors in mechanistic movement models
Integrating individual search and navigation behaviors in mechanistic movement models
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DOI：
10.1007/s12080-010-0081-1
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发表时间：
2011-08-01
影响因子：
1.6
通讯作者：
Grimm, Volker
中科院分区：
文献类型：
--
作者：
Mueller, Thomas;Fagan, William F.;Grimm, Volker
通过机械模型理解复杂的运动行为是运动生态学的一项关键挑战。我们使用经过进化训练的人工神经网络（ANN）建立了一个理论模拟模型，其中个体根据他们搜索和导航的资源景观而进化运动行为。我们区分了响应邻近刺激的非定向运动、利用远处目标感知线索的定向运动，以及假设目标位置的先验知识的记忆机制，然后测试这三种运动行为与资源斑块大小、资源景观的可预测性和运动障碍的发生的相关性。当记忆力有利时，个体在更大的斑块大小和可预测的景观下定位资源的效率更高。然而，记忆也经常用于不可预测的具有中间斑块大小的景观，以系统地搜索整个空间域，正因为如此，我们认为记忆对于解释许多实证研究中观察到的超扩散可能很重要。在可预测的景观下，突然施加的运动障碍会产生最大的影响，并暂时消除记忆的好处。总体而言，我们演示了如何通过人工神经网络中的状态变量来表示与某些认知能力相关的运动行为，以及如何通过改变这些状态变量来测试不同时空资源动态下不同行为的相关性。如果适应经验运动路径，这里描述的方法可以帮助揭示真实动物的行为机制并预测人为景观变化对动物运动的影响。
Understanding complex movement behaviors via mechanistic models is one key challenge in movement ecology. We built a theoretical simulation model using evolutionarily trained artificial neural networks (ANNs) wherein individuals evolve movement behaviors in response to resource landscapes on which they search and navigate. We distinguished among non-oriented movements in response to proximate stimuli, oriented movements utilizing perceptual cues from distant targets, and memory mechanisms that assume prior knowledge of a target's location and then tested the relevance of these three movement behaviors in relation to size of resource patches, predictability of resource landscapes, and the occurrence of movement barriers. Individuals were more efficient in locating resources under larger patch sizes and predictable landscapes when memory was advantageous. However, memory was also frequently used in unpredictable landscapes with intermediate patch sizes to systematically search the entire spatial domain, and because of this, we suggest that memory may be important in explaining super-diffusion observed in many empirical studies. The sudden imposition of movement barriers had the greatest effect under predictable landscapes and temporarily eliminated the benefits of memory. Overall, we demonstrate how movement behaviors that are linked to certain cognitive abilities can be represented by state variables in ANNs and how, by altering these state variables, the relevance of different behaviors under different spatiotemporal resource dynamics can be tested. If adapted to fit empirical movement paths, methods described here could help reveal behavioral mechanisms of real animals and predict effects of anthropogenic landscape changes on animal movement.