From random walks to informed movement
From random walks to informed movement
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DOI：
10.1111/j.1600-0706.2012.21021.x
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发表时间：
2013-06-01
期刊：
影响因子：
3.4
通讯作者：
Poethke, Hans-Joachim
中科院分区：
文献类型：
--
作者：
Fronhofer, Emanuel A.;Hovestadt, Thomas;Poethke, Hans-Joachim
动物运动分析是一个庞大且不断发展的研究领域。运动策略的详细知识是至关重要的理解生态进化动力学在所有尺度上从个人（Meta）种群。这一点以及详细的运动和扩散数据的可用性促使内森和同事们发表了他们备受赞赏的呼吁，将运动生态学建立在更彻底的机械论基础上。到目前为止，大多数运动模型都是基于随机行走的。然而，即使随机行走可以很好地描述真实的运动模式，也没有理由假设动物是随机运动的。因此，觅食策略的机械模型应该基于信息的使用和记忆，以增加我们对导致动物运动决策的过程的理解。我们提出了一个机械运动模型的动物有限的感知范围和基本的信息存储能力。这种空间信息的觅食者通过使用感知、记忆和学习或进化获得的关于景观属性的假设来构建其环境的内部地图。我们分析由此产生的运动模式和搜索效率，并将其与区域限制搜索策略（ARS）和有偏相关随机游走（BCRW）的无所不知的个人进行比较。我们发现，尽管他们的感知范围有限，空间知情的个人提高他们的觅食成功，可能比最好的ARS表现得更好。内部地图的构建和空间信息的使用导致斑块之间高度相关的行走和资源集群内的系统搜索。此外，所得到的移动模式可以包括突袭搜索行为。我们的工作突出了机械建模方法的优势，并为开发用于运动决策和分散的更复杂的记忆模型奠定了基础。
The analysis of animal movement is a large and continuously growing field of research. Detailed knowledge about movement strategies is of crucial importance for understanding eco-evolutionary dynamics at all scales from individuals to (meta-)populations. This and the availability of detailed movement and dispersal data motivated Nathan and colleagues to published their much appreciated call to base movement ecology on a more thorough mechanistic basis. So far, most movement models are based on random walks. However, even if a random walk might describe real movement patterns acceptably well, there is no reason to assume that animals move randomly. Therefore, mechanistic models of foraging strategies should be based on information use and memory in order to increase our understanding of the processes that lead to animal movement decisions. We present a mechanistic movement model of an animal with a limited perceptual range and basic information storage capacities. This spatially informed forager' constructs an internal map of its environment by using perception, memory and learned or evolutionarily acquired assumptions about landscape attributes. We analyse resulting movement patterns and search efficiencies and compare them to area restricted search strategies (ARS) and biased correlated random walks (BCRW) of omniscient individuals. We show that, in spite of their limited perceptual range, spatially informed individuals boost their foraging success and may perform much better than the best ARS. The construction of an internal map and the use of spatial information results in the emergence of a highly correlated walk between patches and a rather systematic search within resource clusters. Furthermore, the resulting movement patterns may include foray search behaviour. Our work highlights the strength of mechanistic modelling approaches and sets the stage for the development of more sophisticated models of memory use for movement decisions and dispersal.