Department of Human Behavior, Ecology, and Culture, Max Planck Institute for Evolutionary Anthropology, Deutscher Platz 6, Leipzig 04103, Germany.
Facultad de Biología, Cuerpo Académico Biología y Ecología del Comportamiento, Universidad Veracruzana, México.
J Theor Biol. 2018 Oct 14;455:357-369. doi: 10.1016/j.jtbi.2018.07.031. Epub 2018 Jul 24.
A theoretical and applied literature has suggested that foragers search using Lévy flights, since Lévy flights can maximize the efficiency of search in the absence of information on the location of randomly distributed prey. Foragers, however, often have available to them at least some information about the distribution of prey, gained either through evolved mechanisms, experience and memory, or social transmission of information. As such, we might expect selection for heuristics that make use of such information to further improve the efficiency of random search. Here we present a general model of random search behavior that includes as special cases: area-restricted search, correlated random walks, Brownian search, and Lévy flights. This generative model allows foragers to adjust search parameters based on encounter-conditional and other heuristics. Using a simulation model, we demonstrate the efficiency gains of these search heuristics, and illustrate the resulting differences in the distributions of step-size and heading angle change they imply, relative to Lévy flights. We conclude by presenting a statistical model that can be fit to empirical data and a set of testable, quantitative predictions that contrast our model of adaptive search with the Lévy flight foraging hypothesis.
理论和应用文献表明，觅食者使用 Lévy 飞行进行搜索，因为 Lévy 飞行可以在没有关于随机分布猎物位置的信息的情况下最大限度地提高搜索效率。然而，觅食者通常至少可以获得一些关于猎物分布的信息，这些信息要么是通过进化机制、经验和记忆获得的，要么是通过信息的社会传递获得的。因此，我们可能期望选择那些利用这些信息来进一步提高随机搜索效率的启发式方法。在这里，我们提出了一个随机搜索行为的通用模型，其中包括：区域限制搜索、相关随机游走、布朗运动和 Lévy 飞行。这个生成模型允许觅食者根据遭遇条件和其他启发式方法来调整搜索参数。我们使用模拟模型演示了这些搜索启发式方法的效率提高，并说明了它们相对于 Lévy 飞行所暗示的步长和航向变化分布的差异。最后，我们提出了一个可以拟合经验数据的统计模型，并提出了一组可测试的定量预测，这些预测对比了我们的适应性搜索模型和 Lévy 飞行觅食假说。
