Self-organized fish schools:: An examination of emergent properties
Self-organized fish schools:: An examination of emergent properties
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DOI：
10.2307/1543482
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发表时间：
2002-06-01
影响因子：
1.6
通讯作者：
Grünbaum, D
中科院分区：
文献类型：
--
作者：
Parrish, JK;Viscido, SV;Grünbaum, D
从细菌到高等脊椎动物，个体空间分布的异质性、“聚集”模式几乎普遍存在于所有生物体中。虽然聚集体的具体特征通常在视觉上对人眼具有冲击力，但基于人类视觉的启发式分析通常不足以回答关于生物体如何以及为什么聚集的基本问题。是什么样的个体行为特征导致了这些特征的产生？当纯粹的物理机制产生了性质上相似的空间模式时，这些模式在生物体中是否具有生物学意义，或者它们只是任何相互作用的自主个体的可能特征的附带现象？如果空间聚集的特定特征确实赋予群体成员的适应性优势或劣势，那么进化是如何在个体和群体层面上平衡成本和收益的过程中塑造个体行为的？鱼类集群等社会行为的数学模型为解决其中一些问题提供了一个有希望的途径。然而，关于学校教育模式的文献缺乏一个共同的框架来客观和定量地描述个人行为和群体模式之间的关系。在本文中，我们简要地调查现有的学校教育模式之间的行为算法和聚合统计的异同。我们目前的初步结果，我们努力开发一个建模框架，综合了以前的工作，并确定行为参数和组级统计之间的关系。
Heterogeneous, "aggregated" patterns in the spatial distributions of individuals are almost universal across living organisms, from bacteria to higher vertebrates. Whereas specific features of aggregations are often visually striking to human eyes, a heuristic analysis based on human vision is usually not sufficient to answer fundamental questions about how and why organisms aggregate. What are the individual-level behavioral traits that give rise to these features? When qualitatively similar spatial patterns arise from purely physical mechanisms, are these patterns in organisms biologically significant, or are they simply epiphenomena that are likely characteristics of any set of interacting autonomous individuals? If specific features of spatial aggregations do confer advantages or disadvantages in the fitness of group members, how has evolution operated to shape individual behavior in balancing costs and benefits at the individual and group levels? Mathematical models of social behaviors such as schooling in fishes provide a promising avenue to address some of these questions. However, the literature on schooling models has lacked a common framework to objectively and quantitatively characterize relationships between individual-level behaviors and group-level patterns. In this paper, we briefly survey similarities and differences in behavioral algorithms and aggregation statistics among existing schooling models. We present preliminary results of our efforts to develop a modeling framework that synthesizes much of this previous work, and to identify relationships between behavioral parameters and group-level statistics.