摘要翻译：
对集体动物行为的研究必须通过数值模型的理论预测和来自经验观察的数据之间的比较来取得进展。为了达到这一目的，开发三维(3D)分析方法是很重要的，这些方法既能提供关于群体结构的信息，又能适用于经验数据。事实上，经验数据比数字数据噪音大得多，而且它们受到几个限制。我们在这里回顾了STARFLAG项目用于描述野外大群椋鸟三维结构的分析工具。我们展示了如何避免最常见的陷阱i定量分析的三维动物组，特别注意问题的边界引入的偏倚组。通过实例证明，在研究群体的三维结构时，忽略边界效应会产生人工制品。此外，我们还表明数学的严谨性对于区分重要的生物学特性和动物群体的琐碎几何特征是必不可少的。
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英文标题：
《The STARFLAG handbook on collective animal behaviour: Part II,
  three-dimensional analysis》
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作者：
Andrea Cavagna, Irene Giardina, Alberto Orlandi, Giorgio Parisi,
  Andrea Procaccini
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最新提交年份：
2008
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分类信息：

一级分类：Quantitative Biology        数量生物学
二级分类：Quantitative Methods        定量方法
分类描述：All experimental, numerical, statistical and mathematical contributions of value to biology
对生物学价值的所有实验、数值、统计和数学贡献
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一级分类：Physics        物理学
二级分类：Statistical Mechanics        统计力学
分类描述：Phase transitions, thermodynamics, field theory, non-equilibrium phenomena, renormalization group and scaling, integrable models, turbulence
相变，热力学，场论，非平衡现象，重整化群和标度，可积模型，湍流
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一级分类：Quantitative Biology        数量生物学
二级分类：Populations and Evolution        种群与进化
分类描述：Population dynamics, spatio-temporal and epidemiological models, dynamic speciation, co-evolution, biodiversity, foodwebs, aging; molecular evolution and phylogeny; directed evolution; origin of life
种群动力学；时空和流行病学模型；动态物种形成；协同进化；生物多样性；食物网；老龄化；分子进化和系统发育；定向进化；生命起源
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英文摘要：
  The study of collective animal behaviour must progress through a comparison between the theoretical predictions of numerical models and data coming from empirical observations. To this aim it is important to develop methods of three-dimensional (3D) analysis that are at the same time informative about the structure of the group and suitable to empirical data. In fact, empirical data are considerably noisier than numerical data, and they are subject to several constraints. We review here the tools of analysis used by the STARFLAG project to characterise the 3D structure of large flocks of starlings in the field. We show how to avoid the most common pitfalls i the quantitative analysis of 3D animal groups, with particular attention to the problem of the bias introduced by the border of the group. By means of practical examples, we demonstrate that neglecting border effects gives rise to artefacts when studying the 3D structure of a group. Moreover, we show that mathematical rigour is essential to distinguish important biological properties from trivial geometric features of animal groups.
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PDF链接：
https://arxiv.org/pdf/802.1674