Testing association patterns of social animals
Testing association patterns of social animals
复制标题
测试社会性动物的关联模式
DOI：
10.1006/anbe.1999.1099
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发表时间：
1999
期刊：
影响因子：
2.5
通讯作者：
H. Whitehead
中科院分区：
文献类型：
--
作者：
H. Whitehead
社会种群中成对动物之间的关联指数自然会有所不同。在社会结构的许多研究中，一个重要的问题是这种变化是否可以纯粹通过随机联想来解释，或者数据是否提供了偏好联想和/或避免联想的证据（Whitehead 1997; Bejder et al. 1998; Whitehead & Dufault 1999）。进行这种检验的最合适的方法可能是使用蒙特-卡罗方法，其中，关联数据在一定的约束条件下随机排列，并将原始关联数据的统计数据与随机数据的统计数据进行比较。通常，原始数据集由在哪个组中观察到的哪些单独识别的动物的记录组成，其中组被定义为瞬时观察到的空间聚集。在这些情况下，通常会限制排列，以使观察到每个个体的组数和每组中的动物数保持恒定（例如Slooten et al. 1993; Bejder et al. 1998）。Bejder et al.（1998）通过使用Manly（1995）为类似的生态问题开发的顺序例程，在这些约束下排列关联矩阵（表1）。这个程序（不像我在这个任务中使用的其他程序：Whitehead et al. 1982; Slooten et al. 1993）是直接的，有效的，适用于所有适当的数据集。因此，它代表了我们在检验社会结构以寻找非随机关联证据的能力上的一个重大发展。Manly（1995）和Bejder等人（1998）（随后称为MBFB）方法的核心是以不影响行和列总数（每组中的动物数量和每只动物的组数）的方式顺序改变1：0组-个体矩阵。这是通过在每个步骤中随机选择两个个体和两个组来实现的，使得每个个体仅在其中一个组中被看到，并且每个组中的每一个组都是独立的。
Indices of association among pairs of animals in a social population naturally vary. An important question in many studies of social structure is whether this variation can be accounted for purely by random association, or whether the data give evidence for preferred association, and/or avoidance of association (Whitehead 1997; Bejder et al. 1998; Whitehead & Dufault 1999). Probably the most appropriate way to carry out such tests is by using Monte-Carlo methods, in which association data are randomly permuted subject to certain constraints, and statistics of the original association data are compared with those from random data. Often the original data set consists of records of which individually identified animals were observed in which group, with group defined as an instantaneously observed spatial aggregation. In these cases it is usual to constrain the permutations so that the number of groups in which each individual was observed, and the number of animals in each group, are held constant (e.g. Slooten et al. 1993; Bejder et al. 1998). Bejder et al. (1998) permuted association matrices under these constraints by using a sequential routine developed by Manly (1995) for an analogous ecological problem (Table 1). This routine (unlike others that I have used for this task: Whitehead et al. 1982; Slooten et al. 1993) is straightforward, efficient and works with all appropriate data sets. Thus it represents a substantial development in our ability to test social structures for evidence of nonrandom associations. The core of the method of Manly (1995) and Bejder et al. (1998) (subsequently called MBFB) is the sequential alteration of a 1:0 group–individual matrix in such a way that row and column totals (number of animals in each group, and number of groups of each animal) are unaffected. This is achieved by randomly selecting, at each step, two individuals and two groups so that each individual is seen in only one of the groups, and each