Analysing animal social network dynamics: the potential of stochastic actor-oriented models.
Analysing animal social network dynamics: the potential of stochastic actor-oriented models.
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DOI：
10.1111/1365-2656.12630
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发表时间：
2017-03
期刊：
影响因子：
--
通讯作者：
Tregenza T
中科院分区：
文献类型：
--
作者：
Fisher DN;Ilany A;Silk MJ;Tregenza T
动物嵌入在动态变化的同种关系网络中。这些动态网络是其环境的基本方面，创造了行为和其他特征的选择。然而，大多数基于社交网络的生态学方法都仅限于将网络视为静态的，尽管有人呼吁此类分析变得更加动态。社会科学中发展的许多统计分析越来越多地应用于动物网络，其中随机行动者导向模型（SAOM）就是一个主要例子。 SAOM 是一类基于个体的模型，旨在对受网络结构和协变量影响的离散时间点之间的网络转换进行建模。然而，目前尚不清楚这些技术对生态学家有多大用处，以及它们是否适合动物社交网络。我们回顾了 SAOM 最近在动物网络中的应用，概述了研究结果并评估了 SAOM 在应用于动物而不是人类网络时的优点和缺点。我们继续强调 SAOM 可用于研究的生态和进化过程的类型。 SAOM 可以包括个体、二元组和群体的效应和协变量，它们可以是恒定的或可变的。这使得可以研究生态学家感兴趣的广泛问题。然而，需要高分辨率数据，这意味着 SAOM 不适用于所有研究系统。目前尚不清楚 SAOM 对缺失数据和社会关系不确定性的鲁棒性如何。最终，我们鼓励在适当的系统中仔细应用 SAOM，动态网络分析可能会提供大量信息。然后，研究人员可以扩展基本方法来解决生态学中的一系列现有问题，并探索新的质疑路线。作为高度社会化的物种，我们对不断变化的社会关系和动物的社会关系着迷。人们经常要求对社交网络进行动态分析，但很少进行。作者回顾了一种动态网络分析技术，该技术可用于将社会关系与生态和进化过程联系起来。
Animals are embedded in dynamically changing networks of relationships with conspecifics. These dynamic networks are fundamental aspects of their environment, creating selection on behaviours and other traits. However, most social network‐based approaches in ecology are constrained to considering networks as static, despite several calls for such analyses to become more dynamic. There are a number of statistical analyses developed in the social sciences that are increasingly being applied to animal networks, of which stochastic actor‐oriented models (SAOMs) are a principal example. SAOMs are a class of individual‐based models designed to model transitions in networks between discrete time points, as influenced by network structure and covariates. It is not clear, however, how useful such techniques are to ecologists, and whether they are suited to animal social networks. We review the recent applications of SAOMs to animal networks, outlining findings and assessing the strengths and weaknesses of SAOMs when applied to animal rather than human networks. We go on to highlight the types of ecological and evolutionary processes that SAOMs can be used to study. SAOMs can include effects and covariates for individuals, dyads and populations, which can be constant or variable. This allows for the examination of a wide range of questions of interest to ecologists. However, high‐resolution data are required, meaning SAOMs will not be useable in all study systems. It remains unclear how robust SAOMs are to missing data and uncertainty around social relationships. Ultimately, we encourage the careful application of SAOMs in appropriate systems, with dynamic network analyses likely to prove highly informative. Researchers can then extend the basic method to tackle a range of existing questions in ecology and explore novel lines of questioning. As a highly social species, we are fascinated by our changing social relationships and those of animals. The dynamic analysis of social networks is regularly called for but infrequently delivered. The authors review a dynamic network analysis technique that can be used to relate social relationships to ecological and evolutionary processes.
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