Data from: Similar but different: dynamic social network analysis highlights fundamental differences between the fission-fusion societies of two equid species, the onager and Grevy's zebra

Understanding why animal societies take on the form that they do has benefited from insights gained by applying social network analysis to patterns of individual associations. Such analyses typically aggregate data over long time periods even though most selective forces that shape sociality have strong temporal elements. By explicitly incorporating the temporal signal in social interaction data we re-examine the network dynamics of the social systems of the evolutionarily closely-related Grevy's zebras and wild asses that show broadly similar social organizations. By identifying dynamic communities, previously hidden differences emerge: Grevy's zebras show more modularity than wild asses and in wild asses most communities consist of solitary individuals; and in Grevy's zebras, lactating females show a greater propensity to switch communities than non-lactating females and males. Both patterns were missed by static network analyses and in general, adding a temporal dimension provides insights into differences associated with the size and persistence of communities as well as the frequency and synchrony of their formation. Dynamic network analysis provides insights into the functional significance of these social differences and highlights the way dynamic community analysis can be applied to other species.

探究动物社会为何呈现出特定形态，相关研究常通过将社交网络分析（social network analysis）应用于个体关联模式来获取研究洞见。尽管塑造社会性的多数选择压力均具有显著的时间维度特征，但此类分析通常会对较长时间段内的数据进行聚合。本研究通过将时间信号明确纳入社交互动数据，重新审视了进化关系紧密、社会组织大致相似的细纹斑马（Grevy's zebra）与野驴（wild ass）的社交系统网络动态。通过识别动态社群，此前被掩盖的差异得以显现：细纹斑马的网络模块性（modularity）强于野驴，且野驴的多数社群由独居个体组成；而在细纹斑马中，泌乳雌性相较于非泌乳雌性与雄性，更倾向于更换社群。静态网络分析（static network analysis）未能捕捉到这两种模式；总体而言，引入时间维度能够帮助我们理解与社群规模、存续时长，以及社群形成的频率与同步性相关的差异。动态网络分析能够揭示这些社交差异的功能意义，并展示了动态社群分析可推广应用于其他物种的可行路径。