Abstract In many group-living animal species, interactions take place in changing social environments, increasing the information processing necessary to optimize social decision-making. Communities with different levels of spatial and temporal cohesion should differ in the predictability of association patterns. While the focus in this context has been on primate species with high fission-fusion dynamics, little is known about the variability of association patterns in species with large groups and high temporal cohesion, where group size and the environment create unstable subgroups. Here, we use sooty mangabeys as a model species to test predictability on two levels: on the subgroup level and on the dyadic level. Our results show that the entirety of group members surrounding an individual is close to random in sooty mangabeys; making it unlikely that individuals can predict the exact composition of bystanders for any interaction. At the same time, we found predictable dyadic associations based on assortative mixing by age, kinship, reproductive state in females, and dominance rank; potentially providing individuals with the ability to partially predict which dyads can be usually found together. These results indicate that animals living in large cohesive groups face different challenges from those with high fission-fusion dynamics, by having to adapt to fast-changing social contexts, while unable to predict who will be close-by in future interactions. At the same time, entropy measures on their own are unable to capture the predictability of association patterns in these groups. Significance statement While the challenges created by high fission-fusion dynamics in animal social systems and their impact on the evolution of cognitive abilities are relatively well understood, many species live in large groups without clear spatio-temporal subgrouping. Nonetheless, they show remarkable abilities in considering their immediate social environment when making social decisions. Measures of entropy of association patterns have recently been proposed to measure social complexity across species. Here, we evaluate suggested entropy measures in sooty mangabeys. The high entropy of their association patterns would indicate that subgroup composition is largely random, not allowing individuals to prepare for future social environments. However, the existence of strong assortativity on the dyadic level indicates that individuals can still partially predict who will be around whom, even if the overall audience composition might be unclear. Entropy alone, therefore, captures social complexity incompletely, especially in species facing fast-changing social environments.

中文翻译：

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乌黑芒果关联模式的可预测性和可变性

摘要在许多群居动物物种中，相互作用发生在不断变化的社会环境中，增加了优化社会决策所需的信息处理。具有不同空间和时间凝聚力的社区在关联模式的可预测性上应该不同。虽然在这方面的重点是具有高裂变融合动力学的灵长类物种，但对于具有大群体和高时间凝聚力的物种的关联模式的可变性知之甚少，其中群体规模和环境会产生不稳定的亚群。在这里，我们使用乌黑曼加比作为模型物种，在两个层面上测试可预测性：亚群层面和二元层面。我们的结果表明，围绕一个人的整个群体成员在乌黑的曼加贝中几乎是随机的；使得个人不太可能预测任何互动的旁观者的确切组成。同时，我们发现了基于年龄、亲属关系、女性生殖状态和优势等级的分类混合的可预测的二元关联；潜在地为个人提供部分预测通常可以一起找到哪些二元组的能力。这些结果表明，生活在大型凝聚力群体中的动物面临着与具有高裂变融合动力学的动物不同的挑战，因为它们必须适应快速变化的社会环境，同时无法预测未来互动中谁会在附近。同时，熵测量本身无法捕捉这些组中关联模式的可预测性。意义陈述虽然动物社会系统中的高裂变融合动力学所带来的挑战及其对认知能力进化的影响相对较好，但许多物种生活在没有明确时空亚群的大群体中。尽管如此，他们在做出社会决定时表现出非凡的能力，可以考虑他们所处的直接社会环境。最近提出了关联模式熵的度量来衡量跨物种的社会复杂性。在这里，我们评估了煤烟芒果中建议的熵测量。他们的关联模式的高熵表明子组的组成在很大程度上是随机的，不允许个人为未来的社会环境做准备。然而，在二元层面上存在的强分类表明个人仍然可以部分预测谁会在谁身边，即使整体受众构成可能不清楚。因此，仅熵就不能完全捕捉社会复杂性，尤其是在面临快速变化的社会环境的物种中。