Network Structure and Conflict Management in a Cooperatively Breeding Fish

人类和非人类动物生活在相互作用和关系的“社会网络”中。例如，一只动物可能经常与它的同伴互动，而另一只动物很少这样做。一种动物可能对另一种动物具有攻击性，但与其他动物和平相处。研究人员将调查这些社会网络在冲突后如何改变，以使冲突在未来变得更糟，或防止冲突再次出现。研究人员假设，我们在自然界观察到的社会网络将倾向于以限制进一步冲突的方式变化，原因有两个。首先，我们可能没有太多机会观察不这样做的网络，否则冲突会变得如此严重，以至于整个群体很快就会分裂。其次，个体动物可能会从避免冲突中受益，因此它们会以减少进一步冲突风险的方式调整自己的行为。为了验证这一假设，研究人员建议利用动态网络模型结合自然界的实验，探索新水螅鱼（Neolamprologus pulcher）的社会网络在冲突后是如何变化的。如果社交网络以限制冲突的方式改变，这将有助于理解具有竞争利益的个人群体如何能够团结在一起，并防止日常争吵升级。该项目涉及在赞比亚开展国际合作，并对学生进行数学和生物学研究方面的培训。该项目由综合有机体系统学部动物行为项目、数学科学部数学生物学项目、生物、数学和物理科学交叉项目BIOMAPS项目以及国际科学与工程办公室共同资助。研究人员建议在实验室和实地干扰N - pulcher群体，例如通过移除个体来为其他人创造改变社会地位的机会。他们将在扰动前后对所有小组成员进行密集的行为观察。他们提出使用随机行动者导向模型（SAOM）来分析社会网络的变化。随机因子导向模型是一种个体行为与网络结构共同演化的时间有序动态网络模型。他们建议使用数学和计算模型来测试从SAOM分析中发现的连接模式是否与低冲突水平和高群体稳定性有关。最后，研究人员建议测量压力激素（皮质醇）水平和繁殖成功率，以调查个体是否从能够更好地限制冲突的群体中受益。这项研究的结果将为以下方面提供见解：个体的社会环境是如何通过相互作用的影响而产生的，社会环境对扰动的恢复能力，以及新兴的社会环境如何反馈给个体的表现和繁殖成功。

Human and non-human animals live in "social networks" of interactions and relationships. For example, one animal might interact frequently with its groupmates, while another rarely does so. An animal might be aggressive to another, but interact peacefully with yet others. The researchers will investigate how these social networks change after conflict to either make conflict worse in the future, or to prevent conflict from re-emerging. The researchers hypothesize that social networks that we observe in nature will tend to change in ways that limit further conflict for two reasons. First, we might not have much of a chance to observe networks that do not do so, as otherwise conflict becomes so severe that the whole group quickly breaks apart. Second, individual animals may benefit from avoiding conflict, and so they modify their behavior in ways that reduce the risks of further conflict. To test this hypothesis, the researchers propose to explore how social networks change after conflict in the cichlid fish Neolamprologus pulcher, which live in stable, long-lasting groups using dynamical network models coupled with experiments in nature. If social networks change in ways that limit conflict, this would help understand how groups of individuals with competing interests are able to stay together and prevent day-to-day quarrels from escalating. This project involves international collaboration in Zambia and training of students in both mathematical and biological research. This project is co-funded by the Animal Behavior program in the Division of Integrative Organismal Systems, the Mathematical Biology program in the Division of Mathematical Sciences, the BIOMAPS program for proposals at the interface of Biology, Math and the Physical Sciences, and from the Office of International Science and Engineering. The researchers propose to perturb groups of N pulcher in the laboratory and the field, for example by removing individuals to create opportunities for others to change social status. They will conduct intensive behavioral observations on all group members before and after perturbation. They propose to use stochastic actor oriented models (SAOM) to analyze social network change. Stochastic actor-oriented models are a type of time ordered dynamic network model in which individual behaviors and network structures coevolve. They propose to use mathematical and computational models to test whether the patterns of connections found from the SAOM analyses are associated with low levels of conflict and high group stability. Finally, the researchers propose to measure stress hormone (cortisol) levels and reproductive success to investigate whether individuals benefit from being in groups that are better able to limit conflict. Findings from this research will provide insights into how individuals' social environment emerges through the effects of their interactions with one another, the resilience of the social environment to perturbation, and how the emergent social environment feeds back on individual performance and reproductive success.