Network Structure and Conflict Management in a Cooperatively Breeding Fish

合作养殖鱼类的网络结构和冲突管理

• 批准号: 1557836
• 负责人: Ian Hamilton
• 金额: $ 32.5万
• 依托单位: Ohio State University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-07-01 至 2022-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1557836&HistoricalAwards=false
• 关键词: Network; Structure; Conflict; Management; Cooperatively

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.

人类和非人类动物生活在相互作用和关系的“社交网络”中。 例如，一种动物可能会频繁地与其同伴互动，而另一种动物则很少这样做。 一种动物可能会对另一种动物产生攻击性，但与其他动物却能和平相处。 研究人员将调查这些社交网络在冲突后如何变化，从而使未来的冲突变得更糟，或者防止冲突再次出现。研究人员假设，我们在自然界中观察到的社交网络将倾向于以限制进一步冲突的方式发生变化，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，否则冲突会变得非常严重，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，因此它们会改变自己的行为，以减少进一步冲突的风险。 为了检验这一假设，研究人员建议利用动态网络模型与自然界的实验相结合，探索丽鱼科鱼 Neolamprologus pulcher 的社交网络在冲突后如何变化，这种鱼生活在稳定、持久的群体中。 如果社交网络以限制冲突的方式发生变化，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。该项目涉及赞比亚的国际合作以及数学和生物研究方面的学生培训。该项目由综合有机系统部门的动物行为项目、数学科学部门的数学生物学项目、生物学、数学和物理科学接口的 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现场扰乱 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。 他们将对扰动前后的所有小组成员进行密集的行为观察。他们建议使用随机参与者导向模型（SAOM）来分析社交网络变化。面向随机参与者的模型是一种时间有序的动态网络模型，其中个体行为和网络结构共同演化。他们建议使用数学和计算模型来测试 SAOM 分析中发现的连接模式是否与低水平的冲突和高群体稳定性相关。 最后，研究人员建议测量压力激素（皮质醇）水平和生殖成功率，以调查个体是否受益于能够更好地限制冲突的群体。 这项研究的结果将深入了解个体的社会环境如何通过彼此互动的影响而出现、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个人表现和繁殖成功。