CNH: Social-Ecological Complexity and Adaptation in Marine Systems

CNH：海洋系统的社会生态复杂性和适应

• 批准号: 1211972
• 负责人: Simon Levin
• 金额: $ 149.89万
• 依托单位: Princeton University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2012
• 资助国家: 美国
• 起止时间: 2012-09-01 至 2018-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1211972&HistoricalAwards=false
• 关键词: CNH; Social; Ecological; Complexity; Adaptation

The oceans are one of the most dynamic environments on Earth, presenting a profound challenge for understanding the complex social, ecological, and physical interactions that occur within them. Fishers are naturally tuned to this complexity and meet their individual goals by targeting their efforts towards particular locations and types of catch, and by adapting their social interactions. Yet we - the scientific community - lack an understanding of how social behavior and ecological dynamics are coupled. Further, these feedbacks are largely ignored in present management approaches. We aim to fill this knowledge gap by answering three questions: (1) How does fisher social behavior (defined as the level to which individual fishers share information) change in response to ecological, technological and management factors? (2) What effect does social behavior have on fisher spatial dynamics and on the social structure of the fishing community? (3) How can management strategies be designed to account for the social behavior of fishers? To answer these questions we will conduct a comprehensive research project involving data gathering and analysis, theoretical modeling and the development of novel mathematical theory. This project will have direct implications for agencies responsible for managing marine resources along the west coast of the U.S. and in Hawaii, some of whom are collaborating in the research. By increasing our understanding of how humans using a natural resource interact with one another and how this in turn affects that resource, the results of this study will be relevant to fields as diverse as finance and global food security. We propose to gather data on the spatial and behavioral dynamics of fishers along the U.S. west coast, in Brazil and in Fiji - three marine systems with contrasting social, ecological, technological and management characteristics. U.S. data will come from collaborations with the NOAA National Marine Fisheries Service, and data from Brazil and Fiji will be obtained using economic field experiments. All three data sources will be used to develop agent-based models that simulate both the dynamics of fish and individual fishers. We will distinguish ourselves from traditional modeling approaches by adopting a Complex Adaptive Systems (CAS) perspective. With a CAS perspective the spatial dynamics of fish and fishers and the social structure of fisher communities, along with other macroscopic properties, emerge from processes operating at low levels of organization, namely the actions of individual fishers and their target species. Our agent-based models will have the CAS perspective at heart, with fisher agents able to adapt and learn different behavioral strategies (e.g. sharing or not sharing information). A combination of variation, fitness and reproduction will create a selective mechanism whereby fisher behaviors converge to evolutionary stable types. We will complement our agent-based modeling with evolutionary game theory and investigate why certain social behaviors are evolutionarily stable in some marine systems and not in others. Last, we will use mechanism design theory to develop management strategies that account for changes in fisher social behavior.

海洋是地球上最具动态的环境之一，对理解其中发生的复杂社会，生态和身体互动构成了深远的挑战。渔民自然会调整这种复杂性，并通过针对特定位置和捕获类型的努力以及调整社交互动来实现他们的个人目标。然而，我们 - 科学界 - 缺乏对社会行为和生态动态如何耦合的理解。此外，在当前的管理方法中，这些反馈在很大程度上被忽略了。我们的目标是通过回答三个问题来填补这一知识差距：（1）渔民社会行为（定义为各个渔民共享信息的水平）如何响应生态，技术和管理因素？ （2）社会行为对渔民的空间动态和捕鱼社区的社会结构有何影响？ （3）如何设计管理策略来说明渔民的社会行为？为了回答这些问题，我们将进行一个全面的研究项目，涉及数据收集和分析，理论建模以及新型数学理论的发展。该项目将对负责管理美国西海岸和夏威夷海洋资源的机构有直接影响，其中一些人正在研究中合作。通过我们对人类使用自然资源之间如何相互作用以及这又如何影响资源的理解，这项研究的结果将与像财务和全球粮食安全一样多样化有关。我们建议收集有关美国西海岸，巴西和斐济的渔民的空间和行为动态的数据 - 三个具有对比的社会，生态，技术和管理特征的海洋系统。美国的数据将来自与NOAA国家海洋渔业服务的合作，巴西和斐济的数据将使用经济实验实验获得。所有三个数据源将用于开发基于代理的模型，以模拟鱼类和单个渔民的动态。我们将通过采用复杂的自适应系统（CAS）观点来区分传统的建模方法。从CAS的角度来看，鱼类和渔民的空间动态以及渔民社区的社会结构以及其他宏观特性，来自组织低水平的过程，即单个渔民及其目标物种的作用。我们的基于代理的模型将核心CAS视角，Fisher Adents能够适应和学习不同的行为策略（例如共享或不共享信息）。变异，健身和繁殖的结合将创建一种选择性机制，从而使Fisher行为融合到进化稳定类型。我们将通过进化游戏理论对基于代理的建模进行补充，并研究为什么某些社会行为在某些海洋系统中而不是在其他海洋系统中稳定。最后，我们将使用机制设计理论来制定造成Fisher社会行为变化的管理策略。