SEES Fellows: Understanding the Dynamics of Resilience in a Social-Ecological System

该项目由美国国家科学基金会（NSF）科学、工程和教育可持续发展研究员（SEES研究员）计划支持，其目标是帮助实现必要的发现，为导致环境、能源和社会可持续发展的行动提供信息，同时创造必要的劳动力来应对这些挑战。可持续发展科学是一个新兴的领域，它解决了在不损害环境的情况下满足人类需求的挑战，同时又不牺牲后代满足其需求的能力。一支强大的科学队伍需要在跨学科研究和思维方面受过教育和训练的个人，特别是在可持续性科学领域。在see奖学金的支持下，该项目将使有前途的早期职业研究人员能够在与可持续发展相关的独立研究生涯中建立自己的职业生涯。该奖学金项目研究社会和生态弹性的动态。长期以来，科学家们一直试图了解人类社会和生态系统如何应对自然灾害、经济崩溃、战争和物理气候变化等破坏性事件并从中恢复过来。随着自然科学和社会科学研究的进步，人类社会和生态系统之间有着千丝万缕的联系。同样地，我们对在面对破坏时影响系统弹性的过程和组件的检查必须结合来自系统的社会和生态组件的数据。尽管人们认识到恢复力的复杂性，但很难找到足够全面的多年数据，其中包括不可预测的破坏性事件。津巴布韦Mazvihwa在提供此类数据方面处于独特的位置，因为一个正在进行的社区研究项目自1980年以来一直在收集有关该地区社会和生态条件的数据。在过去的30年里，Mazvihwa也经历了几次重大的系统冲击（包括公共卫生、土地获取和经济繁荣/萧条方面的巨大变化），使其成为研究弹性的理想场所。了解耦合社会和生态系统的弹性动态可以帮助社区、城市和国家更好地规划和管理意想不到的破坏性变化，从而最大限度地减少负面影响。这项研究利用了Mazvihwa非常强大的数据集，该数据集跟踪了过去30年来该地区的人口统计、健康、营养、农业实践、降雨、土地使用选择、林地动态、家庭资产和土地使用权。定量数据使用多种技术建模（包括状态和转换模型、分层统计状态空间模型和使用元胞自动机的基于代理的模型），以便对弹性测量进行稳健的探索。研究人员还将收集定性数据，捕捉社区成员在变化和恢复力方面的经验，并试验各种技术，将这些定性数据纳入定量模型。定性和定量数据的结合提供了对该系统中起作用的弹性动态的更丰富的理解。最后，研究人员将利用这些模型开发一个决策支持工具，帮助社区成员了解土地使用选择之间的权衡，并更好地规划他们的未来。

The project is supported under the NSF Science, Engineering and Education for Sustainability Fellows (SEES Fellows) program, with the goal of helping to enable discoveries needed to inform actions that lead to environmental, energy and societal sustainability while creating the necessary workforce to address these challenges. Sustainability science is an emerging field that addresses the challenges of meeting human needs without harm to the environment, and without sacrificing the ability of future generations to meet their needs. A strong scientific workforce requires individuals educated and trained in interdisciplinary research and thinking, especially in the area of sustainability science. With SEES Fellowship support, this project will enable a promising early career researcher to establish herself in an independent research career related to sustainability. This fellowship project examines the dynamics of social and ecological resilience. Scientists have long sought to understand how human societies and ecosystems respond to and recover from disruptive events such as natural disasters, economic collapse, war, and changing physical climate. As research in both the natural and social sciences has progressed, it has become clear that human societies and ecosystems are inextricably connected or coupled. Likewise, our examination of the processes and components that influence a system's resilience in the face of disruption must incorporate data from both the social and ecological components of the system. Despite this recognition of the complexity of resilience, it is difficult to find sufficiently comprehensive, multi-year data that includes unpredictable disruptive events. Mazvihwa, Zimbabwe is uniquely situated to provide such data because an ongoing community-based research project has been collecting data about the area's social and ecological conditions since 1980. Mazvihwa has also undergone several significant system shocks (including dramatic change in public health and land access and economic booms/busts) in the last 30 years making it an ideal site for studying resilience. Understanding the dynamics of resilience in coupled social and ecological systems may help communities, cities, and nations better plan for and manage unexpected and disruptive change so that negative consequences are minimized.This research draws on Mazvihwa's remarkably robust dataset that tracks human demography, health, nutrition, agricultural practices, rainfall, land use choices, woodland dynamics, household assets, and land tenure in the area over the last 30 years. The quantitative data are modelled using multiple techniques (including a state-and-transition model, a hierarchical statistical state-space model, and an agent-based model using cellular automata) to allow for a robust exploration of resilience measurements. The researcher will also gather qualitative data that captures community members' experiences with change and resilience and experiment with a variety of techniques for incorporating these qualitative data into the quantitative models. The combination of qualitative and quantitative data provide an even richer understanding of the resilience dynamics at work in this system. Finally, the researcher will use the models to develop a decision support tool that will help community members understand tradeoffs between land use choices and better plan for their future.