Structure and Functionality in Coupled Socio-Ecological Dynamics

现代生态灾难以及生态系统恢复和复原力的例子都表明，人类和生态系统形成了一个单一的、耦合的社会生态系统（SES），人类不仅造成生态系统的影响，而且还对它们做出反应。这种相互关联的性质揭示了无数可能产生积极或消极影响的人类行动，例如生态恢复努力与引入入侵物种或资源过度开发。这类例子往往说明了不同机制之间协同作用的积极反馈所产生的制度转变。大多数现有的SES模型在人类和生态方面都缺乏重要的结构，这对于将理论研究成果应用于现实世界的系统至关重要。长期的目标是开发新的理论框架的中间复杂性研究SES，平衡现实主义和简单性，以确定重要的反馈和协同作用。此外，研究各种各样的可持续环境系统将改善政策制定者在全球变化面前管理生态系统的工具。短期目标包括通过新的功能形式和基于特征的结构为现有的SES模型增加现实性，纳入新的数据类型和来源，以及通过引入新的状态变量和人与环境的相互作用进行更高层次的重新布线。这些新的方法与应用程序的集合种群框架将提高我们的理解SES反馈，特别是在面对人类和环境干扰。这些模型将预测哪些行为/生态参数/反馈对其各自系统的可持续性最重要，跨系统比较将揭示更高层次的见解。问题包括：增加社会学习和行为激励的哪种组合是在特质结构的生态系统和人类群体中实现可持续成果的最佳选择？耦合的SES模型揭示了什么，我们不能知道孤立地研究这些系统？人类投入、产出和社会动态的复杂耦合如何影响我们对生态系统管理的理解？特定的扰动相互作用如何导致灾难性的状态转变？人类和生态集合种群之间的哪些类型的同步对生态系统的恢复力有利/有害，以及如何管理它们？模型将跨越一系列类型，包括从第一原理导出的简单机械模型，到体现系统复杂性的现象学模型，再到更大更详细的系统动态模型-所有这些都与制定政策和获得科学见解有关。

Both modern ecological catastrophes as well as examples of ecosystem recovery and resilience in response to our impacts on the natural world make it clear that humans and ecosystems form a single, coupled social-ecological system (SES) where humans not only cause ecosystem impacts, but also react to them. This interrelated nature reveals the myriad of human actions that can have both positive or negative impacts, for instance ecological restoration efforts versus introducing invasive species or resource over-exploitation. Such examples often illustrate regime shifts resulting from positive feedbacks that represent synergies between disparate mechanisms. Most existing SES models lack important structure on both the human and ecological sides which can be crucial for applying theoretical findings to real-world systems. The long-term objective is to develop novel theoretical frameworks of intermediate complexity for studying SESs, balancing both realism and simplicity to identify important feedbacks and synergies. Additionally, studying a diverse array of SESs will improve on tools for policy makers managing ecosystems in the face of global changes. Short-term objectives include adding realism to existing SES models through new functional forms and trait-based structure, incorporating new data types and sources, as well as higher-level rewiring through the introduction of new state variables and human-environment interactions. These novel approaches with applications to metapopulation frameworks will improve our understanding of SES feedbacks, specifically in the face of human and environmental disturbances. The models will predict what behavioural/ecological parameters/feedbacks are most important to the sustainability of their respective systems and cross-system comparisons will reveal higher-level insights. Questions include: What combination of increased social learning and behavioural incentive is optimal to achieve sustainable outcomes in trait-structured ecosystems and human populations? What do coupled SES models reveal that we cannot know studying these systems in isolation? How does the complex coupling of human inputs, outputs and social dynamics affect our understanding of ecosystem management? How do specific disturbance interactions lead to catastrophic regime shifts? What types of synchrony between human and ecological metapopulations are beneficial/detrimental to ecosystem resilience and how can they be managed? Models will span a range of types including simple mechanistic models derived from first principles, to phenomenological models that subsume chunks of system complexity, to larger and more detailed system dynamic models--all of which can be relevant to both informing policy and gaining scientific insight.