Structure and Functionality in Coupled Socio-Ecological Dynamics

无论是现代生态灾难，还是生态系统恢复和复原的例子，都清楚地表明，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)，在这个系统中，人类不仅造成生态系统影响，而且还对其作出反应。这种相互关联的性质揭示了人类的无数行动，这些行动既有积极的影响，也有消极的影响，例如，生态恢复努力与引入入侵物种或资源过度开发相比。这类例子常常说明不同机制之间协同作用的积极反馈所导致的制度转变。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，这可能是将理论发现应用于现实世界系统的关键。长期目标是为研究SESS开发中等复杂性的新理论框架，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SESS将改进政策制定者在面对全球变化时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增加现有的SES模型的现实性，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交互来进行更高级别的重新布线。这些新的方法与集合种群框架的应用将提高我们对SES反馈的理解，特别是在面对人类和环境干扰的情况下。这些模型将预测哪些行为/生态参数/反馈对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：什么是增加社会学习和行为激励的最佳组合，才能在特征结构的生态系统和人类群体中实现可持续结果？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、产出和社会动力的复杂耦合如何影响我们对生态系统管理的理解？具体的干扰相互作用如何导致灾难性的制度转变？人类和生态集合种群之间的哪种类型的同步性对生态系统的弹性有利/有害，以及如何管理它们？这些模型将跨越一系列类型，包括从基本原理得出的简单机械模型，到包含系统复杂性大块的现象学模型，再到更大和更详细的系统动力学模型--所有这些都可能与提供政策信息和获得科学洞察力有关。

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.