CNH: Collaborative Research: Explaining Socioecological Resilience Following Collapse: Forest Recovery in Appalachian Ohio

CNH：合作研究：解释崩溃后的社会生态恢复力：俄亥俄州阿巴拉契亚地区的森林恢复

• 批准号: 1010314
• 负责人: Darla Munroe
• 金额: $ 124.66万
• 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: 2010
• 资助国家: 美国
• 起止时间: 2010-09-01 至 2016-02-29
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1010314&HistoricalAwards=false
• 关键词: CNH; Collaborative; Research; Explaining; Socioecological

Much is known about the processes leading to forest loss, that understanding must be complemented with new knowledge regarding the socioecological factors that influence forest recovery and sustainability. This knowledge is critical for predicting where and when second- and third-generation forests might emerge and for understanding the conditions necessary to maintain them. Furthermore, such knowledge is urgently required to inform global climate models, climate change mitigation scenarios, and a suite of other environmental issues. This interdisciplinary research project will focus on the human and ecological linkages that give rise to specific forest forms (including forest extent, species composition, and land-cover patterns) and functions (including benefits of the forest like timber, recreation, privacy, and wildlife habitat). The investigators will examine the extent to which those linkages and the forests emergent from them lead to irreversible changes in socioecological systems. They will focus their attention on Appalachian Ohio, an area whose which forests have returned and where there is sufficient time depth to examine the underlying socioecological processes that give rise to them. A former extractive periphery devastated in the 19th and early 20th centuries, its extensive forests have emerged in surprising ways over the last century. Project goals are (1) to compare forest composition between pre-settlement forests and contemporary forests; (2) to describe the social and ecological form and function of recovered forests; (3) to explain the emergence of the forest over time; and (4) to predict how these socioecological systems will function in the future. The methods to be used include an agent-based model representing land-use decision-making and implementation coupled with the landscape disturbance and succession model, which simulates forest succession and regrowth. To inform the models with empirical data, the researchers will collect field data on forest structure and composition, generate time-series classifications of remotely sensed images, and investigate political, economic, infrastructural, and cultural dynamics using archival and field-derived data.This project will make important contributions to theory and methods for understanding dynamically coupled socioecological systems. The project will advance basic understanding of forest transitions by focusing on forest resilience, thus treating forest recovery as an emergent property of complex socioecological systems. The project will focus attention on differences in ecosystem attributes before and long after massive disturbance, showing that humans and ecosystems have together created unexpected ecologies. The project will demonstrate specific ways that forest form and function respond to local forces and distant shocks that are both social and ecological. Methodologically, the project will advance agent-based modeling science by using innovative metrics to incorporate social inequality and power dynamics and by coupling social simulation with landscape-level forest growth models. As a collaborative project between two Ohio-based universities and the U.S. Forest Service, the project will provide practical information regarding pressing social and environmental issues in Appalachian Ohio, including rural poverty, ecological change, and management of public and private forests. Global applications of this portable framework include enhancing the ways that scholars and policy makers understand, plan for, and help to foster ecological recovery, in particular by drawing attention to the role of social inequalities in shaping socioecological resilience. The findings of this research can also inform climate modeling and mitigation. This project is supported by the NSF Dynamics of Coupled Natural and Human Systems (CNH) Program.

关于导致森林损失的过程知之甚少，必须了解有关影响森林恢复和可持续性的社会生态因素的新知识。 这些知识对于预测第二代和第三代森林可能出现以及了解维护它们所需的条件至关重要。 此外，迫切需要这种知识来告知全球气候模型，气候变化情景以及其他一系列其他环境问题。 该跨学科研究项目将集中于引起特定森林形式（包括森林范围，物种组成和土地覆盖模式）的人类和生态联系和功能（包括森林的益处，例如木材，娱乐，隐私和野生动植物栖息地）。 研究人员将研究这些联系和森林从它们出现的程度导致社会生态系统的不可逆转变化。 他们将把注意力集中在俄亥俄州阿巴拉契亚俄亥俄州，俄亥俄州的森林已经返回，并且有足够的时间深度来检查引起它们的潜在社会生态过程。 一个前挖掘的周围在19世纪和20世纪初摧毁了它，其广阔的森林在上个世纪以令人惊讶的方式出现。 项目目标是（1）比较森林前森林与当代森林之间的森林组成； （2）描述回收森林的社会和生态形式和功能； （3）随着时间的流逝，解释森林的出现； （4）预测这些社会生态系统将来将如何运作。 要使用的方法包括代表土地利用决策和实施的基于代理的模型，再加上景观干扰和继任模型，该模型模拟了森林的继任和再生。 为了通过经验数据告知模型，研究人员将收集有关森林结构和组成的现场数据，生成远程感知图像的时间序列分类，并使用档案和现场衍生的数据研究政治，经济，基础设施和文化动态。该项目将对理论和方法做出重要的贡献，以理解动态耦合的社会素质学系统。 该项目将通过关注森林的韧性来提高人们对森林过渡的基本了解，从而将森林恢复视为复杂的社会生态系统的新兴特性。 在大规模干扰之前和很长时间后，该项目将重点关注生态系统属性的差异，这表明人类和生态系统共同创造了意外的生态。 该项目将展示森林形成和功能的特定方式，以应对社会和生态的遥远冲击。 从方法上讲，该项目将通过使用创新指标来融合社会不平等和权力动态，并通过将社会模拟与景观级别的森林增长模型结合起来，从而推动基于代理的建模科学。 作为两所俄亥俄州大学和美国森林服务局之间的合作项目，该项目将提供有关在俄亥俄州阿巴拉契亚俄亥俄州紧迫的社会和环境问题的实用信息，包括农村贫困，生态变化以及公共和私人森林的管理。 该便携式框架的全球应用包括增强学者和政策制定者理解，计划和帮助促进生态恢复的方式，特别是通过提请人们注意社会不平等在塑造社会生态韧性中的作用。 这项研究的发现还可以为气候建模和缓解措施提供信息。 该项目得到了耦合自然和人类系统（CNH）计划的NSF动力学支持。