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.

人们对导致森林丧失的过程了解很多，但这种了解必须辅之以关于影响森林恢复和可持续性的社会生态因素的新知识。 这一知识对于预测第二代和第三代森林何时何地出现以及了解维持这些森林的必要条件至关重要。 此外，迫切需要这些知识来为全球气候模型、气候变化减缓情景和一系列其他环境问题提供信息。 这一跨学科研究项目将侧重于人类和生态的联系，这些联系产生了特定的森林形式（包括森林范围，物种组成和土地覆盖模式）和功能（包括森林的好处，如木材，娱乐，隐私和野生动物栖息地）。 调查人员将审查这些联系和由此产生的森林在何种程度上导致社会生态系统发生不可逆转的变化。 他们将把注意力集中在阿巴拉契亚俄亥俄州，该地区的森林已经恢复，并有足够的时间深度来研究潜在的社会生态过程，引起他们。 在19世纪和20世纪初，这里曾经是一个被掠夺的边缘地带，但在上个世纪，它广阔的森林以令人惊讶的方式出现了。 项目的目标是：（1）比较定居前森林和现代森林的森林组成;（2）描述恢复森林的社会和生态形式和功能;（3）解释森林随时间的推移而出现的原因;（4）预测这些社会生态系统今后将如何发挥作用。 将使用的方法包括代表土地使用决策和执行的基于代理人的模型，以及模拟森林演替和再生的景观干扰和演替模型。 为了使模型具有经验数据，研究人员将收集有关森林结构和组成的实地数据，生成遥感图像的时间序列分类，并利用档案和实地数据调查政治，经济，基础设施和文化动态。该项目将为理解动态耦合的社会生态系统的理论和方法做出重要贡献。 该项目将通过侧重于森林复原力，从而将森林恢复视为复杂社会生态系统的一种紧急性质，来促进对森林过渡的基本了解。 该项目将重点关注大规模干扰之前和之后很长一段时间内生态系统属性的差异，表明人类和生态系统共同创造了意想不到的生态。 该项目将展示森林的形态和功能如何对当地力量和社会及生态方面的遥远冲击作出反应。 在方法上，该项目将通过使用创新的指标来纳入社会不平等和权力动态，并通过将社会模拟与森林生长模型相结合，推进基于代理的建模科学。 作为俄亥俄州两所大学和美国林务局之间的合作项目，该项目将提供有关俄亥俄州阿巴拉契亚地区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。 这一便携式框架的全球应用包括加强学者和政策制定者理解、规划和帮助促进生态恢复的方式，特别是通过提请注意社会不平等在塑造社会生态复原力方面的作用。 这项研究的结果也可以为气候建模和缓解提供信息。 该项目由NSF耦合自然和人类系统动力学（CNH）计划支持。