CNH-L: People, Place, and Payments in Complex Human-Environment Systems

该项目旨在促进对生态系统服务付费(PES)的影响的理解，PES是一种全球保护方法，旨在激励基本自然资源的使用者保护相关生态系统。近几十年来，这些计划越来越受欢迎，越来越多的并行PES计划已被部署，以缓解环境退化和过度开采自然资源。PES计划通常在相同的时间和空间背景下同时实施，但人们对这种相互作用的并行PES计划的机制、途径以及社会和生态影响知之甚少。这个项目将分析并行的PES程序如何以及为什么相互作用，以及如何与相应的自然和人类耦合系统相互作用。这项工作将导致对潜在的、往往是意想不到的社会和生态后果的洞察。PES计划可能会影响人口迁移、教育投资以及当地家庭与自然环境之间的其他联系。此外，该项目将解决这些项目可能如何影响土地覆盖、土地利用和物种栖息地随时间的变化。该项目将通过解决发展保护政策与政府在PES项目中所作投资的有效性之间的相互作用，使社会受益。该项目的更广泛影响还延伸到指导K-12学生及其教师的创新教育和外联活动、创造性地使用新的地理空间技术以有效监测和保护濒危野生动物、开发以学生为中心的教学方法以增进对复杂的人类-环境系统的了解，以及开发相关的电影和一个基于网络的PES中心来传播项目成果。这项研究将培养研究生，并促进美中科学家之间的合作。该项目旨在了解并行的PES计划之间的相互关系，揭示这些计划在空间和时间上的相关社会和生态后果。PIS将回答以下问题：(1)并行的PES计划通过什么途径或中介变量，通过它们对人类和自然子系统的影响相互影响？(2)并行的PES计划在哪里以及在多大程度上导致环境的净变化？(3)考虑到这些干预措施，人-环境系统在空间和时间上是如何以及将如何演变的？在人类子系统内，个人投资指数将侧重于移徙和就业机会的影响，以及参与家庭的教育投资。在自然子系统内，PIS将使用无人驾驶飞行器、相机陷阱和卫星成像技术记录和监测土地覆盖和土地利用的变化，以及主要哺乳动物物种的占有率和栖息地。最后，绩效指标将开发一个复杂的系统框架，其中包括地理空间和统计分析、结构方程建模以及开发一个基于主体的模型(ABM)，该模型综合了各种空间和时间尺度上的定性和定量数据和模型。虽然这个项目将使用中国梵净山国家级自然保护区的数据，但这项研究将为理解许多自然和人类耦合系统中并行的生态系统计划背后的机制提供见解和方法，显著提高生态系统的科学、技术和实践有效性。该奖项反映了国家自然科学基金的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

This project seeks to advance the understanding of the impact of payments for ecosystem services (PES), a global conservation approach that incentivizes users of essential natural resources to protect the related ecosystems. In recent decades these programs have increased in popularity, and an increasing number of parallel PES programs have been deployed to mitigate environmental degradation and the over-exploitation of natural resources. PES programs are often implemented concurrently in the same temporal and spatial context, yet little understanding exists about the mechanisms, pathways, and social and ecological effects of such interacting, parallel PES programs. This project will analyze how and why parallel PES programs interact with one another and with the corresponding coupled natural and human system. This work will result in generating insight into the potential, often unintended, social and ecological consequences. PES programs may impact human migration, investment in education, and other connections between local households and the physical environment. Furthermore, the project will address how such programs may affect changes in land cover, land use, and species habitat over time. This project will benefit society by addressing the interactions between development conservation policy and the effectiveness of governmental investments made in PES programs. This project's broader impacts also extend to innovative education and outreach activities that mentor K-12 students and their teachers, creative use of new geospatial technologies for effective monitoring and conservation of endangered wildlife, development of a student-centered pedagogy that enhances understanding of complex human-environment systems, and the development of a related movie and a web-based PES Center to disseminate project results. The research will train graduate students and foster collaboration between U.S. and Chinese scientists. The project aims to understand the reciprocal relationship between parallel PES programs, shedding light on the related social and ecological consequences of these programs over space and time. The PIs will answer the following questions: (1) Through what pathways or mediating variables do parallel PES programs, through their influence on human and natural subsystems, affect one another? (2) Where and to what extent do parallel PES programs lead to net changes in the environment? (3) How has and will the human-environment system evolve over space and time given these interventions? Within the human subsystem the PIs will focus on the impacts of migration and job opportunities, as well as the educational investments of participating households. Within the natural subsystem, the PIs will document and monitor land cover and land use change, as well as occupancy and habitat of major mammal species using an unmanned aerial vehicle, camera trapping, and satellite imaging techniques. Lastly, the PIs will develop a complex systems framework, which consists of geospatial and statistical analyses, structural equation modeling, and development of an agent-based model (ABM) integrating qualitative and quantitative data and models across various spatial and temporal scales. Although this project will use data from Fanjingshan National Nature Reserve (FNNR) in China, the research will provide insights and approaches for understanding the mechanisms behind parallel PES programs in many coupled natural and human systems, substantially improving the science, technology, and practical effectiveness of PES.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.