DISES: Modeling interactions between community forest dynamics and local livelihoods amidst institutional changes

监管机构和森林管理者长期以来一直在寻找关于哪种制度安排最能平衡农村发展和森林保护的答案。一种这样的安排是社区林业，其中一些森林的护理和管理由当地社区处理。虽然社区林业可以有效地保护森林和改善农村生计，但研究也表明，这种成功是不稳定的。该项目将确定导致积极的社区林业成果的条件。它将在越南、老挝和柬埔寨确定导致成功的情况和安排。这项工作对美国的政府和非政府组织很重要，因为美国社区管理的森林数量正在增加。在社区管理着25%以上森林的低收入和中等收入国家，这也将是有益的。本项目将培养数据科学与建模方向的2名博士生和1名博士后。课程材料将把建模练习带入课堂。该项目将调查社区林业安排对森林条件和生计的影响。它将审查体制变异性以及社会和生态条件在缓和社区林业影响方面的作用。它还将研究推动社会和生态结果变化的反馈。研究人员将建立关于湄公河地区三个国家的森林状况和变化、生计和制度的空间数据集。这将使用卫星图像、公民科学数据集、纵向国家社会经济数据集和社区森林空间数据来完成。他们将采用统计匹配研究设计来检验社区林业更有可能维持和恢复森林覆盖和生物多样性的假设。该方法还将探讨社区林业改善生计的能力，以及这些影响将如何被制度变异性和基线社会和生态条件所缓和。他们将收集现场层面的生态和社会数据，以验证和完善所得模型，并检查导致不同结果的因果机制。最后，他们将建立模型，识别社区林业下森林条件和生计之间的反馈。这些模型将能够在不断变化的制度驱动因素和生态条件下预测不同时空尺度上的景观和生计变化。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

Regulators and forest managers have long looked for answers regarding which institutional arrangements best serve to balance rural development and forest conservation. One such arrangement, is community forestry where some forest care and management is handled by local communities. While community forestry can be an effective in conserving forests and enhancing rural livelihoods, studies also show that that success is variable. This project will identify the conditions that lead to positive community forestry outcomes. It will identify the situations and arrangements that lead to success, across Vietnam, Laos, and Cambodia. This work important for governments and non-governmental organizations in the U.S., where forests under community management are increasing in number. It will also be beneficial in low- and middle-income countries where communities manage over 25% of forests. This project will train two PhD students and a postdoctoral fellow in data science and modeling. Course materials will bring modeling exercises into the classroom. This project will investigate changes in community forestry arrangements on forest condition and livelihoods. It will examine the role of institutional variability and social and ecological conditions in moderating community forestry impacts. It will also study feedback that drive changes in social and ecological outcomes. The researchers will build spatial datasets on forest condition and change, livelihoods, and institutions across three countries in the Mekong Region. This will be done using satellite imagery, citizen science datasets, longitudinal national socio-economic datasets, and community forest spatial data. They will apply a statistical matching research design to test the hypotheses that community forestry is more likely to maintain and restore forest cover and biodiversity. The method will also explore the ability of Community forestry to enhance livelihoods, and how these impacts will be moderated by institutional variability and baseline social and ecological conditions. They will collect site-level ecological and social data to validate and refine the resulting models and examine causal mechanisms leading to varied outcomes. Finally, they will build models that recognize feedbacks between forest condition and livelihoods under community forestry. Those models will be capable of predicting landscape and livelihood changes at various spatial and temporal scales under changing institutional drivers and ecological conditions.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.