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

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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.