Project Details
Description
0729709
Axinn
PIRE: Collaborative Research and Training in Social Context, Population Processes, and Environmental Change
This Partnership for International Research and Education (PIRE) brings together researchers and students from five U.S. universities and four institutions in Nepal and China to undertake comparative studies on the dynamics of population-environment interaction. Principal investigator, William Axinn of the University of Michigan and colleagues from Michigan State University, San Diego State University, the University of North Carolina, and Arizona State University will collaborate with Nepalese partners from the Institute for Social and Environmental Research and Tribhuvan University's Institute of Agriculture and Animal Science, and with Chinese partners from the Research Center for Eco-Environmental Sciences of the Chinese Academy of Sciences and the Wolong Nature Reserve. The field sites in Nepal and China are two high-profile settings where large, growing populations, rapidly changing economies, unique biodiversity and complex institutional structures offer exceptional research and educational opportunities.
The five-year plan for collaborative research focuses on improving understanding of patterns and processes affecting vegetation and habitats for three endangered species (pandas, rhinos, and tigers), achieving cross-case comparisons of the dynamics of population-environment interaction for Nepal and China, and establishing widely applicable tools needed to make such comparisons in other settings. The educational objectives of the collaboration will draw upon the intellectual and physical resources of each partner to train a next generation of scientists skilled in conducting new international cross-case comparisons of the human-environment interaction. This activity provides a unique international research and training experience for forty U.S. graduate and twelve undergraduate students. Educational and research activities are closely integrated with a focus on learning and practicing concepts and methods needed to bridge key disciplines and foster international collaboration while pursuing compelling research questions.
Detailed single site case studies that provide micro-level information have greatly advanced knowledge about human-environment interaction, but cross-case comparison is now an extremely high scientific priority needed for better understanding of global environmental change. Specifically, the strengths of the Nepal case study, which is rich in dynamic measurements of community context will be applied to the China case study. Likewise, the strengths of the China case study, the analysis of remote sensing data and agent-based modeling to study forest and habitat dynamics, will be applied to the Nepal study. The integration will increase the comparability of data and methods across these important cases and the cross-fertilization of research among different case studies will enhance the value of each case study. More importantly, this work will enable the comparative study of human-environment interaction that pushes the field past singular case studies toward multilateral research designs.
This project has the potential to greatly accelerate investigation of important environmental change, ultimately helping to identify effective strategies for preserving biodiversity. The findings are expected to identify models of studying human-environment interaction that transfer across settings to link various studies in a mutually beneficial manner. Further, this project will train a new generation of scientists pioneering international cross-case comparisons of population-environment interactions. Such scientists will be crucial in the coming decades to integrate detailed understandings of specific cases into studies of global environmental change.
This award is funded by the Office of International Science and Engineering together with the
Directorate for Social, Behavioral & Economic Sciences.
Status | Finished |
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Effective start/end date | 10/1/07 → 3/31/14 |
Funding
- National Science Foundation: $2,496,196.00