This National Science Foundation Research Traineeship (NRT) award to the University of Connecticut and the Cary Institute of Ecosystem Studies will support the development of a new model of graduate education focused on predicting, managing, and communicating risks to food, energy, water, and ecosystems in urbanizing regions. Accelerating threats from climate change, habitat degradation, overexploitation, and species invasions threaten access to food, energy, water, and other ecosystem services, especially in cities. Preventing loss of critical services requires accurate predictions of these risks. To meet this demand, we will train graduate student teams to predict alternative futures and design resilient landscapes. This traineeship program will create a novel, cross-cutting framework for STEM education that combines collaborative and problem-based learning and research, while applying techniques aimed at recruiting and retaining a more diverse student cohort. Trainees will develop new skills in an immersive, team-based education model that also applies new knowledge to real-life applications. Enhanced skills in risk assessment, management, and communication will create a highly skilled workforce that can address the 21st Century threats to human life support systems. The traineeship anticipates preparing 64 Ph.D. students, including 32 funded trainees from the life sciences, engineering, geography, geosciences, natural resources, statistics, economics, and computer science. Ultimately the grant will promote the training of students for jobs as risk analysts in a range of careers, not just in academia, but also in industry, non-profit organizations, and government.
In a rapidly urbanizing world, society needs landscapes that are resilient to risks to food, energy, and water, and other ecosystem services (FEWES). The challenge for society is to design landscapes that provide multiple human benefits, maintain ecosystem function, and prove resilient to future risks. This project will address unresolved debates such as whether to develop land intensely, leaving more land untouched (land-sparing) or to develop land moderately, but over a wider area (land-sharing). To resolve such debates and create resilient urban-to-wild landscapes, students will learn how to apply landscape risk assessment to estimate threats, benefits, and uncertainty by exploring landscape linkages and developing scenarios that include socioeconomic impacts for tradeoffs among FEWES. This traineeship will also develop a novel, cross-cutting framework for teaching STEM education by synergistically combining collaborative, problem-based, and service-based learning to assess risks to FEWES. These practices will be combined to develop a unique approach to graduate education called Team-TERRA, or Team-based, Transdisciplinary, Estimation of Risks, and Real-world Analysis. Students will originate from different backgrounds and disciplines and will learn to transcend traditional disciplinary boundaries. Trainees will earn a certificate in Environmental Risk Assessment by learning how to work with stakeholders, develop quantitative analytical skills in predicting risk and uncertainty, explore options for managing such risks, and communicate risk and uncertainty to clients, stakeholders, and the public. Overall, trainees will learn transferable skills by working with real-world problems, while providing an important service to governments, NGOs, and businesses.
The NSF Research Traineeship (NRT) Program is designed to encourage the development and implementation of bold, new potentially transformative models for STEM graduate education training. The program is dedicated to effective training of STEM graduate students in high priority interdisciplinary or convergent research areas through comprehensive traineeship models that are innovative, evidence-based, and aligned with changing workforce and research needs.
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.
