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The need to train sustainability scientists and engineers to address the complex problems of our world has never been more apparent. We organized an interdisciplinary team of instructors from universities in the states of Maine, New Hampshire, and Rhode Island who designed, taught, and assessed a multi-university course to develop the core competencies necessary for advancing sustainability solutions. Lessons from the course translate across sustainability contexts, but our specific focus was on the issues and trade-offs associated with dams. Dams provide numerous water, energy, and cultural services to society while exacting an ecological toll that disrupts the flow of water, fish, and sediment in rivers. Like many natural resource management challenges, effective dam decisions require collaboration among diverse stakeholders and disciplines. We linked key sustainability principles and practices related to interdisciplinarity, stakeholder engagement, and problem-solving to student learning outcomes that are generalizable beyond our dam-specific context. Students and instructors co-created class activities to build capacity for interdisciplinary collaboration and encourage student leadership and creativity. Assessment results show that students responded positively to activities related to stakeholder engagement and interdisciplinary collaboration, particularly when practicing nested discussion and intrapersonal reflection. These activities helped broaden students’ perspectives on sustainability problems and built greater capacity for constructive communication and student leadership. 

Aging infrastructure and growing interests in river restoration have led to a substantial rise in dam removals in the United States. However, the decision to remove a dam involves many complex trade-offs. The benefits of dam removal for hazard reduction and ecological restoration are potentially offset by the loss of hydroelectricity production, water supply, and other important services. We use a multiobjective approach to examine a wide array of trade-offs and synergies involved with strategic dam removal at three spatial scales in New England. We find that increasing the scale of decision-making improves the efficiency of trade-offs among ecosystem services, river safety, and economic costs resulting from dam removal, but this may lead to heterogeneous and less equitable local-scale outcomes. Our model may help facilitate multilateral funding, policy, and stakeholder agreements by analyzing the trade-offs of coordinated dam decisions, including net benefit alternatives to dam removal, at scales that satisfy these agreements.

 

We collected ground‐penetrating radar (GPR) and frequency‐domain electromagnetic induction (FDEM) profiles in 2011 and 2012 to identify the extent of permafrost relative to surface biomass and solar insolation around Twelvemile Lake near Fort Yukon, Alaska. We compared a Landsat‐derived biomass estimate and modeled solar insolation from a digital elevation model to the geophysical measurements. We show correspondence between vegetation type and biomass relative to permafrost extent and seasonal freeze–thaw. Thicker permafrost (≥25 m) was covered by greater biomass, and seasonal thaw depths in these regions were minimal (1 m). Shallow (1–3 m depth) and thin (20–50 cm) newly forming permafrost or frozen layers from the previous winter occurred below northward oriented slopes with thin biomass cover. South‐facing slopes exhibited permafrost when there was enough biomass to shield incoming solar energy. We developed an artificial neural network to predict permafrost extent across the broader region by mapping GPR‐observed instances of permafrost to FDEM, biomass, and terrain observations with 90.2% accuracy. We identified a strong linear correlation (r = −0.77) between permafrost probability and seasonal thaw depth, indicating that our models may also be used to explore thaw patterns and variability in active layer thickness. This study highlights the combined influence of biomass and terrain on the presence of permafrost and the value of evaluating such parameters via remote sensing to predict permafrost spatial or temporal variability. Incorporating diverse geophysical datasets with in‐situ validation into machine learning models demonstrates a useful approach to upscale estimated permafrost extent across large Arctic expanses.