Speaker: Sam Roy, Research Assistant Professor, Mitchell Center for Sustainability Solutions
Dam removal is now common practice for restoring natural flow connectivity in rivers, with significant benefits for migratory fish and their freshwater/marine ecosystems. However, dams are only one of several types of infrastructure fragmenting aquatic habitat. Though dams generally have greater impact, other man-made barriers like road culverts also limit migration. We explore the potential for combining dam removals and road culvert improvements to improve the costs and benefits of restoration using examples from New England. Culverts are more pervasive than dams in developed landscapes, and poorly designed/maintained culverts have similar impacts on migration. However, there are important roadway safety considerations that also factor into improvement, creating opportunities for multicriteria-based collaborations. Coordinating these decisions at greater spatial scales may dramatically improve the cost-effectiveness of ecological restoration, though climate projections indicate more frequent, costly culvert improvements in the future.
Sam Roy has expertise in dynamic socio-ecological systems, machine learning, biophysical modeling, stakeholder engagement, team science leadership, and student training and mentorship. His on-going sustainability research projects include: the Future of Dams and a new partnership with the Department of Energy to develop decision support tools for multi-objective management of US dams; a USGS/WRRI-funded project for development of computer-based tools to improve ecological and transportation safety management of tens of thousands of culverts underlying Maine’s vast road network amid changing climate and land use; and a coastal water quality project to identify potential policy change geared towards regulation of wild caught and aquaculture shellfish harvests. With colleagues in the School of Earth and Climate Sciences, Roy is also collaborating on research to generate a first-of-its-kind global model of bedrock strength and near-surface stress to make spatially-explicit predictions of landslide susceptibility via the integration of geostatistics, physics-based models, and deep learning algorithms.