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This assessment in the Narragansett Bay demonstrates a transparent, defensible method to characterize ecosystem restoration projects in a watershed over large spatial scales. The project team compiled multiple completed projects in the Narragansett Bay watershed including salt marsh restorations, fish passage, and dam removals. The approach included the following: identifying and locating restoration projects, utilizing existing data resources for spatial information, quantifying the gains in area and distance, and extrapolating the potential for collective watershed benefit in fish populations, productivity, water quality and carbon sequestration. In total, 177 projects were identified as being implemented between 1999 and 2015: fish passage restoration (46), marsh restoration (35), eel grass restoration (22), shellfish restoration (43), and other projects (31). The collective efforts to improve fish passage have resulted in more than 800 km of newly accessible river herring habitat in the Narragansett Bay watershed.

Dam removal is a potential habitat restoration alternative through which parties responsible for injuries to natural resources can provide compensation for reductions in fish populations. Predicting the potential response of migratory fish populations to candidate dam removal(s) is a critical step in the natural resource damage assessment process to evaluate whether the proposed action provides adequate compensation. There is currently no standard approach to making such predictions, particularly in cases where data on candidate streams with dams are limited. We considered six modeling approaches for addressing this problem and evaluated the features of each approach for this application. We judged that an approach based on habitat suitability indices and weighted usable area provides the best balance between predictive capacity and cost of model implementation. This balancing act evaluating the cost effectiveness of predictive models is worth consideration in a wide range of fisheries modeling applications.