Prioritizing in-stream barrier removal in Great Lakes tributaries

Background/Question/Methods

Rivers in the Great Lakes basin are highly fragmented due to the presence of thousands of in-stream barriers (dams and road-stream crossings). For migratory fishes such as walleye, lake sturgeon, and coaster brook trout, these barriers restrict breeding migrations and limit access to historical riverine spawning grounds. The removal or modification of in-stream barriers can restore migratory pathways for these species, but the costs (financial, species invasions) and benefits (access to breeding habitats) differ among potential mitigation projects. The restoration community lacks a transparent method for comparing these costs and benefits to assess which barrier removal projects would offer the greatest return on investment. To address this problem, we are undertaking a three-phase project with the goal of providing a decision support tool for prioritizing passability projects. First, we collated several existing barrier databases to create a single, comprehensive database for the Great Lakes basin. Second, we used field surveys (n=1088 barriers) of two components of barrier passability (vertical drop, water velocity) to create a statistical model predicting probability of fish passage for each barrier in the basin. Third, we developed mathematical optimization models to determine the most efficient barrier repair/removal strategies to maximize the amount of available breeding habitat.

Results/Conclusions

Our database of the location of dams (n=7,091) and road-stream crossings (n=268,818) is the most comprehensive inventory to date for the Great Lakes basin. Field surveys of road-stream crossings revealed high variation among watersheds in the number of impassable barriers. In the statistical model built from these surveys, barrier passability was positively related to stream order and upstream drainage area, but negatively related to stream channel slope. Across the Great Lakes basin, predicted barrier passability varied both among and within catchments; within each catchment, the spatial patterning of stream order and drainage area drove several characteristic spatial patterns in barrier passability. Barrier removal strategies generated by the optimization models were characterized by a nonlinear relationship between budget and return on investment (i.e. access to breeding habitats). We will discuss key factors that drive barrier prioritization, future data needs, and the strengths and limitations of an optimization-based approach to river restoration planning.