COS 177-10 - Failure and success points of large-scale barrier removal optimization applied to watersheds

Friday, August 11, 2017: 11:10 AM
B110-111, Oregon Convention Center
Allison T. Moody1, Patrick J. Doran2, Michael Ferris3, Austin W. Milt1 and Peter B. McIntyre1, (1)Center for Limnology, University of Wisconsin, Madison, WI, (2)The Nature Conservancy, Lansing, MI, (3)Wisconsin Institutes for Discovery, University of Wisconsin-Madison, Madison, WI

The access of migratory fish to breeding grounds can be blocked by man-made structures such as dams and culverts. At large scales, these barriers can number in the thousands and be an obvious target for conservation efforts. In the Great Lakes Basin, optimization models allow managers to evaluate alternative sets of barrier removals for their potential to restore access to stream length compared to removal or repair costs. These models are fueled by coarse-grained data on road and stream locations, which do not necessarily reflect exact conditions on the ground. However, local-scale data can be expensive and time-consuming to collect. We partnered with three local groups in the Lake Ontario, Michigan, and Superior basins to access detailed dam and road crossing data that provide an opportunity to evaluate the robustness of large-scale optimization results. Optimizations were compared between runs using our original large-scale data versus detailed passability and locations from local fieldwork. We also ran watershed-specific prioritizations that also included fish management concerns such as barriers to sea lampreys and other non-native fishes.


In all cases, large scale data underestimated the number of barriers and in some cases, misidentified the flowlines. The local data also added several barrier types to the analysis: beaver dams, fords, and fishways. All of these can affect fish passage, and so need to be evaluated at the landscape level. In addition, we found significant differences between the large- and small-scale roads layer in the watershed with a history of timber harvests. As expected, if there were one or more large barriers downstream that were too expensive or important to remove, local data did not change the results of the optimization. Ignoring these major downstream barriers and working just within watersheds, integrating higher-resolution data did change the prioritization results although not in a systematic fashion. This result indicated the large scale data did not tend to miss barriers only on smaller tributaries of less importance to migratory fish, but that missed barriers occurred throughout the watershed. Watersheds with small dirt roads and those with significant fish passage management concerns benefit the most from collecting small scale data.