EP044-02
Great Expectations: Deconstructing Process-based Pathways Underlying Beaver-related Restoration to Guide Evaluation

Friday, 11 December 2020: 19:04
Virtual
Caroline Nash, CK Blueshift, LLC, Boise, ID, United States, Gordon E. Grant, Oregon State University, Corvallis, OR, United States, Susan Charnley, USDA Forest Service, Pacific Northwest Research Station, Portland, OR, United States, Jason Dunham, USGS Forest and Rangeland Ecosystem Science Center, Corvallis, OR, United States, Hannah Gosnell, Oregon State University, CEOAS, Corvallis, OR, United States, Mark B Hausner, Desert Research Institute, Division of Hydrologic Sciences, Reno, NV, United States, David Pilliod, USGS, Boise, ID, United States and Jimmy D Taylor, USDA Animal and Plant Health Inspection Service, National Wildlife Research Center, Corvallis, OR, United States
Abstract:
Beaver-related restoration is a form of process-based restoration that, by re-establishing dam-building in streams, seeks to address wide-ranging ecological restoration goals. Although re-establishing beavers or using analogues have broad popular appeal, especially in water-limited systems, their effectiveness at meeting desired goals and social acceptability are not yet well documented. Here, we present a “process-expectation framework” that links beaver-related restoration tactics to commonly expected outcomes by identifying the set of process pathways that must occur to achieve expected outcomes. We explore the contingency implicit within this framework using social and biophysical data from project and research sites. Preliminary results suggest that outcomes are often predicated on complex process pathways over which humans have limited control. Consequently, expectations may need to shift through the course of projects, suggesting that a more useful paradigm for evaluating process-based restoration should be based on identifying relevant processes and rigorously documenting how projects do, or do not, proceed along expected process pathways using both quantitative and qualitative data.