Effects of Spatial Pattern and Economic Uncertainties on Freshwater Habitat Restoration Planning: A Simulation Exercise

Abstract

Evaluating alternative future scenarios using simulation models is an emerging approach to conservation planning over large spatial and temporal extents. Such an approach is useful when predictions cannot be validated empirically; however, evaluating the sensitivity of scenario-based approaches to key uncertainties is necessary so that managers understand how real-world constraints might impact results. We used a simulation approach to investigate the sensitivity of freshwater habitat restoration plans to spatial pattern (e.g., project locations) and economic uncertainties (e.g., budget level and project costs) in a case study on Pacific salmon in the Lewis River Basin (WA, U.S.A.). We found that some evaluation metrics performed better when restoration was distributed randomly, whereas others improved more when restoration occurred on contiguous stretches of river. Subwatersheds responded differently to restoration, suggesting that intrinsic characteristics limit restoration potential. Changes in budget usually caused proportionate improvements in habitat but disproportionate fish responses, suggesting a cumulative benefit for fish. Most per-dollar benefits increased as project cost decreased; therefore, model predictions are sensitive to project cost. Simulation analyses provided increased information about how economics and spatial pattern might affect habitats and salmon; these insights can help policymakers identify an appropriate restoration strategy.

Prioritization of Watersheds and Restoration Projects

“…The majority of these have focused on terrestrial species or marine reserves, but have more recently been modified and applied to conservation of freshwater fishes (Moilanen et al 2008) and, only recently, specifically to restoration planning (Fullerton et al 2010). These computer modeling efforts range from simple GIS exercises overlaying land cover, land use and species distribution layers to identify priority areas for protection and restorationsuch as for grizzly bears (Ursus arctos horribilis) in Canada or salmon in the United States (Noss et al 2009) -to more complex analysis that incorporate GIS data layers, professional opinion, life history models, and other biological and sociological information -such as those for endangered fishes (Villa et al 2002;Abellán et al 2005;Zafra-Calvo et al 2010).…”

“…Models designed specifically to prioritize restoration projects have often focused on one type of restoration such as barrier removal (Steel et al 2004;Pini-Prato 2008), or examined trade-offs of implementing different restoration scenarios in a basin (Steel et al 2009;Fullerton et al 2010). Models predicting the effects of land use on habitat conditions and fish distribution can be used to develop decision trees for selecting restoration projects (Poppe et al 2008).…”

“…With the exception of these efforts to rate barrier removal or other specific techniques, decision-support models are typically basin or region specific and need to be developed individually for each watershed (i.e. Baker et al 2004;Fullerton et al 2010). As with many other methods for prioritizing watersheds and projects for restoration, simple and complex models are often combined for use with scoring systems (Filipe et al 2004).…”

Review of Tools for Identifying, Planning, and Implementing Habitat Restoration for Pacific Salmon and Steelhead

“…; Fullerton et al. ). However, almost all approaches for prioritizing watersheds, reaches, or restoration projects require data from one or more of the other assessments or tools discussed above.…”

“…The Conservation Success Index developed by Trout Unlimited is an example of a simple MCDA approach to rank watersheds based on restoration and protection potential for different trout species (Williams et al 2007). More complex computer models incorporating a variety of models and data layers have also been used to examine different restoration strategies and prioritize habitat types for restoration (e.g., Greene and Beechie 2004;Scheuerell et al 2006;Fullerton et al 2010). However, almost all approaches for prioritizing watersheds, reaches, or restoration projects require data from one or more of the other assessments or tools discussed above.…”