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Using Geospatial Habitat Suitability Models to Prioritize Estuarine Areas for Conservation or Restoration of Bivalve Shellfish Beds
Citation:
DeWitt, Ted, N. Lewis, AND E. Fox. Using Geospatial Habitat Suitability Models to Prioritize Estuarine Areas for Conservation or Restoration of Bivalve Shellfish Beds. National Conference on Ecosystem Restoration, New Orleans, LA, August 26 - 30, 2018.
Impact/Purpose:
Scientists at EPA/ORDNHEERL/WED have developed ecological models using readily obtained environmental data, that accurately generate maps of the best (e.g., “most suitable”) to worst habitats for locating populations of harvested species of bivalve shellfish within Oregon estuaries. Whereas these bivalves are valued in the recreational and commercial fisheries, and are thus an important final ecosystem good for coastal communities, maps of their distribution are useful to State and local decision-makers for planning conservation, restoration, development or other uses of estuarine lands. The principle advantage of the new modeling approach is that disparate, independent sets of existing data were sufficient to produce and validate maps of habitat suitability. Additionally, by combining the models with changes in precipitation and sea level, this modeling approach can be used prioritize areas where estuarine resource managers could protect prime bivalve habitat, undertake restoration to improve bivalve habitat, or mitigate for potential losses of bivalve habitat.
Description:
Habitat suitability (HS) models can aide in forecasting how environmental changes may affect the distribution of species of interest. This information can then be used to prioritize habitats for conservation or restoration. Here, we demonstrate the use of HS models to identify areas of high suitability for harvested species of bivalves that might change due to reduced precipitation or sea level rise. Estuarine managers might use this information to initiate mitigation, restoration or conservation actions to ensure continued production of this valued ecosystem service. Rule-based HS models were constructed in a GIS for five bay-clam species (Clinocardium nuttallii, Mya arenaria, Tresus capax, Saxidomous gigantea, and Leukoma staminea) that are recreationally and commercially harvested in NE Pacific estuaries. Tolerance limits of each species to four habitat variables (wet-season mean salinity, bathymetric depth, sediment grain size, and burrowing shrimp presence/absence) were determined based on natural history literature. Spatially-explicit maps for each habitat variable were then produced for Yaquina and Tillamook estuaries (Oregon) using empirical data from multiple studies (1953-2015). These maps served as inputs in each species’ HS model, which produced HS classes ranging from 0-4 (lowest to highest suitability). HS models were then validated using bay-clam occurrence data from previous benthic community studies (1996-2012). Results showed that bivalves in the field had the greatest presence probabilities within habitats of highest predicted HS, except for M. areneria in Tillamook Bay. We demonstrate how HS model results can be used to forecast changes in the availability of suitable bivalve habitat by incorporating projected changes in salinity and bathymetric depth. The advantage of this approach is that disparate, independent sets of existing data are sufficient to parameterize the models, as well as produce and validate HS maps. Resource managers can transfer this approach to data-poor systems with modest investment, which can be useful for prioritizing estuarine land-use decisions (i.e., conservation or restoration siting) and estimating the vulnerability of this valued ecosystem service to changes in habitat quality and distribution.