LAUSR

Habitat suitability models are useful to estimate the potential distribution of a species of interest, particularly in the case of infaunal bivalves. Sampling for these bivalves is time- and cost-intensive, which is increasingly difficult for organizations or agencies that are limited by personnel and funds. Consequently, we developed a framework to identify suitable bivalve habitat in estuaries (FISBHE) - a habitat suitability index (HSI) modeling framework for NE Pacific estuaries that was parameterized with published natural-hi story information and existing habitat datasets, without requiring extensive field sampling of bivalves. Spatially explicit, rule-based habitat suitability models were constructed in a GIS for five species of bay-clams (Clinocardium nuttallii, My a arenaria, Tresus capax, Saxidomus gigantea, and Leukoma staminea) that are popular targets for recreational and commercial harvest in estuaries of the U.S. Pacific Northwest. Habitat rasters were produced for Yaquina and Tillamook estuaries (Oregon, USA) using environmental data (bathymetric depth, sediment % silt-clay, wet-season salinity, and burrowing shrimp presence/absence) from multiple studies (1953-2015). These habitat rasters then served as inputs in the final model which produced HSI classes ranging from 0-4 (lowest to highest suitability), dependent upon the number of habitat variables that fell within the sensitivity limits for each species of bay-clam. The models were tested with validation analyses and bay-clam occurrence data (reported in benthic community studies, 1996-2012) within each HSI class; logistic regression and Kendall's correlation coefficient both showed correspondence between predicted HSI classes and bay-clam presence/absence. Results also showed that the greatest presence probabilities occurred within habitats of highest predicted suitability, with the exception of M. arenaria in Tillamook Bay. The advantage of FISBHE is that disparate, independent sets of existing data are sufficient to parameterize the models, as well as produce and validate maps of habitat suitability. This approach can be transferred to data-poor systems with modest investment, which can be useful for prioritizing estuarine land-use decisions and could be used to estimate the vulnerability of this valued ecosystem good to changes in habitat quality and distribution.