Nutrient over-enrichment can produce adverse ecological effects within coastal ecosystems and negatively impact the production of ecosystem goods and services. In small estuaries of the U.S. Pacific Northwest, seasonal blooms of green macroalgae (GMA; Family Ulvaceae) are primarily associated with natural nutrient input, rather than anthropogenic sources. This provided us a unique opportunity to investigate the effects of naturally-stimulated macroalgal blooms on intertidal bivalves. (heart cockles) are an important species for shellfisheries in the region. In summer population surveys, we found that cockles emerged from the sediment with greater frequency as GMA biomass increased. Experimental manipulation of GMA biomass in the field showed that GMA elicited emergence, evoked above-ground lateral movement, inhibited shell growth, and increased mortality (by 34.0 ± 15.2%) in cockles. Laboratory experiments revealed that the interaction of a weighted barrier at the sediment surface and GMA presence elicited rapid emergence among cockles. Risk assessment of the emergence response in cockles showed that the emergent population experienced 11.0 ± 8.0% mortality due to gull predation, while laboratory exposure to elevated temperatures (≥34 °C) slowed valve-closure, inhibited reburial, and increased mortality, which could have translated to 7.1 ± 1.5% mortality. We found that cockles avoided mortality due to burial below GMA mats by emerging from the sediment, but that behavior consequently put them at risk of mortality due to heat stress or gull predation. Regardless of nutrient source, our research showed that GMA blooms pose a threat to the survival of intertidal bivalves.

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 costintensive, 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.

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Coasts and estuaries provide an abundance of ecosystem goods and services (EGS) to humans worldwide. Models that track the supply, demand, and change in EGS within these ecosystems provide valuable insights that have applications in the context of land-use planning, decision-making, and coastal community engagement. However, developing models for use in coastal and estuarine ecosystems is challenging given the multitude and variability of potential input variables, largely due to their dynamic nature and extensive use. Models that can incorporate scenarios of environmental change to forecast changes in EGS endpoints are highly valuable to decision-makers, but only a minor proportion of available EGS models offer this utility. In this chapter, we describe the domain of models most useful to coastal decision-makers, present models at multiple scales that can predict EGS changes, and examine specific examples that epitomize this utility. We also highlight common difficulties in modeling coastal and estuarine EGS and propose suggestions for integrating EGS models into the coastal management decision-making process during times of increasing environmental change. Lessons Learned • Identifying the most suitable model(s) given the scale(s) of a particular question or goal is paramount in the modeling process • Uncertainty is an inherent component of modeling that should be wellcommunicated by users to avoid misinterpretation of results