Type of Presentation

Oral

Session Title

The Role of Eelgrass Ecosystems in the Salish Sea

Description

Climate change and variability has the potential to affect the resilience and restoration potential of eelgrass (Zostera marina) both positively and negatively. As part of a project to identify suitable sites for eelgrass restoration in Puget Sound, we have developed a model of eelgrass biomass production using data on eelgrass productivity and respiration collected from local stocks in Sequim Bay, WA. The model uses modeled or observed time series of water depth, light availability, temperature and salinity to predict the potential for eelgrass growth at a site. To test the model’s sensitivity to climate variability, we compared model predictions with 20 years of observations of eelgrass growth rates in Sequim Bay that show correlations to climate factors including the Oceanic Nino Index. We also compared predictions using observed temperature and sea level data from nearby locations with predictions using output from the Salish Sea hydrodynamic model for several years in the time series. The success of the model in predicting observed eelgrass growth depended in part on the ability of observed environmental data to replicate nearshore and intertidal conditions. Our findings highlight the importance of collecting high-quality time series data on environmental conditions and eelgrass productivity in both highly and marginally suitable eelgrass habitats in order to improve and test models and better understand the role of climate in long-term eelgrass dynamics.

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Modeling climate variability effects on eelgrass productivity and resilience

2016SSEC

Climate change and variability has the potential to affect the resilience and restoration potential of eelgrass (Zostera marina) both positively and negatively. As part of a project to identify suitable sites for eelgrass restoration in Puget Sound, we have developed a model of eelgrass biomass production using data on eelgrass productivity and respiration collected from local stocks in Sequim Bay, WA. The model uses modeled or observed time series of water depth, light availability, temperature and salinity to predict the potential for eelgrass growth at a site. To test the model’s sensitivity to climate variability, we compared model predictions with 20 years of observations of eelgrass growth rates in Sequim Bay that show correlations to climate factors including the Oceanic Nino Index. We also compared predictions using observed temperature and sea level data from nearby locations with predictions using output from the Salish Sea hydrodynamic model for several years in the time series. The success of the model in predicting observed eelgrass growth depended in part on the ability of observed environmental data to replicate nearshore and intertidal conditions. Our findings highlight the importance of collecting high-quality time series data on environmental conditions and eelgrass productivity in both highly and marginally suitable eelgrass habitats in order to improve and test models and better understand the role of climate in long-term eelgrass dynamics.