Eelgrass (Zostera spp.) is a marine flowering plant found in coastal regions worldwide and provides critical habitat, nutrient cycling, and shoreline buffering to ecosystems. Populations are declining in many of these regions due to the negative impacts poor water quality has on light availability and photosynthesis. The Lower Newport Bay (LNB) in Southern California is a heavily developed recreational harbor and estuarine habitat supporting species from varying taxa, including eelgrass. To understand the impact water quality has on eelgrass persistence in LNB, we selected four sites with long-term stable eelgrass populations and four sites with transitional eelgrass populations to collect weekly measurements of weather conditions, light availability, and water quality parameters. Through repeated measure ANOVAs using date as a between factor, we found that stable eelgrass beds had significantly higher light availability, dissolved oxygen concentrations, and bluer water on the Forel-Ule scale. We fit a linear mixed effect model using the log of turbidity, log of chlorophyll, and log of eelgrass bed depth as fixed effects with depth nested in site as random effects, found it to be the best fit model using AIC (Akaike Information Criterion), and found that depth and turbidity are significant predictors of light availability. These results indicate that turbidity, rather than chlorophyll concentration, has a greater impact on light availability and eelgrass health in LNB. This pilot project provides a foundation for future research and recommendations for eelgrass conservation in LNB, and suggests that runoff contributing to higher turbidity may be a leading cause of unhealthy eelgrass beds.

Data collection was performed from kayaks, with relevant tide and weather data recorded from observations in the field and corroborated by online sources. Field equipment included a Photosynthetically Active Radiation (PAR) sensor, YSI® Pro20 dissolved oxygen probe, salinity pen, depth finder, and secchi disk with an attached data logger (HOBO). In the lab, turbidity of collected water samples was analyzed using a LaMotte® turbidity meter. Chlorophyll samples were collected in the field my filtering water samples through a Swinnex filter (GF/F Borosilicate glass microfiber 25 mm) encased in a filter holder, and then later  analyzed in the laboratory using the non-acidification extraction method for the Turner® brand fluorometer.

After combining project and online derived datasets, analysis consisted of the investigation of relationships to test links with light availability and bed stability. We found that our results followed precedents set in the research, with light availability showing significant differences between stable and transitional eelgrass beds. In addition, other variables assessed through ANOVA's such as dissolved oxygen concentration and Forel-Ule color scale were found to have significant differences between eelgrass bed stability categories. We fit a linear mixed effect model using the log of turbidity, log of chlorophyll, and log of eelgrass bed depth as fixed effects with depth nested in site as random effects, found it to be the best fit model using AIC (Akaike Information Criterion), and found that depth and turbidity are significant predictors of light availability.

R Studio and R software.