Oyster food supply in Delaware Bay: Estimation from a hydrodynamic model and interaction with the oyster population

To evaluate oyster food supply, water samples were collected at fifteen sites in Delaware Bay nearmonthly in 2009 and 2010. Food was estimated as the sum of particulate protein, labile carbohydrate, and lipid. Delaware Bay shows a typical spring bloom, centered in March and April, with declining food supply thereafter into early fall, followed sporadically by a minor fall bloom. The geographic and temporal structure of food was more predictable in summer to early fall, and considerably less predictable in spring. Five variables each based on temperature and the spatial and temporal variability of temperature were significant contributors to a multiple regression (R2 = 0.28). Cluster analysis on residuals identified two large groups of sites, one comprising most sites on the eastern side of the bay including all of the sites on the New Jersey oyster beds downestuary of the uppermost beds and one including most of the sites along the central channel and waters west. Food values over the New Jersey oyster beds were often depressed by as much as 50% relative to the bay-wide mean. Food values did not follow an upestuary-downestuary trend anticipated from the salinity gradient. Rather, the differential was cross-bay and was distinctive throughout the estuarine salinity gradient, thus explaining the lack of significance of any salinity-related variable in the multiple regression. The consequence is that food supply cannot be sufficiently predicted or modeled based on observed environmental variables or those predicted from a hydrodynamic model. The cross-bay differential cannot be extracted from such datasets. The oyster reefs of Delaware Bay are dominantly sited on the New Jersey side, where food supply was most depressed and where passive particle residence times were longest. While not conclusive, this dataset suggests that oysters can influence food values on the New Jersey side of the bay at present biomass, and this would explain the cross-bay gradient in food values as an outcome of oyster feeding

Principal Factors Influencing Tree Growth in Low-Lying Mid Atlantic Coastal Forests

Flood frequencies in coastal forests are increasing as sea level rise accelerates from 3–4 mm year−1 to possibly more than 10 mm year−1 by the end of this century. As flooding increases, coastal forests retreat, ghost forests form, and coastal marshes migrate inland. The existence of ghost forests makes the mechanism of forest retreat clear: low-lying trees become more exposed to coastal flooding until they ultimately die. Variability in these retreat rates, however, makes it difficult to predict where and when retreat will continue to occur. Understanding tree growth responses to tidal water levels relative to other environmental factors is a critical step in elucidating the factors that influence retreat variability. Here, dendrochronology was used to study factors that contribute to variations in growth patterns in four coastal forests fringing the Delaware and Barnegat Bays. Species chosen for study included loblolly pine (Pinus taeda), pitch pine (Pinus rigida), and American holly (Ilex opaca). Pearson’s and partial correlation tests showed that growth relationships with monthly environmental conditions varied across sites and were moderate in strength (generally R < 0.5), but each site had at least one significant growth-water level correlation. As coastal flooding exposure is spatially dependent, tree chronologies were also separated into high and low elevation groups. Pearson’s and partial correlation tests of the mean differences between elevation groups showed that at some sites, low elevation trees grew less than high elevation trees when water levels were high, as might be expected. At one site, however, lower elevation trees grew more when water levels were higher, which suggests that other interacting factors—regardless of current flood exposure—potentially have positive, yet likely temporary, influence over tree growth in these low-lying areas

Oyster food supply in Delaware Bay: Estimation from a hydrodynamic model and interaction with the oyster population

To evaluate oyster food supply, water samples were collected at fifteen sites in Delaware Bay nearmonthly in 2009 and 2010. Food was estimated as the sum of particulate protein, labile carbohydrate, and lipid. Delaware Bay shows a typical spring bloom, centered in March and April, with declining food supply thereafter into early fall, followed sporadically by a minor fall bloom. The geographic and temporal structure of food was more predictable in summer to early fall, and considerably less predictable in spring. Five variables each based on temperature and the spatial and temporal variability of temperature were significant contributors to a multiple regression (R2 = 0.28). Cluster analysis on residuals identified two large groups of sites, one comprising most sites on the eastern side of the bay including all of the sites on the New Jersey oyster beds downestuary of the uppermost beds and one including most of the sites along the central channel and waters west. Food values over the New Jersey oyster beds were often depressed by as much as 50% relative to the bay-wide mean. Food values did not follow an upestuary-downestuary trend anticipated from the salinity gradient. Rather, the differential was cross-bay and was distinctive throughout the estuarine salinity gradient, thus explaining the lack of significance of any salinity-related variable in the multiple regression. The consequence is that food supply cannot be sufficiently predicted or modeled based on observed environmental variables or those predicted from a hydrodynamic model. The cross-bay differential cannot be extracted from such datasets. The oyster reefs of Delaware Bay are dominantly sited on the New Jersey side, where food supply was most depressed and where passive particle residence times were longest. While not conclusive, this dataset suggests that oysters can influence food values on the New Jersey side of the bay at present biomass, and this would explain the cross-bay gradient in food values as an outcome of oyster feeding

Modeling Performance and Settlement Windows of Larval Eastern Oyster (\u3ci\u3eCrassostrea virginica\u3c/i\u3e) In Delaware Bay

Oyster population maintenance and growth require a sufficient larval supply competent for metamorphosis and settlement. Larval performance, in terms of growth, development, survival, and metamorphic success, determines the capacity for a larval cohort to effectively settle and establish into an existing population. Exogenous factors influencing larval development include temperature, salinity, food quantity, and food quality. A sufficient diet, composed of balanced protein, lipids, and carbohydrates to meet larval nutritional demands, is required to promote successful metamorphosis. To evaluate the influence of these exogenous factors on oyster settlement potential in Delaware Bay, a well-established biochemically based Crassostrea gigas (Thunberg, 1793) larval model was adapted to simulate Crassostrea virginica (Gmelin, 1791) larval performance under in situ environmental conditions measured during the 2009 to 2011 reproductive seasons at 10 sites across the salinity gradient of Delaware Bay. Variation in the initial egg size and lipid content, and larval food assimilation efficiency was incorporated into the model to represent potential within-cohort phenotypic variability. The middle portion of Delaware Bay along the New Jersey shoreline, bridging the 15-salinity line, generated the most successful larvae each year, whereas the low-salinity reach, on the Delaware side, and Nantuxent Point Reef had more variable success. Survivorship was a function of adequate temperatures and salinities, sufficient food quantity, and favorable food quality defined in part by the protein-to-(lipid-plus-carbohydrate) ratio. Most settlement was predicted by the model to occur between July and September of each year. To validate the model, estimated settlement windows were compared with calculated settlement windows derived from recruitment observations on yearly shell plants. Modeled and recruitment-derived settlement windows agreed well with each other and verified the capacity of the model to accurately forecast in situ larval performance. The oyster larval model, based on measures of lipid, protein, and carbohydrate, successfully passed an important field test, demonstrating the potential of such biochemically based models to reliably evaluate larval performance under real-world conditions