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Holistic understanding of estuarine and coastal environments across interacting domains with high-dimensional complexity can profitably be approached through data-centric synthesis studies. Synthesis has been defined as “the inferential process whereby new models are developed from analysis of multiple data sets to explain observed patterns across a range of time and space scales.” Examples include ecological—across ecosystem components or organization levels, spatial—across spatial scales or multiple ecosystems, and temporal—across temporal scales. Though data quantity and volume are increasingly accessible, infrastructures for data sharing, management, and integration remain fractured. Integrating heterogeneous data sets is difficult yet critical. Technological and cultural obstacles hamper finding, accessing, and integrating data to answer scientific and policy questions. To investigate synthesis within the estuarine and coastal science community, we held a workshop at a coastal and estuarine research federation conference and conducted two case studies involving synthesis science. The workshop indicated that data-centric synthesis approaches are valuable for (1) hypothesis testing, (2) baseline monitoring, (3) historical perspectives, and (4) forecasting. Case studies revealed important weaknesses in current data infrastructures and highlighted opportunities for ecological synthesis science. Here, we list requirements for a coastal and estuarine data infrastructure. We model data needs and suggest directions for moving forward. For example, we propose developing community standards, accommodating and integrating big and small data (e.g., sensor feeds and single data sets), and digitizing ‘dark data’ (inaccessible, non-curated, non-archived data potentially destroyed when researchers leave science).

Scientists have unprecedented access to a wide variety of high-quality datasets. These datasets, which are often independently curated, commonly use unstructured spreadsheets to store their data. Standardized annotations are essential to perform synthesis studies across investigators, but are often not used in practice. Therefore, accurately combining records in spreadsheets from differing studies requires tedious and error-prone human curation. These efforts result in a significant time and cost barrier to synthesis research. We propose an information retrieval inspired algorithm, Synthesize, that merges unstructured data automatically based on both column labels and values. Application of the Synthesize algorithm to cancer and ecological datasets had high accuracy (on the order of 85-100%). We further implement Synthesize in an open source web application, Synthesizer (https://github.com/lisagandy/synthesizer). The software accepts input as spreadsheets in comma separated value (CSV) format, visualizes the merged data, and outputs the results as a new spreadsheet. Synthesizer includes an easy to use graphical user interface, which enables the user to finish combining data and obtain perfect accuracy. Future work will allow detection of units to automatically merge continuous data and application of the algorithm to other data formats, including databases.

Stable nitrogen and carbon isotopes (δ15N and δ13C) and elemental content (% nitrogen, % carbon) in oysters (Crassostrea virginica) grown by a network of 132 citizen–scientists (11,600 km2, 87.9 km2 site−1) were examined to test effects of land use, salinity, flushing time, and oyster size on bioindication of human and/or animal nitrogen sources. Oyster δ15N sampled from shallow waters sites throughout Chesapeake Bay and its tributaries exhibited nested spatial patterns: (1) decreasing toward the mouth of Chesapeake Bay (1000s km2) and (2) decreasing, increasing, and not changing toward tributary mouths (100s km2). Distinct isotopic ‘signatures’ in tributaries were associated with the composition of land use, water quality in tributaries and freshwater streams, human and/or animal nitrogen sources, and marine vs. terrestrial nitrogen and carbon sources. Yet at 1000s km2, oyster δ15N varied with flushing time, salinity, and bioindicator size, thus constraining the upper extent for inferring nitrogen sources from bioindicator δ15N to the scale of gradients in these confounding physical and biological factors. Nevertheless, at 100s km2 isotopic ‘signatures’ can be used to infer nutrient sources and transport mechanisms and might have implications for fishery management/enforcement. Ultimately, δ15N and δ13C in bioindicators distributed to citizen–scientists may add substantial value to existing and ongoing programs, networks, monitoring and databases, and might have some use for imputing data gaps where intensive water quality monitoring is lacking.

Fertig, B.; Carruthers, T.J.B., and Dennison, W.C., 2014. Oyster d 15 N as a bioindicator of potential wastewater and poultry farming impacts and degraded water quality in a subestuary of Chesapeake Bay. Journal of Coastal Research, 30(5), 881–892. Coconut Creek (Florida), ISSN 0749-0208. Anthropogenic nitrogen contributes to water quality degradation, but it is difficult to distinguish sources once they are mixed in coastal ecosystems. Natural abundances of stable nitrogen isotopes (d 15 N) were measured in oyster (Crassostrea virginica) tissues (muscle, gills, and mantle) during summer 2006 to summer 2008 to identify nitrogen sources in Monie Bay (a subestuary of Chesapeake Bay) receiving freshwater inputs from three tributary creeks. The creeks (estimated flushing times: 3.5, 5.7, and 37.2 d) vary in size and potential nitrogen sources: septic systems and poultry operations (Monie Creek), crop fertilizer (Little Monie Creek), and wetlands, forest, or both (Little Creek). Grand mean oyster tissue d 15 N values (11.8 6 0.4% in muscle, 10.4 6 0.4% in gills, and 10.5 6 0.3% in mantle) indicated a mixture of human and animal sources. Potential nitrogen loss from denitrification (15.1–24.5%) likely did not substantially modify isotopic values, and d 15 N values were greater than would be expected from atmospheric sources, refuting these alternative explanations. Though dilute, spatial patterns supported the inference that human waste, poultry waste, or both entered Monie Bay from its watershed and the adjacent Wicomico River watershed (via mixing). Calculated nitrogen generation from poultry manure in the watershed (containing 2.5 3 10 3 people) was 2.9 3 10 4 to 1.0 3 10 6 kg of total nitrogen (TN) per year (equivalent to 6.8 3 10 3 –2.3 3 10 5 people), whereas throughout Delmarva Peninsula (containing 1.2 3 10 6 people) it was 3.9 3 10 6 to 1.3 3 10 8 kg TN y � 1 (equivalent to 9.0 3 10 5 –3.1 3 10 8 people). Conservatively estimated (based on 0.038 kg chicken � 1 y � 1 ), poultry in the Monie Bay watershed generated an amount of nitrogen equivalent to that generated by 263% of the human population. Throughout Delmarva Peninsula, poultry generated an amount of nitrogen equivalent to that generated by 76% of the human population. Estuaries commonly receive nutrients from both inside and outside their watersheds, and oyster d 15 N values elucidated this process locally.

With the increasing appreciation that sea grass habitats are in global decline, there is a great need to be able to efficiently and effectively assess and characterize the status and trends of sea grass in our coastal ecosystems. This paper examines the utility of remotely sensed vs. in situ plot-based monitoring using the Barnegat Bay-Little Egg Harbor (BB-LEH), New Jersey, USA estuarine system as a case study. Eelgrass (Zostera marina) is the dominant species, while widgeon grass (Ruppia maritima) is also common in lower salinity regions of the BB-LEH. Aerial imagery collected during the months of July and August 2009 was interpreted and mapped using object based image analysis techniques, similar to techniques used in the 2003 mapping survey of this system. Boat-based in situ monitoring data were collected concurrently with the aerial photography to assist the image interpretation and for an independent accuracy assessment. We compared the remotely-sensed mapping of sea grass cover change (in 2003 vs. 2009) vs. in situ plot-based monitoring conducted from 2004 through 2009. Comparison of the remotely-sensed vs. the in situ plot-change analysis suggests that the two methodologies had broadly similarly results, with the percent area showing declines in sea grass cover greater than those that exhibited increases. In conclusion, the two studies provide corroborating evidence that sea grass has declined in percent cover in the BB-LEH system during the decade of the 2000’s. While remotely-sensed surveys provide synoptic information for a “big picture” view on sea grass distribution, site specific in situ sampling is required to determine other aspects of sea grass status, e.g. above vs. below-ground biomass, blade length, shoot density, epiphytic loading, etc. Either method alone gives an incomplete picture. As demonstrated in this study, to fully characterize the spatial extent, health, and density of sea grass meadows across the entire estuary, combining remote sensing surveys concomitantly with comprehensive in situ assessment provides the most robust approach.

This study identified drivers of change in Barnegat Bay–Little Egg Harbor Estuary, NJ, USA over multiple long-term time periods by developing an assessment tool (an “Eutrophication Index”) capable of handling data gaps and identifying the condition of and relationships between ecosystem pressures, ecosystem state, and biotic responses. The Eutrophication Index integrates 15 indicators in 3 components: (1) water quality, (2) light availability, and (3) seagrass response. Annual quantitative assessments of condition and its consistency for three geographic segments range from 0 (highly degraded) to 100 (excellent condition). Eutrophication Index values significantly declined (p 1,200 and 100 and

Previous investigations of eelgrass (Zostera marina L.) beds in the Barnegat Bay-Little Egg Harbor Estuary, New Jersey (USA) revealed a significant decline in plant biomass associated with ongoing nitrogen enrichment of the system and declines in light availability. Here, we update trends in eelgrass characteristics (aboveground and belowground biomass, shoot density, percent cover, blade length) for 2008–2010 and compare these to previous datasets (2004–2006). Analysis of quadrat, core, and hand samples collected at 120 transect stations in 4 disjunct eelgrass beds of the estuary showed a distinct, continued reduction in Zostera biomass. In 2010, Z. marina biomass decreased to its lowest level ever recorded in the estuary (mean aboveground biomass = 7.7 g DW m−2; mean belowground biomass = 27.0 g DW m−2). Reduced biomass and other characteristics indicated that eelgrass beds had not recovered from previous reductions observed 2004–2006. The rate of decline was related to nutrient loading and associated symptoms of eutrophication. Additionally, the rate of degradation changed over time. The decline in plant parameters was evident in all of the eelgrass beds investigated, indicating that an estuary-wide stressor was responsible.

a b s t r a c t Eutrophication degrades numerous estuaries worldwide and a myriad of assessment metrics have been developed. Here, we apply an example of a previously developed metric (Lee et al., 2004) designed to indicate incipient estuarine eutrophication to validate this technique in an already eutrophic estuary end-member, Barnegat Bay–Little Egg Harbor, New Jersey. The metric, termed ‘Nutrient Pollution Indicator’ (NPI) uses eelgrass (Zostera marina L.) as a bioindicator and is calculated as the ratio of leaf nitrogen content (%N) to area normalized leaf mass (mg dry wt cm −2 ). Eelgrass samples were collected along the entire length of the Barnegat Bay–Little Egg Harbor from June to October 2008 to determine if leaf chemistry and morphology reflect eutrophication status and a north–south gradient of nitrogen loading from the Barnegat Bay watershed. Nitrogen content, area normalized leaf mass, and NPI values all significantly (p < 0.05) varied temporally but not spatially. NPI values did not significantly correspond to the north–south gradient of nitrogen loading from the Barnegat Bay watershed. The NPI metric is therefore not deemed to reliably indicate estuarine eutrophic status. Differences between sampling effort (number

Stable nitrogen isotopes (delta(15)N) in bioindicators are increasingly employed to identify nitrogen sources in many ecosystems and biological characteristics of the eastern oyster (Crassostrea virginica) make it an appropriate species for this purpose. To assess nitrogen isotopic fractionation associated with assimilation and baseline variations in oyster mantle, gill, and muscle tissue delta(15)N, manipulative fieldwork in Chesapeake Bay and corresponding modeling exercises were conducted. This study (1) determined that five individuals represented an optimal sample size; (2) verified that delta(15)N in oysters from two locations converged after shared deployment to a new location reflecting a change in nitrogen sources; (3) identified required exposure time and temporal integration (four months for muscle, two to three months for gill and mantle); and (4) demonstrated seasonal delta(15)N increases in seston (summer) and oysters (winter). As bioindicators, oysters can be deployed for spatial interpolation of nitrogen sources, even in areas lacking extant populations.

Nitrogen loading from anthropogenic sources, including fertilizer, manure, and sewage effluents, has been linked with declining water quality in coastal lagoons worldwide. Freshwater inputs to mid-Atlantic coastal lagoons of the USA are from terrestrially influenced sources: groundwater and overland flow via streams and agricultural ditches, with occasional precipitation events. Stable nitrogen isotopes ratios (δ15N) in bioindicator species combined with conventional water quality monitoring were used to assess nitrogen sources and provide insights into their origins. Water quality data revealed that nutrients derived from terrestrial sources increased after precipitation events. Tissues from two bioindicator species, a macroalgae (Gracilaria sp.) and the eastern oyster (Crassostrea virginica) were analyzed for δ15N to determine spatial and temporal patterns of nitrogen sources. A broad-scale survey assessment of deployed macroalgae (June 2004) detected regions of elevated δ15N. Macroalgal δ15N (7.33 ± 1.15‰ in May 2006 and 6.76 ± 1.15‰ in July 2006) responded quickly to sustained June 2006 nutrient pulse, but did not detect spatial patterns at the fine scale. Oyster δ15N (8.51 ± 0.89‰) responded slowly over longer time periods and exhibited a slight gradient at the finer spatial scale. Overall, elevated δ15N values in macroalgae and oysters were used to infer that human and animal wastes were important nitrogen sources in some areas of Maryland’s coastal bays. Different nitrogen integration periods across multiple organisms may be used to indicate nitrogen sources at various spatial and temporal scales, which will help focus nutrient management.