Nutrient limitation of phytoplankton in three tributaries of Chesapeake Bay: Detecting responses following nutrient reductions

Geography, Trajectories, and Controls of Coastal Water Quality: More Rapid Improvement in the Shallow Zone of the Chesapeake Bay

Many coastal ecosystems have suffered from cultural eutrophication and dead zones. In the Chesapeake Bay, water quality degradation is manifested in low dissolved oxygen, poor water clarity, and decreased submerged aquatic vegetation acreage. This research combines long-term monitoring data, science-based assessment methods, and novel data analysis approaches (i.e., machine learning) toward understanding the geography, trajectories, and controls of water quality in the Chesapeake Bay, which provides an example for the assessment and management of complex coastal ecosystems. Results showed that the attainment of water quality standards has improved in both the deep zone and the shallow zone since 1985, but the shallow zone has improved more rapidly. In addition, the attainment trajectory has been affected by mainly external drivers (i.e., nutrient reductions) and, to a lesser extent, internal drivers (i.e., water temperature and stratification). Reductions in nutrient loads would improve attainment, whereas warming and stratification would decrease attainment. Furthermore, scenario analyses demonstrated the importance of managing both nitrogen and phosphorus loads. Overall, the deep zone and the shallow zone showed different trajectories and controls, emphasizing the importance of geographical targeting with management actions.

Assessment and prediction of Water Quality Index (WQI) by seasonal key water parameters in a coastal city: application of machine learning models

The Water Quality Index (WQI) provides comprehensive assessments in river systems; however, its calculation involves numerous water quality parameters, costly in sample collection and laboratory analysis. The study aimed to determine key water parameters and the most reliable models, considering seasonal variations in the water environment, to maximize the precision of WQI prediction by a minimal set of water parameters. Ten statistical or machine learning models were developed to predict the WQI over four seasons using water quality dataset collected in a coastal city adjacent to the Yellow Sea in China, based on which the key water parameters were identified and the variations were assessed by the Seasonal-Trend decomposition procedure based on Loess (STL). Results indicated that model performance generally improved with adding more input variables except Self-Organizing Map (SOM). Tree-based ensemble methods like Extreme Gradient Boosting (XGB) and Random Forest (RF) demonstrated the highest accuracy, particularly in winter. Nutrients (Ammonia Nitrogen (AN) and Total Phosphorus (TP)), Dissolved Oxygen (DO), and turbidity were determined as key water parameters, based on which, the prediction accuracy for Medium and Low grades was perfect while it was over 80% for the Good grade in spring and winter and dropped to around 70% in summer and autumn. Nutrient concentrations were higher at inland stations; however, it worsened at coastal stations, especially in summer. The study underscores the importance of reliable WQI prediction models in water quality assessment, especially when data is limited, which are crucial for managing water resources effectively.

Planktonic eukaryotes in the Chesapeake Bay: contrasting responses of abundant and rare taxa to estuarine gradients

Phytoplankton are important drivers of aquatic ecosystem function and environmental health. Their community compositions and distributions are directly impacted by environmental processes and human activities, including in the largest estuary in North America, the Chesapeake Bay. It is crucial to uncover how planktonic eukaryotes play fundamental roles as primary producers and trophic links and sustain estuarine ecosystems. In this study, we investigated the detailed community structure and spatiotemporal variations of planktonic eukaryotes in the Chesapeake Bay across space and time for three consecutive years. A clear seasonal and spatial shift of total, abundant, and rare planktonic eukaryotes was evident, and the pattern recurred interannually. Multiple harmful algal species have been identified in the Bay with varied distribution patterns, such as Karlodinium, Heterosigma akashiwo, Protoperidinium sp., etc. Compared to abundant taxa, rare subcommunities were more sensitive to environmental disturbance in terms of richness, diversity, and distribution. The combined effects of temporal variation (13.3%), nutrient availability (10.0%), and spatial gradients (8.8%) structured the distribution of eukaryotic microbial communities in the Bay. Similar spatiotemporal patterns between planktonic prokaryotes and eukaryotes suggest common mechanisms of adjustment, replacement, and species interaction for planktonic microbiomes under strong estuarine gradients. To our best knowledge, this work represents the first systematic study on planktonic eukaryotes in the Bay. A comprehensive view of the distribution of planktonic microbiomes and their interactions with environmental processes is critical in understanding the underlying microbial mechanisms involved in maintaining the stability, function, and environmental health of estuarine ecosystems.

IMPORTANCE

Deep sequencing analysis of planktonic eukaryotes in the Chesapeake Bay reveals high community diversity with many newly recognized phytoplankton taxa. The Chesapeake Bay planktonic eukaryotes show distinct seasonal and spatial variability, with recurring annual patterns of total, abundant, and rare groups. Rare taxa mainly contribute to eukaryotic diversity compared to abundant groups, and they are more sensitive to spatiotemporal variations and environmental filtering. Temporal variations, nutrient availability, and spatial gradients significantly affect the distribution of eukaryotic microbial communities. Similar spatiotemporal patterns in prokaryotes and eukaryotes suggest common mechanisms of adjustment, substitution, and species interactions in planktonic microbiomes under strong estuarine gradients. Interannually recurring patterns demonstrate that diverse eukaryotic taxa have well adapted to the estuarine environment with a long residence time. Further investigations of how human activities impact estuarine planktonic eukaryotes are critical in understanding their essential ecosystem roles and in maintaining environmental safety and public health.