Regional patterns and drivers of total nitrogen trends in the Chesapeake Bay watershed: Insights from machine learning approaches and management implications

Artificial Intelligence in Aquatic Biodiversity Research: A PRISMA-Based Systematic Review

Freshwater ecosystems are increasingly threatened by climate change and anthropogenic activities, necessitating innovative and scalable monitoring solutions. Artificial intelligence (AI) has emerged as a transformative tool in aquatic biodiversity research, enabling automated species identification, predictive habitat modeling, and conservation planning. This systematic review follows the PRISMA framework to analyze AI applications in freshwater biodiversity studies. Using a structured literature search across Scopus, Web of Science, and Google Scholar, we identified 312 relevant studies published between 2010 and 2024. This review categorizes AI applications into species identification, habitat assessment, ecological risk evaluation, and conservation strategies. A risk of bias assessment was conducted using QUADAS-2 and RoB 2 frameworks, highlighting methodological challenges, such as measurement bias and inconsistencies in the model validation. The citation trends demonstrate exponential growth in AI-driven biodiversity research, with leading contributions from China, the United States, and India. Despite the growing use of AI in this field, this review also reveals several persistent challenges, including limited data availability, regional imbalances, and concerns related to model generalizability and transparency. Our findings underscore AI's potential in revolutionizing biodiversity monitoring but also emphasize the need for standardized methodologies, improved data integration, and interdisciplinary collaboration to enhance ecological insights and conservation efforts.

Enhancing prediction and inference of daily in-stream nutrient and sediment concentrations using an extreme gradient boosting based water quality estimation tool - XGBest

Estimating constituent loads in streams and rivers is a crucial but challenging task due to low-frequency sampling in most watersheds. While predictive modeling can augment sparsely sampled water quality data, it can be challenging due to the complex and multifaceted interactions between several sub-watershed eco-hydrological processes. Traditional water quality prediction models, typically calibrated for individual sites, struggle to fully capture these interactions. This study introduces XGBest, a machine learning-based tool, that integrates hydrological data, land cover, and physical watershed attributes at a regional scale to predict daily concentrations of Total Nitrogen (TN), Total Phosphorus (TP), and Total Suspended Solids (TSS). XGBest leverages 29 environmental variables, including daily and antecedent discharge, temporal features, and landscape characteristics, to comprehensively evaluate water quality dynamics across a large hydrologic region. To explore the robustness of the developed tool, XGBest was validated using observed water quality data in three different hydrologic regions in the eastern United States, encompassing 499 water quality monitoring sites characterized by diverse hydro-climatic conditions and watershed attributes. This study also employed the legacy United States Geological Survey (USGS) tools - LOADEST and WRTDS as benchmarks to evaluate the performance of XGBest in these regions. The results demonstrated that XGBest outperformed LOADEST and WRTDS in predictive accuracy and revealed critical insights into the spatial and temporal variability of nutrient and sediment loads. In addition, SHapley Additive exPlanations (SHAP) values highlighted the importance of integrating static and dynamic watershed attributes, such as land cover, antecedent discharge, and seasonality, in capturing the complex concentration-discharge (C-Q) relationships. This study positions XGBest as a robust and scalable water quality prediction tool that bridges the gap between hydrology and broader environmental management. By combining multiple environmental factors into a unified predictive framework, XGBest enhances our understanding of water quality and supports more effective environmental monitoring and management strategies.

Tracking the optimal watershed landscape pattern for driving pollutant transport: Insights from the integration of mechanistic models and data-driven approaches

Identifying landscape patterns conducive to pollutant transport control is of vitally importance for water quality protection. However, it remains unclear which landscape patterns can weaken the transport capacity of pollutants entering water bodies. To fill this gap, this study proposes a new framework. This framework quantifies the contribution of landscape patterns to pollutant migration; it also identifies the optimal landscape patterns capable of reducing pollutants entering rivers. Furthermore, it analyzes the impact pathways of landscape patterns on pollutant migration by integrating mechanism models, machine learning techniques, and structural equation models (SEM). The results showed that on cultivated land and urban land, when the slope reached 35%, the terrestrial transport intensity of NH₃-N peaked at 34 kg/km2 and 45 kg/km2 respectively, with more pollutants entering the receiving water bodies. Meanwhile, in the forest with a DEM of 900 m, the terrestrial transport intensity of NH₃-N was the highest (50 kg/km2). The complexity of the landscape boundary shape in areas dominated by cultivated land and forest was verified to have a significant impact on the terrestrial migration intensity of NH₃-N, with a contribution rate of over 65%. From the comparison results of multiple land use combinations, it can be seen that the combination of woodland and grassland indirectly weakens the transport capacity of pollutants entering water bodies by directly influencing the connectivity among landscape units. In particular, when the proportion of woodland and grassland reaches 75%, it has a positive effect on improving river pollution and is the optimal landscape combination pattern for reducing the pollution load of the river. The outcomes can be used to develop more efficacious optimization and regulation tactics for landscape patterns and offer a decision - making foundation for the control of pollutant transport in large basins.

Machine learning models for prediction of nutrient concentrations in surface water in an agricultural watershed

Prediction and quantification of nutrient concentrations in surface water has gained substantial attention during recent decades because excess nutrients released from agricultural and urban watersheds can significantly deteriorate surface water quality. Machine learning (ML) models are considered an effective tool for better understanding and characterization of nutrient release from agricultural fields to surface water. However, to date, no systematic investigations have examined the implementation of different classification and regression ML models in agricultural settings to predict nutrient concentrations in surface water using a group of input variables including climatological (e.g., precipitation), hydrological (e.g., stream flow) and field characteristics (i.e., land and crop use). In the current study, multiple classification (e.g., decision trees) and regression (e.g., regression trees) ML models were applied on a dataset pertaining to surface water quality in an agricultural watershed in southern Ontario, Canada (i.e., Upper Parkhill watershed). The target variables of these models were the nutrient concentrations in surface water including nitrate, total phosphorus, soluble reactive phosphorus, and total dissolved phosphorus. These target variables were predicted using physical and chemical water parameters of surface water (e.g., temperature and DO), climatological, hydrological, and field conditions as the input variables. The performance of these different models was assessed using various evaluation metrics such as classification accuracy (CA) and coefficient of determination (R2) for classification and regression models, respectively. In general, both the ensemble bagged trees and logistic regression (CA ≥ 0.72), and exponential Gaussian process regression (R2≥ 0.93) models were the optimal classification and regression ML algorithms, respectively, where they resulted in the highest prediction accuracy of the target variables. The insights and outcomes of the current study demonstrates that ML models can be employed to effectively predict and quantify the nutrient concentrations in surface waters to supplement field-collected monitoring data in agricultural watersheds, assisting in maintaining high quality of the available surface water resources.

Disparities in nitrogen and phosphorus management across time and space: a case study of the Chesapeake Bay using the CAFE framework

Efficient management of nitrogen (N) and phosphorus (P) is imperative for sustainable agriculture, resource conservation, and reducing environmental pollution. Despite progress in on-farm practices and urban wastewater treatment in the Chesapeake Bay (CB) watershed, limited attention has been given to nutrient transport, use, and handling between farms and urban environments. This study uses the hierarchical CAFE (Cropping system, Animal-crop system, Food system, and Ecosystem) framework to evaluate nutrient management performances within the watershed. We first develop a three-decade, county-level nutrient budget database (1985-2019), then analyze the spatiotemporal patterns of N and P budgets, as well as N and P use efficiencies, within the four CAFE hierarchies. Our results indicate a sizable increase in potential N and P losses beyond crop fields (i.e. in the Animal-crop system, Food system, and Ecosystem), surpassing losses from cropland in over 90% of counties. To address these system-wide trade-offs, we estimate the nutrient resources in waste streams beyond croplands, which, if recovered and recycled, could theoretically offset mineral fertilizer inputs in over 60% of counties. Additionally, the growing imbalance in excess N versus P across systems, which increases the N:P ratio of potential losses, could pose an emerging risk to downstream aquatic ecosystems. By utilizing a systematic approach, our novel application of the CAFE framework reveals trade-offs and synergies in nutrient management outcomes that transcend agro-environmental and political boundaries, underscores disparities in N and P management, and helps to identify unique opportunities for enhancing holistic nutrient management across systems within the CB watershed.

Multiscale spatiotemporal characteristics and influencing factors of ecosystem service value of groundwater in China

Ecosystem services are fundamental to human survival on Earth, but studies on ecosystem service value of groundwater (ESV-G) are rare. The multiscale characteristics and influencing factors of ESV-G in China from 2000 to 2020 were analyzed in this study. The results showed that ESV-G decreased first and then increased, the average ESV-G was 130.30 thousand yuan/km2, and ESV-G tended to shift towards middle level (second to fourth class). The Hu Line was the dividing line between the first class (more than half area) and the others. The AI and FRAC values indicated that the patches of ESV-G were more concentrated, with simpler shapes that were more amenable to governance at the province scale. Hot spots and cold spots were mainly located in the eastern and western parts of Hu Line, respectively. The ESV-G of the cold spots per unit area at the province scale was higher than that at the city scale, which indicated that the province scale had the potential for higher ESV-G per unit area and cost advantage. Precipitation and temperature were the main factors affecting ESV-G; the influence of human activities on ESV-G increased on a larger scale as time went by. Combination of precipitation and Digital Elevation Model (DEM) had the greatest influence on ESV-G among the combinational influencing factors. The province scale was the optimal scale to manage ESV-G. Climate change had led to the expansion of hot and cold spots of ESV-G, northern and southern areas should combine existing policies to carry out differentiated governance. This study extended the scope of ecosystem service value studies from land surface to underground, providing a scientific basis for the management of groundwater ecosystem.

Spatiotemporal distribution patterns and coupling effects of aquatic environmental factors in the dry-wet season over a decade from the Beibu Gulf, South China Sea

Since the 21st century, the Beibu Gulf area has been affected by increasing anthropogenic activities, which makes the coastal aquatic ecosystem extremely concerning. However, the comprehensive exploration and analysis of the long-term scale behavior change characteristics of various water quality environment factors is still limited. Through comprehensively detecting coastal surface water environmental behavior information from 33 locations in the Beibu Gulf from 2005 to 2015, we revealed and quantified mutual response characteristics and patterns of various environmental indicators. The main environmental pollution indicators (e.g., COD, NH4+, NO3-, and DIP) showed a gradual decrease in concentration from the coast to the offshore sea area, and significantly increases during the wet season. The semi-enclosed Maowei Sea exhibited the most prominent performance with significant differences compared to other regions in Beibu Gulf. The average Chlorophyll-a (Chla) content in the coastal area of the Beibu Gulf during the wet season was more than twice that of the dry season, yet the interaction pattern between Chla and environmental factors in the two seasons was opposite to its concentration behavior, accompanied by a closely significant relationship with thermohaline structure and the input of nitrogen and phosphorous nutrients. The multivariate statistical analysis results of total nutrient dynamics suggested that the Beibu Gulf was clearly divided into different regions in both dry and wet season clusters. The present study can provide a comprehensive perspective for the spatial and temporal migration patterns and transformation laws of coastal water environmental factor, which should contribute to improve the prevention countermeasure of nutrient pollution in coastal environment.

Development of a basin-scale total nitrogen prediction model by integrating clustering and regression methods

Nutrient runoff into rivers caused by human activity has led to global eutrophication issues. The Nakdong River in South Korea is currently facing significant challenges related to eutrophication and harmful algal blooms, underscoring the critical importance of managing total nitrogen (T-N) levels. However, traditional methods of indoor analysis, which depend on sampling, are labor-intensive and face limitations in collecting high-frequency data. Despite advancements in sensor allowing for the measurement of various parameters, sensors still cannot directly measure T-N, necessitating surrogate regression methods. Therefore, we conducted T-N predictions using a water quality dataset collected from 2018 to 2022 at 157 observatories within the Nakdong River basin. To account for the water quality characteristics of each location, we employed a clustering technique to divide the basin and compared a Gaussian mixture model with K-means clustering. Moreover, optimal regressor for each cluster was selected by comparing multiple linear regression (MLR), random forest, and XGBoost. The results showed that forming four clusters via K-means clustering was the most suitable approach and MLR was reasonably accurate for all clusters. Subsequently, recursive feature elimination cross-validation was used to identify suitable parameters for T-N prediction, thus leading to the construction of high-accuracy T-N prediction models. Clustering was useful not only for improving the regressors but also for spatially analyzing the water quality characteristics of the Nakdong River. The MLR model can reveal causal relationships and thus is useful for decision-making. The results of this study revealed that the combination of a simple linear regression model and clustering method can be applied to a wide watershed. The clustering-based regression model showed potential for accurately predicting T-N at the basin level and is expected to contribute to nationwide water quality management through future applications in various fields.

Evaluating water-quality trends in agricultural watersheds prioritized for management-practice implementation

Many agricultural watersheds rely on the voluntary use of management practices (MPs) to reduce nonpoint source nutrient and sediment loads; however, the water-quality effects of MPs are uncertain. We interpreted water-quality responses from as early as 1985 through 2020 in three agricultural Chesapeake Bay watersheds that were prioritized for MP implementation, namely, the Smith Creek (Virginia), Upper Chester River (Maryland), and Conewago Creek (Pennsylvania) watersheds. We synthesized patterns in MPs, climate, land use, and nutrient inputs to better understand factors affecting nutrient and sediment loads. Relations between MPs and expected water-quality improvements were not consistently identifiable. The number of MPs increased in all watersheds since the early 2010s, but most monitored nutrient and sediment loads did not decrease. Nutrient and sediment loads increased or remained stable in Smith Creek and the Upper Chester River. Sediment loads and some nutrient loads decreased in Conewago Creek. In Smith Creek, a 36-year time-series model suggests that changes in manure affected flow-normalized total nitrogen loads. We hypothesize that increases in nutrient applications may overshadow some expected MP effects. MPs might have stemmed further water-quality degradation, but improvements in nutrient loads may rely on reducing manure and fertilizer applications. Our results highlight the importance of assessing MP performance with long-term monitoring-based studies.

FlowSOM clustering - A novel pattern recognition approach for water research: Application to a hyper-arid coastal aquifer system

Monitoring and understanding of water resources have become essential in designing effective and sustainable management strategies to overcome the growing water quality challenges. In this context, the utilization of unsupervised learning techniques for evaluating environmental tracers has facilitated the exploration of sources and dynamics of groundwater systems through pattern recognition. However, conventional techniques may overlook spatial and temporal non-linearities present in water research data. This paper introduces the adaptation of FlowSOM, a pioneering approach that combines self-organizing maps (SOM) and minimal spanning trees (MST), with the fast-greedy network clustering algorithm to unravel intricate relationships within multivariate water quality datasets. By capturing connections within the data, this ensemble tool enhances clustering and pattern recognition. Applied to the complex water quality context of the hyper-arid transboundary Caplina/Concordia coastal aquifer system (Peru/Chile), the FlowSOM network and clustering yielded compelling results in pattern recognition of the aquifer salinization. Analyzing 143 groundwater samples across eight variables, including major ions, the approach supports the identification of distinct clusters and connections between them. Three primary sources of salinization were identified: river percolation, slow lateral aquitard recharge, and seawater intrusion. The analysis demonstrated the superiority of FlowSOM clustering over traditional techniques in the case study, producing clusters that align more closely with the actual hydrogeochemical pattern. The outcomes broaden the utilization of multivariate analysis in water research, presenting a comprehensive approach to support the understanding of groundwater systems.

Optimizing the development of contaminated land in China: Exploring machine-learning to identify risk markers

Often available for use, previously developed land, which includes residential and commercial/industrial areas, presents a significant challenge due to the risk to human health. China's 2018 release of health risk assessment standards for land reuse aimed to bridge this gap in soil quality standards. Despite this, the absence of representative indicators strains risk managers economically and operationally. We improved China's land redevelopment approach by leveraging a dataset of 297,275 soil samples from 352 contaminated sites, employing machine learning. Our method incorporating soil quality standards from seven countries to discern patterns for establishing a cost-effective evaluative framework. Our research findings demonstrated that detection costs could be curtailed by 60% while maintaining consistency with international soil standards (prediction accuracy = 90-98%). Our findings deepen insights into soil pollution, proposing a more efficient risk assessment system for land redevelopment, addressing the current dearth of expertise in evaluating land development in China.

Our national nutrient reduction needs: Applying a conservation prioritization framework to US agricultural lands

Targeted conservation approaches seek to focus resources on areas where they can deliver the greatest benefits and are recognized as key to reducing nonpoint source nutrients from agricultural landscapes into sensitive receiving waters. Moreover, there is growing recognition of the importance and complementarity of in-field and edge-of-field conservation for reaching nutrient reduction goals. Here we provide a generic prioritization that can help with spatial targeting and applied it across the conterminous US (CONUS). The prioritization begins with identifying areas with high agricultural nutrient surplus, i.e., where the most nitrogen (N) and/or phosphorus (P) inputs are left on the landscape after crop harvest. Subwatersheds with high surplus included 52% and 50% of CONUS subwatersheds for N and P, respectively, and were located predominantly in the Midwest for N, in the South for P, and in California for both N and P. Then we identified the most suitable conservation strategies using a hierarchy of metrics including nutrient use efficiency (proportion of new nutrient inputs removed by crop harvest), tile drainage, existing buffers for agricultural run-off, and wetland restoration potential. In-field nutrient input reduction emerged as a priority because nutrient use efficiency fell below a high but achievable goal of 0.7 (30% of nutrients applied are not utilized) in 45% and 44% of CONUS subwatersheds for N and P, respectively. In many parts of the southern and western US, in-field conservation (i.e., reducing inputs + preventing nutrients from leaving fields) alone was likely the optimal strategy as agriculture was already well-buffered. However, stacking in-field conservation with additional edge-of-field buffering would be important to conservation strategies in 35% and 29% of CONUS subwatersheds for N and P, respectively. Nutrient use efficiencies were often high enough in the Midwest that proposed strategies focused more on preventing nutrients from leaving fields, managing tile effluent, and buffering agricultural fields. Almost all major river basins would benefit from a variety of nutrient reduction conservation strategies, underscoring the potential of targeted approaches to help limit excess nutrients in surface and ground waters.

The changing nitrogen landscape of United States streams: Declining deposition and increasing organic nitrogen

Air quality regulations have led to decreased nitrogen (N) and sulfur deposition across the conterminous United States (CONUS) during the last several decades, particularly in the eastern parts. But it is unclear if declining deposition has altered stream N at large scales. We compared watershed N inputs with N chemistry from over 2,000 CONUS streams where deposition was the largest N input to the watershed. Weighted change analysis showed that deposition declined across most watersheds, especially in the Eastern CONUS. Nationally, declining N deposition was not associated with significant large-scale declines in stream nitrate concentration. Instead, significant increases in stream dissolved organic carbon (DOC) and total organic N (TON) were widespread across regions. Possible mechanisms behind these increases include declines in acidity and/or ionic strength drivers, changes in carbon availability, and/or climate variables. Our results also reveal a declining trend of DOC/TON ratio over the entire study period, primarily influenced by the trend in the Eastern region, suggesting the rate of increase in stream TON exceeded the rate of increase in DOC concentration during this period. Our results illustrate the complexity of nutrient cycling that links long-term atmospheric deposition to water quality. More research is needed to understand how increased dissolved organic N could affect aquatic ecosystems and downstream riverine nutrient export.

Nutrient transport following water transfer through the world's largest water diversion channel

Nutrient levels in the artificial channel constructed for the Middle Route Project are significant indicators of water quality safety and aquatic ecological integrity for this large, inter-basin scheme. However, the distribution and transport of nutrients along the channel were poorly understood. Based on a time-series dataset as well as mass balance and material flow analysis methods, the water and nutrient transport fluxes in the Middle Route of the South-to-North Water Diversion Project were identified in this study. The results indicate that the nutrient concentrations varied considerably with time, but there was no significant difference among the 30 stations of the main channel. Seasonal temperature difference was the major factor in the large fluctuations of water quality indicators over time. The nutrient loadings varied with the water volume outputs from the main channel to the water-receiving cities. Atmospheric deposition was an important source of nutrients in the main channel, accounting for 9.13%, 20.6%, and 0.635% of the nitrogen, phosphorus, and sulfur input from the Danjiangkou Reservoir, respectively. In 2021, a net accumulation of 988 tons of N, 29 tons of P, and 2,540 tons of S, respectively, were present in the main channel. The increase of these external and internal nutrient loadings would cause water quality fluctuation and deterioration in some local sections of the main channel. Our study quantified the spatial and temporal patterns of nutrient transport in the Middle Route and revealed the ecological effects on the aquatic environment, assisting authorities on the project to develop effective water conservation strategies.

Effects of point and nonpoint source controls on total phosphorus load trends across the Chesapeake Bay watershed, USA

Reduction of total phosphorus (TP) loads has long been a management focus of Chesapeake Bay restoration, but riverine monitoring stations have shown mixed temporal trends. To better understand the regional patterns and drivers of TP trends across the Bay watershed, we compiled and analyzed TP load data from 90 non-tidal network stations using clustering and random forest (RF) approaches. These stations were categorized into two distinct clusters of short-term (2013-2020) TP load trends, i.e. monotonic increase (n = 35) and monotonic decline (n = 55). RF models were developed to identify likely regional drivers of TP trend clusters. Reductions in point sources and agricultural nonpoint sources (i.e. fertilizer) both contributed to water-quality improvement in our period of analysis, thereby demonstrating the effectiveness of nutrient management and the importance of continuing such efforts. In addition, declining TP trends have a larger chance to occur in carbonate areas but a smaller chance in Coastal Plain areas, with the latter likely reflecting the effect of legacy P. To provide spatially explicit information, TP trend clusters were predicted for the entire watershed at the scale of river segments, which are more directly relevant to watershed planning. Among the 975 river segments, 544 (56%) and 431 (44%) were classified as 'monotonic increase' and 'monotonic decrease', respectively. Furthermore, these predicted TP trend clusters were paired with our previously published total nitrogen (TN) trend clusters, showing that TP and TN both declined in 185 segments (19%) and neither declined in 337 segments (35%). Broadly speaking, large-scale nutrient reduction efforts are underway in many regions to curb eutrophication. Water-quality responses and drivers may differ among systems, but our work provides important new evidence on the effectiveness of management efforts toward controlling point and nonpoint sources.

Application of classification machine learning algorithms for characterizing nutrient transport in a clay plain agricultural watershed

Excess nutrients in surface water and groundwater can lead to water quality deterioration in available water resources. Thus, the classification of nutrient concentrations in water resources has gained significant attention during recent decades. Machine learning (ML) algorithms are considered an efficient tool to describe nutrient loss from agricultural land to surface water and groundwater. Previous studies have applied regression and classification ML algorithms to predict nutrient concentrations in surface water and/or groundwater, or to categorize an output variable using a limited number of input variables. However, there have been no studies that examined the application of different ML classification algorithms in agricultural settings to classify various output variables using a wide range of input variables. In this study, twenty-four ML classification algorithms were implemented on a dataset from three locations within the Upper Parkhill watershed, an agricultural watershed in southern Ontario, Canada. Nutrient concentrations in surface water were classified using geochemical and physical water parameters of surface water and groundwater (e.g., pH), climate and field conditions as the input variables. The performance of these algorithms was evaluated using four evaluation metrics (e.g., classification accuracy) to identify the optimal algorithm for classifying the output variables. Ensemble bagged trees was found to be the optimal ML algorithm for classifying nitrate concentration in surface water (accuracy of 90.9%), while the weighted KNN was the most appropriate algorithm for categorizing the total phosphorus concentration (accuracy of 87%). The ensemble subspace discriminant algorithm gave the highest overall classification accuracy for the concentration of soluble reactive phosphorus and total dissolved phosphorus in surface water with an accuracy of 79.2% and 77.9%, respectively. This study exemplifies that ML algorithms can be used to signify exceedance of recommended concentrations of nutrients in surface waters in agricultural watersheds. Results are useful for decision makers to develop nutrient management strategies.

Shifts in the composition of nitrogen deposition in the conterminous United States are discernable in stream chemistry

Across the conterminous United States (U.S.), the composition of atmospheric nitrogen (N) deposition is changing spatially and temporally. Previously, deposition was dominated by oxidized N, but now reduced N (ammonia [NH3] + ammonium [NH4+]) is equivalent to oxidized N when deposition is averaged across the entire nation and, in some areas, reduced N dominates deposition. To evaluate if there are effects of this change on stream chemistry at the national scale, estimates of N deposition form (oxidized or reduced) from the National Atmospheric Deposition Program Total Deposition data were coupled with stream measurements from the U.S. Environmental Protection Agency (EPA) National Rivers and Streams Assessments (three stream surveys between 2000 and 2014). A recent fine-scaled N input inventory was used to identify watersheds (<1000 km2) where atmospheric deposition is the largest N source (n = 1906). Within these more atmospherically-influenced watersheds there was a clear temporal shift from a greater proportion of sites dominated by oxidized N deposition to a greater proportion of sites dominated by reduced forms of N deposition. We found a significant positive correlation between oxidized N deposition and stream NO3- concentrations, whereas the correlation between reduced N deposition and stream NO3- concentrations were significant but weaker. Sites dominated by atmospheric inputs of reduced N forms had higher stream total organic N and total N despite lower total N deposition on average. This higher stream concentration of total N is mainly driven by the higher concentration of total organic N, suggesting an interaction between elevated reduced N in deposition and living components of the ecosystem or soil organic matter dynamics. Regardless of the proportion of reduced to oxidized N forms in deposition, stream NH4+ concentrations were generally low, suggesting that N deposited in a reduced form is rapidly immobilized, nitrified and/or assimilated by watershed processes.

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

Many coastal ecosystems suffer from eutrophication, algal blooms, and dead zones due to excessive anthropogenic inputs of nitrogen (N) and phosphorus (P). This has led to regional restoration efforts that focus on managing watershed loads of N and P. In Chesapeake Bay, the largest estuary in the United States, dual nutrient reductions of N and P have been pursued since the 1980s. However, it remains unclear whether nutrient limitation - an indicator of restriction of algal growth by supplies of N and P - has changed in the tributaries of Chesapeake Bay following decades of reduction efforts. Toward that end, we analyzed historical data from nutrient-addition bioassay experiments and data from the Chesapeake Bay long-term water-quality monitoring program for six stations in three tidal tributaries (i.e., Patuxent, Potomac, and Choptank Rivers). Classification and regression tree (CART) models were developed using concurrent collections of water-quality parameters for each bioassay monitoring location during 1990-2003, which satisfactorily predicted the bioassay-based measures of nutrient limitation (classification accuracy = 96%). Predictions from the CART models using water-quality monitoring data showed enhanced nutrient limitation over the period of 1985-2020 at four of the six stations, including the downstream station in each of these three tributaries. These results indicate detectable, long-term water-quality improvements in the tidal tributaries. Overall, this research provides a new analytical tool for detecting signs of ecosystem recovery following nutrient reductions. More broadly, the approach can be adapted to other waterbodies with long-term bioassays and water-quality data sets to detect ecosystem recovery.