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

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

ABSTRACT: 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 tidal 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.

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Explainable machine learning improves interpretability in the predictive modeling of biological stream conditions in the Chesapeake Bay Watershed, USA

Anthropogenic alterations have resulted in widespread degradation of stream conditions. To aid in stream restoration and management, baseline estimates of conditions and improved explanation of factors driving their degradation are needed. We used random forests to model biological conditions using a benthic macroinvertebrate index of biotic integrity for small, non-tidal streams (upstream area ≤200 km2) in the Chesapeake Bay watershed (CBW) of the mid-Atlantic coast of North America. We utilized several global and local model interpretation tools to improve average and site-specific model inferences, respectively. The model was used to predict condition for 95,867 individual catchments for eight periods (2001, 2004, 2006, 2008, 2011, 2013, 2016, 2019). Predicted conditions were classified as Poor, FairGood, or Uncertain to align with management needs and individual reach lengths and catchment areas were summed by condition class for the CBW for each period. Global permutation and local Shapley importance values indicated percent of forest, development, and agriculture in upstream catchments had strong impacts on predictions. Development and agriculture negatively influenced stream condition for model average (partial dependence [PD] and accumulated local effect [ALE] plots) and local (individual condition expectation and Shapley value plots) levels. Friedman’s H-statistic indicated large overall interactions for these three land covers, and bivariate global plots (PD and ALE) supported interactions among agriculture and development. Total stream length and catchment area predicted in FairGood conditions decreased then increased over the 19-years (length/area: 66.6/65.4% in 2001, 66.3/65.2% in 2011, and 66.6/65.4% in 2019). Examination of individual catchment predictions between 2001 and 2019 showed those predicted to have the largest decreases in condition had large increases in development; whereas catchments predicted to exhibit the largest increases in condition showed moderate increases in forest cover. Use of global and local interpretative methods together with watershed-wide and individual catchment predictions support conservation practitioners that need to identify widespread and localized patterns, especially acknowledging that management actions typically take place at individual-reach scales.

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Potomac River Water Quality at Great Falls: 1940 – 2019

The U.S. Army Corps of Engineers operates Washington Aqueduct and provides drinking water to the Washington, D.C. area. Washington Aqueduct routinely samples its source of water, the Potomac River. Each year, it reports the monthly averages for basic water parameters and several pollutants and metals. Reports since 2001 are available online. Reports from 1905 to 2000, however, had limited distribution and their legibility has faded over time.

Dr. Norbert A. Jaworski recognized the historical value of these reports. To prevent their loss, he digitized the monthly values for several parameters. The Interstate Commission on the Potomac River Basin (ICPRB) later updated his dataset through 2019 and checked the entered data for accuracy. This report focuses on changes in temperature, hardness, pH, total solids, chloride, nitrate, and sulfate over the 80 years since ICPRB was formed in 1940. Visual representations (“heatmaps”) and trend analysis show significant increasing trends in all these parameters except nitrate. The report is intended to introduce the historical Washington Aqueduct water quality data to a broader audience and highlight their potential value to Potomac studies.

The Supplemental Materials document contains additional graphical representations of the data.

See the video summary of the report: