In Chesapeake Bay in the United States, decades of management efforts have resulted in modest reductions of nutrient loads from the watershed, but the corresponding improvements in estuarine water quality have not consistently followed. Generalize additive models were used to directly link river flows and nutrient loads from the watershed to nutrient trends in the estuary on a station-by-station basis, which allowed for identification of exactly when and where responses are happening.

Effective stewardship of ecosystems to sustain current ecological status or mitigate impacts requires nuanced understanding of how conditions have changed over time in response to anthropogenic pressures and natural variability. Detecting and appropriately characterizing changes requires accurate and flexible trend assessment methods that can be readily applied to environmental monitoring datasets. A key requirement is complete propagation of uncertainty through the analysis.

Environmental monitoring programs generate multivariate time series for the assessment of ecosystem health. Recent developments in causal inference offer ways to translate these observational data into networks able to explain gains and losses in the trajectories of indicator variables. Here, we present a case study of this approach using surface water dissolved oxygen (DO) criteria attainment across the Chesapeake Bay.

In the Chesapeake Bay, excess nitrogen (N) from both landscape and atmospheric sources has for decades fueled algal growth, disrupted aquatic ecosystems, and negatively impacted coastal economies. Since the 1980s, Chesapeake Bay Program partners have worked to implement a wide range of measures across the region—from the upgrading of wastewater treatment plants to implementation of farm-level best management practices—to reduce N fluxes to the Bay.

Extensive efforts to adaptively manage nutrient pollution rely on Chesapeake Bay Program’s (Phase 6) Watershed Model, called Chesapeake Assessment Scenario Tool (CAST), which helps decision-makers plan and track implementation of Best Management Practices (BMPs). We describe mathematical characteristics of CAST and develop a constrained nonlinear BMP-subset model, software, and visualization framework.

A number of statistical approaches have been developed to quantify the overall trend in river water quality, but most approaches are not intended for reporting separate trends for different flow conditions. We propose an approach called FN2Q, which is an extension of the flow-normalization (FN) procedure of the well-established WRTDS (“Weighted Regressions on Time, Discharge, and Season”) method.

The Orinoco river basin is the third largest river in the world by volume. Its catchment encompasses 27 major sub-basins including the Bita with a catchment area of about 825,000 ha, which originates in the Colombian high plains in the Llanos ecoregion.

Susan Lynn Williams (1951–2018) was an exceptional marine ecologist whose research focused broadly on the ecology of benthic nearshore environments dominated by seagrasses, seaweeds, and coral reefs. She took an empirical approach founded in techniques of physiological ecology. Susan was committed to applying her research results to ocean management through outreach to decision-makers and resource managers.

Understanding the temporal and spatial roles of nutrient limitation on phytoplankton growth is necessary for developing successful management strategies. Chesapeake Bay has well-documented seasonal and spatial variations in nutrient limitation, but it remains unknown whether these patterns of nutrient limitation have changed in response to nutrient management efforts.

Nutrient and sediment transport exhibit strong spatial and temporal inequality, with a small percentage of locations and events contributing to the vast majority of total annual loads. The processes for determining how to reduce total annual loads at a watershed scale often target spatial, but not temporal, components of inequality.