Measuring and modeling diel oxygen dynamics in a shallow hypereutrophic estuary: Implications of low oxygen exposure on aquatic life.

Hypoxia, or low dissolved oxygen (DO) is a common outcome of excess nitrogen and phosphorus delivered to coastal waterbodies. Shallow and highly productive estuaries are particularly susceptible to diel-cycling hypoxia, which can exhibit DO excursions between anoxia (DO ≤1 mg L-1) and supersaturated concentrations within a day. Shallow estuaries exhibiting diel-cycling hypoxia are understudied relative to larger and deeper estuaries, with very few mechanistic models that can predict diel oxygen dynamics. We utilized continuous monitoring data and the Coastal Generalized Ecosystem Model (CGEM) coupled with an Environmental Fluid Dynamics Code (EFDC) hydrodynamic model to simulate diel DO dynamics in Weeks Bay, AL. Low oxygen conditions ranging from anoxia to DO ≤4 mg L-1 were consistently observed and simulated in the lower water column for periods of minutes to >11 h. High frequency observations and model simulations also identified significant vertical gradients in near bottom DO that varied as much as 0.8 to 3.1 mg L-1 within 0.4 m from the bottom. This spatiotemporal variability presents unique challenges to adequately quantify DO dynamics and the potential exposure of aquatic life to low oxygen conditions. Our results demonstrate the need for detailed measurements to adequately quantify the complex DO dynamics in shallow estuaries. We also demonstrate that simulation models can be successfully applied to evaluate diel oxygen dynamics in complex estuarine environments when calibrated with fine time scale data and effective parameterization of water column and benthic metabolic processes.

From Protein Sequence to Structure: The Next Frontier in Cross-Species Extrapolation for Chemical Safety Evaluations.

Computational screening for potentially bioactive molecules using advanced molecular modeling approaches including molecular docking and molecular dynamic simulation is mainstream in certain fields like drug discovery. Significant advances in computationally predicting protein structures from sequence information have also expanded the availability of structures for nonmodel species. Therefore, the objective of the present study was to develop an analysis pipeline to harness the power of these bioinformatics approaches for cross-species extrapolation for evaluating chemical safety. The Sequence Alignment to Predict Across Species Susceptibility (SeqAPASS) tool compares protein-sequence similarity across species for conservation of known chemical targets, providing an initial line of evidence for extrapolation of toxicity knowledge. However, with the development of structural models from tools like the Iterative Threading ASSEmbly Refinement (ITASSER), analyses of protein structural conservation can be included to add further lines of evidence and generate protein models across species. Models generated through such a pipeline could then be used for advanced molecular modeling approaches in the context of species extrapolation. Two case examples illustrating this pipeline from SeqAPASS sequences to I-TASSER-generated protein structures were created for human liver fatty acid-binding protein (LFABP) and androgen receptor (AR). Ninety-nine LFABP and 268 AR protein models representing diverse species were generated and analyzed for conservation using template modeling (TM)-align. The results from the structural comparisons were in line with the sequence-based SeqAPASS workflow, adding further evidence of LFABL and AR conservation across vertebrate species. The present study lays the foundation for expanding the capabilities of the web-based SeqAPASS tool to include structural comparisons for species extrapolation, facilitating more rapid and efficient toxicological assessments among species with limited or no existing toxicity data. Environ Toxicol Chem 2023;42:463-474. © 2022 SETAC. This article has been contributed to by U.S. Government employees and their work is in the public domain in the USA.

Inter-model comparison of simulated Gulf of Mexico hypoxia in response to reduced nutrient loads: effects of phytoplankton and organic matter parameterization.

Complex simulation models are a valuable tool to inform nutrient management decisions aimed at reducing hypoxia in the northern Gulf of Mexico, yet simulated hypoxia response to reduced nutrients varies greatly between models. We compared two biogeochemical models driven by the same hydrodynamics, the Coastal Generalized Ecosystem Model (CGEM) and Gulf of Mexico Dissolved Oxygen Model (GoMDOM), to investigate how they differ in simulating hypoxia and their response to reduced nutrients. Different phytoplankton nutrient kinetics produced 2-3 times more hypoxic area and volume on the western shelf in CGEM compared to GoMDOM. Reductions in hypoxic area were greatest in the western shelf, comprising 72% (~4,200 km2) of the total shelfwide hypoxia response. The range of hypoxia responses from multiple models suggests a 60% load reduction may result in a 33% reduction in hypoxic area, leaving an annual hypoxic area of ~9,000 km2 based on the latest 5-yr average (13,928 km2).

The impact of alternative nutrient kinetics and computational grid size on model predicted primary production and hypoxic area in the northern Gulf of Mexico.

Model structure uncertainty is seldom calculated because of the difficulty and time required to perform such analyses. Here we explore how a coastal model using the Monod versus Droop formulations and a 6 km × 6 km versus 2 km 2 × km computational grid size predict primary production and hypoxic area in the Gulf of Mexico. Results from these models were compared to each other and to observations, and sensitivity analyses were performed. The different models fit the observations almost equally well. The 6k-model calculated higher rates of production and settling, and especially a larger hypoxic area, in comparison to the 2k-model. The Monod-based model calculated higher production, especially close to the river delta regions, but smaller summer hypoxic area, than the model using the Droop formulation. The Monod-based model was almost twice as sensitive to changes in nutrient loads in comparison to the Droop model, which can have management implications.

Modeling the Relative Importance of Nutrient and Carbon Loads, Boundary Fluxes, and Sediment Fluxes on Gulf of Mexico Hypoxia.

The Louisiana continental shelf in the northern Gulf of Mexico experiences bottom water hypoxia in the summer. In this study, we applied a biogeochemical model that simulates dissolved oxygen concentrations on the shelf in response to varying riverine nutrient and organic carbon loads, boundary fluxes, and sediment fluxes. Five-year model simulations demonstrated that midsummer hypoxic areas were most sensitive to riverine nutrient loads and sediment oxygen demand from settled organic carbon. Hypoxic area predictions were also sensitive to nutrient and organic carbon fluxes from lateral boundaries. The predicted hypoxic area decreased with decreases in nutrient loads, but the extent of change was influenced by the method used to estimate model boundary concentrations. We demonstrated that modeling efforts to predict changes in hypoxic area on the continental shelf in relationship to changes in nutrients should include representative boundary nutrient and organic carbon concentrations and functions for estimating sediment oxygen demand that are linked to settled organic carbon derived from water-column primary production. On the basis of our model analyses using the most representative boundary concentrations, nutrient loads would need to be reduced by 69% to achieve the Gulf of Mexico Nutrient Task Force Action Plan target hypoxic area of 5000 km(2).

The Lake Michigan eutrophication model, LM3-Eutro: model development and calibration.

A eutrophication model developed to generate primary-production estimates in Lake Michigan can simulate 17 state variables, including three plankton classes and several nutrients. The model, known as the Lake Michigan Eutrophication model (LM3-Eutro), has a high-resolution computational grid that enables good spatial description of spring temperature and phytoplankton concentrations, which have significant gradients in the lakes. The grid also allows the model to predict concentrations in nearshore areas and other regions of interest. The model provided more accurate estimates of algal light limitation based on three-hour intervals compared to daily averages that are used in most eutrophication models, especially during sunny summer days when algal photo-inhibition often occurs. Model output was compared to field data using statistical parameters such as squares of the correlation coefficients to determine the best model fit. The calibrated model output fit the field data reasonably well for nutrients and phytoplankton, which provided confidence in the framework, governing equations, and coefficients used.