Using a coupled ecosystem modeling approach to evaluate effects of reductions in nutrients and hypoxia on living marine resources
Bibliographic record
Abstract
ABSTRACT Objective The objective of this study was to evaluate effects of planned reductions in hypoxia on fish and fisheries in the northern Gulf of Mexico. To specifically address goals established by the Hypoxia Task Force, a short-term goal of 20% reduction in nitrogen and phosphorus loading from the Mississippi River and long-term goals of 40% and 50% reductions in nitrogen and phosphorus loading (encompassing the goal of reducing the 5-year average hypoxic area size to 5,000 km2) were used as model scenarios. Methods An Ecospace model was co-produced representing the northern Gulf of Mexico food web, with 66 groups of fish, shellfish, and other marine organisms. Four species of high economic and/or ecological interest were the focus of this paper: Red Snapper Lutjanus campechanus, Gulf Menhaden Brevoortia patronus, Atlantic Croaker Micropogonias undulatus, and white shrimp Penaeus setiferus. The Ecospace model was linked to a calibrated physical–biological Regional Ocean Modeling System-based model that passed dissolved oxygen, phytoplankton, and temperature output of the simulation scenarios on to Ecospace. Novel spatial Monte Carlo simulations were used to determine the probability of the outcomes and calculate uncertainty ranges. Results Hypoxia affected all organisms to some extent, either by avoidance of hypoxic areas or by a decrease in biomass. Under simulated nutrient reduction scenarios, the biomass of some species increased (e.g., Gulf Menhaden and white shrimp), while the biomass of other species decreased (e.g., Red Snapper and Atlantic Croaker). Although hypoxia affected the spatial distribution of species biomass, the total biomass changes in response to the nutrient reduction scenarios for the most part did not exceed the uncertainty bounds of the scenario in which nutrients were not reduced. Conclusions Exploring reductions in nutrient loading from the Mississippi River and the subsequent reductions in hypoxia separately and together revealed that reducing hypoxia has a positive effect on living resources, while reducing nutrients has a negative effect. The small net effects were specific to each species due to species-specific hypoxia sensitivities and trophic interactions. Nutrient reductions affected the spatial distribution by increasing fisheries species biomass in areas closer to the coast. The output of this coupled modeling approach supports managers in assessing effects of planned nutrient reduction goals on ecosystem function, living resources, and fisheries landings.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".