Simulating the Trophic Impacts of Fishery Policy Options on the West Florida Shelf Using Ecopath with Ecosim
Bibliographic record
Abstract
Abstract The recovery of several top predators in the Gulf of Mexico is likely to increase predation on and competition with other target and nontarget species, possibly causing the abundance of those species to decline. While changes are taking place at the upper trophic levels, exploitation of prey species and climate change are altering productivity at the lower levels. An Ecopath with Ecosim model was developed to simulate the ecosystem impacts of Reef Fish Fishery Management Plan Amendment 30B (which aims to rebuild Gag Mycteroperca microlepis) and Amendment 31 (which reduces effort in the longline fishery). We also evaluated the impact of a hypothetical increase in the exploitation of baitfish and future changes to phytoplankton productivity. The model predicted that rebuilding Gag will cause the biomass of Black Sea Bass Centropristis striata to be 20% lower than it is now and those of Black Grouper M. bonaci, King Mackerel Scomberomorus cavalla, and other shallow-water groupers to be 5–10% lower. Reducing effort in the longline fishery will lead to biomass declines for Black Sea Bass (13%) and Vermilion Snapper Rhomboplites aurorubens (7%). Harvesting baitfish at historically high levels caused the biomass of Red Snapper Lutjanus campechanus, Vermilion Snapper, Greater Amberjack Seriola dumerili, King Mackerel, and numerous species of dolphins and seabirds to be 5–12% lower after 20 years, while biomass increased for species whose diet consists of benthic-associated prey. This paper demonstrates that ecosystem models can be used to quantify the potential ecological impacts of management goals and that the predictions of such models should be considered alongside stock projections from single-species models that assume a constant environment. We intend for this research effort to lead to a more focused and coherent strategy for ecosystem-based fishery management in the Gulf of Mexico. Received January 30, 2014; accepted September 9, 2014
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 teacher head, 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".