Sea‐surface temperature anomalies mediate changes in fish richness and abundance in Atlantic and Gulf of Mexico estuaries
Bibliographic record
Abstract
Abstract Aim Anthropogenic warming of marine systems has caused biological and physiological responses that are fundamentally altering ecosystem structure. Because estuaries exist at the land‐ocean interface, they are particularly vulnerable to the effects of ocean warming as they can undergo rapid biogeochemical and hydrological shifts due to climate and land‐use change. We explored how multiple components of estuarine fish diversity—turnover, richness, and abundance—have changed in the North Atlantic and Gulf of Mexico estuaries across space and time and the drivers of change. Location North Atlantic and Gulf of Mexico. Taxa Fish. Method We compiled long‐term (>30 years), continent‐wide fisheries independent trawl surveys conducted in estuaries—from the Gulf of Maine to the Gulf of Mexico (U.S. waters)—and combined these with climate and land‐use‐land‐cover data to examine trends and ecological drivers of fish richness, abundance and turnover using mixed‐effect models. Results Species richness, abundance and turnover have increased in North Atlantic and Gulf of Mexico estuaries in the last 30 years. These changes were mediated largely by sea‐surface temperature anomalies, especially in more northern estuaries where warming has been relatively pronounced. Main Conclusion The increasing trajectory of turnover in many estuaries suggests that fish communities have changed fundamentally from the baselines. A fundamental change in community composition can lead to an irreversible trophic imbalance or alternative stable states among other outcomes. Thus, predicting how shifting community structures might influence food webs, ecosystem stability, and human resource use remain a pertinent task.
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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 | 0.000 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".