Ocean Futures for the World’s Largest Yellowfin Tuna Population Under the Combined Effects of Ocean Warming and Acidification
Bibliographic record
Abstract
The impacts of climate change are expected to have profound effects on the fisheries of the Pacific Ocean, including its tuna fisheries, the largest globally. This study examined the combined effects of climate change on the yellowfin tuna population using the ecosystem model SEAPODYM. Yellowfin tuna fisheries in the Pacific contribute significantly to the economies and food security of Pacific Island Countries and Territories and Oceania. We use an ensemble of earth climate models to project yellowfin populations under a high greenhouse gas emissions (IPCC RCP8.5) scenario, which includes, the combined effects of a warming ocean, increasing acidification and changing ocean chemistry. Our results suggest that the acidification impact will be smaller in comparison to the ocean warming impact, even in the most extreme ensemble member scenario explored, but will have additional influences on yellowfin tuna population dynamics. An eastward shift in the distribution of yellowfin tuna was observed in the projections in the model ensemble in the absence of explicitly accounting for changes in acidification. The extent of this shift did not substantially differ when the three-acidification induced larval mortality scenarios were included in the ensemble; however, acidification was projected to weaken the magnitude of the increase in abundance in the eastern Pacific. Together with intensive fishing, these potential changes are likely to challenge the global fishing industry as well as the economies and food systems of many small Pacific Island Countries and Territories. The modelling framework applied in this study provides a tool for evaluating such effects and informing policy development.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".