Timing and magnitude of climate‐driven range shifts in transboundary fish stocks challenge their management
Bibliographic record
Abstract
Climate change is shifting the distribution of shared fish stocks between neighboring countries' Exclusive Economic Zones (EEZs) and the high seas. The timescale of these transboundary shifts determines how climate change will affect international fisheries governance. Here, we explore this timescale by coupling a large ensemble simulation of an Earth system model under a high emission climate change scenario to a dynamic population model. We show that by 2030, 23% of transboundary stocks will have shifted and 78% of the world's EEZs will have experienced at least one shifting stock. By the end of this century, projections show a total of 45% of stocks shifting globally and 81% of EEZs waters with at least one shifting stock. The magnitude of such shifts is reflected in changes in catch proportion between EEZs sharing a transboundary stock. By 2030, global EEZs are projected to experience an average change of 59% in catch proportion of transboundary stocks. Many countries that are highly dependent on fisheries for livelihood and food security emerge as hotspots for transboundary shifts. These hotspots are characterized by early shifts in the distribution of an important number of transboundary stocks. Existing international fisheries agreements need to be assessed for their capacity to address the social-ecological implications of climate-change-driven transboundary shifts. Some of these agreements will need to be adjusted to limit potential conflict between the parties of interest. Meanwhile, new agreements will need to be anticipatory and consider these concerns and their associated uncertainties to be resilient to global change.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".