The transboundary nature of the world’s exploited marine species
Bibliographic record
Abstract
Regulatory boundaries and species distributions often do not align. This is especially the case for marine species crossing multiple Exclusive Economic Zones (EEZs). Such movements represent a challenge for fisheries management, as policies tend to focus at the national level, yet international collaborations are needed to maximize long-term ecological, social and economic benefits of shared marine species. Here, we combined species distributions and the spatial delineation of EEZs at the global level to identify the number of commercially exploited marine species that are shared between neighboring nations. We found that 67% of the species analyzed are transboundary (n = 633). Between 2005 and 2014, fisheries targeting these species within global-EEZs caught on average 48 million tonnes per year, equivalent to an average of USD 77 billion in annual fishing revenue. For select countries, over 90% of their catch and economic benefits were attributable to a few shared resources. Our analysis suggests that catches from transboundary species are declining more than those from non-transboundary species. Our study has direct implications for managing fisheries targeting transboundary species, highlighting the need for strengthened effective and equitable international cooperation.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".