Small fish biomass limits the catch potential in the High Seas
Bibliographic record
Abstract
The High Seas, lying beyond the boundaries of nations’ Exclusive Economic Zones, cover the majority of the ocean surface and host roughly two thirds of marine primary production. Yet, only a small fraction of global wild fish catch comes from the High Seas, despite intensifying industrial fishing efforts. The surprisingly small fish catch could reflect economic features of the High Seas - such as the difficulty and cost of fishing in remote parts of the ocean surface - or ecological features resulting in a small biomass of fish relative to primary production. We use the coupled biological-economic model BOATS to estimate contributing factors, comparing observed catches with simulations where: (i) fishing cost depends on distance from shore and seafloor depth; (ii) catchability depends on seafloor depth or vertical habitat extent; (iii) regions with micronutrient limitation have reduced biomass production; (iv) the trophic transfer of energy from primary production to demersal food webs depends on depth; and (v) High Seas biomass migrates to coastal regions. Our results suggest that the most important features are ecological: demersal fish communities receive a large proportion of primary production in shallow waters, but very little in deep waters due to respiration by small organisms throughout the water column. Other factors play a secondary role, with migrations having a potentially large but uncertain role, and economic factors having the smallest effects. Our results stress the importance of properly representing the High Seas biomass in future fisheries projections, and clarify their limited role in global food provision.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".