Safeguarding Seafood Security, Marine Biodiversity and Threatened Species: Can We Have Our Fish and Eat It too?
Bibliographic record
Abstract
The ocean contains an abundance of biodiversity that is vital to global food security. However, marine biodiversity is declining. Marine protected areas and marine reserves have been used to protect biodiversity, conserve threatened species and rebuild exploited species, but are perceived as restrictive to fishing, which has slowed progress towards ocean protection targets. Here, we perform a spatial prioritisation of the ocean to protect biodiversity, threatened species and food security. Food security was quantified using catch in tonnes per km2, per 0.5-degree cell of the ocean, using data from the Sea Around Us, a global database of industrial, artisanal, subsistence, and recreational fishing catches. Using Representative Biodiversity Areas [RBAs (the top 30% of the ocean based on holistic measures of biodiversity)], maps of 974 threatened species, and catch data for 2,170 exploited species, we find that these multiple, competing objectives are achievable with minimal compromise. Protecting 30% of the ocean using a multi-objective solution could protect 89% of RBAs, 89% of threatened species and maintain access to fishing grounds that provide 89% of global catch. Even when prioritising food security above conservation objectives we find significant protection for biodiversity and threatened species (85% RBAs, 73% threatened species). We highlight four exploited species for improved management, as they are consistently caught in areas of high conservation importance (skipjack tuna,Katsuwonus pelamis; yellowfin tuna,Thunnus albacares; Atlantic cod,Gadus morhua; Chilean jack mackerel,Trachurus murphyi). We show that a globally coordinated approach to marine conservation and food security is necessary, as regional scale strategies are shown to be less efficient and may result in conflict between food security and conservation objectives. Our results add support for calls to protect 30% of the ocean by 2030, and show where protection would best protect food security and conserve biodiversity and threatened species.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| 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".