Rebuilding Mediterranean marine resources under climate change
Bibliographic record
Abstract
The Mediterranean Sea ranks among the most overexploited and fastest-warming ocean regions. This situation calls for urgent development of global change scenarios and models of marine biodiversity to anticipate changes and support ecosystem-based management strategies across the entire Mediterranean Sea. Using a new end-to-end modelling chain for the whole Mediterranean Sea, we explored the potential effects of changes in fishing pressure on marine resources and ecosystem structure and functioning under a worst-case climate change scenario (RCP8.5). We found that a decrease in fishing mortality or an improvement in fishing selectivity could increase the total biomass and total catch of high trophic level species by the middle and end of the 21st century, especially the biomass of demersal, large pelagic and benthic species, thereby reversing the projected climate-induced decrease in their biomass and catch by the end of the century in the western Mediterranean basin. In contrast, climate change could offer opportunities for some eastern Mediterranean fisheries to increase catches of thermophilic and/or exotic species benefiting from new favourable environmental conditions. Based on a suite of ecological indicators, our results indicated clear positive effects of a more sustainable fisheries management on ecosystem structure and functioning. However, a decrease in fishing pressure may not fully compensate for climate-induced changes on marine resources and ecosystems, but rather buffer some projected negative impacts. Our study highlights the need for a more sustainable exploitation of fisheries resources to restore marine ecosystems and increase their resilience in a global change context.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".