Temporal ecotrophic impacts of fisheries and climate change in the Aegean Sea
Bibliographic record
Abstract
Mediterranean fisheries face a crisis with rapidly depleting stocks due to overexploitation, exacerbated by various stressors, including climate change, alien species and pollution. To address these challenges, modelling tools like ECOPATH with ECOSIM (EwE) are being used to assess marine ecosystems within the context of the ecosystem-based fisheries management (EBFM). In this study, an ECOSIM model was developed for the Aegean Sea based on a previously developed ECOPATH configuration (calibration period: 2006-2021; projection period: 2022-2050), to assess the current state of the food web and predict future conditions. Forecast scenarios incorporating fisheries and climatic stressors were explored to propose management measures that will ensure fisheries sustainability. The baseline simulation demonstrated a decline in biomass and catch in most functional groups (FGs). Fishing effort reduction scenarios led to biomass increase in several FGs, but resulted in lower catches. Climate scenarios with sea surface temperature time series from the Intergovernmental Panel on Climate Change triggered varied responses among FGs, with biomass gains observed in the demersal compartment, while the most negatively affected pelagic groups were mackerels and horse mackerels. Combined fisheries and climatic scenarios revealed synergistic effects, emphasizing the complex ecological interactions in the Aegean Sea. Ecological indicators from the ECOIND analysis highlighted losses in the demersal compartment in relation with the fisheries and climate scenarios, while trophic-based indices exhibited the most notable variations, suggesting cascading effects in the food web. The findings of this study will provide valuable insights for fisheries management and climate adaptation strategies in the Aegean Sea.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".