Assessing Anthropogenic Underwater Noise Pollution in the Mediterranean Sea and its Implications for Marine Ecosystem Protection and Restoration
Bibliographic record
Abstract
The protection and restoration of marine ecosystems are paramount to meeting the Kunming-Montreal Global Biodiversity Framework’s (GBF) ambitious targets, especially in addressing biodiversity threats in ecologically sensitive regions like the Mediterranean Sea. Underwater noise pollution, primarily from human activities such as shipping, seismic exploration and maritime works, has emerged as a critical stressor for marine biodiversity, impacting species reliant on sound for navigation, communication, and survival. In 2023, the first comprehensive regional assessment of underwater noise in the Mediterranean was conducted by ACCOBAMS and UNEP/MAP, focusing on its effects on cetacean populations as indicator species of ecosystem health.Utilizing innovative risk-based assessment methods, this work evaluates two critical indicators: impulsive noise (e.g., explosions, sonar, pile-driving) and continuous noise (mainly shipping-related) across sub-regions of the Mediterranean. Findings show that noise levels exceed tolerable thresholds in significant habitats, especially in the Western Mediterranean and Aegean sub-regions, where up to 35% of potential cetacean habitat is affected. Such results reveal unprecedent information about risks to the sustainability of key species in this area, highlighting the need of implementing effective area-based management approaches, such as marine protected areas and noise-mitigation policies, to protect and restore these ecosystems.The results underscore the need for robust data on noise pollution to guide marine spatial planning and policy-making, supporting equitable management of marine resources. Mitigating noise pollution impacts is one essential component in fostering resilient and healthy marine ecosystems, a goal that can be effectively pursued through enhanced cross-sectoral collaboration, scientific innovation, and policy frameworks aligned with the Kunming-Montreal Global Biodiversity Framework.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".