The Mediterranean Sea under siege: spatial overlap between marine biodiversity, cumulative threats and marine reserves
Bibliographic record
Abstract
ABSTRACT Aim A large body of knowledge exists on individual anthropogenic threats that have an impact on marine biodiversity in the Mediterranean Sea, although we know little about how these threats accumulate and interact to affect marine species and ecosystems. In this context, we aimed to identify the main areas where the interaction between marine biodiversity and threats is more pronounced and to assess their spatial overlap with current marine protected areas in the Mediterranean. Location Mediterranean Sea. Methods We first identified areas of high biodiversity of marine mammals, marine turtles, seabirds, fishes and commercial or well‐documented invertebrates. We mapped potential areas of high threat where multiple threats are occurring simultaneously. Finally we quantified the areas of conservation concern for biodiversity by looking at the spatial overlap between high biodiversity and high cumulative threats, and we assessed the overlap with protected areas. Results Our results show that areas with high marine biodiversity in the Mediterranean Sea are mainly located along the central and north shores, with lower values in the south‐eastern regions. Areas of potential high cumulative threats are widespread in both the western and eastern basins, with fewer areas located in the south‐eastern region. The interaction between areas of high biodiversity and threats for invertebrates, fishes and large animals in general (including large fishes, marine mammals, marine turtles and seabirds) is concentrated in the coastal areas of Spain, Gulf of Lions, north‐eastern Ligurian Sea, Adriatic Sea, Aegean Sea, south‐eastern Turkey and regions surrounding the Nile Delta and north‐west African coasts. Areas of concern are larger for marine mammal and seabird species. Main conclusions These areas may represent good candidates for further research, management and protection activities, since there is only a maximum 2% overlap between existing marine protected areas (which cover 5% of the Mediterranean Sea) and our predicted areas of conservation concern for biodiversity.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".