Twelve Recommendations for Advancing Marine Conservation in European and Contiguous Seas
Bibliographic record
Abstract
Like most ocean regions today, the European and contiguous seas experience cumulative impacts from local human activities and global pressures. They are largely in poor environmental condition with deteriorating trends. Despite several success stories, European policies for marine conservation fall short of being effective. Acknowledging the challenges for marine conservation, a four-year multi-national network, MarCons, supported collaborative marine conservation efforts to bridge the gap between science, management and policy, aiming to contribute in reversing present negative trends. By consolidating a large network of more than 100 scientists from 26 countries, and conducting a series of workshops over four years (2016-2020), MarCons analysed challenges, opportunities and obstacles for advancing marine conservation in the European and contiguous seas. Here, we synthesize the major issues that emerged from this analysis and make 12 key recommendations for policy makers, marine managers, and researchers. To increase the effectiveness of marine conservation planning, we recommend (1) designing coherent networks of marine protected areas (MPAs) in the framework of marine spatial planning (MSP) and applying systematic conservation planning principles, including re-evaluation of existing management zones, (2) designing MPA networks within a broader transboundary planning framework, and (3) implementing integrated land-freshwater-sea approaches. To address inadequate or poorly informed management, we recommend (4) developing and implementing adaptive management plans in all sites of the Natura 2000 European conservation network and revising the Natura 2000 framework, (5) embedding and implementing cumulative effects assessments into a risk management process and making them operational, and (6) promoting actions to reach ‘good environmental status’ in all European waters. To account for global change in conservation planning and management, we further recommend (7) developing conservation strategies to address the impacts of global change, and (8) incorporating biological invasions in conservation plans and prioritizing management actions to control invasive species. Finally, to improve current practices that may compromise the effectiveness of conservation actions, we recommend (9) reinforcing the collection of high-quality open-access data, (10) improving mechanisms for public participation in MPA planning and management, (11) prioritizing conservation goals in full collaboration with stakeholders, and (12) addressing gender inequality in marine sciences and conservation.
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 | 0.024 | 0.046 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.011 | 0.010 |
| Open science | 0.005 | 0.007 |
| Research integrity | 0.017 | 0.012 |
| Insufficient payload (model declined to judge) | 0.026 | 0.008 |
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".