Operationalising Ecologically or Biologically Significant Marine Area Criteria for Ecosystem-Based Conservation and Management: The Bay of Biscay Case
Bibliographic record
Abstract
Achieving global and regional policy goals to protect 30% of marine areas by 2030 requires an integrated strategy that incorporates the full complexity of ecosystem processes and functions. This study (i) introduces a framework for enhancing ecosystem-based conservation and management by utilising the Ecologically or Biologically Significant Marine Area (EBSA) criteria established under the Convention on Biological Diversity (CBD), and (ii) demonstrates its application in the Bay of Biscay (Northeast Atlantic), explicitly accounting for benthic and pelagic aspects of the ocean environment.The framework provides a structured workflow for identifying the areas of high ecological significance within an analysed region, moving beyond single-species or habitat protection to incorporate ecosystem-level processes and stressors, including those driven by climate change. It guides key stages of EBSA criteria operationalisation, including area delineation, identification of target ecological features, data collection, evaluation of data quality and coverage, and spatial analysis and interpretation. This approach allows relative ecological significance to be assessed at a spatial resolution suitable for informing regional or national decision-making. An embedded method for evaluating data quality and spatial coverage further enables spatially explicit assessments of uncertainty and data gaps.The framework was tested in the Bay of Biscay, a transboundary region characterised by high productivity and structurally complex seafloor sustaining a rich diversity of marine life. Using a systematic conservation prioritization approach that integrates both benthic and pelagic realms and accounts for climate-driven changes, we demonstrate how diverse datasets can be aligned with EBSA criteria to evaluate ecological significance. This assessment offers valuable insights into key ecological processes and forms a foundation for ecosystem-based management, supporting the identification of priority areas for conservation while also informing broader marine spatial planning, such as guiding regulations of fisheries and other human activities.The showcased framework can be adapted in other regions, whether data-rich or data-poor, enhancing broader ecosystem-based conservation and spatial management efforts while ensuring transparency and reproducibility.
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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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".