Building towards the marine conservation end‐game: consolidating the role of MPAs in a future ocean
Bibliographic record
Abstract
Progress on spatial conservation efforts in marine environments is often summarized with the simplistic metric of extent. However, targets require a more nuanced view, where ecological effectiveness, biodiversity, representation, connectivity and ecosystem services must all be recognized. Furthermore, these targets must be achieved through equitable processes and produce equitable outcomes.This paper calls for a clearer definition of what is to be ‘counted’ in assessing progress in marine conservation, through the use of both traditionally defined marine protected areas and a limited subset of other equivalent areas. It calls for future effort to draw a clear distinction between non‐extractive areas such as no‐take marine reserves, and the more numerous extractive areas. To be considered protected, sites must be ecologically effective, and be equitably managed to support all stakeholders.Spatial extent of coverage is only one constituent part of conservation effort, however, and much greater effort is needed to ensure that sites are selected to achieve optimum conservation outcomes for biodiversity and for ecosystem services. The paper reviews some of the existing views and approaches to defining and delimiting marine protection priorities.It recommends that with a clearer set of metrics for defining protection, and for assessing progress and setting future targets, marine conservation will be better placed to achieve lasting outcomes, including halting biodiversity loss and securing or enhancing ecosystem service provision. Protected spaces will continue to play a major role in future oceans, but they also need to be configured within a wider spatial framework.
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 | 0.010 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.010 |
| Scholarly communication | 0.014 | 0.018 |
| Open science | 0.002 | 0.016 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".