Contribution of area-based fisheries management measures to fisheries sustainability and marine conservation: a global scoping review
Bibliographic record
Abstract
Abstract Area-based fisheries management measures (ABFMs) are commonly related to the sustainable use of resources but are increasingly considered broader conservation measures. This Scoping Review (ScR) identified and mapped the evidence base regarding the contribution of ABFMs to fisheries sustainability and marine conservation. The ScR was conducted following the JBI methodology and the PRISMA statement. A total of 2,391 documents were identified, and following a two-stage screening process, 151 documents were eventually included in the ScR for full review and data extraction. Most of the documents were published during the last 12 years. Studies had a wide geographical distribution (mainly located in Europe and North America), were primarily conducted at the subnational level, concerned fishing restrictions of towed gears, and assessed ABFMs from a single stock’s perspective. ABFMs identified were of various types, and the use of terminology was not consistent in the literature. Multiple combinations of data collection (primarily experimental surveys/sampling and open data sources) and analysis methods (most commonly, fisheries and ecological analyses and modelling) were applied (often in combination), using a variety of different metrics. Various knowledge gaps emerged, mainly related to the study of ABFMs networks and the application of an interdisciplinary and ecosystem-based approach for assessing ABFMs. The social, economic, and environmental impact of ABFMs was positive in most cases (56%) and in less cases negative (22%) or mixed (14%). This ScR is a valuable source of information for the contribution of ABFMs to rebuilding marine ecosystems and attaining CBD conservation targets through the lens of the OECM concept. Graphical abstract
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 | 0.022 | 0.084 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.031 | 0.028 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".