Evidence for spatiotemporal shift in demersal fishery management priority areas in the western Mediterranean
Bibliographic record
Abstract
Marine protected areas (MPAs) are a promising management tool for the conservation and recovery of marine ecosystems, as well as fishery management. MPAs are generally established as permanent closures but marine systems are dynamic, which has generated debate in favour of more dynamic designs. As a consequence, the identification of priority areas should assess their persistence in space and time. Here, we develop a step-by-step approach to assess the spatiotemporal dynamics of fishery management priority areas using standard fishery-independent survey data. To do so, we fit Bayesian hierarchical spatiotemporal SDM (species distribution model) models to different commercially important demersal species and use the resulting maps to fit different spatial prioritisation configurations. The proposed method is illustrated through a western Mediterranean case study using fishery-independent trawl survey data on six commercially important species collected over 17 years. We use these results to assess the spatiotemporal dynamics of fishery priority areas. We identified two fishery priority area patterns in the study area, each predominant during a different time period of the study, asserting the importance of regularly reassessing MPA designs.
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 | 0.001 | 0.003 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".