Effects on faunistic composition and population characteristics of decapod crustaceans after the implementation of a fisheries no-take area in the NW Mediterranean
Bibliographic record
Abstract
The establishment of fisheries no-take areas is considered an effective method for the recovery of populations of exploited species and their habitats. Here we study the faunistic composition of decapod crustaceans after the implementation of a no-take area in the Gulf of Roses (NW Mediterranean) in 2014. We studied the occurrence (presence/absence) and density of all decapod crustaceans sampled by trawling inside and outside a no-take area from March 2015 to July 2018. Sizes were assessed for all common species. A total of 33 species of decapod crustaceans were recorded. Four species showed significantly higher occurrences in the no-take area and three in the open area, while significantly higher densities were found for four species in the no-take area and three in the open area. Multivariate analysis showed marked differences between the no-take area and the open area, while also showing that the two areas were undergoing a divergence. The comparison of sizes between the two zones showed species-specific patterns that in many cases showed that both the smallest and the largest individuals were present in the no-take area, suggesting that the closing of this area would be important for recruitment and juvenile development, as well as for protection of large-sized individuals. All evidence indicates that the establishment of the no-take area has led to an improvement in biodiversity and species population descriptors.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".