Factors contributing to the disease ecology of brown crab ( <i>Cancer pagurus</i> ) in a temperate marine protected area
Bibliographic record
Abstract
ABSTRACT Marine ecosystems are affected by multiple, well-known stressors like fishing and climate change, but a less documented concern is disease. Marine reserves have been successful in replenishing stocks and aiding recruitment but studies have shown that high population abundances in marine reserves may lead to unwanted secondary effects such as increase in predators and competition, altering trophic webs, and disease. Here, we investigate factors contributing to disease prevalence in a brown crab ( Cancer pagurus ) population around Lundy Island (the UK’s first MPA) after 7 years of no-take protection. Population parameters (size, sex, and abundance), disease (shell disease, Hematodinium spp. infection) and injury presence (a known precursor to some disease conditions) were assessed over two years in both fished and unfished areas of the MPA. We found no significant difference in prevalence between the disease prevalence in fished and unfished areas, however overall, the number of injured crabs increased significantly over the two years (12%), as did the prevalence of shell disease (15%). The probability of crabs having shell disease increased significantly in male crabs, and in those missing limbs. The probability of crabs being injured increased significantly in crabs below the minimum landing size. In terms of population parameters, crabs were more prevalent in the fished area compared to the unfished area, thought to be a result of an increase in the predatory European lobster. The findings of the present study highlight potential secondary community changes as a result of MPA implementation. Therefore, surveillance for such changes, as part of MPA management, would provide useful information on the health and overall function of the protected ecosystem.
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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 | 0.000 |
| 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".