Impact of disease on the survival of three commercially fished species
Bibliographic record
Abstract
Recent increases in emergent infectious diseases have raised concerns about the sustainability of some marine species. The complexity and expense of studying diseases in marine systems often dictate that conservation and management decisions are made without quantitative data on population-level impacts of disease. Mark-recapture is a powerful, underutilized, tool for calculating impacts of disease on population size and structure, even in the absence of etiological information. We applied logistic regression models to mark-recapture data to obtain estimates of disease-associated mortality rates in three commercially important marine species: snow crab (Chionoecetes opilio) in Newfoundland, Canada, that experience sporadic epizootics of bitter crab disease; striped bass (Morone saxatilis) in the Chesapeake Bay, USA, that experience chronic dermal and visceral mycobacteriosis; and American lobster (Homarus americanus) in the Southern New England stock, that experience chronic epizootic shell disease. All three diseases decreased survival of diseased hosts. Survival of diseased adult male crabs was 1% (0.003-0.022, 95% CI) that of uninfected crabs indicating nearly complete mortality of infected crabs in this life stage. Survival of moderately and severely diseased striped bass (which comprised 15% and 11% of the population, respectively) was 84% (70-100%, 95% CI), and 54% (42-68%, 95% CI) that of healthy striped bass. The disease-adjusted yearly natural mortality rate for striped bass was 0.29, nearly double the previously accepted value, which did not include disease. Survival of moderately and severely diseased lobsters was 30% (15-60%, 95% CI) that of healthy lobsters and survival of mildly diseased lobsters was 45% (27-75%, 95% CI) that of healthy lobsters. High disease mortality in ovigerous females may explain the poor recruitment and rapid declines observed in this population. Stock assessments should account for disease-related mortality when resource management options are evaluated.
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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 | 0.001 | 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".