Latitudinal drivers of oyster mortality: deciphering host, pathogen and environmental risk factors
Bibliographic record
Abstract
Diseases pose an ongoing threat to aquaculture, fisheries and conservation of marine species, and determination of risk factors of disease is crucial for management. Our objective was to decipher the effects of host, pathogen and environmental factors on disease-induced mortality of Pacific oysters (Crassostrea gigas) across a latitudinal gradient. We deployed young and adult oysters at 13 sites in France and we monitored survival, pathogens and environmental parameters. The young oysters came from either the wild collection or the hatchery while the adults were from the wild only. We then used Cox regression models to investigate the effect of latitude, site, environmental factors and origin on mortality risk and to extrapolate this mortality risk to the distribution limits of the species in Europe. We found that seawater temperature, food level, sea level atmospheric pressure, rainfall and wind speed were associated with mortality risk. Their effect on hatchery oysters was generally higher than on wild animals, probably reflecting that hatchery oysters were free of Ostreid herpesvirus 1 (OsHV-1) whereas those from the wild were asymptomatic carriers. The risk factors involved in young and adult oyster mortalities were different, reflecting distinct diseases. Mortality risk increases from 0 to 90% with decreasing latitude for young hatchery oysters, but not for young wild oysters or adults. Mortality risk was higher in wild oysters than in hatchery ones at latitude > 47.6°N while this was the opposite at lower latitude. Therefore, latitudinal gradient alters disease-induced mortality risk but interacts with the initial health status of the host and the pathogen involved. Practically, we suggest that mortality can be mitigated by using hatchery oysters in north and wild collected oysters in the south.
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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 | 0.001 | 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) | 0.000 | 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".