Unraveling multifactorial risks in Pacific oyster mortality: a four-year study of environmental and age-related impacts
Bibliographic record
Abstract
The mortality of Pacific oysters Crassostrea gigas remains a persistent challenge for aquaculture, driven by complex interactions between environmental conditions, pathogen dynamics, and host factors. We monitored 96 sentinel oyster cohorts across 8 French sites over 4 yr (2014-2018) to assess age-specific mortality risks and environmental influences. Survival analyses and Cox hazard models confirmed that spat exhibit the highest mortality, while juveniles and adults demonstrate increased survival, reflecting age-related physiological resilience. Seawater temperature emerged as the strongest predictor of mortality, with risks increasing significantly between 16 and 24°C, highlighting a critical temperature threshold. Wind speed and relative humidity also modulated survival, likely influencing pathogen dispersal and physiological stress. Importantly, the impact of these factors was neither constant nor always significant over time, emphasizing the need for non-proportional risk functions to accurately capture mortality dynamics. Over the monitoring period, no significant increase in spat survival was observed, suggesting that resistance to infectious agents in farmed oysters has not markedly improved under natural environmental conditions. In response, oyster farming practices have evolved to integrate mortality risks, notably by increasing spat input and adjusting rearing conditions. This study underscores the necessity of incorporating environmental and life-history parameters into predictive models for improved risk assessment. By providing long-term insights into mortality patterns, our findings support the development of sustainable management strategies to enhance oyster resilience in the face of climate change and evolving pathogen threats.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".