Abundance of Vibrio aestuarianus, water temperature, and stocking density are associated with summer mortality of Pacific oysters in suspended culture
Bibliographic record
Abstract
Abstract High mortality rates of cultured Pacific oysters ( Crassostrea gigas ) during the summer months have regularly occurred on oyster farms in British Columbia, Canada over the last 10 years, but little is known about the microbial and environmental conditions that contribute to such mortality events. The objective of the study was to determine correlative factors associated with the onset of a summer mortality event in oysters (mean ± SD shell height: 14.2 ± 0.5 mm) grown in suspended culture at four stocking densities (150, 300, 450, 600 oysters tray −1 ) from May 11 to September 17, 2018. Variables examined included both biotic (oyster growth, mortality, reproductive development, and microbiome (approximately every week); Vibrio and harmful algal species abundance) and abiotic (temperature, salinity, turbidity, dissolved oxygen, p CO 2 , pH, and aragonite saturation) ones. Both the absolute abundance of V. aestuarianus and the relative abundance of Vibrio spp. increased with observed oyster mortality and declining health. Mortality was highest on August 12 and associated with a prior period of elevated temperatures (i.e., increasing temperatures from early July to early August) and increased oyster growth/reproductive development. At that time, systemic mixed microbial infections and necrotic gill tissue in histological cross sections were observed in 19% of oysters that appeared healthy macroscopically. Cumulative percent mortalities per tray ranged from 34 to 75%, the highest-density trays having significantly less mortality and smaller shell width, shell length, and gonad length than lower-density trays. This study demonstrates the significant impact of summer mortality on Pacific oysters and highlights the biotic (host growth, reproductive development, and microbiome composition as well as Vibrio spp. abundance) and abiotic (water temperature) factors associated with the observed mortality in this region.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".