The killer within: Endogenous bacteria accelerate oyster mortality during sustained anoxia
Bibliographic record
Abstract
Abstract Sustained periods of anoxia, driven by eutrophication, threaten coastal marine systems and can lead to mass mortalities of even resilient animals such as bivalves. While mortality rates under anoxia are well‐studied, the specific mechanism(s) of mortality are less clear. We used a suite of complementary techniques (LT50, histology, 16S rRNA amplicon sequencing, and valvometry) to show that the proliferation of anaerobic bacteria within eastern oysters (Crassostrea virginica) accelerates mortality rate under anoxic conditions. Manipulative laboratory experiments revealed that oyster survival under anoxic conditions was halved when bacteria were present compared to when they were excluded by the broad‐spectrum antibiotic chloramphenicol. Histological assessments supported this mechanism and showed infiltration of bacteria in oysters that were not treated with antibiotics compared to a general lack of bacteria when oysters were treated with antibiotics. 16S rRNA amplicon sequencing failed to identify any particular genera of bacteria responsible for mortality, rather a diversity of endogenous anaerobic and/or sulfate‐reducing bacteria were common among oysters. In addition, monitoring of oyster valve gaping behavior in the field revealed that oysters showed remarkable valve closure synchrony when first exposed to anoxia. However, oysters periodically opened throughout anoxia/hypoxia in both the lab and field, suggesting that the infiltration of exogenous bacteria from the environment may also influence mortality rates under natural settings. Coupled with previous studies, we posit that mass mortality events in a wide range of coastal bivalves are likely the result of co‐morbidity from asphyxiation and bacterial processes.
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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 | 0.001 |
| 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".