Species- and site-specific circulating bacterial DNA in Subantarctic sentinel mussels Aulacomya atra and Mytilus platensis
Bibliographic record
Abstract
Impacts of climate changes are particularly severe in polar regions where warmer temperatures and reductions in sea-ice covers threaten the ecological integrity of marine coastal ecosystems. Because of their wide distribution and their ecological importance, mussels are currently used as sentinel organisms in monitoring programs of coastal ecosystems around the world. In the present study, we exploited the concept of liquid biopsy combined to a logistically friendly sampling method to study the hemolymphatic bacterial microbiome in two mussel species (Aulacomya atra and Mytilus platensis) in Kerguelen Islands, a remote Subantarctic volcanic archipelago. We found that the circulating microbiome signatures of both species differ significantly even though their share the same mussel beds. We also found that the microbiome differs significantly between sampling sites, often correlating with the particularity of the ecosystem. Predictive models also revealed that both species have distinct functional microbiota, and that the circulating microbiome of Aulacomya atra was more sensitive to changes induced by acute thermal stress when compared to Mytilus platensis. Taken together, our study suggests that defining circulating microbiome is a useful tool to assess the health status of marine ecosystems and to better understand the interactions between the sentinel species and their habitat.
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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 | 0.000 |
| 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".