Abiotic stress destabilizes the bacterial community of sugar kelp, <i>Saccharina latissima</i> (Phaeophyceae)
Bibliographic record
Abstract
As climate change progresses, the intensity and variability of freshwater outflow into the ocean are predicted to increase. The resulting increase in low-salinity events, paired with other abiotic stressors (including increasing temperatures), will be a source of stress for the kelp Saccharina latissima (Saccharina hereafter) and potentially Saccharina-associated bacteria. Bacteria influence host health and can facilitate or hinder host survival and acclimation to stressful abiotic conditions. Therefore, understanding how bacterial communities change under abiotic stress is critical for understanding how abiotic stress will affect kelp physiology. We investigated the effect of abiotic stress on Saccharina and associated bacteria by surveying the bacterial community associated with Saccharina across naturally occurring salinity and temperature gradients, coupled with salinity manipulation experiments. Overall, Saccharina harbored a stable core bacterial community, which decreased in relative abundance under abiotic stress. In the field, both salinity and temperature shaped the bacterial community, with temperature having higher explanatory power most of the time. In the lab, we confirmed that the patterns observed in the field could be replicated by manipulating salinity alone. Decreased relative abundance of core bacteria and increased community dissimilarity in low-salinity in the lab suggest that low-salinity alone can induce a stress response, detectable in the bacterial community of Saccharina.
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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".