Temperatures beyond the community optimum promote the dominance of heat-adapted, fast growing and stress resistant bacteria in alpine soils
Bibliographic record
Abstract
Alpine soils are warming strongly, leading to profound alterations in carbon cycling and greenhouse gas budgets, mediated via the soil microbiome. To explore microbial responses to global warming, we incubated eight alpine soils between 4 and 35 °C and linked the temperature dependency of bacterial growth with alterations in community structures and the identification of temperature sensitive taxa. The temperature optimum for bacterial growth was between 27 and 30 °C and was higher in soils from warmer environments. This temperature framing the upper limit of naturally occurring temperatures was a tipping point above which the temperature range for growth shifted towards higher temperatures together with pronounced changes in community structures and diversity based on both 16S rRNA gene and transcript sequencing. For instance, at the highest temperature, we observed a strong increase in OTUs affiliated with Burkholderia-Paraburkholderia, Phenylobacterium, Pseudolabrys, Edaphobacter and Sphingomonas. Dominance at high temperature was explained by a priori adaptation to high temperature, high growth potential as well as stress resistance. At the highest temperature, we moreover observed an overall increase in copiotrophic properties in the community along with high growth rates. Further, temperature effects on community structures depended on the long-term climatic legacy of the soils. These findings contribute to extrapolating from single to multiple sites across a large range of conditions.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".