Shifts in soil microorganisms in response to warming are consistent across a range of Antarctic environments
Bibliographic record
Abstract
Because of severe abiotic limitations, Antarctic soils represent simplified systems, where microorganisms are the principal drivers of nutrient cycling. This relative simplicity makes these ecosystems particularly vulnerable to perturbations, like global warming, and the Antarctic Peninsula is among the most rapidly warming regions on the planet. However, the consequences of the ongoing warming of Antarctica on microorganisms and the processes they mediate are unknown. Here, using 16S rRNA gene pyrosequencing and qPCR, we report highly consistent responses in microbial communities across disparate sub-Antarctic and Antarctic environments in response to 3 years of experimental field warming (+0.5 to 2 °C). Specifically, we found significant increases in the abundance of fungi and bacteria and in the Alphaproteobacteria-to-Acidobacteria ratio, which could result in an increase in soil respiration. Furthermore, shifts toward generalist bacterial communities following warming weakened the linkage between the bacterial taxonomic and functional richness. GeoChip microarray analyses also revealed significant warming effects on functional communities, specifically in the N-cycling microorganisms. Our results demonstrate that soil microorganisms across a range of sub-Antarctic and Antarctic environments can respond consistently and rapidly to increasing temperatures.
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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 | 0.001 |
| 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".