Cross-continental soil prokaryotic traits driven by precipitation regime and land cover
Bibliographic record
Abstract
Trade-offs among traits determine microbial community dynamics and affect soil carbon-cycling feedback to climate change. Here, we determined soil prokaryotic traits based on a novel marker gene-based workflow using cross-continental temperature and aridity gradients. Genome streamlining and high 16S rRNA gene copy numbers per genome (RRN) conferred high maximal growth rates, possibly by allowing for smaller cells with higher surface to volume ratio. Small genomes and high maximal growth rates were found under high precipitation seasonality and in barren soils. Large genomes and low maximal growth rates were found in forests, characterized by high water availability and by abundant and complex organic resources. Our findings suggest that large genomes confer versatility to cope with resource fluctuations and moderate climatic fluctuations while extreme climatic fluctuations and scarcity of resources promote genome streamlining. Seasonal fluctuations in water availability were associated with the ability to form spores and with rapid resuscitation, promoted by high RRN. Moreover, Prokaryotes were less dispersal limited compared to Fungi, presumably due to their smaller size, but within Prokaryotes, small taxa were not more ubiquitous. Our trait-based framework highlights that particularly changes in precipitation patterns and vegetation type will cause changes in microbial processes under future climate. Small genomes and high maximal growth of soil prokaryotes occurred in barren soils and soils with high precipitation seasonality, while large genomes and low growth appeared in moist, resource-rich forests, based on marker gene-based trait analysis of samples from Greenland, Europe, and South Africa.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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 teacher head, 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".