Emergence of soil bacterial ecotypes along a climate gradient
Bibliographic record
Abstract
The high diversity of soil bacteria is attributed to the spatial complexity of soil systems, where habitat heterogeneity promotes niche partitioning among bacterial taxa. This premise remains challenging to test, however, as it requires quantifying the traits of closely related soil bacteria and relating these traits to bacterial abundances and geographic distributions. Here, we sought to investigate whether the widespread soil taxon Curtobacterium consists of multiple coexisting ecotypes with differential geographic distributions. We isolated Curtobacterium strains from six sites along a climate gradient and assayed four functional traits that may contribute to niche partitioning in leaf litter, the top layer of soil. Our results revealed that cultured isolates separated into fine-scale genetic clusters that reflected distinct suites of phenotypic traits, denoting the existence of multiple ecotypes. We then quantified the distribution of Curtobacterium by analysing metagenomic data collected across the gradient over 18 months. Six abundant ecotypes were observed with differential abundances along the gradient, suggesting fine-scale niche partitioning. However, we could not clearly explain observed geographic distributions of ecotypes by relating their traits to environmental variables. Thus, while we can resolve soil bacterial ecotypes, the traits delineating their distinct niches in the environment remain unclear.
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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 | 0.001 | 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".