Climate controls prokaryotic community composition in desert soils of the southwestern United States
Bibliographic record
Abstract
Aridisols are the dominant soil type in drylands, which occupy one-third of Earth's terrestrial surface. We examined controls on biogeographical patterns of Aridisol prokaryotic (bacterial and archaeal) communities at a regional scale by comparing communities from 100 Aridisols throughout the southwestern United States using high-throughput sequencing of the 16S rRNA gene. We found that microbial communities differed among global biomes and deserts of the Southwest. Differences among biomes were driven by differences in taxonomic identities, whereas differences among deserts of the Southwest were driven by differences in relative sequence abundance. Desert communities were dominated by Actinobacteria, Proteobacteria and Crenarchaeota, supporting the notion of a core set of abundant taxa in desert soils. Our findings contrast with studies showing little taxonomic overlap at the OTU level (97% sequence similarity) across large spatial scales, as we found ∼90% of taxa in at least two of the three deserts. Geographic distance structured prokaryotic communities indirectly through the influence of climate and soil properties. Structural equation modeling suggests that climate exerts a stronger influence than soil properties in shaping the composition of Aridisol microbial communities, with annual heat moisture index (an aridity metric) being the strongest climate driver. Annual heat moisture index was associated with decreased microbial diversity and richness. If the Desert Southwest becomes hotter and drier as predicted, these findings suggest that prokaryotic diversity and richness in Aridisols will decline.
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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".