Dispersal alters soil microbial community response to drought
Bibliographic record
Abstract
Microbial communities will experience novel climates in the future. Dispersal is now recognized as a driver of microbial diversity and function, but our understanding of how dispersal influences responses to novel climates is limited. We experimentally tested how the exclusion of aerially dispersed fungi and bacteria altered the compositional and functional response of soil microbial communities to drought. We manipulated dispersal and drought by collecting aerially deposited microbes after precipitation events and subjecting soil mesocosms to either filter-sterilized rain (no dispersal) or unfiltered rain (dispersal) and to either drought (25% ambient) or ambient rainfall for 6 months. We characterized community composition by sequencing 16S and ITS rRNA regions and function using community-level physiological profiles. Treatments without dispersal had lower soil microbial biomass and metabolic diversity but higher bacterial and fungal species richness. Dispersal also altered soil community response to drought; drought had a stronger effect on bacterial (but not fungal) community composition, and induced greater functional loss, when dispersal was present. Surprisingly, neither immigrants nor drought-tolerant taxa had higher abundance in dispersal treatments. We show experimentally that natural aerial dispersal rate alters soil microbial responses to disturbance. Changes in dispersal rates should be considered when predicting microbial responses to climate change.
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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) | 0.001 | 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".