Mechanisms and implications of bacterial–fungal competition for soil resources
Bibliographic record
Abstract
Elucidating complex interactions between bacteria and fungi that determine microbial community structure, composition, and functions in soil, as well as regulate carbon (C) and nutrient fluxes, is crucial to understand biogeochemical cycles. Among the various interactions, competition for resources is the main factor determining the adaptation and niche differentiation between these two big microbial groups in soil. This is because C and energy limitations for microbial growth are a rule rather than an exception. Here, we review the C and energy demands of bacteria and fungi-the two major kingdoms in soil-the mechanisms of their competition for these and other resources, leading to niche differentiation, and the global change impacts on this competition. The normalized microbial utilization preference showed that bacteria are 1.4-5 times more efficient in the uptake of simple organic compounds as substrates, whereas fungi are 1.1-4.1 times more effective in utilizing complex compounds. Accordingly, bacteria strongly outcompete fungi for simple substrates, while fungi take advantage of complex compounds. Bacteria also compete with fungi for the products released during the degradation of complex substrates. Based on these specifics, we differentiated spatial, temporal, and chemical niches for these two groups in soil. The competition will increase under the main five global changes including elevated CO2, N deposition, soil acidification, global warming, and drought. Elevated CO2, N deposition, and warming increase bacterial dominance, whereas soil acidification and drought increase fungal competitiveness.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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 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".