Structure and functions of soil microbial communities and tree composition are more closely associated with keystone microbes than rare microbes in a subtropical forest
Bibliographic record
Abstract
Abstract The significance of microbes for ecosystem functioning is well known; however, within a single system, the relative contributions of keystone and rare taxa to soil microbial functions are less well quantified, as are their shared or unique responses to abiotic conditions. Furthermore, their associations with tree community composition in natural forest ecosystems are not well understood. In this study, a total of 1287 soil samples were collected from a 20-ha subtropical forest plot and analyzed using high-throughput sequencing. Based on co-occurrence network analyses, we conducted a comparison of the associations between keystone and rare taxa with the structure, functions and stability of soil microbial communities. Additionally, we examined their associations with tree community composition. Results showed that keystone taxa made a significantly greater contribution than rare taxa in all comparisons of microbial functions and stability. Keystone taxa had direct effects on microbial community structure and also mediated indirect effects of abiotic conditions. Neither effect was evident for rare taxa. The importance of keystone taxa also extended to aboveground composition, where tree community composition was more closely associated with keystone taxa than with rare taxa. While it may still be premature to establish causality, our study represents one of the initial attempts to compare the relative importance of keystone and rare microbial taxa in forest soils. These findings offer the potential to improve natural forest ecosystem functioning and tree diversity through the manipulation of a small number of keystone soil microbial taxa, as has been demonstrated in agroecosystems.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".