Optimizing tradeoff strategies of soil microbial community between metabolic efficiency and resource acquisition along a natural regeneration chronosequence
Bibliographic record
Abstract
The tradeoff between community-level soil microbial metabolic efficiency and resource acquisition strategies during natural regeneration remains unclear. Herein, we examined variations in soil extracellular enzyme activity , microbial metabolic quotient ( q CO 2 ), and microbial carbon use efficiency (CUE) along a chronosequence of natural regeneration by sampling secondary forests at 1, 10, 20, 30, 40, and 100 years after rubber plantation (RP) clearance. The results showed that the natural logarithms of carbon (C)-, nitrogen (N)-, and phosphorus (P)-acquiring enzyme activities were 1:1.68:1.37 and 1:1.54:1.38 in the RP and secondary forests, respectively, thus demonstrating that microbial metabolism was co-limited by N and P. Moreover, the soil microbial C limitation initially increased (1–40 years) and later decreased (100 years). Overall, the q CO 2 increased, decreased, and then increased again in the initial (< 10 years), middle (10–40 years), and late (100 years) successional stages, respectively. Except for specific P-acquiring enzyme activities, the changes in other indicators with natural regeneration were consistent in the dry and wet seasons . Both q CO 2 and CUE were mainly predicted by microbial community composition and physiological traits. These results indicate that soil microbial communities could employ tradeoff strategies between metabolic efficiency and resource acquisition to cope with variations in resources. Our findings provide new information on tradeoff strategies between metabolic efficiency and resource acquisition during natural regeneration.
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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".