Dynamics of ectomycorrhizal fungal communities and soil enzyme activities in <i>Pinus massoniana</i> forests across developmental stages
Bibliographic record
Abstract
Ectomycorrhizal (ECM) fungi are essential in regulating nutrient cycling and plant–soil interactions in forest ecosystems, yet their dynamics and functional roles across developmental stages remain underexplored. We characterized the ECM fungal community composition, diversity, and soil enzyme activities in Pinus massoniana forests of three developmental stages (15, 25, and 35 years) to uncover their relationships and ecological implications. The results revealed distinct shifts in ECM fungal communities, with young forests exhibiting higher diversity and dominance of short-distance exploration types, while mature forests showed reduced diversity but increased abundance of long-distance exploration types. Soil enzyme activities associated with carbon, nitrogen, and phosphorus cycling varied significantly with forest development. Mature forests exhibited the highest soil urease and soil β-glucosidase activities, aligning with the dominance of long-distance exploration types. Soil pH and nutrient stoichiometry emerged as key drivers shaping ECM fungal communities and enzyme activities, with lower pH in mature forests favoring acidophilic fungal taxa. Redundancy analysis further highlighted the strong influence of soil chemical properties and enzyme activities on ECM fungal community structure. These findings underscore the critical role of ECM fungi in modulating soil nutrient dynamics and plant growth during forest development, providing insights into sustainable forest management and the ecological functioning of subtropical pine plantations.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".