Responses of Soil Fungal Populations and Communities to the Thinning of <i>Cryptomeria Japonica</i> Forests
Bibliographic record
Abstract
Forest management activities, such as tree thinning, alter forest ecology, including key components of forest ecosystems, including fungal communities. In the present study, we investigate the effects of forest thinning intensity on the populations and structures of fungal soil communities in the Cryptomeria japonica forests of central Taiwan as well as the dynamics of soil fungi communities in these forests after a thinning disturbance. Although the populations of soil fungi significantly increased in the first 6 months after thinning, these increases had subsided by 9 months. This pulse was attributed to a transient increase in the populations of rapid colonizers. A multiple regression analysis positively correlated fungal populations with organic matter content and cellulase activity. Thinning initially provided large amounts of fresh leaves and roots as nutrient-rich substrates for soil fungi. Denaturing gradient gel electrophoresis (DGGE) profiles indicated that soil fungal communities significantly differed among plots with 0% (control), 25%, and 50% tree thinning in the first 21 months post-thinning, with no significant differences being observed after 21 months. The fungal communities of these forest soils also changed with the seasons, and an interactive relationship was detected between seasons and treatments. Seasonal variations in fungal communities were the most pronounced after 50% tree thinning. The results of the present study demonstrate that the soil fungi of Taiwanese C. japonica forests are very sensitive to thinning disturbances, but recover stability after a relatively short period of time.
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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".