Fungal wood decomposer activity induces niche separation between two dominant tree species seedlings regenerating on coarse woody material
Bibliographic record
Abstract
Decomposition subsystems have an essential role in forest dynamics but few studies demonstrate the effect of microbial decay traits on seedling regeneration. In the present study, we focused on seedling regeneration on coarse woody material (CWM), which is an important regeneration site for forest tree species, and the effects of wood decay type according to fungal decay preference for wood structural components on seedling colonization. Effects of log properties including wood decay type and other environmental variables on seedling density were evaluated by ordination methods and generalized linear models. In total, 22 woody species were recorded as seedlings on Pinus densiflora logs. By ordination analysis, white rot in heartwood and brown rot in sapwood, as well as canopy openness and log diameter, showed significant association with seedling communities. The factors selected for a generalized linear model for explaining seedling densities of the two dominant seedling species Cryptomeria japonica and P. densiflora included brown rot in sapwood and white rot in heartwood, but the effects were different: a positive effect of brown rot on C. japonica and a negative effect of white rot on P. densiflora. These results suggested that wood decay type could induce niche separation between dominant tree species regenerating on CWM.
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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) | 0.001 | 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".