Annual and seasonal dynamics of ectomycorrhizal fungi colonizing white pine (<i>Pinus strobus</i>) seedlings following catastrophic windthrow in northern Georgia, USA
Bibliographic record
Abstract
The effect of wind disturbance on ectomycorrhizal fungal (EMF) communities in forests remains largely uninvestigated. We monitored EMF colonizing Pinus strobus L. seedlings in an oak–pine forest in northern Georgia, USA, after catastrophic wind disturbance. Over three years, we sampled naturally regenerating P. strobus seedlings across three growing seasons in the windthrow area and an adjacent undisturbed forest, identifying 53 unique EMF taxa using molecular techniques. The diversity of EMF colonizing seedlings in the undisturbed forest was consistently greater than in the windthrow area. Although the EMF compositional similarity between EMF in the gap and in the undisturbed forest was low throughout the study, many EMF taxa colonized seedlings in both the gap and the undisturbed forest. Seasonal differences in EMF composition and diversity were pronounced in the undisturbed forest, with diversity increasing from spring to fall. In contrast, EMF composition and diversity were relatively constant throughout seasons in the windthrow gap. We hypothesize that the extent of host community mortality and the extent of EMF host community regeneration following disturbance drive both EMF species composition and community dynamics. However, this hypothesis warrants further study.
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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".