Data from: Ectomycorrhizal fungal community succession and fragmentation across subalpine forest edges nearly 3 decades postharvest
Bibliographic record
Abstract
Ectomycorrhizal fungi (EMF) are sensitive to stand-removing disturbances. The re-establishment of diverse EMF communities, including return of the rarer fungi typical of old-growth stands, may take longer than a century. Using smaller cutovers, with their high edge to area ratio, has the potential to accelerate postharvest EMF community recovery by increasing proximity to mature forests. By contrast, forest edges produce a fragmentation effect that could eventually inflict a negative influence on EMF taxa of the remaining forest. We compared both succession and fragmentation patterns across small openings at 27 years postharvest in a high-elevation coniferous forest. We sampled 90 m transects across forest edges of nine 1-ha openings, using long-read metabarcoding of EMF from mycorrhizal root tips, and Sanger sequencing of epigeous sporocarps to assess EMF community response. A timber harvesting effect on root tip EMF communities was still evident, with significant differences among species assemblages between the opening, edge and interior forest habitats. Furthermore, EMF richness of root tips in the openings declined by 27%, on average, beginning at 10 m from the forest edge, illustrating how the previously observed influence of overstory trees had not expanded into the openings during this time. An influx of multi-seral taxa into the young stands was instead likely driven by spore dispersal, which may have been facilitated in these small cutovers where regenerating saplings were never more than 50 m from mature trees. While EMF richness on tree roots was maintained in the interior forest, sporocarp fruiting was curtailed within forest edges, alongside some reductions in forest- dependent taxa, likely reflecting fragmentation stress. Overall, the potential benefits of small openings appeared constrained by environmental factors, such as open canopies and limited rooting density, that delay the re-establishment of late-seral EMF species. The negative effect of edges on EMF reproduction and abundance of some taxa in the forest adjacent to the openings should be considered when designing retention forestry systems as part of sustainable forest management. Our results suggest medium to large patches of retained trees may reduce fragmentation stress and better sustain an array of late-seral forest fungi.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 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) | 0.003 | 0.001 |
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".