High-Throughput DNA Metabarcoding of Stem Sections from Trees with Cavities Describes Fungal Communities Associated with Variable Wood Decay, Position on Stem and Tree Species
Bibliographic record
Abstract
The presence of unculturable fungi, variability in timing and frequency of fungal fruiting, hyper-rich fungal communities, and genetic and environmental variability explains the difficulty in adopting ideal sampling schemes and fungal identification approaches in studies of fungal communities in wood at variable stages of decay. Here, we use intensive within-tree sampling of five standing trees with cavities paired with high-throughput DNA metabarcoding, to study fungal communities in decayed and healthy wood of trees from two Populus species in British Columbia, Canada. The amplification of over 3000 fungal DNA sequence variants shows the presence of a hyper-rich wood fungal community that not only varied depending on PCR primers, tree species, tree stem portion and wood decay stage. but also included a large number of taxa unassignable to any known sub-kingdom taxonomic order based on published DNA sequences. Our data show that the use of two different primer sets greatly increases the power of the metabarcoding analysis. By testing three alternative models of fungal community composition, we identify the model that best explains fungal community by considering the position on the stem and distance from the cavity. We suggest this model may be used to design optimal sampling schemes to describe fungal communities in trees experiencing discrete decay pockets or cavities.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.000 | 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".