Ectomycorrhizal fungal communities and soil chemistry in harvested and unharvested temperate Nothofagus rainforests
Bibliographic record
Abstract
The composition of ectomycorrhizal (EcM) fungal communities in Nothofagus rainforests and the responses of the fungal communities to timber harvesting have been unknown. We investigated EcM communities in two sites, 9 to 11 years after timber harvesting, and tested whether changes in the communities were driven by soil chemistry. The fungal communities in both sites were highly diverse, yet 53 out of 140 distinct terminal restriction fragment length polymorphism (T-RFLP) patterns were shared between the sites. At both sites, timber harvesting reduced the presence of EcM roots and caused shifts in the fungal community in the organic soil horizons. At one site, Laccaria spp. increased in harvested areas, which partially correlated with an increase in soil mineralizable nitrogen. The other site showed a decreased abundance of Russula sp. (cf. R. purpureotincta , R. roseostipitata ) in harvested areas, which correlated with declines in soil carbon and organic horizon depth, and a decline in the abundance of rare species at the edge of harvested areas, which was related to inorganic phosphorus. The results show that EcM fungal communities in Nothofagus temperate rainforest are highly diverse at the local scale, yet have a high degree of similarity across sites. These communities are directly affected by timber harvesting and by shifts in soil chemistry following timber harvesting.
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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 | 0.001 | 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".