Fungal Elevational Rapoport pattern from a High Mountain in Japan
Bibliographic record
Abstract
Little is known of how fungal distribution ranges vary with elevation. We studied fungal diversity and community composition from 740 to 2940 m above sea level on Mt. Norikura, Japan, sequencing the ITS2 region. There was a clear trend, repeated across each of the fungal phyla (Basidiomycota, Ascomycota, Zygomycota, Chytridomycota and Glomeromycota), and across the whole fungal community combined, towards an increased elevational range of higher elevation OTUs, conforming to the elevational Rapoport pattern. It appears that fungi from higher elevation environments are more generalized ecologically, at least in terms of climate-related gradients. These findings add to the picture from latitudinal studies of fungal ranges, which also suggest that the classic Rapoport Rule (broader ranges at higher latitudes) applies on a geographical scale. However, there was no mid-elevation maximum in diversity in any of the phyla studied, and different diversity trends for the different phyla, when different diversity indices were used. In terms of functional guilds, on Norikura there were trends towards increased saprotrophism (Zygomycota), symbiotrophism (Basidiomycota), symbiotrophism and saprotrophism (Ascomycota) and pathotrophism (Chytridiomycota) with elevation. The causes of each of these trends require further investigation from an ecological and evolutionary viewpoint.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".