Limited divergence in the spatially subdivided population of the Hawaiian mushroom<i>Rhodocollybia laulaha</i>
Bibliographic record
Abstract
Here we report the first population genetic examination of a fungus in Hawaii and, to our knowledge, the only investigation of a saprotrophic fungal population distributed across an oceanic archipelago. Rhodocollybia laulaha fruits abundantly in the native rain forests of Hawaii from June through December. Its range includes the geographic extent of the Hawaiian Archipelago; however, this range is highly fragmented because of the discontinuous distribution of the native forest habitat where R. laulaha occurs. We hypothesized that significant patterns of population structure would be recovered within the geographic range of the Hawaiian mushroom R. laulaha resulting from divergence between isolated subpopulations. We tested for population structure and related inferred patterns of restricted gene flow to geographic distance, major geographic features such as mountain peaks and oceans, elevation zones, and spore morphology. We included 120 R. laulaha collections using data from the rRNA IGS1 region, two microsatellite loci, and 184 AFLP loci. Analyses of these genetic data suggest limited genetic structure among R. laulaha subpopulations in Hawaii correlated mostly with geographic distance. Patterns associated with specialization to elevation or spore morphology were not recovered. The limited geographic structure observed in R. laulaha is consistent with relatively recent population fragmentation.
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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) | 0.001 | 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".