Dispersal analysis of three<i>Peltigera</i>species based on landscape genetics data
Bibliographic record
Abstract
Lichens can either disperse sexually through fungal spores or asexually through vegetative propagules and fragmentation. Understanding how genetic variation in lichens is distributed across a landscape can be useful to infer dispersal and establishment events in space and time as well as the conditions needed for this establishment. Most studies have sampled lichens across large spatial distances on the order of hundreds of kilometers, while here we sequence the internal transcribed spacer (ITS) for 113 samples of three Peltigera species sampling at a variety of small spatial scales. The maximum distance between sampled lichens was 3.7 km and minimum distance was approximately 20 cm. We find significant amounts of genetic diversity across all three species. For P. praetextata, two out of the three most common ITS genotypes exhibit spatial autocorrelation supporting short-range dispersal. Using rarefaction we estimate that all ITS genotypes in our sampling area have been found for P. praetextata and P. evansiana, but not P. canina. Comparing our results with other ITS data in the literature provides evidence for global dispersal for at least one sequence followed by the evolution of endemic haplotypes with wide dispersal and rare haplotypes with more local dispersal.
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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 | 0.002 | 0.001 |
| 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".