Genetically divergent algae shape an epiphytic lichen community on Jack Pine in Manitoba
Bibliographic record
Abstract
Abstract Algal genotypes should freely associate with different lichen fungi that grow in the same confined habitat giving the appearance of low levels of selectivity and specificity. If genetic compatibility between algal and fungal partners limits the combination of partners, then some degree of taxonomic specificity should be evident. This study examined the photobiont composition in a community of epiphytic lichens on Jack Pine to investigate selectivity and specificity. The objectives of the study were to infer algal identity, to infer photobiont dispersal, and to investigate the distribution of algal genotypes relative to the fungal partner. Photobiont variability was determined by Restriction Fragment Length Polymorphism (RFLP) and nucleotide sequences of the Internal Transcribed Spacer (ITS) of ribosomal DNA (rDNA). Seven species of lichen-forming fungi are reported to associate with five divergent algal genotypes, with only one species,Evernia mesomorpha, showing some degree of selectivity and specificity. The algae represent at least two species (Trebouxia jamesiiandT. impressa) for the area confined to 200cm2on the north side of 20 Jack Pine trees. Gene flow was inferred in this tightly defined community of lichenized algae. The algal sharing and inferred gene flow may suggest that soredia provide a means of algal transport and distribution among lichen-forming fungi in the habitat.
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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 | 0.001 | 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".