Bacterial community of reindeer lichens differs between northern and southern lichen woodlands
Bibliographic record
Abstract
Lichens cover nearly 7% of the earth’s surface, and in eastern Canada, lichen woodlands occupy over 300 000 km 2 . Reindeer lichens (genus Cladonia) are the main component of lichen woodlands and they play a crucial role in boreal forest ecology. We study, for the first time, the bacterial community of four species of reindeer lichens from eastern North America’s boreal forests. Using the 16S rRNA gene, we characterize the bacterial community of 189 lichen samples. We aim to analyse the effect of geography and host identity in the bacterial community composition and structure, verify the presence of a common core bacteria, and identify the most abundant core taxa. Our results suggest that host-lichen identity does not determine bacterial community composition and structure in reindeer lichens, but we confirm the influence of geography in shaping the diversity and abundance of bacteria associated with Cladonia stellaris. We also reveal that reindeer lichens share a reduced common core bacteria composed exclusively by Alphaproteobacteria. Northern lichen woodlands exhibit a significantly higher diversity and abundance of bacteria associated with C. stellaris than southern lichen woodlands do. The presence of the species Methylorosula polaris in the core bacteria is evident and may have a particular importance for reindeer lichens.
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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) | 0.001 | 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".