Geography, not host identity, shapes bacterial community in reindeer lichens
Bibliographic record
Abstract
Background and Aims Tremendous progress have been recently achieved in host-microbe research, however, there is still a surprising lack of knowledge in many taxa. Despite its dominance and crucial role in boreal forest, reindeer lichens have until now received little attention. We characterize, for the first time, the bacterial community of four species of reindeer lichens from Eastern North America’s boreal forests. We analysed the effect of two factors (host-identity and geography) in the bacterial community composition, we verified the presence of a common core bacteriota and identified the most abundant core taxa. Methods Morphological and molecular lichen species delimitation was performed based on the ITS region. The bacterial community of around 200 lichen samples was characterised using the 16S rRNA gene. Key Results Our results showed that host-lichen identity does not determine bacterial community composition in reindeer lichens, but we confirmed the influence of geography in shaping the diversity and abundance of bacteria associated to the species Cladonia stellaris from lichen woodlands. We also revealed that reindeer lichens share a reduced common core bacteriota composed exclusively by Proteobacteria. Conclusions The bacterial community in reindeer lichens is not host-selective. Northern lichen woodlands exhibit a significant higher diversity and abundance of bacteria associated to Cladonia stellaris . Nevertheless, the specific role of those bacteria as well as the process of host colonization remains to be determined. Elucidating these two aspects would be key to have a better understanding of the whole boreal ecosystems. The reduced and not diverse core bacteriota of reindeer lichens might be due to the larger size of our study area. The presence of the species Methylorosula polaris in the core bacteriota is evident and might 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 | 0.001 | 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".