Decoding cryptic diversity of moss populations in a forest-tundra ecotone
Bibliographic record
Abstract
ABSTRACT Cryptic speciation is widespread among bryophytes, and it appears common in arctic and subarctic mosses where cryptic lineages may occur sympatrically in the environment. However, cryptic lineages are rarely considered in genetic diversity assessments. This situation poses a challenge, as the complex population structure resulting from cryptic speciation, along with factors like the predominance of clonality, can lead to inaccurate estimates of biodiversity. In this sense, we studied two populations of the subarctic moss Racomitrium lanuginosum in the forest-tundra ecotone to test the impact of accounting for differentiated genetic groups on moss genetic diversity estimates, clonal structure and microbial covariation. We performed genotyping-by-sequencing to infer genetic diversity and structure in the forest tundra and the shrub tundra. Genetic groups were identified using haplotype-based coancestry matrices and phylogenetic analyses. The clonal structure was explored by determining multilocus genotypes at the population and a finer scale (225 cm 2 ). The covariation between genetic groups and microbial communities (bacterial and diazotrophic) was explored. The recognition of cryptic lineages in genetic diversity estimations revealed differences between habitats that remained undetected when treating R. lanuginosum as a single species. Clonal growth seemed to predominate and affect the genetic structure at the local scale. Finally, genetic groups did not host specific microbiomes, suggesting that moss microbial associations in the forest-tundra ecotone did strongly respond to a genetic component. This study highlights the importance of accounting for cryptic lineages in genetic diversity estimations for a precise biodiversity assessment in subarctic and arctic ecosystems.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".