The microbial diversity of watermelon snow
Bibliographic record
Abstract
During summer months in alpine systems around the world patches of green, orange and red snow appear. The phenomenon is sometimes called watermelon snow and is caused by a bloom of microalgae. The resulting microbiome is teeming with life, including algae, fungi, metazoans, other protists and bacteria. Here, I present data documenting the biological diversity in the ephemeral snow algae microbiome, which grows on snows threatened by global warming. I began by asking: what microbes are found in snow algae blooms? I focussed my work in the southwestern Coastal Mountain Range in B.C., Canada. The data I present detail the algal, bacterial, fungal, metazoan and other protist diversity in blooms and their distribution across the region. These data included sequences from undescribed algal species, with some potentially belonging to species names with no DNA data available. I therefore did an analysis, including five novel algal isolates, to clarify the taxonomy of Raphidonema and its sister genera, using genetic data. I was able to identify my five isolates as R. sempervirens, and in the process name two novel species: R. catena and R. monicae. As bacteria are commonly important mutualistic symbionts of microalgae, I next described their communities living alongside snow algae. I found that, unlike algae, the bacterial community composition does not change with elevation, and instead there are regionally widespread bacteria. I therefore wanted to learn more about the metabolic capabilities of these bacteria common to snow algae blooms. Using a shotgun metagenomics approach, I analyzed the bacterial metagenome, and metagenomically assembled genomes. These data included representative from the widespread bacterial families found during metabarcoding, and I furthered that analysis by describing their metabolic genes related to: nitrogen and sulfur cycling as well as biosynthesis of osmolytes/cryoprotectants, B-vitamins, phytohormones, and xanthophyll pigments. These data act as observations to form hypotheses on the biogeochemistry and microbial ecology of snow algae microbiomes.
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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 | 0.001 |
| Science and technology studies | 0.001 | 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".