Multiple Strategies for Light-Harvesting, Photoprotection, and Carbon Flow in High Latitude Microbial Mats
Bibliographic record
Abstract
Microbial mats are ubiquitous in polar freshwater ecosystems and sustain high concentrations of biomass despite the extreme seasonal variations in light and temperature. Here we aimed to resolve genomic adaptations for light-harvesting, bright-light protection and carbon flow in mats that undergo seasonal freeze-up. To bracket a range of communities in shallow water habitats, we sampled cyanobacterial mats in the thawed littoral zone of two lakes situated at the northern and southern limits of the Canadian Arctic permafrost zone. We applied a multiphasic approach using pigment profiles from high performance liquid chromatography, Illumina MiSeq sequencing of the 16S and 18S rRNA genes, and metagenomic analysis. The mats shared a taxonomic and functional core microbiome, dominated by oxygenic cyanobacteria with light-harvesting and photoprotective pigments, bacteria with bacteriochlorophyll, and bacteria with light-driven Type I rhodopsins. Organisms able to use light for energy related processes represented up to 85% of the total microbial community, with 15-30% attributable to cyanobacteria and 55-70% attributable to other bacteria. The proportion of genes involved in anaplerotic CO2 fixation was greater than for genes associated with oxygenic photosynthesis. Diverse heterotrophic bacteria, eukaryotes (including metazoans and fungi) and viruses co-occurred in both communities. The results indicate a broad range of strategies for capturing sunlight and CO2, and for the subsequent flow of energy and carbon in these complex, light-driven microbial ecosystems.
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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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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 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".