Large‐scale biogeography and environmental regulation of methanotrophic bacteria across boreal inland waters
Bibliographic record
Abstract
Aerobic methanotrophic bacteria (methanotrophs) use methane as a source of carbon and energy, thereby mitigating net methane emissions from natural sources. Methanotrophs represent a widespread and phylogenetically complex guild, yet the biogeography of this functional group and the factors that explain the taxonomic structure of the methanotrophic assemblage are still poorly understood. Here, we used high-throughput sequencing of the 16S rRNA gene of the bacterial community to study the methanotrophic community composition and the environmental factors that influence their distribution and relative abundance in a wide range of freshwater habitats, including lakes, streams and rivers across the boreal landscape. Within one region, soil and soil water samples were additionally taken from the surrounding watersheds in order to cover the full terrestrial-aquatic continuum. The composition of methanotrophic communities across the boreal landscape showed only a modest degree of regional differentiation but a strong structuring along the hydrologic continuum from soil to lake communities, regardless of regions. This pattern along the hydrologic continuum was mostly explained by a clear niche differentiation between type I and type II methanotrophs along environmental gradients in pH, and methane concentrations. Our results suggest very different roles of type I and type II methanotrophs within inland waters, the latter likely having a terrestrial source and reflecting passive transport and dilution along the aquatic networks, but this is an unresolved issue that requires further investigation.
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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 | 0.001 | 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) | 0.000 | 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".