Patterns of Endemism and Habitat Selection in Coalbed Microbial Communities
Bibliographic record
Abstract
Microbially produced methane, a versatile, cleaner-burning alternative energy resource to fossil fuels, is sourced from a variety of natural and engineered ecosystems, including marine sediments, anaerobic digesters, shales, and coalbeds. There is a prevailing interest in developing environmental biotechnologies to enhance methane production. Here, we use small-subunit rRNA gene sequencing and metagenomics to better describe the interplay between coalbed methane (CBM) well conditions and microbial communities in the Alberta Basin. Our results show that CBM microbial community structures display patterns of endemism and habitat selection across the Alberta Basin, consistent with observations from other geographical locations. While some phylum-level taxonomic patterns were observed, relative abundances of specific taxonomic groups were localized to discrete wells, likely shaped by local environmental conditions, such as coal rank and depth-dependent physicochemical conditions. To better resolve functional potential within the CBM milieu, a metagenome from a deep volatile-bituminous coal sample was generated. This sample was dominated by Rhodobacteraceae genotypes, resolving a near-complete population genome bin related to Celeribacter sp. that encoded metabolic pathways for the degradation of a wide range of aromatic compounds and the production of methanogenic substrates via acidogenic fermentation. Genomic comparisons between the Celeribacter sp. population genome and related organisms isolated from different environments reflected habitat-specific selection pressures that included nitrogen availability and the ability to utilize diverse carbon substrates. Taken together, our observations reveal that both endemism and metabolic specialization should be considered in the development of biostimulation strategies for nonproductive wells or for those with declining productivity.
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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 | 0.000 | 0.001 |
| 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".