Local and Regional Diversity Reveals Dispersal Limitation and Drift as Drivers for Groundwater Bacterial Communities from a Fractured Granite Formation
Bibliographic record
Abstract
Microorganisms found in terrestrial subsurface environments make up a large proportion of the Earth's biomass. Biogeochemical cycles catalyzed by subsurface microbes have the potential to influence the speciation and transport of radionuclides managed in geological repositories. To gain insight on factors that constrain microbial processes within a formation with restricted groundwater flow we performed a meta-community analysis on groundwater collected from multiple discrete fractures underlying the Chalk River Laboratories site (located in Ontario, Canada). Bacterial taxa were numerically dominant in the groundwater. Although these were mainly uncultured, the closest cultivated representatives were from the phenotypically diverse Betaproteobacteria, Deltaproteobacteria, Bacteroidetes, Actinobacteria, Nitrospirae, and Firmicutes. Hundreds of taxa were identified but only a few were found in abundance (>1%) across all assemblages. The remainder of the taxa were low abundance. Within an ecological framework of selection, dispersal and drift, the local and regional diversity revealed fewer taxa within each assemblage relative to the meta-community, but the taxa that were present were more related than predicted by chance. The combination of dispersion at one phylogenetic depth and clustering at another phylogenetic depth suggest both niche (dispersion) and filtering (clustering) as drivers of local assembly. Distance decay of similarity reveals apparent biogeography of 1.5 km. Beta diversity revealed greater influence of selection at shallow sampling locations while the influences of dispersal limitation and randomness were greater at deeper sampling locations. Although selection has shaped each assemblage, the spatial scale of groundwater sampling favored detection of neutral processes over selective processes. Dispersal limitation between assemblages combined with local selection means the meta-community is subject to drift, and therefore, likely reflects the differential historical events that have influenced the current bacterial composition. Categorizing the study site into smaller regions of interest of more closely spaced fractures, or of potentially hydraulically connected fractures, might improve the resolution of an analysis to reveal environmental influences that have shaped these bacterial communities.
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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 | 0.000 |
| 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".