Contrasting Variable and Stable Subsurface Microbial Populations: an ecological time series analysis from the Deep Mine Microbial Observatory, South Dakota, USA
Bibliographic record
Abstract
Summary The deep subsurface contains a vast reservoir of microbial life. While recent studies have revealed critical details about this biosphere including the sheer diversity of microbial taxa and their metabolic potential, long-term monitoring of deep subsurface microbial populations is rare, thus limiting our understanding of subsurface microbial population dynamics. Here we present a four-year time series analysis of subsurface microbial life from the Deep Mine Microbial Observatory (DeMMO), Lead, SD, USA. We find distinct and diverse populations inhabiting each of 6 sites over this ~1.5 km deep slice of terrestrial crust, corresponding to distinct geochemical habitats. Alpha diversity decreases with depth and beta diversity measures clearly differentiate samples by site over time, even during substantial perturbations. Population dynamics are driven by a subset of variable (and often relatively abundant) OTUs, but the vast majority of detected OTUs are stable through time, constituting a core microbial community. The phylogenetic affiliations of both stable and variable taxa, including putative sulfate reducers, methanogens, spore formers, and many uncultivated lineages, are similar to those found previously in subsurface environments. This work reveals the dynamic nature of the terrestrial subsurface, contributing to a more holistic understanding than can be achieved when viewing shorter timeframes. Originality-Significance Statement This four-year record of deep mine microbial diversity and geochemistry is the first of its kind and allows for direct investigation of temporal trends in deep subsurface biogeochemistry. We identify disparate populations of variable and stable taxa, suggesting the presence of a core deep subsurface microbiome with unique niche partitioning.
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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 | 0.001 |
| 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 | 0.000 | 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".