A global perspective on microbial diversity in the terrestrial deep subsurface
Bibliographic record
Abstract
Summary While recent efforts to catalogue Earth’s microbial diversity have focused upon surface and marine habitats, 12% to 20% of Earth’s bacterial and archaeal biomass is suggested to inhabit the terrestrial deep subsurface, compared to ∼1.8% in the deep subseafloor 1–3 . Metagenomic studies of the terrestrial deep subsurface have yielded a trove of divergent and functionally important microbiomes from a range of localities 4–6 . However, a wider perspective of microbial diversity and its relationship to environmental conditions within the terrestrial deep subsurface is still required. Here, we show the diversity of bacterial communities in deep subsurface groundwater is controlled by aquifer lithology globally, by using 16S rRNA gene datasets collected across five countries on two continents and from fifteen rock types over the past decade. Furthermore, our meta-analysis reveals that terrestrial deep subsurface microbiota are dominated by Betaproteobacteria, Gammaproteobacteria and Firmicutes, likely as a function of the diverse metabolic strategies of these taxa. Despite this similarity, evidence was found not only for aquifer-specific microbial communities, but also for a common small consortium of prevalent Betaproteobacteria and Gammaproteobacterial OTUs across the localities. This finding implies a core terrestrial deep subsurface community, irrespective of aquifer lithology, that may play an important role in colonising and sustaining microbial habitats in the deep terrestrial subsurface. An in-silico contamination-aware approach to analysing this dataset underscores the importance of downstream methods for assuring that robust conclusions can be reached from deep subsurface-derived sequencing data. Understanding the global panorama of microbial diversity and ecological dynamics in the deep terrestrial subsurface provides a first step towards understanding the role of microbes in global subsurface element and nutrient cycling.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".