Influence of lithogenic energy on subglacial microbial community composition
Bibliographic record
Abstract
Chemosynthesis, the generation of biomass using chemical energy, supported life on early Earth and continues to sustain contemporary light-independent ecosystems. The mechanisms of nutrient release from the geosphere are critical to understanding the present and historical distribution and diversity of life. Glaciers release such nutrients through comminution of bedrock, continuously resurfacing reactive minerals that can be colonized and exploited by chemosynthetic microorganisms. Bedrock mineralogy influences the nutrients available in these environments, but little is known about which nutrients are most important or how they affect microbial community composition, particularly in catchments overlying igneous bedrock like basalt. Iron and silicate minerals, common in basalt, readily generate both reductants such as H 2 and oxidants such as Fe(III) through interactions with water. Abundant H 2 in meltwaters of the basalt-based Icelandic glacier Kotlujokull (KJ) were found to support sediment microbial communities better adapted to use H 2 in chemosynthetic metabolism than those found beneath the carbonate-based Robertson Glacier (RG), Canada. KJ communities exhibited shorter lag-times and faster rates of net H 2 oxidation and dark carbon dioxide (CO 2) fixation than those from RG. A KJ sediment enrichment culture provided with H 2, CO 2, and Fe(III) produced a chemolithoautotrophic population related to Rhodoferax ferrireducens, which was also detected using molecular techniques in sediments from Kaldalonsjokull (Kal), another basalt-based Icelandic glacier. The abundance and composition of microbial communities that colonized defined minerals incubated for 12 months in Kal meltwater streams were examined by extracting DNA and sequencing PCR-amplifiable 16S rRNA genes. DNA quantities and the composition of 16S rRNA genes recovered from Kal sediments were most similar to those recovered from incubated Fe(III)-bearing minerals hematite and magnetite, with putative Fe(III) reducers dominating all three communities. These findings point to the importance of bedrock mineral composition in influencing the supplies of nutrients like H 2 and Fe(III) that, in turn, influence the diversity, abundance, and activity of microbial communities in subglacial environments. They further indicate the potential for subglacial habitats to serve as refugia for microbial communities in the absence of sunlight, such as during Snowball Earth episodes, or on icy planets without photosynthetic life.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".