Humboldt’s spa: microbial diversity is controlled by temperature in geothermal environments
Bibliographic record
Abstract
Over 200 years ago Alexander von Humboldt (1808) observed that plant and animal diversity peaks at tropical latitudes and decreases toward the poles, a trend he attributed to more favorable temperatures in the tropics. Studies to date suggest that this temperature-diversity gradient is weak or nonexistent for Bacteria and Archaea. To test the impacts of temperature as well as pH on bacterial and archaeal diversity, we performed pyrotag sequencing of 16S rRNA genes retrieved from 165 soil, sediment and biomat samples of 36 geothermal areas in Canada and New Zealand, covering a temperature range of 7.5-99 °C and a pH range of 1.8-9.0. This represents the widest ranges of temperature and pH yet examined in a single microbial diversity study. Species richness and diversity indices were strongly correlated to temperature, with R(2) values up to 0.62 for neutral-alkaline springs. The distributions were unimodal, with peak diversity at 24 °C and decreasing diversity at higher and lower temperature extremes. There was also a significant pH effect on diversity; however, in contrast to previous studies of soil microbial diversity, pH explained less of the variability (13-20%) than temperature in the geothermal samples. No correlation was observed between diversity values and latitude from the equator, and we therefore infer a direct temperature effect in our data set. These results demonstrate that temperature exerts a strong control on microbial diversity when considered over most of the temperature range within which life is possible.
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".