Temperature adaptation of soil bacterial communities along an Antarctic climate gradient: predicting responses to climate warming
Bibliographic record
Abstract
Abstract Soil microorganisms, the central drivers of terrestrial Antarctic ecosystems, are being confronted with increasing temperatures as parts of the continent experience considerable warming. Here we determined short‐term temperature dependencies of Antarctic soil bacterial community growth rates, using the leucine incorporation technique, in order to predict future changes in temperature sensitivity of resident soil bacterial communities. Soil samples were collected along a climate gradient consisting of locations on the Antarctic Peninsula (Anchorage Island, 67 °34′S, 68 °08′W), Signy Island (60 °43′S, 45 °38′W) and the Falkland Islands (51 °76′S 59 °03′W). At each location, experimental plots were subjected to warming by open top chambers (OTCs) and paired with control plots on vegetated and fell‐field habitats. The bacterial communities were adapted to the mean annual temperature of their environment, as shown by a significant correlation between the mean annual soil temperature and the minimum temperature for bacterial growth (Tmin). Every 1 °C rise in soil temperature was estimated to increase Tmin by 0.24–0.38 °C. The optimum temperature for bacterial growth varied less and did not have as clear a relationship with soil temperature. Temperature sensitivity, indicated by Q10 values, increased with mean annual soil temperature, suggesting that bacterial communities from colder regions were less temperature sensitive than those from the warmer regions. The OTC warming (generally <1 °C temperature increases) over 3 years had no effects on temperature relationship of the soil bacterial community. We estimate that the predicted temperature increase of 2.6 °C for the Antarctic Peninsula would increase Tmin by 0.6–1 °C and Q10 (0–10 °C) by 0.5 units.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".