Impact of Century-Scale Soil Warming on Soil Organic Matter Dynamics and Microbial Communities in a Subarctic Ecosystem
Bibliographic record
Abstract
Soil organic matter (SOM) dynamics under long-term warming are critical to understanding how climate change may impact carbon cycling. This study investigates the effects of century scale soil warming on SOM dynamics and microbial communities in a subarctic deciduous forest near the Takhini Hot Springs in Yukon Territory, Canada. Utilizing a natural geothermal gradient, we examine changes in soil microbial community composition and functional potential as carbon use efficiency. Initial findings indicate that warming increases microbial decomposition of litter and native SOM, with significant substrate preference of plant-derived particulate organic matter to microbially-derived compounds, particularly in deeper soil layers. We hypothesize that warming enhances microbial activity, leading to increased decomposition and altered SOM composition. As a result, microbial communities adapt to relatively oligotrophic conditions, observable as an increase in traits associated with a high carbon use efficiency (CUE), like higher codon use bias, as it enhances translational efficiency and reduces metabolic costs. Our methodology incorporates the 18O-CUE method to measure microbial CUE by tracking microbial growth using 18O-labeled water under steady-state conditions. Incubation experiments will quantify CUE across different temperatures, testing the mechanisms of temperature adaptation in the soil microbial communities. Additionally, exoenzyme analysis, of enzymes involved in SOM decomposition, e.g. N-acetyl glucosaminidase, β-glucosidase, along the same temperature gradients will be performed to connect changes in soil properties to soil functions. To decouple the immediate effects of temperature on enzyme activity from the sustained impacts of long-term warming, we will use Arrhenius plots as a framework. This research will enhance our understanding of the link between SOM dynamics under climate change and microbial adaptation, providing a framework for predicting long-term ecological responses in subarctic ecosystems. The outcomes will inform broader ecological models and potential mitigation strategies for climate change impacts on soil health and carbon 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 | 0.000 | 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".