Interactive biotic and abiotic regulators of soil carbon cycling: evidence from controlled climate experiments on peatland and boreal soils
Bibliographic record
Abstract
Partially decomposed plant and animal remains have been accumulating in organic soils (i.e. >40% C content) for millennia, making them the largest terrestrial carbon store. There is growing concern that, in a warming world, soil biotic processing will accelerate and release greenhouse gases that further exacerbate climate change. However, the magnitude of this response remains uncertain as the constraints are abiotic, biotic and interactive. Here, we examined the influence of resource quality and biological activity on the temperature sensitivity of soil respiration under different soil moisture regimes. Organic soils were sampled from 13 boreal and peatland ecosystems located in the United Kingdom, Ireland, Spain, Finland and Sweden, representing a natural resource quality range of C, N and P. They were incubated at four temperatures (4, 10, 15 and 20 °C) at either 60% or 100% water holding capacity (WHC). Our results showed that chemical and biological properties play an important role in determining soil respiration responses to temperature and moisture changes. High soil C : P and C : N ratios were symptomatic of slow C turnover and long-term C accumulation. In boreal soils, low bacterial to fungal ratios were related to greater temperature sensitivity of respiration, which was amplified in drier conditions. This contrasted with peatland soils which were dominated by bacterial communities and enchytraeid grazing, resulting in a more rapid C turnover under warmer and wetter conditions. The unexpected acceleration of C mineralization under high moisture contents was possibly linked to the primarily role of fermented organic matter, instead of oxygen, in mediating microbial decomposition. We conclude that to improve C model simulations of soil respiration, a better resolution of the interactions occurring between climate, resource quality and the decomposer community will be required.
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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) | 0.000 | 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 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".