Deforestation for agriculture increases microbial carbon use efficiency in subarctic soils
Bibliographic record
Abstract
Abstract Agriculture is likely to expand poleward with climate change, encouraging deforestation for agriculture in subarctic regions, which alters soil physical, chemical and biological properties and potentially affects microbial metabolic efficiency. Deciphering how and by which mechanisms land-use change affects microbial carbon use efficiency (CUE) will enable the development of mitigation strategies to alleviate C losses. We assessed CUE using 18 O-labelled water in a paired-plot approach on soils collected from 19 farms across the subarctic region of Yukon, Canada, comprising 14 pairs of forest-to-grassland conversion and 15 pairs of forest-to-cropland conversion. Microbial CUE significantly increased following conversion to grassland and cropland. Land-use conversion resulted in a lower estimated abundance of fungi, while the archaeal abundance increased. Interestingly, structural equation modelling revealed that increases in CUE were mediated by a rise in soil pH and a decrease in soil C:N ratio rather than by shifts in microbial community composition, i.e. the ratio of fungi, bacteria and archaea. Our findings indicate a direct control of abiotic factors on microbial CUE via improved nutrient availability and facilitated conditions for microbial growth. Overall, this implies that to a certain extent CUE can be managed to achieve a more efficient build-up of stabilised soil organic C (SOC), as reflected in increased mineral-associated organic C under agricultural land use. These insights may also help constrain SOC models that generally struggle to predict the effects of deforestation, something that is likely to take place more frequently in the subarctic.
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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".