Shrub and tree encroachment alter plant-soil interactions in low Canadian Arctic
Bibliographic record
Abstract
Rapid expansion of deciduous shrubs and evergreen trees on the Arctic tundra could induce large losses of soil carbon stocks through increased rhizosphere priming. Through the use of isotopic and molecular techniques, we investigated whether the belowground carbon cycling differed between three plant species that are encroaching Canadian tundra. 13CO2 pulse chase labelling showed that dwarf shrubs (Betula glandulosa) had faster turnover of recent 13C-photosynthates belowground than tall shrubs (Alnus viridis) and black spruces (Picea Mariana). Depth-resolved 13C flux estimations and partial 13C source isolation, both from field and lab measurements, elucidated multiple drivers of the differences in belowground carbon cycling. Turnover rates were strongly dependent on relative belowground carbon allocation, source of respiration and soil depth. Carbon cycling data will be compared with microbial community composition in bulk and rhizosphere soil to disentangle the specific interactions between encroaching plants and their soils. Overall, both plant and soil characteristics were key influences on the fate of recently assimilated carbon belowground. Our work suggests that changing plant communities will influence the belowground carbon cycling of the Arctic tundra. Our data pinpoints towards multiple factors influencing the feedback from northern ecosystems to on-going climate change, which further complicates accurate predictions of soil carbon losses in the northern hemisphere.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".