Ecosystem carbon dynamics differ between tundra shrub types in the western Canadian Arctic
Bibliographic record
Abstract
Shrub expansion at high latitudes has been implicated in driving vegetation 'greening' trends and may partially offset CO 2 emissions from warming soils. However, we do not yet know how Arctic shrub expansion will impact ecosystem carbon (C) cycling and this limits our ability to forecast changes in net C storage and resulting climate feedbacks. Here we quantify the allocation of photosynthate between different ecosystem components for two common deciduous Arctic shrubs, both of which are increasing in abundance in the study region; green alder ( Alnus viridis (Chaix) DC.) and dwarf birch ( Betula glandulosa Michx., B.). Using 13 C isotopic labelling, we show that carbon use efficiency (i.e. the fraction of gross photosynthesis remaining after subtracting respiration) in peak growing season is similar between the two shrubs (56 ± 12% for A. viridis , 59 ± 6% for B. glandulosa ), but that biomass production efficiency (plant C uptake allocated to biomass production, per unit gross photosynthesis) is 56 ± 14% for A. viridis , versus 31 ± 2% for B. glandulosa. A significantly greater proportion of recent photosynthate is allocated to woody biomass in A. viridis dominated plots (27 ± 5%), compared to plots dominated by B. glandulosa (4 ± 1%) . Allocation of C to belowground pools also differs significantly; after 2.5 weeks we recovered 28 ± 6% of recent photosynthate in root-free soil under B. glandulosa , but under A. viridis we were unable to detect recent photosynthate in the soil. We provide the first evidence that the impact of shrub expansion on Arctic C cycling will be species dependant. Where Betula dominates, ~1/3 of recently photosynthesised C will be rapidly allocated belowground to soil and microbial pools. Where Alnus dominates, more recently fixed C will be allocated to woody biomass. We conclude that models driven by remotely-sensed aboveground canopy characteristics alone (i.e. greenness) will be unable to accurately represent the impact of vegetation change on Arctic C storage.
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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 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".