Elevated CO2 Exacerbates Flooding-Induced Mortality in Black Spruce While Enhancing Adventitious Root Formation in Tamarack
Bibliographic record
Abstract
Anthropogenic increases in atmospheric CO₂ concentrations, coupled with climate change-induced alterations in hydrological regimes, are increasing flood frequency across many boreal regions.Prolonged flooding creates oxygen-deprived soils that force roots to shift from aerobic respiration to anaerobic fermentation, an inefficient metabolic pathway that compromises carbohydrate utilization and leads to root dieback.We examined the interactive effects of flooding and CO₂ levels on two ecologically important boreal species: black spruce (Picea mariana [Mill.]Britton, Sterns & Poggenb.) and tamarack (Larix laricina [Du Roi] K. Koch).Seedlings were exposed to a 28-day flooding event under ambient (AC, 400μmol mol⁻¹) and elevated (EC, 800μmol mol⁻¹) CO₂ concentrations.Flooding significantly reduced root respiration (-16% during treatment; -36% 35 days post-recovery), root biomass (-66%), root volume (-58%), and overall growth (-70%).While Photosystem II quantum efficiency declined under flooding, photosynthetic rates remained stable.Species responses diverged markedly: flooded black spruce exhibited 2.1× higher mortality under EC (59%) versus AC (28%), whereas tamarack developed adventitious roots 2.9× more frequently under EC (53%) than AC (18%) with no mortality.Contrary to our hypothesis, EC failed to mitigate flood stress or enhance recovery through carbohydrate supplementation.These findings demonstrate that elevated CO₂ exacerbates flood vulnerability in black spruce while promoting tamarack's adaptive capacity through morphological plasticity.Such differential responses suggest future climate conditions may drive compositional shifts in boreal forests, favoring tamarack in increasingly flood-prone landscapes.
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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".