Decoding carbon allocation in boreal forests: Integrating multi-proxy observations and process-based modelling
Bibliographic record
Abstract
• Stem structural C allocation is linked to GPP in two boreal forests. • Jack pine shows an active NSC role while black spruce shows buffering strategy. • NSC dynamics reflect species-specific cold adaptation and stand conditions. • Incorporating NSC-mediated trade-offs improves carbon allocation modelling. Understanding how photosynthetic carbon (C) is allocated to woody biomass remains a critical gap in predicting forest responses to climate change, especially in cold-limited ecosystems, due to the pervasive lack of comprehensive carbon-based data at the whole-stand level. We applied a multi-proxy approach integrating eddy covariance, process-based modelling, and quantitative wood anatomy to assess C fluxes and stem-level C allocation in two mature boreal stands in Canada—black spruce ( Picea mariana Mill.) and jack pine ( Pinus banksiana Lamb.)—from 1999 to 2021. At both stands, we found that stem structural C allocation (measured as cell wall area, CWA) was tightly coupled with observed and modelled gross primary productivity (GPP). Modelled non-structural carbohydrates (NSC) dynamics revealed contrasting temporal patterns between species: jack pine showed an immediate response to available NSC and annual CWA, suggesting an active role of NSC in supporting growth under fluctuating environmental conditions. In contrast, black spruce exhibited a delayed effect, suggesting a more passive and buffering role of NSC in stem structural C allocation. Notably, at the jack pine site, extreme cold years corresponded to reduced CWA alongside elevated NSC concentrations, which might indicate a shift in C allocation priorities toward storage over growth. Our findings, based on a multi-proxy approach, provide novel insights into species-specific and possible trade-offs between storage and growth, useful for improving C budget models and adaptive forest management under climate change.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".