Multi-proxy analysis confirms the tight coupling of carbon assimilation and allocation, with divergent NSCs strategies in two boreal forest species
Bibliographic record
Abstract
Understanding the link between photosynthesis and carbon allocation to woody biomass remains a critical gap in predicting forest responses to climate change due to the pervasive lack of comprehensive carbon-based data at the whole-stand level. We employed an integrated approach combining micrometeorological techniques (Eddy Covariance, EC), process-based and biogeochemical modelling, tree ring width (TRW), and quantitative wood anatomy to assess changes in carbon fluxes and allocation dynamics over mature stands of black spruce (Picea mariana Mill.) and jack pine (Pinus banksiana Lamb.) from 1999 to 2021 in Canada. We used Gross Primary Production (GPP) from EC to calibrate and validate GPP simulations from the 3D-CMCC-FEM model, incorporating tree ring width (TRW) and wood anatomical traits, such as cell wall area (CWA), as proxies for carbon fixation.Our findings demonstrated that the forest ecosystem model effectively captured GPP at daily, monthly, and annual scales, strongly correlating with EC-based estimates (P < 0.001). Both stands revealed a strong association between observed and modelled GPP and CWA, highlighting that CWA better reflects carbon assimilation in woody biomass than TRW. Species-specific differences in non-structural carbohydrates (NSCs) dynamics were also evident, as model simulations indicated that Pinus banksiana actively utilized NSCs for growth, while Picea mariana relied on NSCs as a buffer under cold conditions. This multi-proxy approach enhanced our understanding of carbon dynamics and temporal and spatial carbon flux pathways. Our findings provide critical insights into carbon allocation strategies, contributing valuable knowledge for refining climate change models in boreal ecosystems.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".