Revealing how intra- and inter-annual variability of carbon uptake (GPP) affects wood cell biomass in an eastern white pine forest
Bibliographic record
Abstract
Abstract Forests are major terrestrial carbon (C) sinks and play a crucial role in climate change mitigation. Despite extensive studies on forest C sequestration, the relationship between seasonal C uptake and its allocation to woody biomass is poorly understood. Here we used a novel dendro-anatomical approach to investigate the relationships between climate variability, C uptake, and woody biomass growth in an 80 year-old eastern white pine ( Pinus strobus ) plantation forest in Ontario, Canada. We used eddy covariance (EC) gross primary productivity (GPP) from 2003–2018 and woody biomass estimated from chronologies of cell wall area (CWA, a proxy for C storage in individual wood cells) and ring wall area (RWA) for earlywood (EW) and latewood (LW) from 1970–2018. Warm temperatures in early spring and high precipitation in mid-spring and summer positively and strongly affected GPP, while high temperature and high vapor pressure deficit in the summer had a negative effect. From 2003 to 2018, there was a steady increase in both GPP and woody cell biomass. Moreover, we found strong positive correlations between GPP and CWA both in EW (May—July GPP, r = 0.65) and LW (July—August GPP, r = 0.89). Strong positive correlations were also found between GPP and RWA both in EW and LW (April—September, r = ⩾ 0.79). All these associations were stronger than the association between annual GPP and tree-ring width ( r = 0.61) used in previous studies. By increasing the resolution of tree-ring analysis to xylem-cell level, we captured intra-annual variability in biomass accumulation. We demonstrated a strong control of seasonal C assimilation (source) over C accumulation in woody biomass at this site. Coupling high-resolution EC fluxes (GPP) and wood anatomical measurements can help to reduce existing uncertainties on C source-sink relationships, opening new perspectives in the study of the C cycle in forests.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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 teacher head, 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".