Large seasonal fluctuations in whole-tree carbohydrate reserves: is storage more dynamic in boreal ecosystems?
Bibliographic record
Abstract
BACKGROUND AND AIMS: Carbon reserves are a critical source of energy and substrates that allow trees to cope with periods of minimal carbon gain and/or high carbon demands, conditions which are prevalent in high-latitude forests. However, we have a poor understanding of carbon reserve dynamics at the whole-tree level in mature boreal trees. We therefore sought to quantify the seasonal changes in whole-tree and organ-level carbon reserve pools in mature boreal Betula papyrifera. METHODS: Non-structural carbohydrate (NSC; soluble sugars and starch) tissue concentrations were measured at key phenological stages throughout a calendar year in the roots, stem (inner bark and xylem), branches and leaves, and scaled up to estimate changes in organ and whole-tree NSC pool sizes. Fine root and stem growth were also measured to compare the timing of growth processes with changes in NSC pools. KEY RESULTS: The whole-tree NSC pool increased from its spring minimum to its maximum at bud set, producing an average seasonal fluctuation of 0.96 kg per tree. This fluctuation represents a 72 % change in the whole-tree NSC pool, which greatly exceeds the relative change reported for more temperate conspecifics. At the organ level, branches accounted for roughly 48-60 % of the whole-tree NSC pool throughout the year, and their seasonal fluctuation was four to eight times greater than that observed in the stemwood, coarse roots and inner bark. CONCLUSIONS: Branches in boreal B. papyrifera were the largest and most dynamic storage pool, suggesting that storage changes at the branch level largely drive whole-tree storage dynamics in these trees. The greater whole-tree seasonal NSC fluctuation in boreal vs. temperate B. papyrifera may result from (1) higher soluble sugar concentration requirements in branches for frost protection, and/or (2) a larger reliance on reserves to fuel new leaf and shoot growth in the spring.
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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 | 0.001 | 0.001 |
| 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".