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 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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 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".