Relating the Growth Phenology and Biomass Allocation in Seedlings of 13 Acadian Tree Species With Their Drought Tolerance
Bibliographic record
Abstract
Climate models predict an increase in the frequency and intensity of drought events in the Acadian Forest Region, with higher risk of tree growth decline and mortality. To accurately predict future species response, we need to better compare drought-coping traits between Acadian tree species, especially at early life stages as young trees tend to display increased sensitivity to small environmental changes than mature trees. Here, we compared the seasonal growth phenology and biomass allocation in seedlings of 13 Acadian tree species in a controlled environment to assess their ability to predict species drought tolerance rankings. We focused on two traits associated with drought tolerance, namely biomass allocation to root systems, which affects water uptake, and phenology of seasonal growth, where earlier growth can avoid the peak drought period in late summer. We find an earlier onset of height growth in drought-tolerant species (P < 0.05), supporting the late-summer drought avoidance hypothesis. The observed biomass allocation patterns did not support the hypothesis of a higher allocation to roots with drought tolerance. In fact, we report an initially higher shoot-to-root ratio in drought tolerant species (P < 0.05), which becomes non-significant as the season progresses. Our results highlight the complexity of drought response strategies, as the developmental traits reported here only account for a fraction of each species overall drought tolerance. Yet, the important differences in growth phenology observed here between species, and their linkage with drought tolerance indices, could help predict species response to future drought regime.
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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 | 0.000 | 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 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".