The Importance of Initial Seedling Characteristics in Controlling Allocation to Growth and Reserves under Different Soil Moisture Conditions
Bibliographic record
Abstract
After disturbance, forest regeneration and resiliency depend on the ability of seedlings to respond, survive, and grow under a variety of stress conditions, including drought. Despite recent efforts to improve our fundamental knowledge surrounding plant response mechanisms to stress and their application in seedling quality research, initial seedling characteristics are often ignored when exploring seedling responses to stress in field plantings or ecophysiological studies. Here, we explore how initial differences in size, biomass allocation, and non-structural carbohydrate (NSC) storage affect the subsequent partitioning of new biomass, growth potential, and drought response in seedlings of a deciduous broad-leaved (Populus tremuloides) and an evergreen coniferous species (Pinus banksiana). We exposed seedlings of both species to different growing conditions in their first growing season in order to manipulate the aforementioned seedling characteristics. In a second growing season, we exposed these different seedling types to a subsequent drought stress. While drought reduced both structural growth and NSC storage in all seedling types, the expected shift in allocation favoring roots was only observed in seedling types with initially low root:shoot or root:stem ratios. Overall, we also found that the traits associated with greater growth were quite different between pine and aspen. While larger seedlings led to greater growth in pine, it was the smallest seedling type in aspen with the largest root:stem ratio that produced the most new growth. In aspen, this smaller seedling type was the only one that did not undergo a shift in biomass relative to its initial allometry, suggesting that adjustments in biomass allocation made by other, larger seedling types must have come at the cost of lower growth. In contrast, adjustments in allocation did not appear to negatively impact pine, possibly because reduced root:shoot ratios of larger seedlings did not reduce NSC storage, as it did in aspen. Our results highlight (1) the complexity of how differences in biomass allocation and changes in seedling size may alter storage and the response of species to drought, and (2) the importance of accounting for initial seedling characteristics (both morphological and physiological) when predicting seedling growth and the impacts of environmental stressors.
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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 | 0.000 |
| 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 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".