Budburst timing within a functional trait framework
Bibliographic record
Abstract
Abstract Phenology, the timing of recurring life history events, can vary substantially in different environments and for different species. While climate change has shifted phenology by altering its environmental triggers, such as temperature, changes in the drivers that select for species‐level variation remain poorly explained. Theory suggests that species‐level variation in phenology can result from shifting environmental pressures that favour different strategies across the spring growing season: from the early season, where higher abiotic risks and greater availability of nutrients and light favour cheaper leaves and acquisitive growth strategies, to later, when a more benign environment and lower levels of light and nutrients favour conservative growth strategies. This framework predicts a suite of traits that may covary with species phenologies, but the high variability in phenology across environments has made testing its role within a trait framework challenging. Using a modelling framework that accommodates this variability, with phenological data from a database of controlled environment experiments and tree trait data from two major databases we tested for relationships between traits and spring phenology in trees. Specifically, we examined the cues that drive early to late budburst: spring temperatures (forcing), winter temperatures (chilling) and daylength (photoperiod). We found mixed support for our predictions for how traits relate to budburst timing and phenology. Species with cues that lead to earlier budburst (small responses to experimental chilling and photoperiod) were shorter with higher leaf nitrogen content, both traits related to acquisitive strategies and thus are in line with our predictions. However, our one reproductive trait of seed mass showed no relationship with phenology, and other traits (e.g. specific leaf area) showed relationships in the opposite direction to our predictions. Synthesis : Our findings show how spring budburst phenology partially fits within a functional trait framework of acquisitive to conservative growth strategies. Leveraging these relationships could improve predictions of how communities shift in their growth strategies alongside changing phenology with future warming.
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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) | 0.001 | 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 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".