Leaf out times of temperate woody plants are related to phylogeny, deciduousness, growth habit and wood anatomy
Bibliographic record
Abstract
Leaf out phenology affects a wide variety of ecosystem processes and ecological interactions and will take on added significance as leaf out times increasingly shift in response to warming temperatures associated with climate change. There is, however, relatively little information available on the factors affecting species differences in leaf out phenology. An international team of researchers from eight Northern Hemisphere temperate botanical gardens recorded leaf out dates of c. 1600 woody species in 2011 and 2012. Leaf out dates in woody species differed by as much as 3 months at a single site and exhibited strong phylogenetic and anatomical relationships. On average, angiosperms leafed out earlier than gymnosperms, deciduous species earlier than evergreen species, shrubs earlier than trees, diffuse and semi-ring porous species earlier than ring porous species, and species with smaller diameter xylem vessels earlier than species with larger diameter vessels. The order of species leaf out was generally consistent between years and among sites. As species distribution and abundance shift due to climate change, interspecific differences in leaf out phenology may affect ecosystem processes such as carbon, water, and nutrient cycling. Our open access leaf out data provide a critical framework for monitoring and modelling such changes going forward.
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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 | 0.001 | 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) | 0.001 | 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".