Weak evidence of provenance effects in spring phenology across Europe and North America
Bibliographic record
Abstract
Forecasting the biological impacts of climate change requires understanding how species respond to warmer temperatures through interannual flexible variation vs through adaptation to local conditions. Yet, we often lack this information entirely or find conflicting evidence across studies, which is the case for spring phenology. We synthesized common garden studies across Europe and North America that reported spring event dates for a mix of angiosperm and gymnosperm tree species in the northern hemisphere, capturing data from 384 North American and 101 European provenances (i.e. populations) with observations from 1962 to 2019, alongside autumn event data when provided. Across continents, we found no evidence of provenance effects in spring phenology, but strong clines with latitude and mean annual temperature in autumn. These effects, however, appeared to diverge by continent and species type (gymnosperm vs angiosperm), with particularly pronounced clines in North America in autumn events. Our results suggest flexible, likely plastic responses, in spring phenology with warming, and potential limits - at least in the short term - due to provenance effects for autumn phenology. They also highlight that, after over 250 yr of common garden studies on tree phenology, we still lack a holistic predictive model of clines across species and phenological events.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".