Higher‐latitude spring‐flowering herbs advance their phenology more than trees with warming temperatures
Bibliographic record
Abstract
Abstract The phenologies of co‐occurring trees and spring‐blooming understory herbs in northeastern North American hardwood forests appear to be regulated by different environmental drivers – air temperature and soil temperature/snowpack, respectively. Accordingly, it has been hypothesized that climate change–driven asymmetry in the advancement of canopy leaf‐out relative to the timing of understory growth could reduce photosynthetic rates and reproductive success of understory herbs through greater early‐season shading. To determine whether trees and spring‐flowering forest herbs are advancing their phenologies at different rates with respect to increasing global temperatures, we examined the phenological responses to warming of 10 species of trees and 11 species of spring‐flowering forest herbs (8045 observations from 965 sites) in northeastern North America using 13 years of data collected by citizen scientists under the auspices of the USA‐National Phenology Network. Contrary to expectation, the degree of advancement of leaf‐out as a function of temperature was greater in spring‐flowering forest herbs than in trees, with a mean response rate of −4.9 days/°C (95% BCI [−5.2, −4.6]) for spring‐flowering forest herbs vs. −3.3 days/°C (95% BCI [−3.5, −3.1]) for trees. However, the response to temperature was not consistent across the latitudinal range, with spring‐flowering forest herbs responding more strongly to warming than trees at middle (40–44°N) and higher (45–48°N) latitudes but not at lower latitudes (35–39°N). Synthesis . In contrast to previous suggestions, our study shows spring‐flowering forest herbs advancing their phenology at a higher rate than trees with respect to warming through most of the latitudinal range investigated, which could translate into a longer growing season and increased carbon uptake for spring‐flowering forest herbs as spring temperatures rise.
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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 | 0.001 |
| 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) | 0.002 | 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".