Progress towards an interdisciplinary science of plant phenology: building predictions across space, time and species diversity
Bibliographic record
Abstract
Climate change has brought renewed interest in the study of plant phenology - the timing of life history events. Data on shifting phenologies with warming have accumulated rapidly, yet research has been comparatively slow to explain the diversity of phenological responses observed across latitudes, growing seasons and species. Here, we outline recent efforts to synthesize perspectives on plant phenology across the fields of ecology, climate science and evolution. We highlight three major axes that vary among these disciplines: relative focus on abiotic versus biotic drivers of phenology, on plastic versus genetic drivers of intraspecific variation, and on cross-species versus autecological approaches. Recent interdisciplinary efforts, building on data covering diverse species and climate space, have found a greater role of temperature in controlling phenology at higher latitudes and for early-flowering species in temperate systems. These efforts have also made progress in understanding the tremendous diversity of responses across species by incorporating evolutionary relatedness, and linking phenological flexibility to invasions and plant performance. Future research with a focus on data collection in areas outside the temperate mid-latitudes and across species' ranges, alongside better integration of how risk and investment shape plant phenology, offers promise for further progress.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".