The mechanisms of phenology: the patterns and processes of phenological shifts
Bibliographic record
Abstract
Abstract Species across a wide range of taxa and habitats are shifting phenological events in response to climate change. While advances are common, shifts vary in magnitude and direction within and among species, and the basis for this variation is relatively unknown. We examine previously suggested patterns of variation in phenological shifts in order to understand the cue–response mechanisms that underlie phenological change. Here, we review what is known about the mechanistic basis for nine factors proposed to predict phenological change (latitude, elevation, habitat type, trophic level, migratory strategy, ecological specialization, species’ seasonality, thermoregulatory mode, and generation time). We find that many studies either do not identify a specific underlying mechanism or do not evaluate alternative mechanistic hypotheses, limiting the ability of scientists to predict future responses to global change with accuracy. We present a conceptual framework that emphasizes a critical distinction between environmental (cue‐driven) and organismal (response‐driven) mechanisms causing variation in phenological shifts and discuss how this distinction can reduce confusion in the field and improve predictions of future phenological change.
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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 | 0.001 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".