Toward a synthetic understanding of the role of phenology in ecology and evolution
Bibliographic record
Abstract
Phenology affects nearly all aspects of ecology and evolution. Virtually all biological phenomena-from individual physiology to interspecific relationships to global nutrient fluxes-have annual cycles and are influenced by the timing of abiotic events. Recent years have seen a surge of interest in this topic, as an increasing number of studies document phenological responses to climate change. Much recent research has addressed the genetic controls on phenology, modelling techniques and ecosystem-level and evolutionary consequences of phenological change. To date, however, these efforts have tended to proceed independently. Here, we bring together some of these disparate lines of inquiry to clarify vocabulary, facilitate comparisons among habitat types and promote the integration of ideas and methodologies across different disciplines and scales. We discuss the relationship between phenology and life history, the distinction between organismal- and population-level perspectives on phenology and the influence of phenology on evolutionary processes, communities and ecosystems. Future work should focus on linking ecological and physiological aspects of phenology, understanding the demographic effects of phenological change and explicitly accounting for seasonality and phenology in forecasts of ecological and evolutionary responses to climate 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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.006 |
| Scholarly communication | 0.003 | 0.008 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".