Phenological sensitivity to temperature at broad scales: opportunities and challenges of natural history collections
Bibliographic record
Abstract
The seasonal timing of biological events (i.e. phenology) has been frequently observed to shift in response to recent climate change. While many of these events now occur earlier due to warmer temperatures, there is considerable variation in the direction and magnitude of these shifts across species. This variation could have consequences for species interactions and ecological communities, especially when the relative timing of key life cycle events among species is disrupted. As a first step to better understand the causes and consequences of variation in species’ phenological responses to climate change, we used natural history collections to quantify and compare broad-scale patterns in phenology-temperature relationships for Canadian butterflies and their nectar food plants over the past century. The phenology of both groups advanced in response to warmer temperatures - both across years and sites. Across butterfly-plant associations, flowering time was significantly more sensitive to temperature than the timing of butterfly flight. However, the sensitivities were not correlated across associations. The findings we will present indicate that warming-driven shifts in the timing of species interactions are likely to be prevalent. The opportunities and challenges associated with using natural history collections for detecting and linking phenological responses to climate change will also be discussed.
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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 | 0.130 | 0.207 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.009 | 0.012 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.008 | 0.008 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".