Data from "Vulnerability of alpine butterfly eggs to early winter warming"
Bibliographic record
Abstract
Metadata are provided in each data sheet. Refer to manuscript for full details.Parnassius smintheus is an alpine butterfly that overwinters as a first-instar caterpillar within its egg and often beneath the snow. While extreme temperatures in early winter appear pivotal to year-to-year population change of P. smintheus in the Rocky Mountains of Canada, the sources of mortality for these eggs are unclear. Here we tested three hypotheses about the vulnerability of eggs to warming and extreme weather in early winter (i.e. upper quartile of daily maximum and minimum microclimate temperatures in November): (1) warming disrupts the acquisition of cold tolerance, making eggs susceptible to subsequent cold snaps; (2) warming drives premature development and hatch, such that precocious hatchlings either starve or freeze; and (3) warming depletes the energy stores of dormant eggs. We then used these hypotheses to guide a simulation of the risk of winter mortality for eggs over the last half-century (1971 – 2020) in the Rocky Mountains of Canada. Early winter warming did not interrupt the acquisition of cold tolerance by freeze-avoidant eggs. Eggs did risk lethal freezing in simulated winters when extreme low temperatures coincided with an absence of snow cover. Early winter warming increased the risk of subsequent hatching, and precocious larvae were less cold-tolerant than eggs. Our simulation found that precocious larvae risked freezing during snow-free cold snaps in spring. Early winter warming did not appreciably drawdown energy stores, and we found that P. smintheus could not only fuel overwintering but tolerate several days of starvation after hatch. We conclude that eggs risk precocious development after early winter warming and are likely vulnerable in winters that lack persistent snow cover. Together, these sources of winter mortality may explain year-to-year change in P. smintheus populations. Identifying unidirectional thresholds, such as hatching and freezing, may be important for predicting the susceptibility of some alpine insects to future winter warming.Kurtis F. Turnbull1†, Alkistis Elliott-Graves2,3, Susan E. Anthony1, Jens Roland4, Stephen F. Matter4,5 and Brent J. Sinclair1,61Department of Biology, University of Western Ontario, London, ON, N6A 5B7, Canada2The Rotman Institute of Philosophy, University of Western Ontario, London, ON, N6A 5B7, Canada3Present address: Department of Philosophy, Bielefeld University, P.O. Box 100131, D-33501 Bielefeld, Germany4Department of Biological Sciences, University of Alberta, Edmonton, AB, T6G 2E9, Canada5Department of Biological Sciences, University of Cincinnati, Cincinnati, OH, 45221-0006, USA6Present address: Department of Entomology, Cornell University, Ithaca, NY 14853, USA†Corresponding author: Brent J. Sinclair, Department of Entomology, Comstock Hall, 129 Garden Ave, Ithaca, NY 14853, USA. Email: bjs299@cornell.edu; telephone +1-607-255-6413.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.033 | 0.009 |
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".