Pivotal effect of early‐winter temperatures and snowfall on population growth of alpine<i>Parnassius smintheus</i>butterflies
Bibliographic record
Abstract
Abstract Geographic range shifts in species’ distributions, due to climate change, imply altered dynamics at both their northern and southern range limits, or at upper and lower elevational limits. There is therefore a need to identify specific weather or climate variable(s), and life stages or cohorts on which they act, and how these affect population growth. Identifying such variables permits prediction of population increase or decline under a changing climate, and shifts in a species’ geographic range. For relatively well studied groups, such as butterflies, geographic range shifts are well documented, but weather variables and mechanisms causing those shifts are not well known. The Holarctic butterfly genusParnassius(Papilionidae) inhabits northern and alpine environments subject to variable and extreme weather. As such,Parnassiusspecies are vulnerable not only to long‐term changes in average conditions but especially to short‐term extreme weather events. We use population growth estimates for the alpine butterfly,Parnassius smintheus, from 21 populations in the Rocky Mountains of Canada over a 20‐yr interval combined with techniques of machine learning (randomForests) and parametric modeling to identify the important weather variables determining population growth. We do this to determine the seasons and life stages ofP. smintheusmost affected by climate change. Extreme minimum and maximum temperatures in November, in combination with November snowfall, affect annual population growth most, more so than do mean temperatures in November, and more so than weather at any other time of year. Populations decline both in years with low extreme minimum temperatures in November and especially in years with high extreme maximum temperatures in November, indicating that overwintering eggs are particularly vulnerable to early‐winter weather. Snowfall ameliorates the negative effects of extreme temperatures, particularly for extreme warm events. Results provide insight into biological mechanisms by which overwintering eggs might be affected by early winter weather. Short‐term extreme weather in November, acting on a single pivotal life stage (egg) is a far better predictor of population change of alpineP. smintheusbutterflies than is the general index of climate, the Pacific Decadal Oscillation.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".