The highest butterfly in North America
Bibliographic record
Abstract
Despite the expansive old-growth forests of California's Sierra Nevada, its greatest diversity of butterflies is found in non-forested habitats, such as alpine meadows and fell-fields. These unique “sky island” habitats support a number of endemic butterflies, such as the Ivallda Arctic (Oeneis chryxus ivallda). Unlike other, more colorful butterflies in the region, the dark, cryptic coloration of O c ivallda is hypothesized to aid in both thermoregulation and camouflage in the relatively cool, rocky environments they inhabit. Faced with warming temperatures, some alpine butterfly populations may track their climatic niche and stay ahead of advancing treelines by moving up mountain slopes. However, many O c ivallda populations already occur at or near mountain summits, limiting their potential for elevational shifts. On 2 July 2022, we observed a previously unrecorded O c ivallda population at the summit of Mount Whitney (4421 m). Popular data repositories (eg GBIF and iNaturalist) confirmed that no other butterflies have been observed here. Mount Whitney is the highest mountain in the conterminous US, and all higher summits in Canada and Alaska are—at least for the moment—permanently snow- or glacier-covered, unsuitable for butterfly occupancy. This observation therefore marks what we believe is the highest extant butterfly population in North America. Of the 12 O c ivallda individuals observed during a one-hour survey, three were collected for whole-genome resequencing as part of the California Conservation Genomics Project (CCGP; https://www.ccgproject.org/). Two individuals are pictured, one from the summit of Mount Whitney (above) and the other from the summit of Mount Dana (3981 m; below), approximately 170 km to the northwest of Whitney. In light of this observation, alpine butterflies in the Sierra Nevada are clearly exhausting their potential for elevational shifts in the face of warming temperatures. Preventing extinction may require proactive conservation practices, such as translocation and even assisted migration. Detailed population genomic data, such as those produced by the CCGP, will help inform these efforts.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.005 | 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".