Supplemental Material - 1000 randomly-chosen candidate topologies for the Canadian butterfly phylogeny
Bibliographic record
Abstract
Climate change is driving rapid and accelerating shifts in range limits, both poleward expansions and equatorward contractions. However, many species are falling behind the pace of change in their dispersal into newly suitable habitats and now show “climate debts”, lags between predicted and observed range expansions under changing climates. Failure to track changing climates may be due to interspecific interactions such as particular food availability for specialists, abiotic barriers such as mountain ranges, or intrinsic traits such as dispersal limitation. A trait-based analysis of climate change performance would help identify causes of climate debt.\nTo understand the correlates of climate debt within a large clade of organisms we use historical and modern observations of butterflies from western Canada as a case study to construct and project individual climate-based environmental niche models. By comparing projected distributions based on historical records to observed modern distributions we are able to construct estimates of climate debt and evaluate the effect of dispersal ability, diet breadth and a proxy for range size on these species' measured climate debt.\nHigh levels of climate debt are accumulating within the butterflies of Western Canada, independently of dispersal ability, diet breadth and phylogeny. Range size emerges as the only variable that significantly reduces climate debt, suggesting that more narrowly-ranged species may be at risk of being squeezed out by both a reduction of suitable habitat in their current range and the failure to colonize newly available habitat. These findings underscore the need to investigate potential landscape-level determinants of climate debt that may be limiting range expansions in this group.
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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 | 0.016 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.255 | 0.037 |
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".