Historically calibrated predictions of butterfly species' range shift using global change as a pseudo‐experiment
Bibliographic record
Abstract
Global changes have the potential to cause a mass extinction. Predicting how species will respond to anticipated changes is a necessary prerequisite to effectively conserving them and reducing extinction rates. Species niche models are widely used for such predictions, but their reliability over long time periods is known to vary. However, climate and land use changes in northern countries provide a pseudo-experiment to test model reliability for predicting future conditions, provided historical data on both species distributions and environmental conditions are available. Using maximum entropy, a prominent modeling technique, we constructed historical models of butterfly species' ranges across Canada and then ran the models forward to present-day to test how well they predicted the current ranges of species. For the majority of species, projections of how we predicted species would respond to known climate changes corresponded with species' observed responses (mean autoregressive R2 = 0.70). This correspondence declined for northerly and very widely distributed species. Our results demonstrate that at least some species are tracking shifting climatic conditions across very large geographic areas and that these shifts can be predicted accurately using niche models. We also found, however, that models for some species fail when projected through time despite high spatial model accuracies during model training, highlighting the need to base management decisions on species assemblages, not individual species.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".