Historically calibrated predictions of butterfly species' range shift using global change as a pseudo‐experiment
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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) | 0.067 | 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 it