Canadian butterfly climate debt is significant and correlated with range size
Bibliographic record
Abstract
Climate change is causing rapid shifts in species’ range limits, leading to poleward expansions and range losses toward the equator. However, ‘climate debt’, the gap between required and realized range shifts under changing climates, can accumulate when species are unable to track shifting conditions sufficiently rapidly to keep pace with climate changes. Currently, we do not know the rate at which species will keep pace via dispersal to track their climate envelopes, yet understanding potential differences in climate debt is central to estimating how climate change will influence extinction risk. Here, we use historical observations of 155 butterfly species found in Canada to construct climate‐based environmental niche models for each species and then compare projections with observed modern distributions to quantify climate debts. This approach suggests that high levels of climate debt are accumulating within the vast majority of these species. Such failure to track changing climates may arise from some combination of interspecific interactions such as particular food availability for specialists, abiotic barriers such as mountain ranges, or species’ intrinsic dispersal capacities. Our linear models relating climate debt to a variety of biological predictors suggest that the debts we documented are accumulating independently of dispersal ability, diet breadth, and phylogeny. A proxy for range size is the only significant predictor of climate debt, with species with narrower ranges accumulating more debt: this suggests that species with narrow ranges may be at risk from both a reduction of suitable habitat in their current range and the failure to colonize newly available habitat. Identifying the factors, whether intrinsic or imposed by local environmental conditions, that limit species’ capacities to colonize areas beyond their historical limits is vital to conservation planning.
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How this classification was reachedexpand
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) | 0.050 | 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; both teacher heads agree on what is shown here.
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".