Thermal physiology and species distribution models reveal climate vulnerability of temperate amphibians
Bibliographic record
Abstract
Abstract Aim High‐latitude ectotherms are predicted to be less physiologically vulnerable to climate warming than tropical species based on their larger thermal safety margins, the distance between ambient temperatures and species' thermal optima. We sought to test the prediction that high latitude amphibians are buffered against the impacts of climate warming. Location British Columbia, Canada. Methods We estimated the risk from climate change for three high‐latitude amphibian species ( Spea intermontana , Rana aurora and Pseudacris regilla ) by combining thermal performance experiments with species distribution models and predicted changes in maximum summer temperatures through the 2080s, in order to demonstrate temporal and geographical trends in vulnerability to climate warming among and within species. Results We found that species have thermal safety margins of 3.2–3.8 °C based on current maximum summer temperatures. However, by the 2080s (emissions scenario A1B), we estimate that 45–82% of our focal species' current distributions will experience maximum summer temperatures above their thermal optima. We also found that by using long‐term average temperatures, as some studies have done, there were almost no scenarios in which populations of any species were experiencing temperatures greater than their thermal optima. Main conclusions Combining spatially explicit species distribution models with performance physiology allows us to predict where limiting temperatures will occur in the coming decades, and can guide climate mitigation and conservation efforts before populations decline. Despite moderate thermal safety margins, high‐latitude ectotherms can be highly vulnerable to climate warming when spatio‐temporal variation is incorporated into estimates of risk as a result of climate change.
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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 | 0.001 | 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) | 0.002 | 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 teacher head, 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".