Assessing the risks to United States and Canadian mammals caused by climate change using a trait-mediated model
Bibliographic record
Abstract
Abstract A set of 182 populations of 76 mammal species in the United States and Canada, examined in natural conditions with minimized disturbances or management effects, shows that responses to climate change include negative responses, such as elevational range contractions, upward shifts and decreases in abundance, positive responses, such as range expansions, and no detectable responses. Responses vary among and within mammal species but many are correlated with species traits, particularly the responses linked to high extinction risks (= climate change risk: decreases in population sizes, range contractions, local extirpations). The traits showing the strongest links to differential responses to climate change are 1) body size—large mammals respond more often and most negatively to climate change, 2) activity times—few mammals with flexible active times respond to climate change, and 3) spatial distribution—high-latitude and high-elevation mammals responded more often to climate change. Using these traits and two approaches to trait weighting, I modeled the relative climate change risk for all 328 terrestrial, nonvolant mammal species in the United States and Canada across 10 levels of risk (low = 1–2, moderate = 3–4, moderate-high = 5–6, high = 7–8, very high = 9–10). The models predicted that 15% of these mammalian species are in the high- and very high-risk categories, including species from most orders. Many mammal populations and species listed as of conservation concern due to other human impacts by national or international agencies are also predicted by my models to be in the higher categories of climate change risk. My intention for these models is to clarify for managers and researchers which, where, and how mammals are responding to climate change relatively independent of other anthropogenic stressors (e.g., large-scale habitat change, overhunting) and to provide a preliminary assessment of species most in need of careful monitoring for climate change impacts.
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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) | 0.004 | 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".