A Global-Scale Evaluation of Mammalian Exposure and Vulnerability \nto Anthropogenic Climate Change
Bibliographic record
Abstract
There is considerable evidence demonstrating that anthropogenic climate change is impacting species living in the wild. The vulnerability of a given species to such change may be understood as a combination of the magnitude of climate change to which the species is exposed, the sensitivity of the species to changes in climate, and the capacity of the species to adapt to climatic change. I used species distributions and estimates of expected changes in local temperatures per teratonne of carbon emissions to assess the exposure of terrestrial mammal species to human-induced climate change. I evaluated species vulnerability to climate change by combining expected local temperature changes with species conservation status, using the latter as a proxy for species sensitivity and adaptive capacity to climate change. I also performed a global-scale analysis to identify hotspots of mammalian vulnerability to climate change using expected temperature changes, species richness and average species threat level for each km2 across the globe. The average expected change in local annual average temperature for terrestrial mammal species is 1.85 oC/TtC. Highest temperature changes are expected for species living in high northern latitudes, while smaller changes are expected for species living in tropical locations. Hotspots of terrestrial mammalian vulnerability to climate change include northern Eurasia and Canada, central China, and the Amazon basin. This study is intended to provide a guide for conservation research and planning in the identification of individual mammal species as well as regions of mammalian habitat that may become increasingly vulnerable with continued climate change.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".