How can physiology best contribute to wildlife conservation in a warming world?
Bibliographic record
Abstract
Abstract Global warming is now predicted to exceed 1.5°C by 2033 and 2°C by the end of the 21st century. This level of warming and the associated environmental variability are already increasing pressure on natural and human systems. Here we emphasize the role of physiology in the light of the latest assessment of climate warming by the Intergovernmental Panel on Climate Change. We describe how physiology can contribute to contemporary conservation programmes. We focus on thermal responses of animals, but we acknowledge that the impacts of climate change are much broader phylogenetically and environmentally. A physiological contribution would encompass environmental monitoring, coupled with measuring individual sensitivities to temperature change and upscaling these to ecosystem level. The latest version of the widely accepted Conservation Standards designed by the Conservation Measures Partnership includes several explicit climate change considerations. We argue that physiology has a unique role to play in addressing these considerations. Moreover, physiology can be incorporated by institutions and organizations that range from international bodies to national governments and to local communities, and in doing so, it brings a mechanistic approach to conservation and the management of biological resources.
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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 | 0.006 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.009 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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; 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".