To what extent do physiological tolerances determine elevational range limits of mammals?
Bibliographic record
Abstract
Abstract A key question in biology concerns the extent to which distributional range limits of species are determined by intrinsic limits of physiological tolerance. Here, we use common‐garden data for wild rodents to assess whether species with higher elevational range limits typically have higher thermogenic capacities in comparison to closely related lowland species. Among South American leaf‐eared mice (genus Phyllotis), mean thermogenic performance is higher in species with higher elevational range limits, but there is little among‐species variation in the magnitude of plasticity in this trait. In the North American rodent genus Peromyscus, highland deer mice (Peromyscus maniculatus) have greater thermogenic maximal oxygen uptake () than lowland white‐footed mice (Peromyscus leucopus) at a level of hypoxia that matches the upper elevational range limit of the former species. In highland deer mice, the enhanced thermogenic in hypoxia is attributable to a combination of evolved and plastic changes in physiological pathways that govern the transport and utilization of O2 and metabolic substrates. Experiments with Peromyscus mice also demonstrate that exposure to hypoxia during different stages of development elicits plastic changes in cardiorespiratory traits that improve thermogenic via distinct physiological mechanisms. Evolved differences in thermogenic capacity provide clues about why some species are able to persist in higher‐elevation habitats that lie slightly beyond the tolerable limits of other species. Such differences in environmental tolerance also suggest why some species might be more vulnerable to climate change than others. image
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".