Phenotypic plasticity and genetic adaptation to high-altitude hypoxia in vertebrates
Bibliographic record
Abstract
High-altitude environments provide ideal testing grounds for investigations of mechanism and process in physiological adaptation. In vertebrates, much of our understanding of the acclimatization response to high-altitude hypoxia derives from studies of animal species that are native to lowland environments. Such studies can indicate whether phenotypic plasticity will generally facilitate or impede adaptation to high altitude. Here, we review general mechanisms of physiological acclimatization and genetic adaptation to high-altitude hypoxia in birds and mammals. We evaluate whether the acclimatization response to environmental hypoxia can be regarded generally as a mechanism of adaptive phenotypic plasticity, or whether it might sometimes represent a misdirected response that acts as a hindrance to genetic adaptation. In cases in which the acclimatization response to hypoxia is maladaptive, selection will favor an attenuation of the induced phenotypic change. This can result in a form of cryptic adaptive evolution in which phenotypic similarity between high- and low-altitude populations is attributable to directional selection on genetically based trait variation that offsets environmentally induced changes. The blunted erythropoietic and pulmonary vasoconstriction responses to hypoxia in Tibetan humans and numerous high-altitude birds and mammals provide possible examples of this phenomenon. When lowland animals colonize high-altitude environments, adaptive phenotypic plasticity can mitigate the costs of selection, thereby enhancing prospects for population establishment and persistence. By contrast, maladaptive plasticity has the opposite effect. Thus, insights into the acclimatization response of lowland animals to high-altitude hypoxia can provide a basis for predicting how altitudinal range limits might shift in response to 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 | 0.000 | 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".