Physiological insight into the evolution of complex phenotypes: aerobic performance and the O2 transport pathway of vertebrates
Bibliographic record
Abstract
Evolutionary physiology strives to understand how the function and integration of physiological systems influence the way in which organisms evolve. Studies of the O2 transport pathway - the integrated physiological system that transports O2 from the environment to mitochondria - are well suited to this endeavour. We consider the mechanistic underpinnings across the O2 pathway for the evolution of aerobic capacity, focusing on studies of artificial selection and naturally selected divergence among wild populations of mammals and fish. We show that evolved changes in aerobic capacity do not require concerted changes across the O2 pathway and can arise quickly from changes in one or a subset of pathway steps. Population divergence in aerobic capacity can be associated with the evolution of plasticity in response to environmental variation or activity. In some cases, initial evolutionary divergence of aerobic capacity arose exclusively from increased capacities for O2 diffusion and/or utilization in active O2-consuming tissues (muscle), which may often constitute first steps in adaptation. However, continued selection leading to greater divergence in aerobic capacity is often associated with increased capacities for circulatory and pulmonary O2 transport. Increases in tissue O2 diffusing capacity may augment the adaptive benefit of increasing circulatory O2 transport owing to their interactive influence on tissue O2 extraction. Theoretical modelling of the O2 pathway suggests that O2 pathway steps with a disproportionately large influence over aerobic capacity have been more likely to evolve, but more work is needed to appreciate the extent to which such physiological principles can predict evolutionary outcomes.
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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 | 0.001 |
| 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 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".