Evolutionary Physiology and Genomics in the Highly Adaptable Killifish ( <i>Fundulus heteroclitus</i> )
Bibliographic record
Abstract
Abstract By investigating evolutionary adaptations that change physiological functions, we can enhance our understanding of how organisms work, the importance of physiological traits, and the genes that influence these traits. This approach of investigating the evolution of physiological adaptation has been used with the teleost fish Fundulus heteroclitus and has produced insights into (i) how protein polymorphisms enhance swimming and development; (ii) the role of equilibrium enzymes in modulating metabolic flux; (iii) how variation in DNA sequences and mRNA expression patterns mitigate changes in temperature, pollution, and salinity; and (iv) the importance of nuclear‐mitochondrial genome interactions for energy metabolism. Fundulus heteroclitus provides so many examples of adaptive evolution because their local population sizes are large, they have significant standing genetic variation, and they experience large ranges of environmental conditions that enhance the likelihood that adaptive evolution will occur. Thus, F. heteroclitus research takes advantage of evolutionary changes associated with exposure to diverse environments, both across the North American Atlantic coast and within local habitats, to contrast neutral versus adaptive divergence. Based on evolutionary analyses contrasting neutral and adaptive evolution in F. heteroclitus populations, we conclude that adaptive evolution can occur readily and rapidly, at least in part because it depends on large amounts of standing genetic variation among many genes that can alter physiological traits. These observations of polygenic adaptation enhance our understanding of how evolution and physiological adaptation progresses, thus informing both biological and medical scientists about genotype‐phenotype relationships. © 2020 American Physiological Society. Compr Physiol 10:637‐671, 2020.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| 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".