Hormones as Mediators of Phenotypic and Genetic Integration: an Evolutionary Genetics Approach
Bibliographic record
Abstract
Evolutionary endocrinology represents a synthesis between comparative endocrinology and evolutionary genetics. This synthesis can be viewed through the breeder's equation, a cornerstone of quantitative genetics that, in its univariate form, states that a population's evolutionary response is the product of the heritability of a trait and selection on that trait (R = h(2)S). Under this framework, evolutionary endocrinologists have begun to quantify the heritability of, and the strength of selection on, a variety of hormonal phenotypes. With specific reference to our work on testosterone and corticosterone in birds and lizards, we review these studies while emphasizing the challenges of applying this framework to hormonal phenotypes that are inherently plastic and mediate adaptive responses to environmental variation. Next, we consider the untapped potential of evolutionary endocrinology as a framework for exploring multivariate versions of the breeder's equation, with emphasis on the role of hormones in structuring phenotypic and genetic correlations. As an extension of the familiar concepts of phenotypic integration and hormonal pleiotropy, we illustrate how the hormonal milieu of an individual acts as a local environment for the expression of genes and phenotypes, thereby influencing the quantitative genetic architecture of multivariate phenotypes. We emphasize that hormones are more than mechanistic links in the translation of genotype to phenotype: by virtue of their pleiotropic effects on gene expression, hormones structure the underlying genetic variances and covariances that determine a population's evolutionary response to selection.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".