Natural selection acts on phenotypes, regardless of their genetic basis, and produces immediate phenotypic effects within a generation that can be measured without recourse to principles of heredity or evolution. In contrast, evolutionary response to selection, the genetic change that occurs from one generation to the next, does depend on genetic variation. Animal and plant breeders routinely distinguish phenotypic selection from evolutionary response to selection (Mayo, 1980; Falconer, 1981). Upon making this critical distinction, emphasized by Haldane (1954), precise methods can be formulated for the measurement of phenotypic natural selection. Correlations between characters seriously complicate the measurement of phenotypic selection, because selection on a particular trait produces not only a direct effect on the distribution of that trait in a population, but also produces indirect effects on the distribution of correlated characters. The problem of character correlations has been largely ignored in current methods for measuring natural selection on quantitative traits. Selection has usually been treated as if it acted only on single characters (e.g., Haldane, 1954; Van Valen, 1965a; O'Donald, 1968, 1970; reviewed by Johnson, 1976 Ch. 7). This is obviously a tremendous oversimplification, since natural selection acts on many characters simultaneously and phenotypic correlations between traits are ubiquitous. In an important but neglected paper, Pearson (1903) showed that multivariate statistics could be used to disentangle the direct and indirect effects of selection to determine which traits in a correlated ensemble are the focus of direct selection. Here we extend and generalize Pearson's major results. The purpose of this paper is to derive measures of directional and stabilizing (or disruptive) selection on each of a set of phenotypically correlated characters. The analysis is retrospective, based on observed changes in the multivariate distribution of characters within a generation, not on the evolutionary response to selection. Nevertheless, the measures we propose have a close connection with equations for evolutionary change. Many other commonly used measures of the intensity of selection (such as selective mortality, change in mean fitness, variance in fitness, or estimates of particular forms of fitness functions) have little predictive value in relation to evolutionary change in quantitative traits. To demonstrate the utility of our approach, we analyze selection on four morphological characters in a population of pentatomid bugs during a brief period of high mortality. We also summarize a multivariate selection analysis on nine morphological characters of house sparrows caught in a severe winter storm, using the classic data of Bumpus (1899). Direct observations and measurements of natural selection serve to clarify one of the major factors of evolution. Critiques of the "adaptationist program" (Lewontin, 1978; Gould and Lewontin, 1979) stress that adaptation and selection are often invoked without strong supporting evidence. We suggest quantitative measurements of selection as the best alternative to the fabrication of adaptive scenarios. Our optimism that measurement can replace rhetorical claims for adaptation and selection is founded in the growing success of field workers in their efforts to measure major components of fitness in natural populations (e.g., Thornhill, 1976; Howard, 1979; Downhower and Brown, 1980; Boag and Grant, 1981; Clutton-Brock et

https://www.onlinelibrary.wiley.com/doi/pdf/10.1111/j.1558-5646.1983.tb00236.x

The measurement of selection on correlated characters

A new method is described for estimating genetic relatedness from genetic markers such as protein polymorphisms. It is based on Grafen's (1985) relatedness coefficient and is most easily interpreted in terms of identity by descent rather than as a genetic regression. It has several advantages over methods currently in use: it eliminates a downward bias for small sample sizes; it improves estimation of relatedness for subsets of population samples; and it allows estimation of relatedness for a single group or for a single pair of individuals. Individual estimates of relatedness tend to be highly variable but, in aggregate, can still be very useful as data for nonparametric tests. Such tests allow testing for differences in relatedness between two samples or for correlating individual relatedness values with another variable.

Estimating relatedness using genetic markers.

Arthur Jensen argues that the failure of recent compensatory education efforts to produce lasting effects on children's IQ and achievement suggests that the premises on which these efforts have been based should be reexamined.

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How Much Can We Boost IQ and Scholastic Achievement

A pesar de la relativamente corta historia de la Psicologia como ciencia, existen pocos constructos psicologicos que perduren 90 anos despues de su formulacion y que, aun mas, continuen plenamente vigentes en la actualidad. El factor «g» es sin duda alguna uno de esos escasos ejemplos y para contrastar su vigencia actual tan solo hace falta comprobar su lugar de preeminencia en los modelos factoriales de la inteligencia mas aceptados en la actualidad, bien como un factor de tercer orden en los modelos jerarquicos o bien identificado con un factor de segundo orden en el modelo del recientemente desaparecido R.B.Cattell.

The g Factor: The Science of Mental Ability

Although the potential genetic risks associated with rare or endangered plants and small populations have been discussed previously, the practical role of population genetics in plant conservation remains unclear. Using theory and the available data, we examine the effects of genetic drift, inbreeding, and gene flow on genetic diversity and fitness in rare plants and small populations. We identify those circumstances that are likely to put these plant species and populations at genetic risk. Warning signs that populations may be vulnerable include changes in factors such as population size, degree of isolation, and fitness. When possible, we suggest potential management strategies.

/pdf/population-genetic-consequences-of-small-population-size-3onkvtfxng.pdf

POPULATION GENETIC CONSEQUENCES OF SMALL POPULATION SIZE: Implications for Plant Conservation

So far in this course we have dealt entirely with the evolution of characters that are controlled by simple Mendelian inheritance at a single locus. There are notes on the course website about gametic disequilibrium and how allele frequencies change at two loci simultaneously, but we didn’t discuss them. In every example we’ve considered we’ve imagined that we could understand something about evolution by examining the evolution of a single gene. That’s the domain of classical population genetics. For the next few weeks we’re going to be exploring a field that’s actually older than classical population genetics, although the approach we’ll be taking to it involves the use of population genetic machinery. If you know a little about the history of evolutionary biology, you may know that after the rediscovery of Mendel’s work in 1900 there was a heated debate between the “biometricians” (e.g., Galton and Pearson) and the “Mendelians” (e.g., de Vries, Correns, Bateson, and Morgan). Biometricians asserted that the really important variation in evolution didn’t follow Mendelian rules. Height, weight, skin color, and similar traits seemed to

Grüneberg H

Papers

Human biochemical genetics