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Natural Selection on Phenotypes Conner and Hartl – p. 6-1 From: Conner, J. and D. Hartl, A Primer of Ecological Genetics. In prep. for Sinauer Chapter 6: Natural selection on phenotypes Natural selection and adaptation have been recurring themes throughout this book, from the very beginning of chapter 1. We discussed selection on genotypes (and discrete phenotypes) in chapter 3, and now that we have a good understanding of the genetics of continuously distributed traits we turn to selection on these common and ecologically important phenotypes. We discuss the very general and widely used regression-based approaches to measuring selection, and cover ways to identify the phenotypic traits that are the direct targets of selection, as well as ways to determine the environmental agents that are causing selection. Identifying selective agents and targets is a powerful approach to understanding adaptation. Finally, we integrate this material with the concepts covered in chapters 4 and 5 to show how short-term phenotypic evolution can be modeled and predicted, and how this undertaking sheds light on constraints on adaptive evolution. Throughout the chapter the effects of genetic and phenotypic correlations among traits are highlighted. Evolution by natural selection has three parts (Figure 6.1; Endler 1986): 1. There is phenotypic variation for the trait of interest. 2. There is some consistent relationship between this phenotypic variation and variation in fitness. 3. A significant proportion of the phenotypic variation is caused by additive genetic variance, that is, the trait is heritable. Numbers 1 and 2 represent selection on phenotypes, which occurs within a generation and can be quantified using the selection differential (S) just as with artificial selection. Number 3 is heritability, which can be thought of as determining the amount of phenotypic change that is passed on to the next generation, because as with artificial selection, the amount of evolutionary change, R, is determined by the product h2S. Increases in either h2 or S produce a greater evolutionary change across generations. Conner and Hartl – p. 6-2 A 1. Phenotypic variation B 2. Consistent relationship between fitness and phenotype Fitness function; slope = S C 1 3. Some of the phenotypic 0 variation is heritable Offspring-parent regression; -1 2 Offspring flower number Offspring slope = h -3 -2 -1 0 1 2 Parental flower number (standardized) Figure 6.1. Graphical depiction of the three elements of phenotypic evolution by natural selection. A. Histogram showing the frequency distribution for flower number in wild radish (data in A and B from Conner et al. 1996). Flower number is standardized to have a mean of zero and a standard deviation of one. B. The relationship between flower number and fitness. The spread of points along the X-axis shows exactly the same phenotypic variation as in part A. The fitted regression line is the estimated fitness function; the slope of this line is the selection differential S. C. Hypothetical offspring-parent regression for flower number, showing that the trait is heritable. 6.1 The Chicago school approach to phenotypic evolution An extremely fruitful approach to studying phenotypic evolution in nature is based on the scheme depicted in Figure 6.1. This approach is sometimes referred to as the ‘Chicago School’, because it was developed at the University of Chicago in the late 1970s and early 1980s (Lande 1979; Lande and Arnold 1983). In this scheme, the bivariate plot shown in Figure 6.1B is the fundamental depiction of natural selection. On the X-axis is the phenotype, which can be any quantitative trait in the phenotypic hierarchy, including physiology, morphology, behavior, or life-history traits. For Conner and Hartl – p. 6-3 measurements of selection it is very useful to standardize the phenotypic values by taking each individual value and subtracting the population mean and dividing by the population standard deviation. This produces measures of selection that have useful statistical properties and are comparable across traits and organisms (e.g., Kingsolver et al. 2001). On the Y-axis is fitness, which is a fundamental and very difficult concept, and therefore a number of different definitions of fitness have been proposed, each with strengths and weaknesses (Dawkins 1982, ch. 10; Endler 1986, ch. 2; deJong 1994). In addition to this conceptual difficulty, in practice fitness is very difficult to measure in natural populations. For the Chicago School methods, the best measure may be lifetime number of offspring produced, and this is the working definition that we will use in this chapter. This is certainly an excellent practical measure of fitness for organisms with non-overlapping generations and stable population sizes, and is better than the fitness estimates that have been used in most field studies of selection. For the actual analysis relative fitness values are used, which are calculated by dividing the fitness of each individual by the average fitness of the population. This is different from how relative fitness was calculated for genotypes (Chapter 3), but the symbol (w) and the reason are the same – selection operates through the fitnesses of individuals relative to other individuals in the population, not through absolute numbers of offspring produced. The Chicago School methods estimate the strength of natural selection by regressing fitness on the phenotype. This provides a statistical estimate of the fitness function, which describes the relationship between fitness and the phenotype; this relationship is natural selection. There are three basic types of phenotypic selection defined according to the shape of the fitness function. Directional selection is characterized by a linear fitness function (a straight line), and can be positive or negative depending on whether fitness increases or decreases with increasing phenotypic value (Figure 6.1 B and Figure 6.2 A respectively). Linear regression is used to fit a line through the data, and the slope of this line measures the strength of selection. If standardized data are used, then this slope equals the selection differential (S). This is the same measure as is used for artificial selection (Chapter 5), but is calculated in a different way because fitnesses in natural selection are continuously distributed (truncation selection is rare or nonexistent in nature). Recall that the selection differential is for phenotypic selection, and is not the same as the selection coefficient (lower case s) that is used to measure genotypic selection (Chapter 3). The results of directional selection are very similar to truncation selection – if the trait is heritable, it will change the mean and may decrease the variance (Fig. 6.2B). Conner and Hartl – p. 6-4 A C E Fitness B D F Frequency Phenotypic trait Figure 6.2 Hypothetical fitness functions (top graphs) and the effect of each kind of selection on the frequency distribution of the population for that trait (bottom graphs). A and B show negative directional selection, C and D stabilizing selection, and E and F disruptive selection. In the bottom graphs the gray curves are the population distribution before selection, and the black curves after selection. Fitness functions are not always straight lines, so when the fitness function has curvature quadratic regression is used to estimate the strength of selection: γ 2 Y = βX + X 6.1 2 In this equation, β is the slope of the fitness function (equivalent to S with standardized data) and γ measures the degree that the slope changes with increasing X. In other words, γ estimates the amount of curvature in the fitness function, and is variously called the non-linear, variance, or stabilizing/disruptive selection gradient (non-linear is preferable, and stabilizing/disruptive should be avoided). If β = 0 and γ is negative, then there is no overall trend in the data but the slope is constantly decreasing (Fig. 6.2C); this is called stabilizing selection. The key characteristic of stabilizing selection is that there is an intermediate optimum for fitness; in other words, fitness is highest at some intermediate fitness and is lower at the phenotypic extremes. Stabilizing selection by itself does not change the trait mean but does decrease trait variance (Fig. 6.2D). A particularly clear pattern of stabilizing selection occurs on the size of galls formed by flies in the genus Eurosta on goldenrod plants (Weis and Gorman 1990; Fig. 6.3). Female flies lay their eggs inside goldenrod stems, and this causes the plant cells in the Conner and Hartl – p. 6-5 area to divide profusely, producing a large, hard ball inside which the larva feeds and develops. A parasitoid wasp often attacks these larvae, but only in galls that are small enough in diameter that the ovipositor of the wasp can reach the fly larva. The fly larvae are also eaten by birds, who are more attracted visually to the larger galls. Therefore, the two extreme gall sizes are each attacked at a higher frequency by different predators, reducing survivorship relative to the intermediate phenotypes. Fig. 6.3 Stabilizing selection on gall size of Eurosta flies on goldenrod. Survival of the fly larvae was the measure of fitness, and the phenotypic trait was gall diameter measured to the nearest mm. Over 3500 galls were measured and scored for survival to adulthood; the points in the graph represent the proportion that survived at each gall size. If β = 0 and γ is positive, then the slope is steadily increasing with X (Fig. 6.2E); when the fitness function has this shape the term disruptive selection is used. The key characteristic of disruptive selection is opposite to that for stabilizing selection -- there is an intermediate minimum for fitness, i.e., fitness is lowest at some intermediate fitness and is higher at the phenotypic extremes. Like stabilizing selection, disruptive selection by itself does not change the population mean, but it can increase the variance, at least in the short term (Fig.
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