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High-Dimensional Fitness Landscapes and Speciation 3 High-Dimensional Fitness Landscapes and Speciation 3 Sergey Gavrilets The Modern Evolutionary Synthesis of the 1930s and 1940s remains the paradigm of evolutionary biology (Futuyma 1998; Gould 2002; Pigliucci 2007; Ridley 1993). The progress in understanding the process of evolu- tion made during that period had been a direct result of the development of theoretical population genetics by Fisher, Wright, and Haldane, who built a series of mathematical models, approaches, and techniques showing how natural selection, mutation, drift, migration, and other evolutionary factors are expected to shape the genetic and phenotypic characteristics of biological populations. These theoretical advances pro- vided “a great impetus to experimental work on the genetics of popula- tions” (Sheppard 1954) and a “guiding light for rigorous quantitative experimentation and observation” (Dobzhansky 1955), and had many other far-reaching implications. According to Provine (1978), the work of Fisher, Wright, and Haldane had significant influence on evolutionary thinking in at least four ways. First, their models showed that the processes of selection, mutation, drift, and migration were largely sufficient to account for microevolution. Second, they showed that some directions explored by biologists were not fruitful. Third, the models complemented and lent greater significance to particular results of field and laboratory research. Fourth, they stimulated and provided framework for later empirical research. Since the time of the Modern Synthesis, evolutionary biology has arguably remained one of the most mathematized branches of the life sciences, in which mathe- matical models and methods continuously guide empirical research, provide tools for testing hypotheses, explain complex interactions between multiple evolutionary factors, train biological intuition, identify crucial parameters and factors, evaluate relevant temporal and spatial scales, and point to the gaps in biological knowledge, as well as provide simple and intuitive tools and metaphors for thinking about complex J2 phenomena. Pigliucci_03_Ch03.indd 45 9/15/2009 4:18:20 PM 46 Sergey Gavrilets In this chapter, I discuss two particular areas of theoretical evolution- ary biology that have experienced significant progress since the late 1980s: a theory of fitness landscapes and a theory of speciation. I also outline two particular directions for theoretical studies on the origins of biodiversity which are especially important, in my opinion, for unification of different branches of the life sciences. One is the develop- ment of a theory of large-scale evolutionary diversification and adaptive radiation. The other is a quantitative theory of the origins of our own species. Classical Fitness Landscapes The theoretical notion of fitness landscapes (also known as “adaptive landscapes,” “adaptive topographies,” and “surfaces of selective value”), which emerged at the onset of the Modern Synthesis, has become a standard tool both for formal mathematical modeling and for the intui- tive metaphorical visualizing of biological evolution, adaptation, and speciation. This notion was first introduced by Sewall Wright in a classic paper delivered at the 1932 International Congress of Genetics. Wright wanted to illustrate his ideas and mathematical results on the interaction of selection, random drift, mutation, and migration during adaptation in a nontechnical way accessible to biologists lacking quantitative skills (Wright 1932, 1988). Wright’s metaphor of fitness landscapes is widely viewed as one of his most important contributions to evolutionary biology (Coyne et al. 1997; Pigliucci and Kaplan 2006; Provine 1986). Over the ensuing 70 years, the notion of fitness landscapes has been substantially expanded and has found numerous applications well outside of evolutionary biology (e.g., in computer science, engineering, economics, and biochemistry). A key idea of evolutionary biology is that individuals in a population differ in fitness (due to the differences in genes and/or environments experienced). Differences in fitness that have genetic bases are the most important ones because it is the changes in genes that make innovations and adaptation permanent. The relationship between genes and fitness (direct or mediated via phenotype) is obviously of fundamental impor- tance. In the most common modern interpretation, a fitness landscape specifies a particular fitness component (e.g., viability, that is, the prob- ability to survive to the age of reproduction) as a function defined on a particular set of genotypes or phenotypes. J2 Pigliucci_03_Ch03.indd 46 9/15/2009 4:18:20 PM High-Dimensional Fitness Landscapes and Speciation 47 a) b) 1.2 1.2 1 1 s 0.8 0.8 0.6 0.6 Fitness 0.4 Average fitnes 0.4 0.2 0.2 0 0 AA Aa aa 0 0.2 0.4 0.6 0.8 1 Genotype Frequency of allele A c) d) 1 0.8 0.8 0.6 0.6 0.4 Fitness 0.4 0.2 Average fitness 0.2 0 2 10 8 0 Average 0 6 –4 –2 0 2 4 –2 4 trait value –4 2 Trait value 0 Trait variance Figure 3.1 Examples of fitness landscapes. (a) Fitness landscape in a one-locus, two-allele model. (b) The average fitness landscape in a one-locus, two-allele model. (c) Fitness landscape for a quantitative trait model. (d) The average fitness landscape for a quantitative trait model. For example, consider a very large, randomly mating diploid popula- tion under constant viability selection. Let us focus on a particular locus with two alleles, A and a, controlling fitness (viability). Then there are three different genotypes: two homozygotes, AA and aa, and a hetero- zygote, Aa. An example of a fitness landscape for this simple model is given in figure 3.1a. The fitness landscape illustrated in this figure cor- responds to disruptive selection, that is, selection acting against inter- mediate genotypes (here, heterozygotes Aa). One may imagine an individual as a point on a fitness landscape and a population as a cloud of points which changes both its structure and its position as a result of action of different evolutionary factors (e.g, natural selection and sexual selection, mutation, recombination, drift, migration). The peaks and J2 Pigliucci_03_Ch03.indd 47 9/15/2009 4:18:20 PM 48 Sergey Gavrilets valleys of the landscape represent high-fitness and low-fitness combina- tions of genes (or phenotypic values), respectively; natural selection is imagined as a force pushing the population uphill, and adaptive evolu- tion is visualized as hill-climbing. In his original 1932 paper, Wright introduced two versions of fitness landscapes. The first corresponds to a relationship between a set of genes and individual fitness as illustrated in figure 3.1a. The second describes a relationship between variables characterizing the population’s genetic state (e.g., allele frequencies) and the average fitness of the population. The fitness landscape for the average fitness can be derived from a fitness landscape for individual fitness in a straightforward way. For example, figure 3.1b illustrates the fitness landscape for the average fitness corre- sponding to the fitness landscape for individual fitness shown in figure 3.1a. In fitness landscapes for the average fitness, it is the population (rather than an individual) that is imagined as a point climbing the slope toward a nearby peak. The attractive feature of this interpretation of fitness landscapes is the fact that in some simple models, the change in allele frequencies induced by selection is directly proportional to the gradient of the average fitness (Wright 1931). In this case one can intuit the general features of the evolutionary change just from the shape of the corresponding fitness landscape, without the need to solve the under- lying dynamic equations. The generalization of the notion of fitness landscapes for the case of continuously varying traits (such as size, weight, or a concentration of a particular gene product) was introduced by Simpson (1953). An example of a fitness landscape for a single quantitative character is shown in figure 3.1c. The fitness landscape illustrated in this figure describes stabilizing selection, that is, selection favoring an intermediate optimum (here, at trait value 0). Figure 3.1d illustrates the average fitness landscape corre- sponding to the individual fitness landscape shown in figure 3.1c. In figure 3.1d the independent variables are the average and variance of the trait values in the population which jointly control the average fitness. Lande (1976, 1980) showed that the change in the average trait value induced by selection is proportional to the gradient of the average fitness. The theoretical work of Lande (1976, 1980), Barton (e.g., 1989a; Barton and Rouhani 1987; Barton and Turelli 1987), and others in the 1970s and 1980s made such landscapes an indispensable part of the theoretical toolbox of evolutionary biology. In the original formulation and in most of the latter work, the fitness J2 component under consideration was viability and, as such, it was a prop- Pigliucci_03_Ch03.indd 48 9/15/2009 4:18:20 PM High-Dimensional Fitness Landscapes and Speciation 49 erty of an individual. Later the notion of fitness landscapes was general- ized to other fitness components, such as fertility (i.e., the number of offspring) or mating success (i.e., the probability of mating), which can be a property of a mating pair rather than of an individual (Gavrilets 2004). In most interpretations, fitness landscapes are static, that is, they do not change over time. However, models also exist in which landscapes
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