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Landscapes, Surfaces, and Morphospaces: What Are They Good For?

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Landscapes, Surfaces, and Morphospaces: What Are They Good For? CHAPTER 3 Landscapes, Surfaces, and Morphospaces: What Are They Good For? Massimo Pigliucci 3.1 Introduction is precisely what in philosophy of science is meant by reduction; see Brigandt 2008). This time the non- Few metaphors in biology are more enduring fitness axes of the landscape were phenotypic traits, than the idea of Adaptive Landscapes, originally not genetic measures at all. Even more recently, proposed by Sewall Wright (1932) as a way to Lande and Arnold (1983) proposed a mathematical visually present to an audience of typically non- formalism aimed at estimating actual (as opposed mathematically savvy biologists his ideas about the to Simpson’s hypothetical) fitness surfaces, making relative role of natural selection and genetic drift use of standard multiple regression analyses. But in the course of evolution. The metaphor, how- while Simpson was talking about macroevolution- ever, was born troubled, not the least reason for ary change involving speciation, Lande and Arnold which is the fact that Wright presented different were concerned with microevolutionary analyses diagrams in his original paper that simply can- within individual populations of a single species. not refer to the same concept and are therefore In principle, it is relatively easy to see how hard to reconcile with each other (Pigliucci 2008). one can go from individual-genotype landscapes For instance, in some usages, the landscape’s non- to genotypic-frequency landscapes (the two Wright fitness axes represent combinations of individual versions of the metaphor). However, the (implied) genotypes (which cannot sensibly be aligned on a further transition from either of these to pheno- linear axis, and accordingly were drawn by Wright types (either in the Lande–Arnold or in the Simpson as polyhedrons of increasing dimensionality). In version) is anything but straightforward because of other usages, however, the points on the diagram the notorious complexity and non-linearity of the represent allele or genotypic frequencies,andsoare so called genotype–phenotype mapping function actually populations, not individuals (and these can (Alberch 1991; Pigliucci 2010). This is a serious issue indeed be coherently represented along continuous if—as I assume is the case—we wish to use the land- axes). scape metaphor as a unified key to an integrated Things got even more confusing after the land- treatment of genotypic and phenotypic evolution scape metaphor began to play an extended role (as well as of micro- and macroevolution). Without within the Modern Synthesis in evolutionary biol- such unification evolutionary biology would be left ogy and was appropriated by G. G. Simpson (1944) in the awkward position of having two separate to further his project of reconciling macro- and theories, one about genetic change, the other about microevolution, i.e. to reduce paleontology to pop- phenotypic change, and no bridge principles to con- ulation genetics (some may object to the characteri- nect them. zation of this program as a reductive one, but if the One more complication has arisen in more recent questions raised by one discipline can be reframed years, this one concerning Wright-style fitness land- within the conceptual framework of another, that scapes. Work by Gavrilets (2003; see also Chapter 17 26 LANDSCAPES, SURFACES, AND MORPHOSPACES: WHAT ARE THEY GOOD FOR? 27 this volume) and collaborators, made possible by context of the Modern Synthesis (Mayr and Provine the availability of computing power exceeding by 1980) as a theory essentially rooted in (though cer- several orders of magnitudes what was achievable tainly not limited to) population genetics, where throughout the twentieth century, has explored the evolution is often simply defined as change in features of truly highly dimensional landscapes—as allelic frequencies (Futuyma 2006). opposed to the standard two- or three-dimensional Despite some confusion due to the often inter- ones explicitly considered by Wright and by most changeable use of terms like “fitness” and “Adap- previous authors. As it turns out, evolution on tive” Landscape, I will use and build here on the so-called “holey” landscapes is characterized by more rigorous terminology followed by authors qualitatively different dynamics from those sug- such as McGhee (2007) and distinguish the follow- gested by the standard low-dimensional version ing four types of landscapes: of the metaphor—a conclusion that has led some Fitness landscapes. These are the sort of entities authors to suggest abandoning the metaphor alto- originally introduced by Wright and studied in a gether, in favor of embracing directly the results of high-dimensional context by Gavrilets and collabo- formal modeling (Kaplan 2008; though see Plutyn- rators. The non-fitness dimensions are measures of ski 2008 and Chapter 2 for a somewhat different genotypic diversity. The points on the landscape are take). population means, and the mathematical approach In this essay I wish to discuss the implications is rooted in population genetics. of four versions of the metaphor, often referred Adaptive Landscapes. These are the non- to as fitness landscapes, Adaptive Landscapes, fit- straightforward “generalizations” of fitness ness surfaces, and morphospaces. I will argue that landscapes introduced by Simpson, where the moving from one to any of the others is signifi- non-fitness dimensions now are phenotypic traits. cantly more difficult than might at first be surmised, The points on the landscape are populations and that work with morphospaces has been unduly speciating in response to ecological pressures or neglected by the research community. even above-species level lineages (i.e. this is about macroevolution). Fitness surfaces.ThesearetheLande–Arnoldtype 3.2 Four types of landscapes of landscapes, where phenotypic traits are plot- As I mentioned earlier, there are several versions of ted against a surrogate measure of fitness. They the “landscape” metaphor that have proliferated in are statistical estimates used in quantitative genetic the literature since Wright’s original paper. Indeed, modeling, and the points on the landscape can be Wright himself was referring—in that very same either individuals within a population or popu- paper—to at least two conceptions of landscapes, lation means, in both cases belonging to a single one with individual genotypes as the points on the species (i.e. this is about microevolution). (necessarily non-continuous) landscape, the other Morphospaces. These were arguably first intro- with populations identified by gene or genotype duced by Raup (1966), and differ dramatically from frequencies (in a continuous space). I will ignore the other types for two reasons: (a) they do not the distinction between the two types of Wright have a fitness axis; (b) their dimensions, while rep- landscapes for two reasons: first, they are con- resenting phenotypic (“morphological”) traits, are nected in a conceptually straightforward manner, generated via a priori geometrical or mathematical since it is obviously possible to go from individ- models, i.e. they are not the result of observa- ual genotypes to populations of genotypes with- tional measurements. They typically refer to across- out any theoretical difficulty. Second, most of the species (macroevolutionary) differences, though post-Wright literature, including the presentation they can be used for within-species work. of the metaphor in textbooks and the more recent work on “holey” landscapes, is framed in terms Let us take a look in a bit more detail at each type of gene/genotype frequencies, not individual geno- of landscape, to familiarize ourselves with their types. This is understandable within the broader respective similarities and differences. Throughout 28 PART I HISTORICAL BACKGROUND AND PHILOSOPHICAL PERSPECTIVES Iwilluseboththeterminologyjustsummarized work of Cowperthwaite and Meyers (2007) on 30- and the names of the relevant authors, to reduce nucleotide long binary RNA molecules. This is confusion at the cost of some redundancy. The first arguably one of the simpler models of genotype– thing we notice from even a cursory examination phenotype–fitness relations, and it is computation- of the literature is that there are few actual biolog- ally tractable and empirically approachable. Still, ical examples of fitness landscapes (Wright-style) 30-nucleotide binary molecules correspond to a or Adaptive Landscapes (Simpson-style) available, bewildering one billion unique sequences! These while there is a good number of well understood in turn generate “only” about 220,000 unique fold- examples of morphospaces (Raup-style) and partic- ing shapes in a G/U landscape and a “mere” 1000 ularly of adaptive surfaces (Lande–Arnold style). shapes in the A/U landscape, both of which these These differences are highly significant for my dis- authors have tackled. In other words, a genotypic cussion of the metaphor. space of a billion sequences corresponds (obviously Beginning with Wright-type, fitness landscapes, in a many-to-many manner) to a phenotypic space many of the examples in the literature are entirely of thousands to hundreds of thousands of possibil- conceptual, i.e. presented by various authors only ities, each characterized by its own (environment- for heuristic purposes. It should be obvious why dependent, of course) fitness value. it is so: to actually draw a “real” fitness landscape Things get even more complex when we we need a reasonably complete description of the move from RNA to proteins: Wroe et al. (2007) genotype fitness mapping function, i.e. we need have explored evolution in protein structure- → data about the fitness value of each relevant combi- defined phenotypic space, focusing in particular nation of genotypes that we happen to be interested on so-called “promiscuity,” the ability of a given in. For most purposes this is next to impossible. protein to perform two functions because of the Dobzhansky (1970, p. 25) famously put it this way: alternation between multiple thermodynamically stable configurations. Proteins are, of course, much Suppose there are only 1000 kinds of genes in the more complicated biochemical objects than RNA world, each gene existing in 10 different variants or molecules.
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