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Introduction to Evolutionary Processes
In this chapter we introduce the basic elements and the empirical evidence of evolutionary processes. Since the groundbreaking work The Origin of Species by Charles Darwin (1859), a great deal of effort has been dedicated to the subject (see, e.g., Fisher, 1930; Haldane, 1932; Dobzanski, 1937; Mayr, 1942, 1963, 1982; Wright, 1969; Dawkins, 1976, 1982, 1986; Cavalli-Sforza and Feldman, 1981; Maynard Smith, 1989, 1993; Maynard Smith and Szathmary, 1995, just to mention a few masterpieces). Our discussion on the origin of evolutionary theory is mainly taken from the introduction by Ernst Mayr (2001) to the seventeenth printing of Darwin’s famous book, and from Dieckmann (1994, Chapter 1), Schrage (1995), Rizzoli-Larousse (2003), and the web pages of the University of California Museum of Paleontology. Throughout the exposition we emphasize that, even though the major scientists who developed evolutionary theory were stimulated by the study of nature, their ideas not only apply to the biological realm, but also capture many phenomena of self-organization encountered in social sciences, economics, and engineering.
1.1 ORIGINS OF EVOLUTIONARY THEORY
The idea that living organisms have been diversifying themselves through time, starting from a common origin, goes back to the Greek naturalistic philosophy. Among the precursors of evolutionary theory, as we define it today, we can mention Anaximander of Miletus (610–546 BC), Empedocles of Acragas (495–435 BC), and later some clergymen, such as Saint Augustine (354–430). The evolutionary conceptions of Greek philosophy were known during the Renaissance. However, no further contribution arose until the eighteenth century, when European scholars still believed that the universe was created in essentially its present and final state. During the eighteenth century, the work of intellectuals known as “encyclopedists” spread the Illuminism doctrine and, in particular, the results of pioneering research in systematic biology, aimed at hierarchically classifying organisms into groups that successively share more and more visible structural characteristics. Their work brought a better understanding of the concept of species and highlighted fundamental similarities between widely disparate organisms. Such similarities were in contrast with the hypothesis of creation in final state and prepared the ground for evolutionary theory. A considerable contribution came from Georges-Louis Leclerc Buffon (1707–1788), author of a compendium of biological history, and from Erasmus Darwin (1731–1802), Charles’ grandfather, who first discussed the conjecture that life could have evolved from a common ancestor and posed the question of how a species could evolve into another. The first explicit evolutionary theory was formulated by Jean Baptiste Lamarck (1744–1829), disciple of Buffon, who introduced the notion of inheritance. The “Lamarckian” hypothesis, that simple life forms continually come into existence from dead matter and continually become more complex, was strongly criticized by most naturalists of the time. In particular, one of the most active antievolutionists, Georges Cuvier (1769–1832), paradoxically provided evidence to the evolutionary hypothesis with his research in systematic biology, comparative anatomy, and paleontology.
At this point Charles Darwin (1809–1882) and Alfred Russel Wallace (1823– 1913) formulated the evolutionary theory that we still accept today. In their papers published in the same issue of the Journal of the Proceedings of the Linnean Society (Darwin, 1858; Wallace, 1858, often cited as a single paper with the title “On the Tendency of Species to form Varieties; and on the Perpetuation of Varieties and Species by Natural Means of Selection”) they presented their theory of evolution by natural selection, arguing that
- there is individual variation in innumerable characteristics of populations, some of which may affect the individual ability to survive and reproduce;
- there is likely to be a hereditary component to much of this variation, but evolutionary change ultimately relies on the appearance of new variant forms of organisms, called mutants;
- generation by generation there is a natural selection of the characteristics associated with greater survival and reproductive success, whose frequencies in the populations increase over time;
- the cumulative effects of mutations and natural selection, over a long period of time, alter the characteristics of species from those of their ancestors;
- all living organisms have descended with modifications from a common ancestor, thus developing hierarchical patterns of similarities.
Darwin and Wallace combined empirical observations with theoretical insights gained from Malthus’ (1798) work on competition and population growth. They had a precise idea of natural selection and realized the need of mutations. However, they were not aware of the laws of heredity, discovered seven years later by Gregor Mendel (1822–1884), who realized the discrete nature of heredity determinants, which we now call genes (Mendel, 1865). Darwin actually introduced the concept of natural selection and deserves, more than anyone else, the credit for having started, and firmly supported, the scientific and philosophical revolution from the dogma of creation and constancy of species to evolutionary theory.
The decisive event in Darwin’s life was the five-year period spent as a naturalist on the vessel Her Majesty’s Ship Beagle (from December 1831 to October 1836), in which he surveyed the coast of South America and the off-lying islands, collecting invaluable observations on the tropical forests of Brazil, on fossils in the Pampas of Argentina, on the geology of the Andes, and on the animal life of the Galapagos Islands (Darwin, 1839). After the return of the Beagle, Darwin spent most of his time in the analysis and interpretation of his findings and became later more acknowledged than Wallace, thanks to his famous book The Origin of Species (1859).
A third important scientist in the development of evolutionary theory, though by far less acknowledged than Darwin and Wallace, is Patrick Matthew (1790–1874). In a letter to Charles Lyell (April 10, 1860) Darwin says: “In last Saturday Gardeners’ Chronicle, a Mr. Patrick Matthew publishes long extract from his work on Naval Timber & Arboriculture published in 1831, in which he briefly but completely anticipates the theory of Natural Selection. — I have ordered the book, as some few passages are rather obscure but it is, certainly, I think, a complete but not developed anticipation.” Matthew’s evolutionary insights lie buried in an appendix of a book he wrote on raising trees of optimal quality for the Royal Navy (Matthew, 1831). In that appendix Matthew expressed his theory based on how tree species might vary in form and how artificial selection might improve cultivated trees.
Let us now listen directly to Darwin, Wallace, and Matthew. “The affinities of all the beings of the same class have sometimes been represented by a great tree. I believe this simile largely speaks the truth. The green and budding twigs may represent existing species; and those produced during each former year may represent the long succession of extinct species. . . The limbs divided into great branches, and these into lesser and lesser branches, were themselves once, when the tree was small, budding twigs; and this connexion of the former and present buds by ramifying branches may well represent the classification of all extinct and living species in groups subordinate to groups. . . From the first growth of the tree, many a limb and branch has decayed and dropped off, and these lost branches of various sizes may represent those whole orders, families, and genera which have now no living representatives, and which are known to us only from having been found in a fossil state. . . As buds give rise by growth to fresh buds, and these, if vigorous, branch out and overtop on all a feebler branch, so by generation I believe it has been with the Tree of Life, which fills with its dead and broken branches the crust of the earth, and covers the surface with its ever branching and beautiful ramifications” (Darwin, 1859).
“We have also here an acting cause to account for that balance so often observed in Nature — a deficiency in one set of organs always being compensated by an increased development of some others — powerful wings accompanying weak feet, or great velocity making up for the absence of defensive weapons; for it has been shown that all varieties in which an unbalanced deficiency occurred could not long continue their existence. The action of this principle is exactly like that of the centrifugal governor of the steam engine, which checks and corrects any irregularities almost before they become evident; and in like manner no unbalanced deficiency in the animal kingdom can ever reach any conspicuous magnitude, because it would make itself felt at the very first step, by rendering existence difficult and extinction almost sure soon to follow” (Wallace, 1858).
“As Nature, in all her modifications of life, has a power of increase far beyond what is needed to supply the place of what falls by Time’s decay, those individuals who possess not the requisite strength, swiftness, hardihood, or cunning, fall prematurely without reproducing — either a prey to their natural devourers, or sinking under disease, generally induced by want of nourishment, their place being occupied by the more perfect of their own kind, who are pressing on the means of subsistence. . . a law universal in Nature, tending to render every reproductive being as the best possibly suited to its condition. . . There is more beauty and unity of design in this continual balancing of life to circumstance, and greater conformity to those dispositions of Nature which are manifest to us, than in total destruction and new creation. It is improbable that much of this diversification is owing to commixture of species nearly allied, all change by this appears very limited, and confined within the bounds of what is called species; the progeny of the same parents, under great differences of circumstance, might, in several generations, even become distinct species, incapable of co-reproduction” (Matthew, 1831).
After more than a century since the publication of Darwin’s famous book, we can say that the impact it had on man’s concept of himself and his activities has been dramatic and has gone far beyond biology. Reduced to the essential, evolutionary change can be described as a two-step process: the first step consists of innovation, namely the production of variations, while the second is ruled by competition and leads to the selection of the best-performing variants. This abstract paradigm can be applied to many processes of self-organization that drive the evolution of complex natural and artificial systems, composed of several interacting agents, or units, each characterized by individual traits that are transmitted, with possible modifications, to agents or units of next generation, and naturally or artificially selected by their effectiveness or by optimization criteria. In other words, after the Darwinian “revolution,” networks of socio-cultural relationships, the global economy, and the design of several industrial processes can be interpreted and studied as evolutionary processes.
In particular, Richard Dawkins reformulated the paradigm of evolution independently of genetic inheritance to explain the evolution of culture. In his books The Selfish Gene (1976) and The Extended Phenotype (1982) he argued that cultures, namely the clouds of ideas, behavioral traits, and artifacts developed and produced by animal and human populations, compete, cooperate, mutate, and are transmitted as well as genetic traits. Thus, the constitutive elements of cultures do evolve, and actually coevolve with genetic traits in a whole biological-sociocultural evolutionary process. Dawkins introduced the concept of a replicator as the minimum natural or artificial unit evolvable through a mutation-selection (or better innovation-competition) process. Replicators are characterized by four fundamental properties:
- replication: units generate or are replaced by new units;
- transmission: units are characterized by distinctive features that are passed to the units of next generation;
- innovation: transmission is not a perfect copy but allows for variations;
- competition: survival and effectiveness of units are regulated by the characteristic features of interacting units.
Dawkins’ idea of a replicator was elaborated on just a few years after Francis Crick and James Watson’s (1953) discovery of the double helix molecular structure of the deoxyribonucleic acid (DNA). Although this idea might simply seem an abstract summary of Darwin and Wallace’s evolutionary theory, its conceptual impact is enormous because it offers a paradigm of evolution that can be scaled from DNA to macroscopic natural and artificial systems. In fact, even if Dawkins’ work is focused on animal and, in particular, human biological and cultural coevolution, the concept of replicator is fully independent of biology and can in principle be applied to describe any innovation-competition process. However, for tradition, simplicity, and uniformity, we will mainly refer throughout this chapter to biological evolution, pointing out here and there, and, in particular, in Section 1.9, the analogy with the evolution of social and economic systems. Later, in Chapter 4, we will interpret and analyze the process of technological change as an evolutionary process, where replicators are commercial products competing in a market, which replicate in production and transmit, with possible innovations, technological characteristics to new generations of products. Through a stylized model we will show that the evolutionary interpretation of technological change supports the emergence of technological variety from a single ancestral technology.
In the next three sections we introduce the basic biological elements of evolutionary processes, namely the structure of the genetic material and the laws of heredity, underpinning the way of being and reproducing of all living organisms, the appearance of mutations, ultimate source of organisms variability, and the mechanisms of selection of successful mutants. Among the innumerable options, we closely follow Charlesworth and Charlesworth (2003), a lucid and concise introduction to evolutionary theory, presenting some of their contributions in a form appropriate for this book. Sections 1.5–1.8 describe the evolutionary patterns we might expect to emerge as long-term consequences of evolutionary processes, while the last section shows many examples of such patterns in biological as well as nonbiological contexts.
1.2 GENOTYPES AND PHENOTYPES
Starting with Mendel’s (1865) work and passing through the achievements of modern molecular biology, we now know that the similarities between living organisms are not confined to visible structural characteristics, but are profound and extend to the smallest microscopic scale. All living organisms other than viruses (which are on the borderline of life) are composed of a single unit or an assembly of essentially similar units, the cells. In so-called eukaryote organisms, which include all multicellular species (animals, plants, and fungi) as well as some unicellular species, cells are delimited by a membrane and contain the cytoplasm, a gel with floating subcellular structures, and the nucleus, which carries the genetic material. The remaining unicellular organisms, called prokaryotes and including bacteria and similar organisms called archaea, are simpler cells in which the genetic material is floating in the gel with no subcellular structures and nucleus. Viruses are parasites that reproduce inside the cells of other organisms and consist of a chemical coat surrounding the genetic material.
Cells are miniaturized factories that produce the chemicals needed by the organism, generate energy from food, and provide body structures. Most chemical products of a cell, as well as the machinery employed for the production of chemicals and energy, are proteins. Some proteins are enzymes that take a chemical and perform a task on it, like breaking molecules in food into smaller pieces that can be assimilated by cells. Some other proteins have storage or transport functions, like binding to iron and storing it in the liver, or to oxygen and carrying it through the blood. Some others are communication proteins, like hormones, which circulate in the blood and control many functions, or signaling proteins, located on the cell surface and responsible of communications with other cells. Finally, there are structural proteins, e.g., those forming skin and bones. Proteins are large molecules composed of a chain of amino acids typically arranged in a suitable spatial configuration. There are only twenty different amino acids across the entire range of living organisms. This points out how similar all organisms are at a microscopic scale and indirectly supports the evolutionary hypothesis of a common ancestor.
The genetic material of all species is organized in so-called chromosomes, composed of a DNA molecule and a protein coat. As explained below, there are different types of chromosomes, which control different functions of the organism and are characterized by a different size and structure of their DNA molecule. The number of different types of chromosomes is strongly species-specific, e.g., 23 in humans.
A DNA molecule consists of two helices of alternating sugar and phosphate molecules, where each sugar binds to one of four possible molecules: adenine (A), cytosine (C), guanine (G), and thymine (T), called DNA bases. Each base in one helix binds to the base at the corresponding position of the other helix, but only the bindings A–T and C–G are possible, so that given the sequence of bases in one helix, the sequence in the other helix is simply obtained by exchanging A with T and C with G. Thus, a molecule of DNA is represented by a sequence of the “genetic letters” A, C, G, and T, whose length is specific to the considered type of chromosome.
Each type of chromosome is also characterized by a logical structure of its DNA molecule. In fact, within the sequence of genetic letters representing the chromosome there are particular subsequences, called loci, whose number, lengths, and positions are also specific of the type of chromosome. The portion of the DNA molecule corresponding to a locus is a gene and may take one of several forms, called alleles, corresponding to different subsequences of genetic letters that are possible for that gene. Chromosomes of the same type can therefore contain different alleles at the same loci.
The function of most genes is to code the structure of a protein. For this, each triplet of genetic letters identifies an amino acid, and the sequence of triplets identifies the amino acid chain of the protein. Since there are 43 = 64 different triplets and only 20 amino acids, several triplets code the same amino acid. Other than genes, there are also DNA portions which control the activity of the cell. In particular, they determine the proteins to be produced, and this is the main mechanism of cell diversification.
The genetic material is transferred by parents to the progeny and this transmission is called heredity. There are two mechanisms of reproduction: asexual and sexual. In asexual species (all prokaryotes and a few eukaryotes) all cells are haploid, i.e., they contain only one chromosome for each type of chromosome characterizing the species. An individual produces an offspring by simply replicating its genetic material through the process of cell division (mitosis), where chromosomes are first duplicated and then the cell divides into two identical daughter cells.
In sexual species some cells are polyploid (e.g., diploid), i.e., they contain more than one (e.g., two) chromosome for each type (except for special chromosomes identifying sex), which form a group of so-called homologous chromosomes. There are fundamentally two mechanisms of sexual reproduction. In diploid species (animals and many plants) all cells are diploid except for eggs and sperms, called gametes, which are haploid. The production of gametes (meiosis) is a special type of cell division, where first each pair of homologous chromosomes exchanges the alleles at some loci (recombination), then all new chromosome pairs are duplicated, and finally the cell divides into four gametes, each with one chromosome for each type. The union of an egg and a sperm restores a diploid cell, the fertilized egg, which develops into a new individual by successive cell divisions (mitosis). The second mechanism of sexual reproduction characterizes sexual haploid species (most fungi, some plants, and some unicellular eukaryotes), where the fusion of haploid cells produces polyploid cells in which the recombination of homologous chromosomes takes place before cell division gives rise to new haploid cells. Asexual reproduction may also occur in some sexual haploid species, where it is either occasional or the predominant mechanism of reproduction. Notice that haploid cells are temporary in the first mechanism of sexual reproduction, while polyploid cells are temporary in the second. Moreover, in both cases, homologous chromosomes in polyploid cells are inherited one from each parent (one from the mother and one from the father in diploid species), so that the genetic material of the progeny is a mix of that of the parents.
The genome of a species is the set of all possible chromosomes characterizing an individual of the species and is therefore identified by all allelic forms of all genes of the species. By contrast, the genotype of an individual is a particular genome realization, given by the chromosomes carried by the individual. Notice that in asexual species and in sexual haploid species the genotype is identified by one chromosome for each type and is contained in all cells, while in diploid species the genotype is given by pairs of homologous chromosomes, i.e., by two chromosomes for each type, and is contained in all cells except gametes.
Any individual characteristic determined (to some extent) by the genotype is called a phenotype (or phenotypic trait) and is therefore a heritable characteristic from parents to the progeny. Almost every imaginable kind of characteristic is genetic-dependent, from physical traits, like body size or colors, to mental traits, like character, disposition, and intelligence. Phenotypic variability within populations, called polymorphism, may take the form of discrete differences, as for the number of limbs and blood type, or that of a continuous range of values measurable on a metric scale, as for body size and weight. Discrete phenotypes are typically controlled by differences in one or a few genes and are unaffected, or altered only slightly, by the environmental circumstances experienced by the individual. By contrast, continuous phenotypes are typically influenced by many genes and by the environmental conditions as well.
The map between genotypes and phenotypes can be astonishingly complex and in most cases it is not yet completely understood. Phenotypes can be controlled by many genes, and some genes control several phenotypes (so-called pleiotropic genes), so that different phenotypes can be controlled by different but overlapping sets of genes. The effect of a gene on a phenotype can be simply a direct contribution to the phenotypic value, but there are also genes whose effect is to alter or switch off the direct effect of other genes (so-called epistatic genes). A further complicacy in diploid species is the mechanism of dominance between alleles. An allele is dominant over another allele (called recessive) if the presence of the two alleles at the same locus of two homologous chromosome gives only the phenotypic effect of the dominant allele. If the same allele is present at a certain locus of a homologous pair of chromosomes, the locus is said to be homozygous (as well as the genotype with respect to that locus); otherwise, it is heterozygous. Thus, the phenotypic effect of a dominant homozygous locus is the same as that of an heterozygous locus with a dominant and a recessive allele.
For example, there are two genes responsible of our blood type. One is coding for a protein present on the surface of red corpuscles, which can be of two different types or absent, corresponding to three allelic forms (say a, b, and o). The other is coding for the so-called Rh-factor, a protein that can be present in red corpuscles (allele +) or absent (allele −). Alleles a and b are dominant on o, and the positive Rh allele is dominant on the negative one, so that there are eight possible blood types, as summarized in Table 1.1. This is one of the rare examples in which the genotype-to-phenotype map is known. Notice, however, that such a map is not invertible, not only because of the mechanism of dominance in diploid species, but also because of the existence of genetic differences with no effect on phenotypes, as, for example, the existence of different triplets of genetic letters coding for the same amino acid.
As Darwin and Wallace first realized, evolutionary change relies on the appearance of new forms of organisms, called mutants, characterized by changes in their phenotypes with respect to their conspecifics. Such phenotypic changes reflect heritable changes in the organism genetic material, i.e., what biologists call mutations. Heritable phenotypic variations have been documented in many organisms and for all kind of characters, including aspects of intelligence and behavioral strategies, like the role of dominance in the social hierarchy and the altruistic propensity among nonrelated individuals. Variations in such characters may confer competitive advantages ordisadvantages in terms of survival and/or reproductive success.
Genetic and phenotypic variability within a population does not necessarily require mutations. In fact, as we have seen in the previous section, different environmental conditions may shape the phenotypes of different individuals, and the mechanisms of sexual reproduction, in particular the recombination of homologous chromosomes, result in offspring genotypes and phenotypic values different from those of the parents. However, if we imagine that all individuals of a population experience the same environmental conditions and that the processes of chromosome recombination and duplication are error-free, then the genetic material of the progeny is a mix of that of the parents, so that no new phenotypic value can appear other than those already observed or potentially observable by suitably mixing individual genotypes. Over a sufficiently long period of time, the genetic and phenotypic variability would halt and all statistics of the genotypic and phenotypic distributions would not evolve anymore. Thus, mutations, i.e., miscopying errors in the duplication of DNA molecules, are the ultimate source of genetic variability and form the raw material upon which selection acts, as shown in the next section.
The simplest mutation consists of a base substitution in a gene. As a result, a wrong amino acid may be placed in the protein coded by the gene, and the protein may be unable to perform its task properly. Other types of observed mutations involve the modifications, insertions, or deletions of entire DNA portions and may thus alter the length and the logical structure of chromosomes. Mutations can cause serious diseases. For example, the development of a cancer is favored by mutations involving genes coding for proteins controlling cell division. But mutations can also be advantageous, for example, by increasing the resistance of animals and plants against diseases, as well as chemical resistance in pests and antibiotic resistance in bacteria.
The phenotypic effect of a mutation may vary greatly in magnitude. Some mutations have no phenotypic effect and are known only because it is nowadays possible to observe the genetic material directly. Most mutations affect a small portion of a single gene and therefore have small phenotypic effects, especially on phenotypes controlled by several genes. But even on phenotypes controlled by a few genes, like the blood type, a huge mutation replacing most DNA bases of a gene would be needed to change the phenotype. Large phenotypic effects, like a blood type not obtainable from the combination of parental genotypes or the heritable possession of an extra leg, are, in principle, possible but extremely unlikely to occur.
Large mutations are more plausible if we extend the evolutionary paradigm beyond the biological realm. As discussed in Section 1.1, cultures, behavioral strategies, companies, goods, technology, and many other artificial replicators evolve through innovation-competition processes analogous to the mutation-selection process that drives the evolution of living organisms. Most of the time, innovations consist of slight modifications, but revolutionary ideas, like the invention of differential calculus by Isaac Newton, or relevant technological innovations, like the steam engine, are certainly instances of large mutations. However, not all important technological innovations can be associated with large mutations. For example, the first personal computer was huge and expensive like a big mainframe, and the first mobile phone was a heavy car phone, different from a traditional phone only for the presence of an antenna instead of a wire.
Finally, notice that a mutation is not the only way to inject a new variant into an evolving system, since similar or radically different traits can come from outside. Examples are immigration in biological systems, communication through fast and worldwide media such as the Internet in social systems, and import-export of goods in economic systems. New traits originated by mutations or external influences typically appear in a single replicator or in a tiny fraction of the population. Thus, a mutation can have a long-term impact on the system only if other processes can cause the new trait to increase in frequency within the population. This is the topic discussed in the following section.
From an evolutionary point of view, what matters is the effect of a mutation on the phenotypes of the mutants. In fact, the performance of an individual depends on its characteristic phenotypes and on the environmental conditions it experiences. The environment experienced by an individual is identified by all physical factors, such as climate, altitude, oceans level, and air or water pollution, which define the so-called abiotic environment, and by all individuals of the same or other species interacting with the considered individual, which form its biotic environment (see, e.g., Lewontin, 1983). The performance of an individual continuously involved in intra-and interspecific and environmental interactions therefore depends on its phenotypes, on the abundances and phenotypes of the interacting individuals, and on the abiotic environmental conditions, which typically fluctuate in time.
Individuals with phenotypic values different from those of the rest of the population may thus differ in their probability to survive to reproductive age (viability), in their mating success, or in their progeny abundance (fertility). As a result, if we imagine no mutations or injections of new phenotypes from outside in a constant abiotic environment, namely in the absence of mutations, immigrations, seasonalities, and other external perturbations, then the demography of populations (i.e., birth and death of individuals) must promote the best-performing phenotypic values, which come to dominate the populations. In other words, what Darwin called selection is demography in the absence of mutations and external influences.
In nonbiological contexts, the success of artificial replicators, like different behavioral strategies in social relationships or products competing in a market, depends on the characteristic traits of interacting replicators (the equivalent of phenotypes), on their abundances, and on the rules and conditions constraining the system (the equivalent of the abiotic environment in biological systems). Thus, the best-performing replicators become dominant if there are no further innovations and external influences.
Demography is the dynamical process that regulates population abundances. The state of a population is determined by the genotypic distribution, which gives the abundances of all genotypes present in the population at a given time, and the dynamics of the population are the changes in time of such a distribution that result from birth, death, and migration of individuals. Given the genotypic distribution of a population, the distribution of one of its phenotypes is, in principle, obtainable by applying the corresponding genotype-to-phenotype map. By contrast, the genotypic distribution cannot be inferred from the phenotypic distribution because, as we have seen in Section 1.2, the genotype-to-phenotype map is not invertible. Unfortunately, we have also seen that such a map is usually not known, so that the joint distribution of all relevant phenotypes must generally be measured or assumed to be known to completely characterize the population. Thus, the state of an evolving system is given by the genotypic and phenotypic distributions of all interacting populations, i.e., by the state of the entire community or, in other words, by the biotic environment. Given such a state at a given time, the future abundances are not predictable if there is no a priori information on mutations and possible external influences. In technical words, demography is a nonautonomous dynamical process, i.e., a process whose future is not solely determined by its current state. On the contrary, selection is an autonomous process (since it works in the absence of mutations and external influences) and, as such, drives the system toward a regime, which can be stationary as well as nonstationary (periodic or wilder, so-called chaotic, regime; see, e.g., Turchin, 2003a). Such regimes correspond to the so-called attractors of the dynamical process, since they attract nearby states.
When approaching an attractor, some of the phenotypic values may disappear from the community, because the groups of individuals bearing them are outcompeted by better-performing groups. Once the state of the community is close to the attractor, the genotypes and phenotypic values that coexist in the populations are called resident, as the groups of individuals bearing them. If a phenotype is characterized by a single resident value or, more weakly, if the resident phenotypic distribution is concentrated around its mean value, then the population is said to be monomorphic with respect to that phenotype. In reality, selection may be interrupted by a mutation, by the arrival of a new phenotype from outside, or by a perturbation of the abiotic environment, events that prevent the system from reaching an attractor. However, the time between successive mutations and external influences is often long enough to enable selection to define the resident groups of the community.
In this book, the characteristic timescale on which the process of selection drives the system toward one of its attractors is called the demographic timescale. Analogously, we respectively call demographic dynamics and attractors the population dynamics driven by selection and their possible attractors. In the biological literature the demographic timescale, dynamics, and attractors are often called “ecological” or “short-term,” since ecology typically studies the relations between living organisms and their environment on a timescale that is so short that mutations and external influences can be neglected (see, e.g., Roughgarden, 1983b). As we will see in more detail in Chapter 8, there can be multiple demographic attractors, each one attracting a different set of states, which means that there can be different selection pressures acting on different genotypic/phenotypic distributions.
As pointed out by Dawkins (1976), selection is a selfish process, since each individual is moved by the ambition to maximize its impact on the next generation, by maximizing the transfer of its genes. Thus, it may seem difficult to explain altruistic behaviors and cooperation, which are commonly observed in many species, including the human one. Some biologists, starting with Wynne-Edwards (1962) (see also Wilson, 1980), have invoked a higher level of selection, called group selection, acting on groups rather than on individuals, hence promoting phenotypic values that are beneficial to the group at the expense of the single individual. However, the idea of group selection is founded on a weaker genetic basis and has been therefore severely criticized (see, e.g., Williams, 1966). Moreover, as we will show in Chapter 6, group selection is not necessary to explain the evolutionary emergence of altruistic behaviors, which can be the direct result of “individual selection.” A related concept is that of kin selection, originally proposed by Darwin as an explanation of the existence of sterile individuals in various species of social insects (e.g., bees, wasps, and ants), where some of the females are workers who do not reproduce, thus apparently forgoing their ambition (Hamilton, 1963, 1964a,b; Eshel and Motro, 1981). However, members of a social group are typically close relatives, often sharing the same mother or father (e.g., all worker bees are daughters of the queen), so that the sacrifice of individual reproduction may considerably improve the reproductive success of relatives. Other altruistic behaviors toward relatives account for other details of animal societies. For example, young males guarding the nest, instead of attempting to mate, increase the viability of their relatives at the expense of their own reproductive success, possibly leading to an overall higher transfer of genes in the next generation. Thus, altruistic behaviors toward relatives can be interpreted as selfish behaviors, promoted by selection as well as phenotypic values improving individual survival and/or reproduction (Grafen, 1984).
For brevity, and to allow one to think in general terms, the word fitness is often used in the biological context to stand for overall ability to survive and reproduce. Quantitatively, the fitness of an individual is defined as the abundance of its progeny in the next generation or, equivalently, as the per-capita growth rate of the group of individuals characterized by the same phenotypic values (i.e., the abundance variation per unit of time relative to the total abundance of the group). These two definitions say that the abundance of individuals characterized by a given set of phenotypic values is increasing, at a given time, if the associated fitness is, respectively, larger than one or positive. As we will see in the next chapter, the quantitative approaches for modeling evolutionary dynamics make an extensive use of the concept of fitness, and adopt the first or the second of the above definitions depending on the fact that the description of the time is discrete, i.e., by generations, or continuous.
By definition, the fitness of an individual depends on both the abiotic and biotic components of its environment. In particular, the dependence on the biotic component, i.e., on the phenotypic distributions of all interacting populations, is often emphasized by saying that the fitness, or selection, is frequency-and/or density-dependent (see, e.g., Li, 1955; Lewontin, 1958; Haldane and Jayakar, 1963; Wright, 1969; and the discussion in Heino et al., 1998). For example, speed is crucial for antelopes living in savannas, especially at low abundances, since they cannot defend themselves by gathering in big groups, but it is not so important in other habitats where they find refuges from predators. Analogously, some technological innovations are useful in particular market sectors and conditions and not in others, and the benefit of behavioral strategies typically depends on the socio-cultural context.
As discussed in the previous section, mutations appear in a single individual, or in a tiny minority with respect to the resident groups. Disadvantageous mutations, e.g., those causing the malfunction of important proteins, reduce the survival and reproductive success of affected individuals, so that mutants will be underrepresented in the next generations and will eventually be eliminated by the competition with similar residents. One of the major roles of selection is indeed to keep undesired characteristics under control. By contrast, mutants that are at some advantage in terms of survival and reproduction with respect to resident individuals initially tend to grow in number. However, since they are few in number, they face the risk of accidental extinction and may fail to invade, i.e., to spread among the resident groups. The mutant fitness when mutants are just appeared is usually called invasion fitness, and its value gives a quantitative indication of whether the mutation gives some advantage or disadvantage to its bearers. As described in the next section, evolutionary change develops whenever there are phenotypic traits affecting the fitness of invading mutants that escape accidental extinction and become new resident individuals.
Intuitively, we would like to define evolutionary dynamics as the long-term dynamics of the phenotypic distributions of interacting populations. If there are several populations of different species, interacting directly like predator and prey and symbiotic partners, or indirectly like populations competing for common resources, most likely the evolution of one population affects the evolution of the others, so that the coupled evolution of all interacting populations, called coevolution (see, e.g., Futuyma and Slatkin, 1983; Thompson, 1994; Futuyma, 1998), must be studied.
As Darwin pertinently pointed out in the first chapter of The Origin of Species (1859), the evidence that the combination of the processes of mutation and selection leads to evolutionary change is given by artificial selection, the human activity aimed to the breeding of animals and plants with desirable phenotypic values. Artificial mutations have been achieved in the past by intentionally and suitably mixing the genotypes of wild species, and, recently, by directly modifying their genotypes, hence producing transgenic individuals. The obtained groups of mutants with desirable characteristics, like all kinds of dogs or many fruits and vegetables, are then intensively selected by favoring or allowing only intragroup reproduction. Artificial mutations and selection have produced striking evolutionary changes over a relatively short timescale, compared to the typical timescale of most natural evolutionary processes. In nature, mutations are often rare events on the demographic timescale (the typical probability of mutation per locus is of the order of one per million). Moreover, natural selection is the result of individual interactions such as predation, unidirectional or mutualistic symbiosis, competition for resources, and exploitation of environmental niches, which generally are not so intense as artificial selection can be.
So far, we have considered phenotypes affecting the demography of coevolving populations, i.e., affecting individual survival and/or reproductive success or, in a word, fitness, and we have seen that the combination of the processes of mutation and selection drives their evolution. Phenotypes with an effect on fitness are able to adapt to the environmental conditions experienced by individuals and are therefore said to be adaptive; they are often called adaptive traits and, most of the time, simply traits in the following chapters. In principle, there can also be phenotypes with no effect on fitness, as well as mutations with no effect on phenotypes. At each generation, the genes that control selectively neutral phenotypes, or that are altered by mutations with no phenotypic effect, are a sample of the genes present in the parental population, whose offspring are not filtered by any selection pressure. The accumulation of differences in such genes (and related phenotypes) produces an evolutionary change that biologists call genetic drift. Thus, we can distinguish between two sources of evolutionary change: mutation-selection processes involving phenotypes affecting fitness and the sole process of reproduction resulting in genetic or phenotypic differences with no effect on fitness. Notice, however, that individuals characterized by new phenotypic values (due to the recombination of parental genotypes and/or mutations) are initially few in number, so that if they are neutral to selection they have a much higher chance of being lost by accidental extinction than of spreading in the population. Thus, genetic drift is a very slow process, typically dominated by mutation-selection processes.
To focus on the basic dynamical features of mutation-selection processes, it is convenient to identify two contrasting timescales: the demographic timescale on which selection is acting and the timescale on which the cumulative effects of several mutations followed by the selection of successful mutants are noticeable. When these two timescales are strongly separated it is possible to qualitatively predict interesting features of evolution. In fact, as we have seen in the previous section, the time between successive mutations is in most cases long enough to allow selection to reach a demographic attractor and define the resident groups. More precisely, if a mutation confers some advantage to affected individuals and if mutants are able to escape accidental extinction, then selection brings the community in a new attractor. In the opposite case, mutants quickly disappear and the community is back to the previous attractor. Thus, in the idealized case of extremely rare mutations, we can define evolutionary dynamics as the sequences of attractors visited by the demographic dynamics. The timescale on which such a sequence develops is called in the following the evolutionary timescale. In this ideal frame, the demographic and evolutionary timescales are completely separated, i.e., any finite time on the evolutionary timescale corresponds to an infinite time on the demographic timescale. In the case of contrasting but not completely separated timescales, selection may not be able to reach an attractor before the occurrence of the next mutation, so that the difference between demographic and evolutionary dynamics is faded. As we will see in the next section, this problematic distinction has sometimes led to questionable interpretations of field and laboratory data.
The separation between demographic and evolutionary timescales not only allows a precise definition of demographic and evolutionary dynamics, but points out their coupling as well. In fact, demographic dynamics are determined by the biotic component of the environment, since they are defined under constant abiotic conditions in the absence of mutations and external influences. Immediately after a mutation, the biotic environment is given by the current attractor of the demographic dynamics, i.e., the current evolutionary state, since mutants are scarce and therefore have no effects. Thus, the current evolutionary state determines the demographic dynamics, which, in turn, define the new evolutionary state. Ford (1949) was perhaps the first to document that demographic and evolutionary changes are entangled in a “feedback loop,” a concept that has been subsequently formalized by Pimentel (1961, 1968), Stenseth and Maynard Smith (1984), and Metz et al. (1992).
Other than being rare on the demographic timescale, mutations have often small effects on phenotypes, as we saw in Section 1.3, so that the evolutionary dynamics are slow and smooth. In fact, imagine a sequence of mutations with small phenotypic effects and assume that each time the mutant group is at advantage, it replaces the similar resident group, thus becoming itself the new resident. Then, we can picture a smooth evolutionary trajectory as follows. At a given time on the evolutionary timescale, define the trait space as the space with one axis for each adaptive trait characterizing each resident group. Then, the current evolutionary state corresponds to a point in trait space, which smoothly moves in the course of evolution. Notice that the dimension of the trait space changes each time a new resident group appears in the community or an old resident group disappears from it (see Sections 1.7 and 1.8).
As we will see in the next chapter, the assumption of rare mutations with small phenotypic effects is at the core of some of the mathematical descriptions of evolutionary processes, including the approach of adaptive dynamics. Although this assumption might seem a crude approximation of reality, we will see that it allows a mathematical description of the demographic and evolutionary dynamics that, at least qualitatively, provides useful insights on the real dynamics of the community.
As done in Section 1.4 for the demographic dynamics, we can now focus on the attractors of the evolutionary dynamics. However, the evolutionary process is intrinsically nonautonomous, since evolutionary dynamics depend on the particular sequence of mutations and on the fluctuations of the abiotic environment. Abiotic environmental conditions typically fluctuate in time as stationary processes or as (nonstationary) processes with relevant periodic components at particular frequencies, like seasonalities. Here and almost everywhere in the book we consider constant abiotic environments. This choice not only greatly simplifies the analytical treatment, but allows one to concentrate on the evolutionary dynamics endogenously generated by the mutation-selection process and not on those entrained by the variability of the abiotic environment. Of course, results obtained under this assumption remain qualitatively sound when the abiotic environmental fluctuations are sufficiently mild or when their characteristic frequencies are out from the frequency range of demographic and evolutionary dynamics. In conclusion, at least in a probabilistic sense, namely by averaging on all possible sequences of mutations, evolutionary change can be described as an autonomous dynamical process. Evolutionary dynamics are therefore characterized by stationary and nonstationary attractors, i.e., by points and periodic or chaotic evolutionary trajectories in trait space attracting nearby evolutionary states. Moreover, as we will better see in Chapter 5, evolutionary attractors can be multiple, so that different evolutionary trajectories may approach different attractors, making long-term evolutionary implications strongly dependent on the ancestral evolutionary state.
1.6 THE RED QUEEN HYPOTHESIS
So far, we have seen that evolutionary dynamics proceed by apparently random mutations followed by demographic selections of the individuals best adapted to the current environmental conditions. Thus, externally imposed perturbations of abiotic environmental factors, such as climate changes in biology, the availability of new communication media or transportation services in social systems, the opening of new markets, or the establishment of new trading rules or policies in economics, might be thought to be the ultimate driving forces of evolution. A very common idea is that in a constant environment mutation-selection processes should have the time to adjust the relevant traits to a state that confers the maximum fitness. As a consequence, little evolutionary change is expected, until the environment poses some new challenge. In other words, it is generally conjectured that in the absence of external forcing, evolutionary dynamics converge to a stationary attractor, namely to an evolutionary state at which mutants are always at disadvantage with respect to well-adapted residents. This belief, however, neglects the fact that the coevolving populations define the biotic component of the environment in which they live, so that the expression “evolution in a constant environment” actually becomes nonsense if both the abiotic and biotic components of the environment are taken into account. Adapting to an environment that is itself adaptive can thus prevent the evolutionary dynamics to reach a stationary regime.
The question as to whether the interaction between species may drive never-ending evolutionary change in their phenotypes has been first posed by Van Valen (1973), who called this hypothetically endless evolutionary story Red Queen dynamics (see also Rosenzweig and Schaffer, 1978a,b; Stenseth and Maynard Smith, 1984; Rosenzweig et al., 1987). The name was inspired by the book Through the Looking-Glass and What Alice Found There by Lewis Carroll (1871), where Alice says: “Well, in our country, you’d generally get to somewhere else — if you ran very fast for a long time as we’ve been doing.” And the Queen replies: “A slow sort of country! Now, here, you see, it takes all the running you can do, to keep in the same place.”
The term the “Red Queen” is also associated in the biological literature with the evolution of sex, since chromosome recombination maintains genetic variability and phenotypic polymorphism and therefore tends to keep sexual populations “in the same place” (see, e.g., Bell, 1982; Lively, 1996). In particular, recombinations have the potential to combine favorable parental alleles in the loci of the progeny and to break up deleterious allelic pairs, thus conferring an evolutionary advantage to sexual versus asexual species. In this book, independently of sex, we refer to Red Queen dynamics as evolutionary dynamics that in the absence of external forcing, namely in a constant abiotic environment, lead to nonstationary evolutionary regimes.
Since Van Valen (1973), there have been many contributions in the literature on Red Queen dynamics (including our Chapters 5–10). However, there is essentially no neat empirical evidence of Red Queen evolutionary regimes, since data are often not available on a sufficiently long time interval or there is not enough information on the constancy of the abiotic environment to distinguish between externally imposed and autonomous evolutionary oscillations (Lythgoe and Read, 1998).
Some studies on living populations show interesting oscillations in population abundances and phenotypic traits, though the evidence is not conclusive. An example is provided by some African fish species, whose individuals eat scales from prey flanks and have developed an asymmetry toward left or right in the direction of mouth opening to improve the effectiveness of attacks from behind while approaching the right or left prey flank. Hori (1993; see also the commentary by Lively, 1993) reported oscillations in the abundance of left-and right-handed populations andconcomitant oscillations of the antipredator traits (or behaviors) of the prey species, such as alertness to attacks from left or right. A second example comes from lizards, where oscillations of male reproductive strategies have been observed (Sinervo and Lively, 1996; Sinervo et al., 2000; see also Maynard Smith, 1996). Finally, Yoshida et al. (2003) recently showed oscillations in genetic characters in prey-predator (algal-rotifer) laboratory microcosms.
However, in all three cases, the observed oscillations develop on a demographic timescale and indicate the demographic nonstationary coexistence of different phenotypic values, rather than nonstationary evolutionary dynamics. Although some evolutionary biologists argue that in some species and under particular environmental conditions evolutionary change may be rapid and develop on a timescale comparable to the demographic timescale (see, e.g., Lively, 1993; Thompson, 1998; Zimmer, 2003), more investigations are needed. Recent and well-controlled experiments on host-parasite and bacterial evolution seem to be promising (see Dybdahl and Storfer, 2003, for a review), but at the moment the empirical evidence of Red Queen dynamics remains scant and it is plausible that Red Queen dynamics will remain, for a while, a conjecture.
1.7 THE EMERGENCE OF DIVERSITY
One of the major hypotheses of evolutionary theory is that all living organisms are the descendants of self-replicating molecules that were formed by chemicals more than 3 billion years ago. The successive forms of life have been produced by the natural selection of successful mutations: the process of “descent with modification,” as Darwin called it.
Throughout this chapter, we have often used the concept of species without giving a precise definition, but rather relying on an intuitive notion. However, in most cases, a species can be defined as a group of morphologically and genetically similar individuals that, when reproduction is sexual, are capable of interbreeding and reproductively isolated from other such groups (Mayr, 1942). Notice that while two sexual species are kept separated by the lack of interbreeding, the distinction between two asexual species critically depends on the considered measure of similarity. Biologists identify each species with two names, the genus, identifying a group of extant or extinct species by a set of morphological and physiological characters not shared by other genera, and the name of the species itself, both conventionally written in italics, e.g., Homo sapiens. Genera are then grouped into families, families into orders, orders into classes, classes into phyla, and phyla into kingdoms, thus forming the so-called taxonomic classification of organisms, e.g., Hominidae, Primates, Mammalia, Chordata, Animalia, respectively, from family to kingdom in our case.
The formation of new species, called speciation, is certainly one of the core issues of evolutionary theory and a great deal of research has been devoted to it (see, e.g., Hutchinson, 1959; Mayr, 1963; Maynard Smith, 1966; Felsenstein, 1981; Kondrashov and Mina, 1986; Rice and Hostert, 1993; Kawecki, 1996; Dieckmann and Doebeli, 1999; Higashi et al., 1999; Doebeli and Dieckmann, 2000, 2003; Matessi et al., 2001; Dieckmann et al., 2004; Coyne and Orr, 2004; Gavrilets, 2004). Speciation occurs through the genetic and phenotypic divergence of conspecific populations that adapt to different environmental niches in the same or different habitats. In the case of sexual reproduction, the populations must diverge far enough to develop some barrier to interbreed, such as morphological or physiological incompatibilities, inviability or sterility of crossbred offspring (hybrids), or simply different preferences in habitats or in mating periods, sites, and rituals.
The mechanism of speciation traditionally accepted by the scientific community (and first proposed by Darwin) imagines that two populations of a given species become geographically isolated and follow separate evolutionary paths. Since different selection pressures and different genetic drifts may act on different environments, the isolated populations may eventually become separate species. This form of speciation is called allopatric when the two populations become geographically separated by natural or artificial barriers, and parapatric when they evolve toward geographic isolation by exploiting different environmental niches of contiguous habitats. While allopatric and parapatric speciations are supported by the evidence that variants of the same species, some of which may be candidate new species, often occupy different territories, their key ingredient, geographic isolation, remains a somehow exogenous cause of speciation rather than an evolutionary consequence.
A different mechanism of speciation, put forward by Maynard Smith (1966) and called sympatric, considers populations in the same geographic location. The key ingredient here is a selection pressure favoring phenotypic values at the extremes of a polymorphic range over those in the middle of the range. This so-called disruptive selection may result, for example, from the competition for alternative environmental niches, where specialization for specific niches may be advantageous with respect to be generalist. Under the effect of disruptive selection a monomorphic population may turn dimorphic with respect to some relevant phenotypes, undergoing what is called an evolutionary branching. The monomorphic population splits into two initially similar resident groups, which then diverge by following separate evolutionary paths (branches), each one driven by its own mutations. Pictured in trait space, an evolutionary trajectory approaches an evolutionary state, called a branching point, at which the dimension of the trait space increases by gaining the adaptive traits characterizing the new resident group.
The potential of disruptive selection as a mechanism of speciation has been hotly debated. Some evolutionary biologists do not believe that sympatric speciation can generically occur in sexual species, because interbreeding would constantly produce phenotypes that are intermediate between the two incipient branches, thus contrasting any phenotypic difference that might appear under disruptive selection. Speciation would then require the evolution of mechanisms of assortative mating (i.e., the mating preference for similar phenotypes) disfavoring intermediate phenotypes. For example, morphological differences, reduced viability or fertility of crossbred offspring, or different reproductive timings within the season may accompany and therefore favor the phenotypic divergence fostered by disruptive selection. Based on these arguments, recent empirical and theoretical studies (see, e.g., Schliewen et al., 1994; Schluter, 1994; Feder, 1995; Doebeli, 1996a; Johnson et al., 1996; Kawecki, 1996; Dieckmann and Doebeli, 1999; Higashi et al., 1999; Matessi et al., 2001; Barluenga et al., 2006; see also the review by Gavrilets, 2003, and its commentary by Doebeli et al., 2005) suggest that sympatric speciation is, after all, not as rare as commonly believed.
Both geographic isolation and sympatric speciation consider the splitting of an existing population into two initially similar resident groups, which then genetically and phenotypically diverge and eventually become separate species. An alternative speciation route in sexual species, called hybrid speciation (Stebbins, 1959), occurs when offspring crossbred from two similar species are viable, fertile, and perform better than parental individuals in exploiting suitable environmental niches, where they can develop reproductive isolation and eventually form a new species. Although theoretically possible, hybrid speciation is regarded as rare and hence of little importance, especially in animals (Buerkle et al., 2000; Schwarz et al., 2005).
The last three centuries of biological studies have organized all known living organisms in a branching genealogy, called the tree of life. The root of the tree represents the hypothetical common ancestor, from which all species have been derived by subsequent speciations. All species ever derived are represented by the branches of the tree, whose leaves thus correspond to extant or extinct species. By dating all speciation events, the tree of life can be drawn on a vertical time axis with the present day at the top, as in Figure 1.1. Two species are more closely related at a given time, and therefore share more characteristics, the closer in time is their common ancestral species. Figure 1.1 is very schematic and shows only the major groups of organisms, namely prokaryotes (bacteria and archaea), unicellular eukaryotes (where the dotted branches stay for several not shown speciations), and multicellular eukaryotes (animals, plants, and fungi) (see, e.g., Woese et al., 1990, and Wray, 2001, for an up to date and more detailed representation).
The classification of species and their genealogical relationships have been based for a long time on easily detectable morphological and structural characteristics, observed in living populations and in fossil records. Nowadays, it is possible to compare DNA and protein sequences among different extant species. Under the evolutionary hypothesis, the sequences of DNA bases of a given gene, or the chains of amino acids of a given protein, should be more similar for more closely related species than those of distantly related species. Thus, the times of speciations can be estimated under the assumption that the amount of differences increases proportionally with time since speciation. This is roughly true if mutational rates are constant and if the compared DNA and protein sequences have no effect on individual fitness, so that, as we have seen in Section 1.5, their evolutionary change consists of a random genetic drift solely driven by mutations. The accurate estimation of speciation times is therefore a complex problem and various techniques have been developed to provide better and better estimates (see, e.g., Wray, 2001). The result is that the evolutionary hypothesis is strongly supported, since the estimated times of speciations are in broad agreement with the times at which the major groups of animals and plants appear in fossil records. This confidence allows biologists to use molecular techniques to date the divergence between species for which there is no or little fossil evidence. By filling the gaps of fossil records, we are therefore getting closer and closer to the complete reconstruction of the tree of life.
As anticipated in Section 1.1, the evolutionary paradigm goes far beyond biology. The spontaneous emergence and maintenance of diversity are observable in many different areas of science and engineering and evolutionary theory may be used to identify the analogue of the biological mechanisms of speciation. For example, in Section 1.3 we have mentioned that the gradual innovation of technological products has led to different “product species,” such as personal computers and mainframes, or fixed and mobile phones, which originated by a common ancestral product. As shown in Section 1.9 and formally in Chapter 4, the selection pressure exerted on the market by customers or other economic agents may be disruptive, so that evolutionary branching may occur and explain the emergence of product diversity. Goods that appear to us as radically different, like a horse carriage and a modern car, were very similar just after the branching. The socio-cultural history is also rich in gradual diversifications that we can describe through the mechanism of evolutionary branching. For example, different languages have common roots, as well as different political organizations and behavioral strategies.
In closing this discussion on the emergence of diversity in nature (biodiversity) as well as in other contexts, we like to emphasize the role of evolutionary branching, because it provides an autonomous evolutionary explanation for speciation, in the sense that it does not require exogenous triggers, like geographic isolation. However, notice again that an evolutionary branching only marks an increased polymorphism in a phenotypic distribution and thus only gives an indication for a possible speciation.
1.8 EVOLUTIONARY EXTINCTION
We have just seen how speciation mechanisms provide an evolutionary explanation to the diversity of life. It is estimated that roughly forty million species are living today on Earth. However, between five and fifty billion species are estimated to have existed at one time or another during the history of life (Wilson, 1988). These numbers reveal that the present diversity is the result of a small surplus of generation over loss of species, so that the causes of species extinction are as relevant as those of speciation.
Since the origin of life, diversity has generally increased over time, as shown for example in Figure 1.2, which reports the number of families of marine animals through the past 600 million years. Figure 1.2 also points out periods of diversity decline, where extinction was more intense than speciation, and abrupt extinction events, among which are the five so-called mass extinctions (vertical dotted lines, see Raup and Sepkoski, 1982). Minor extinction events, not visible at the scale of Figure 1.2, are noticeable in fossil records, so that biologists and paleontologists now believe that extinction and speciation have smoothly shaped the tree of life over the evolutionary timescale, with interruptions characterized by abrupt extinctions.
Clearly, abrupt extinction events, where many species living in different areas disappear in a relatively short time interval on the evolutionary timescale, are the fingerprints of some global catastrophe or rapid climate change that has little to do with evolution. In particular, the last mass extinction, where dinosaurs disappeared, thus making mammals and human evolution possible, is now well documented and associated with the impact of an asteroid in the Yucatan peninsula (Alvarez et al., 1980). The impact should have produced shock and tidal waves, darkness caused by dust with associated reduction of plant photosynthesis, acid rain, forest fires, heating due to the greenhouse effect, or cooling due to reduced sunlight. Other frequent causes of abrupt extinctions are glaciations and variations in oceans level, salinity, and oxygen concentration (Huey and Ward, 2005), while for minor extinction events the debate is much more open (see, e.g., Raup, 1981, 1991). External causes of minor extinctions may be several, e.g., earthquakes, volcanic eruptions, hurricanes, epidemic diseases, and even small-scale meteorite impacts (Raup, 1992).
Notice that the extinction of species is not a roulette-like random process, even when it is triggered by a catastrophe. In fact, a global change of the abiotic environment, occurring rapidly on the evolutionary timescale, begets an evolutionary transient in which species try to adapt to the new conditions. Some species may fail and go extinct, so that it is fully correct to refer to this evolutionary consequence as evolutionary extinction.
As we have repeatedly observed, the evolution of populations shapes the biotic environment in which they live, which in turn affects their evolution. Thus, as a change in the abiotic conditions may lead to the extinction of some of the coevolving populations, the same is, in principle, true for a change in the biotic conditions. In other words, evolution in a constant abiotic environment may drive the phenotypes of coevolving populations toward values at which some of the populations cannot persist. The fact that evolution may autonomously lead to the extinction of species was first claimed by Darwin, who observed that the mutation-selection process may favor phenotypic values that, in the long run, turn out to be inconvenient or even harmful to the survival of a population. Darwin concluded that if any part comes to be injurious, then either it will be modified by evolution, or the being will become extinct. Haldane (1932) noticed that fossil records are full of cases where enormous horns and spines have been the prelude to extinction and concluded that in some cases, one of which is presented in the next section, the species literally sank under the weight of their own armaments.
Much more recently, the possibility of evolution toward extinction in a constant abiotic environment has been theoretically investigated and three basic mechanisms have been identified (Matsuda and Abrams, 1994a; Ferrière, 2000; Gyllenberg and Parvinen, 2001; Dieckmann and Ferri`ere, 2004; Parvinen, 2005). The first, called evolutionary runaway by Matsuda and Abrams (1994a), is present when selection drives a population toward phenotypic values at which the population persists at low abundances, thus facing high risk of accidental extinction. For example, there are species in which a large, robust, or colorful body is accompanied by reduced fertility or increased predation risk and, hence, by low abundances of the population. However, selection between individuals may always favor larger and more robust or colorful bodies, because of their success in intraspecific competition for food or mates.
The other two mechanisms are based on the observation that the region of the trait space that allows the persistence of all coevolving populations is typically limited by an extinction boundary, since extreme phenotypic values are usually morphologically and/or physiologically incompatible with the abiotic and biotic environmental conditions. For example, a population may be able to persist up to a certain value of individual body size, which may depend on habitat morphology as well as on the phenotypes of coevolving predator or competing populations. In the course of evolution the abundance of the population may gradually vanish, when approaching the extinction boundary, or it may remain high even in the vicinity of the boundary but suddenly collapse (on the demographic timescale) when the boundary is reached, as is often the case for exploited renewable resources where a threshold abundance is needed for persistence. In the former case, the rate of phenotypic change of the population, being proportional to the number of mutants generated per unit of time, vanishes together with the population abundance. Thus, the extinction boundary is reached due to the evolution of other coevolving populations, which act as murderers, so that the extinction is called an evolutionary murder. By contrast, in the latter case, called evolutionary suicide, the population actively evolves toward self-destruction, i.e., mutants closer to the extinction boundary are at advantage with respect to resident individuals, even though they are unconsciously closer to extinction. In other words, as suggested by Darwin, what is advantageous for the individual may ultimately be disastrous for the population.
The subtle differences between evolutionary runaway, murder, and suicide are hard to observe based on empirical data, so that the debate on the possible causes of extinction remains wide open. In any case, we believe that theoretical investigations are important because in the lack of clear empirical evidence they conceptually show that evolution is able to autonomously destroy species. For example, in a recent review of fossil records Rohde and Muller (2005) have shown that diversity in marine animals, once detrended from environmental drivers, oscillated with a base period of about 62 million years, suggesting that speciation and extinction might have autonomously repeated. In Chapter 7 we will show that the mechanisms of evolutionary branching and extinction can indeed autonomously alternate if, after a branching, one of the two emerging lineages evolves to extinction while the remaining one evolves back to the branching point, thus forming a Red Queen evolutionary attractor called a branching-extinction evolutionary cycle.
We close this introductory chapter by reporting empirical evidence of evolutionary phenomena in a variety of fields, including biology, social sciences, economics, and engineering. In each example, we identify the basic characteristics and dynamical features discussed in the previous sections. Most examples present empirical data, some others discuss theoretical studies, and a few simply speculate on the applicability and descriptive power of the mutation-selection (innovation-competition) paradigm.
We start with our own evolutionary history, naively depicted in Figure 1.3. As we have seen in Section 1.7, we belong to the Hominidae family, also including great apes (bonobos, chimpanzees, gorillas, and orangutans) and extinct humanlike species. No fossil data connecting great apes and humans were available when Darwin published The Descent of Man (1871), arguing on the basis of anatomical similarities that humans are closely related to gorillas and chimpanzees. Since then, many fossil remains have been found and accurately dated (see, e.g., the recent discovery of Moy`a-Sol`a et al., 2004), showing that humans and great apes share a common ancestor 6 to 7 million years ago. Figure 1.4 sketches skulls of present-day gorillas and humans (A, F) and intermediate human-like species (B–E), while Figure 1.5 reports the evolution of brain size in the Hominidae family. All together, Figures 1.3–1.5 show the evolutionary dynamics of various human phenotypes, spinal curvature, hair abundance, skull shape, and brain volume, and confirm our speciation from great apes.
The causes of the evolutionary branching that led to humans and great apes have been largely discussed. An intriguing option comes from recent studies on the evolution of language. The ability to develop articulate speech, and thus complex languages and cultures, relies on the fine control of the larynx and mouth, which is absent in great apes. Lai et al. (2001) showed that such an ability is regulated by a particular gene, while Enard et al. (2002) recently studied its evolution. Their results show that the gene is present in all great apes and even in other mammals, but that subtle differences are specific to the human lineage. From such human-specific mutations to the development of modern human languages the gap seems to be filled by cultural, rather than biological, evolution. In fact, the language is culturally transmitted and mutations are represented by the addition of new words or signals to the language. A more articulate language may confer several advantages to its speakers, especially when the language is still very poor, and such advantages may ultimately lead to a higher fitness, i.e., to a better reproductive success. This has been shown theoretically by Nowak et al. (2000, 2001), who studied the transition from nonsyntactic to syntactic communication, a step considered essential in the evolution of human languages (Hauser, 1996; Hauser et al., 2002). Animal communication is typically nonsyntactic, which means that there is a word or a signal for each event to be identified. By contrast, human languages are syntactic, i.e., events are identified by messages composed of words taken from a finite vocabulary. The fitness of an individual adopting a certain language is defined as the probability of successful communication with a randomly selected individual. The main result of these studies is that selection favors the emergence of syntax if the number of events to be identified exceeds a threshold value. This might explain why only humans evolved syntactic communication without requiring biological or genetic justification.
Many other examples of evolutionary dynamics may be found in fossil records, our only direct source of information along the evolutionary timescale, and actually the only source before the advent of the indirect methods of molecular biology that we have seen in Section 1.5. Fossilization is the replacement or molding of the body of a dead organism with a mineralized replica as minerals infiltrate or deposit around the body. Thus, fossilization is most likely to happen at the bottom of aquatic environments, where precipitation of minerals is regular, while for birds and other flying species we have almost no fossil record. New fossils continue to be found and systematically confirm the inferences previously made in accordance with the evolutionary hypothesis. In particular, there are cases of almost complete temporal sequences of fossils that provide examples of smooth evolutionary dynamics, as we have defined in Section 1.5. We now show five of such astonishing examples, three of them coming, not surprisingly, from columns of rock extracted from the bottom of oceans.
Figure 1.6 shows the evolution of body size of a zooplanktonic species over several million years. Planktonic species are unicellular eukaryotes floating in the ocean, and include algae, called phytoplankton, and organisms feeding on bacteria and algae, called zooplankton.
Figure 1.7, one of the two nonmarine examples, describes the coevolution of ungulate species and of their predators by showing two indexes positively correlated to running speed. These data provide an example of coevolution, where an increase of the prey speed is followed by an increase of the speed of some of the predators, though the evolution of predator indexes is less pronounced than that of prey indexes (we will focus on prey-predator (resource-consumer) coevolution in Chapters 5, 9, and 10).
The next three examples provide evidence for the three evolutionary patterns described in Sections 1.6–1.8, namely Red Queen dynamics, evolutionary branching, and evolutionary extinction. Figure 1.8 shows several millennia of reconstructed population abundances of two sardine species and points out repeated oscillations between commonness and rarity. Tens and even hundreds of consecutive generations at either high or very low abundance may have given time to contrasting selective pressures to operate on phenotypes. For example, a physiological trade-off between competitive abilities and fertility often characterizes wild species (see, e.g., Roff, 1992; Stearns, 1992). An intuitive explanation is that at high population density individuals are engaged in many competitive contests, so that more competitive mutants are favored, even though characterized by a reduced fertility. By contrast, at low population density encounters are so rare that there is little to be gained from improved competitive abilities, so that one may expect advantageous mutants to be characterized by an increased fertility at the expense of competitive abilities. Thus, different selection pressures may have operated at high and low abundances on phenotypes related to competitive performance, and caused their evolutionary oscillations. Unfortunately, measures of such phenotypes are not available, so that the Red Queen dynamics remain a conjecture, that we formally support in Chapter 8.
Figure 1.9 illustrates evolutionary branching and speciation in the genus Rhizosolenia of (predominantly asexual) phytoplanktonic species. The evolutionary branching occurred about three million years ago and eventually led to two distinct species, Rhizosolenia bergonii (upper branch) and Rhizosolenia praebergonii (lower branch). The represented phenotype is the height of the region of the organism cell connecting the conical part with the top thin part (see sketch). Other phenotypes were measured and showed similar patterns of lineage splitting.
Our last example from fossil records documents the evolutionary extinction of the family Brontotheriidae of the order Perissodactyla, which includes modern horses, rhinoceroses, and tapirs. Brontotheres are known in fossil records of western United States from about 55 million years ago, when they were small and, in many respects, very similar to their contemporary relatives (primitive horses, rhinoceroses, and tapirs). Since then brontotheres underwent a diversification by evolving horns and attaining very large sizes, as illustrated in Figure 1.10. Both males and females showed this evolutionary pattern, though male horns were significantly more robust, since they probably conferred an evolutionary advantage in the competition for mating and food. The cause of brontotheres extinction is still debated among paleontologists, but most likely brontotheres were victims of their own armaments and their large and cumbersome bodies, thus suggesting a case of evolutionary suicide.
Evidence for evolutionary patterns may also be found in living populations. One of the most known and studied cases is that of Darwin’s finches. During his visit to the Galapagos islands, Darwin noticed thirteen species of finches found nowhere else before. These species were different one from each other in the shape and size of the beak, in overall body size, and in resources exploited, e.g., seeds of different size and hardness (see Figure 1.11). He then realized that those variations that conferred survival advantages in a given environment come to dominate the population, all else being equal, and he argued that the drastic specialization of finches was the result of speciation from a common ancestral species coming fromCentral or South America. Darwin suggested the geographic diversity of the islands, characterized by different environments and food sources, as the cause of finches speciation. Since then, Darwin’s finches have provided a prototypical case study for speciation (see, e.g., Grant et al., 1976, 1985; Schluter et al., 1985; Schluter, 1988; Grant and Grant, 2002; see also the book by Grant, 1986) and both morphological and genetic studies have confirmed that different species are derived from a single ancestral finch (Petren et al., 1999; Sato et al., 2001). However, morphological and genetic differences are sometimes larger between species occurring on the same island than between species on different islands, suggesting that competition for different resources may have produced disruptive selective forces on the same island. Thus, the finches example probably provides a case where both geographic isolation and sympatric speciation have played a role in sexual populations (see, e.g., Benkman, 1999, 2003).
Birds share a common ancestor with all other vertebrates about 500 million years ago, a fish-like creature from which the evolution of fins and limbs led to vertebrate fish and land reptiles. The further evolution of limbs into leathery and feathered wings led to flying reptiles and birds, while other evolutionary paths led to mammals about 300 million years ago. The evolution of limbs has also involved important speciations. Whale flippers, for example, though similar to fish fins, actually derive from mammals feet with increased number of fingers. The evolution of the number and morphology of fingers, recently reviewed by Galis et al. (2001), reveals another interesting case of speciation in birds. Modern techniques of molecular biology have shown that the ancestral position of toes in birds is that of one backward-and three forward-pointing toes. In aquatic birds, the three forward-pointing toes generally form the paddle, but in some species, like pelicans and cormorants, the backward-pointing toe has moved forward and is incorporated in the paddle, as shown in Figure 1.12 (left). The absence of a backward support for the foot explains the poor walking ability of pelicans, which have been subject, in the course of evolution, to a trade-off between swimming and walking. By contrast, in tree-dwelling birds, like toucans, parrots, and woodpeckers, there has been a selective advantage in having an extra backward-pointing toe, e.g., for climbing the trunk of trees and grasping branches (see Figure 1.12, right).
Perhaps the best example of sympatric speciation is provided by species of freshwater fishes, commonly called three-spined sticklebacks (genus Gasterosterus), deeply studied by Dolph Schluter and colleagues (Schluter and McPhail, 1992; Schluter, 1994; Rundle et al., 2000; McKinnon and Rundle, 2002; see also Schluter, 2000). They have found two different species of three-spined sticklebacks in each of five different lakes in southwestern British Columbia: a large species with a large mouth primarily living on the lake bottom and feeding on large prey, and a smaller species with a smaller mouth feeding on plankton in open water (see Figure 1.13). DNA analysis (Taylor and McPhail, 1999) indicates that each lake was colonized independently, presumably by a marine ancestor, about two million years ago, and that the two species in each lake are more closely related to each other than to any of the species in the other lakes. Moreover, the two species in each lake are reproductively isolated, while individuals of the same species from different lakes can interbreed and show mating preferences for similar size individuals (Rundle and Schluter, 1998; Nagel and Schluter, 1998). Thus, the most likely explanation is that in each lake an initially single population faced disruptive selection due to competition for different resources and habitats. Such a competition favored fishes at either extremes of body and mouth size over those closer to the mean. Disruptive selection, coupled with assortative mating, split the population into two distinct groups, exploiting different types of food in different parts of the lake, and eventually led to the two different species that we observe today.
Another remarkable example of evolutionary branching in fish populations is provided by cichlid fish (family Cichlidae) in African lakes, in which the analysis of DNA sequences suggests that the hundreds of present species, differing in size, colors, male courtship traits, and female preferences, have arisen within just the last two million years (Meyer et al., 1990; Schliewen et al., 1994; Albertson et al., 1999; Schliewen et al., 2001; Barluenga et al., 2006). The cause of this large and rapid diversification is probably what Darwin first realized and called sexual selection, namely selection for male traits associated with dominance in the social hierarchy or with attractiveness to females. As selection for limited resources, sexual selection may become disruptive and lead to repeated speciations, as shown by Higashi et al. (1999).
In plant populations, speciation can be due to resource competition (see, e.g., Knox and Palmer, 1995) and to the coevolution with pollinator species in flower plants (see, e.g., Schemske and Bradshaw, 1999; Bradshaw and Schemske, 2003; Ramsey et al., 2003). In Chapter 6 we will show how the selection pressure governing the evolution of mutualistic interactions may turn disruptive.
While adaptation to resource consumption, to female mating preferences, and to the phenotypes of partner species certainly increases reproduction and therefore fitness, the alternative way to improve fitness is to reduce mortality, by adapting to environmental conditions and by evolving antipredator traits or behaviors. Thus, prey-predator, host-parasite, or, more in general, resource-consumer coevolution has often attracted the attention of evolutionary biologists and there is both empirical evidence and theoretical support that predation pressure may be a disruptive selection force leading to prey speciation. For example, Walker (1997) suggests that predation pressure may have contributed to the body size diversification of three-spined sticklebacks described above. Other examples of evolutionary branching induced by predation pressures are proposed by van Damme and Pickford (1995) and Stone (1998) in mollusks and by Chown and Smith (1993) in beetles, while other studies on resource-consumer coevolution have been recently reviewed by Abrams (2000). Further theoretical support will be given in Chapter 5.
Notice that, for all the above examples on living populations we have not shown figures representing evolutionary dynamics, as we have done for the examples from fossil records. The reason is simply that the length of significant evolutionary experiments is often impracticable and the experimental conditions are hardly controllable over long periods of time. However, these limitations are strongly attenuated for small and simple organisms, like bacteria, which typically have short generation times and large population sizes, so that their evolutionary dynamics develop on reasonably short timescales. Moreover, laboratory experiments can be easily controlled and repeated under the same or different conditions. Thus, laboratory experiments on simple organisms are best suited for testing theoretical evolutionary hypotheses and evolutionary models, as those described in this book (see the sequence of papers by Lenski and coauthors on long-term evolution in Escherichia coli, from Lenski et al. (1991) to Rozen et al. (2005), and references therein). However, though empirical evidence for evolutionary branching in bacteria is available (see, e.g., Rainey and Travisano, 1998), testing the Red Queen hypothesis is still a rather open problem.
Before closing this chapter, we now show that evolutionary phenomena not driven by genetic inheritance are relevant in biological as well as nonbiological contexts. As we first noted in Section 1.1, the mutation and selection processes do not necessarily concern genetically inherited phenotypes, but they can, as well, drive the evolution of culturally transmitted traits, like animal and human behavioral strategies (see, e.g., Cavalli-Sforza and Feldman, 1981; Boyd and Richerson, 1985; see also Le Galliard et al., 2005, for a recent theoretical study). The evolution of cooperation among nonrelated individuals is one of the most discussed problems in biology and social sciences and may have an evolutionary explanation (Axelrod and Hamilton, 1981). Sociologists investigate this problem by so-called public goods games, in which cooperative individuals contribute to a public resource from which both cooperative and cheating individuals benefit. Experimental results, typically obtained by means of interviews or by letting individuals play repeated games, show that, in the absence of a mechanism punishing cheaters, groups composed of cooperators perform better than groups composed of cheaters, though cheaters always outperform cooperators in mixed groups (Boyd and Richerson, 1992; Fehr and Gächter, 2000; Fischbacher et al., 2001; Fehr and Gächter 2002). Thus, even if the cooperators’ progeny tends to cooperate due to cultural transmission, the imitation of more successful cheating neighbors will eventually lead mixed groups to groups of cheaters and to the loss of the public goods. This is known as “The Tragedy of the Commons” (Hardin, 1968), which says that reciprocal altruism fails to provide an explanation for cooperation to get established, unless cheaters can be identified and punished.
In a recent theoretical study Hauert et al. (2002) draw attention to a simple mechanism to explain the evolution of cooperation. It consists in allowing individuals to quit the game (loner behavior) and get a low but safe income that does not depend on the strategy played by other individuals (see Doebeli et al., 2004; Hauert and Doebeli, 2004, for different mechanisms promoting or inhibiting the evolution of cooperation). Such risk-averse optional participation prevents cheating to become the dominant strategy and allows the evolutionary persistence of cooperation, even if individuals have no way of discriminating against cheaters. Mutations, namely the change of the strategy culturally learned from the family, and selection, favoring the switch to the better-performing strategies of the moment, drive the volunteering public goods game in a sort of “rock-scissors-paper” cycle, where individuals tend to quit the game in groups dominated by cheaters, to cooperate in groups dominated by loners, and start to cheat in groups dominated by cooperators. Hauert et al. (2002) concluded that volunteering is a “Red Queen mechanism” for the persistence of cooperation in public goods games. However, the game is characterized by three strategies that never change. What oscillates are the fractions of the population adopting the three strategies. Thus, variations occur on the demographic timescale and the game describes the cyclic coexistence of different strategies rather than their evolutionary change.
An example of evolutionary dynamics of a socio-cultural trait is shown in Figure 1.14, which reports the evolution of the skirt length of women’s formal evening dresses reconstructed from the analysis of dress pictures appeared in fashion magazines over 200 years. The role of fashion is identity display. Two opposed selective forces are the tendency to imitate certain stereotypes with desirable characteristics and the tendency to diverge from them to proclaim an identity. The trade-off between imitation and personalization might induce complex evolutionary dynamics of fashion traits. For example, both diagrams in Figure 1.14 show an almost synchronous slow oscillating trend, with superposed year by year variations, suggesting the existence of a Red Queen evolutionary regime in which particular trait values periodically, or, better, erratically, become fashionable.
As already discussed in Section 1.1, Dawkins’ replicators, the basic units able to evolve through innovation and competition processes, do not need to be living organisms. All material products as well as ideas and social norms in use in our societies are replicators in the sense of Dawkins. Not surprisingly, the evolution of norms and, more in general, of the history of human societies have been recently addressed (see Ehrlich and Levin, 2005, and references therein, and Turchin, 2003b, 2006), while the technological evolution of commercial products will be described in Chapter 4. Commercial products replicate each time a new product is purchased, die out each time a product is dismissed, transmit their characteristic traits to newly produced products, which from time to time happen to be new versions characterized by slight innovations, and interact with each other in local or global markets. Market interactions are often competitive, as between same category cars or watches of different producers and brands, but they can also be cooperative, as between cars and tires or watches and batteries. Technological change is therefore a mutation-selection process, driven by technological innovations and by supply and demand selective forces.
Figure 1.15 shows the case of the evolution of various telecommunication services in Sweden. The appearance of digital fixed phones (diamonds) has been a successful innovation, which led in about ten years to the substitution of previous analog fixed phones (squares). This is an example of trait substitution, the basic step of evolution. In Section 1.5 we have seen that if substitutions are quick with respect to the rate of occurrence of innovations, then the demographic timescale, here better called market timescale, can be thought as separated from the evolutionary timescale, and this separation allows us to define the products resident in the market, the evolution of their characteristic traits, and the corresponding evolution of their market abundances, i.e., the evolution of the market share. In Figure 1.15, however, as in all real situations, the demographic and evolutionary timescales are not completely separated. Thus, we might interpret digital fixed phones as a mutant group that replaced the resident group of analog fixed phones, or as a mutant group that became resident by coexisting with other resident groups, thus marking an evolutionary branching in the group of fixed phones. In this second case, subsequent mutation-substitution sequences in each resident group (not shown in Figure 1.15) would have led to the evolutionary extinction (probably an evolutionary murder) of analog fixed phones. In fact, we might think that in ten years of development, digital phones have gradually changed, e.g., improving the quality of speech, allowing faster and faster data communication, or simply changing design, begetting trait substitution sequences in the telephone market, which eventually wiped out analog phones. Analogously, we can see that digital mobile phones (triangles up) are replacing analog mobile phones (triangles down), while they seem to coexist with digital fixed phones and Internet hosts (dots). By contrast, public pay phones (stars) are declining, possibly approaching an evolutionary extinction.
The somehow ambiguous interpretation of Figure 1.15 also applies to some of the next figures, which show the evolution of the market share between several technologies on a relatively long period of time, without reporting the concurrent evolution of their characteristic traits. Figure 1.16 shows examples of technological substitutions in steel manufacturing. Puddel steel was replaced by bessemer steel, quite rapidly relative to the time required by the substitution of bessemer steel by other technologies. Thus, we can reasonably interpret the bessemer technology as a successful innovation that replaced the previously resident puddel technology. By contrast, the advent of open-hearth and of more recent technologies are better described as technological speciations, due to different technologies utilized in isolated markets or to the mechanism of evolutionary branching. Notice that while the bessemer technology vanished gradually, the decline of the open-hearth technology has been much more sudden, two extinction patterns that we may interpret as evolutionary murder and suicide, respectively.
Analogous considerations apply to Figures 1.17 and 1.18, which show the evolution of the market share between different transport and energy production systems, while evolutionary dynamics of related traits are given in Figures 1.19–1.22.
The paradigm of mutation-selection evolution is also important in the design of complex machineries and computer programs. Engineers have often found that an efficient and effective way to find the optimal design is to successively make small and random changes, test the obtained design, and keep the version that perform better. Since the advent of modern and cheap powerful computers, this evolutionary approach has had an enormous impact on complex problem solving. Design or optimization algorithms inspired by the evolutionary analogy are generally called genetic algorithms and have been pioneered by Holland (1975; see also Goldberg, 1989). They are useful whenever we do not have a solution in mind for a complex problem but only the desired functional characteristics of the solution.
On the other hand, computer simulations are of interest to evolutionary biologists because they provide an artificial world in which experiments can be performed quickly and at low cost. As recognized by Maynard Smith (1992), If we want to discover generalizations about evolving systems, we have to look at artificial ones. This is why the mathematical modeling of evolutionary systems is today more than ever an important and challenging problem.
As a last speculative example of the wide applicability and descriptive power of evolutionary paradigm, we report an intriguing problem that comes from the cosmologist Smolin (1997). Seeking an explanation of the physical constants of the universe, Smolin argued that universes themselves can be considered as replicators. His idea is that universes reproduce by means of black holes, which form when large stars collapse into a region of gravity so intense that nothing, not even light, can escape. New universes are then generated from black holes of parental universes, through expansions like the “Big Bang” that generated our universe. Each time a new universe is generated, its properties may vary slightly from those of the parent, thus providing a source of mutation. Thus, according to this hazardous evolutionary conjecture, universes most capable of reproducing, i.e., of forming black holes, would have been selected. In fact, the physical constants of our universe seem to be well adapted for the formation of black holes, and since the properties required for life are strongly correlated with those required for black holes, this conjecture would also explain why our universe is optimized for life.
The message emerging from this gallery of examples seems to be that the mechanisms of mutation (innovation) and selection (competition) are pervasive at each level of organization of natural and artificial systems. Dawkins (1989) even argued that evolvability is a trait that can be, and has been, selected for. In fact, the ability to be responsive to the surrounding environment through the mechanisms of mutation and selection is what allows living and artificial replicators to fit to their local conditions, and adaptation of structure to function is apparent in living organisms as well as in the human design of artificial systems. Not surprisingly, Ernst Mayr (2001) concludes his introduction to the seventeenth printing of Darwin’s masterpiece by saying that the philosophical consequences of the Darwinian “revolution” have not yet been fully exploited.
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