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This chapter provides an overview of basic principles of coevolutionary biology, including both microevolutionary (ecological) and macroevolutionary (historical) approaches and their integration. It defines the main terminology used in coevolutionary biology. It discusses the relationship between coadaptation, codiversification, and coevolution. The chapter considers the relationship of reciprocal phenotypic selection to coadaptive responses of heritable traits. It introduces the geographic mosaic theory of coevolution and considers the central roles of dispersal and population structure, as well as GxGxE interactions. Chapter 1 briefly reviews the fossil evidence for coevolution. It introduces co-phylogenetic approaches and the cospeciation - host-switching continuum. The chapter concludes by considering why permanent parasites are unusually tractable model systems for studies of coevolution, and by introducing the concept of “ecological replicates.”

The preceding chapters of the book focused on interactions between hosts and parasites. This chapter focuses on competitive interactions between different species of parasites, and the role of the host in mediating those interactions. Parasites do not live in isolation, but are members of diverse parasite communities that share hosts. While no individual harbors all parasites known from that host species, most individuals support more than one species of parasite at a time. The presence of a given parasite species can have a negative effect on other parasites due to competition for limiting resources in or on the shared host. Even parasites that exploit very different parts of a host's body may compete, because each host individual is ultimately a single resource. Interspecific competition can lead to the coadaptation of traits that reduce the intensity of competition. Thus, in addition to coevolving with the host species, parasites coevolve with other parasites that share that same host species. The chapter begins by considering competition between parasite species, in general. It then provides a more detailed overview of competition between species of lice.

Coadaptive diversification occurs when one of two interacting lineages diversifies in response to coadaptation between those lineages. This chapter reviews evidence suggesting that bird lice have undergone coadaptive diversification. For example, coadaptation between lice and their hosts can trigger the diversification of lice across different host species. Lineages of feather lice on different host species live largely in isolation, punctuated by occasional dispersal between hosts. The isolation causes lice to diverge in behavior and morphology in response to interactions with their respective host species. This, in turn, leads to coadaptive diversification of lice across host species. Lice also compete with other species of lice, further contributing to the coadaptive radiation of lice. Divergence and speciation, followed by periodic dispersal, may also result in divergent lineages coming into secondary contact with former conspecifics, triggering even more competition and diversification. In summary, the distribution of feather louse ecomorphs across the feather louse phylogeny is a consequence of infrequent dispersal events, followed by the repeated, independent evolution of similar ecomorphs.

This chapter summarizes the book using a graphical framework that integrates the coadaption and codiversification ends of coevolution with five “zones” of coevolution. This framework can be applied to any host-parasite system. Indeed, it can be used with any coevolving system. Differences in the five coevolutionary zones are operational, yet subtle. Delineating these different zones helps to clarify the ways in which dispersal and selection influence the adapation, coadapation, diversification, and codiversification of interacting groups. Coevolution in this framework varies from a purely microevolutionary focus in the case of coadapation, to a purely macroevolutionary focus in the case of codiversification. These extremes are first considered in more detail. Then the three combinations of adaptation and diversification that comprise the middle portions of the framework are discussed. The importance of integrating phylogenetic, comparative, and experimental approaches cannot be overstated in studies of coevolution, or evolutionary ecology in general.

Population structure is a ubiquitous feature of natural populations that has an important influence on evolutionary change. In the real world, populations are not homogenous units; instead, they develop an internal structure, created by the physical properties of the environment and the biological characteristics of the species (such as dispersal ability). However, our basic ecological and population genetic models generally ignore population structure and focus on randomly mating (panmictic) populations. Such structure can profoundly change the evolution of a population. In fact, the myriad of influences that population structure exerts can only be hinted at in a single chapter. Since an exhaustive review is not possible, I will focus on presenting the conceptual issues linking mathematical models of population structure to empirical studies. To do this, it is useful to recognize two different kinds of population structure that both reflect and influence evolutionary change. The first is genetic structure. This is defined as the nonrandom distribution of genotypes in space and time. Thus, genetic structure reflects the genetic differences that develop among the different components of one or more populations. The second is what I will call proximity structure, defined by the size and composition of the group of neighbors that influence an individual’s fitness. Fitness is commonly influenced by local intraspecific interactions. Perhaps the most obvious example is competition. When individuals compete for some resource, they don’t usually compete equally with every other member of the population; in general, they compete only with a few of the most proximate individuals. These two forms of population structure, genetic structure and proximity structure, provide a foundation for understanding why we have shifted away from viewing populations as homogenous units. For good reason, this is a theme that is explored in many of the other chapters in this book. Genetic structure can develop within a population over a single generation, generally either as a result of local family associations or as a result of spatial variation in selection. For example, limited seed dispersal results in genetic correlations among neighbors even in the face of long-distance pollen movement, due to the clustering of maternal half sibs.

Coevolution is reciprocal evolutionary change in interacting species driven by natural selection. It is a pervasive evolutionary process that has shaped many of the major events in the history of life, including the origin of the eukaryotic cell, the origin of plants, the evolution of coral reefs, and the formation of lichens, mycorrhizae, and rhizobia, all of which are crucial in the development of terrestrial communities. Just as important, evidence is increasing that Coevolution is an important ongoing ecological process, continually shaping and reshaping interactions among species, sometimes over time spans of only a few decades. This chapter is an evaluation of coevolution as an ongoing process shaped by the geographic structure of interactions among species. It is an analysis of what we have learned recently as we have taken a broader geographic view of how coevolution continually remolds the relationships among taxa. The first mathematical models of geographically structured coevolution were developed only in the past few years, and there are still fewer than a dozen empirical studies that have analyzed any aspects of coevolutionary structure and dynamics across geographic landscapes. Nevertheless, these theoretical and empirical studies have together suggested that coevolution is very likely a much more dynamic process than suggested by the previous several decades of study in evolutionary ecology. Coevolution is a hierarchical process. Local populations of species interact with one another and sometimes coevolve. These local populations are in turn connected through gene flow to populations in other communities, and this geographic structuring adds another level to the coevolutionary process. Local geographic clusters of populations may show metapopulation dynamics, and yet broader geographic groupings of populations may show considerable genetic differentiation in the traits of interacting species. Only a subset of locally or regionally coevolving traits will eventually sweep through all populations. Hence, coevolution as seen in comparisons of interacting phylogenetic lineages will show only a small fraction of the Coevolutionary dynamics found at the population, metapopulation, and broader geographic scales. Within this hierarchical structure of coevolution, many of the dynamics may occur above the level of local populations and below the level of the fixed traits of species for three reasons: Many species are collections of genetically differentiated populations, the outcomes of species interactions commonly differ among communities, and interacting species often do not have identical geographic ranges.

Müller reacted to Darwin in three phases. First, between 1861 and 1863 he restructured his own research after reading Darwin’s Origin. Next, in 1865, he initiated correspondence with Darwin in direct engagement of Darwin’s projects. Third, after returning to Blumenau in 1867, he developed research on new groups of organisms. Three topics pursued by Müller between 1865 and 1867 were stimulated directly by Darwin: climbing plants (over 50 genera near Desterro with disparate ways of twining, some new to Darwin); heterostyly; and complex orchid adaptations affecting fertilization involving coadaptations with insect pollinators. Much of Müller’s research on orchids is now lost; it was set aside for an abandoned book project. His independent research often engaged Darwinian topics, including species formation, adaptations of plants to prevent self-fertilization, and the role of natural selection in shaping plant–insect coadaptations. The chapter also covers the changing fortunes of the Blumenau colony and August and Fritz Müller’s efforts to devise and teach improved agricultural methods.

This chapter deals with diverse natural historical projects Müller pursued between 1868 and1876. He investigated numerous plants (including their means of pollen and seed dispersal) and animals (including leaf-cutter ants, armadillos, stingless bees, caddis flies, crabs, and termites). His correspondence and publications cover several Darwinian themes, including intraspecific variation, the importance of outcrossing, effects of inbreeding, systems of sexuality (e.g., alternation of sexual and asexual generations), coadaptations in complex interspecific mutualisms (such as, ant–acacia symbioses), and the importance for phylogeny (e.g., among wasps, bumblebees, stingless bees, and hive bees) of utilizing comparative studies, not only of morphologies but also of the development patterns, behaviors, and social systems of the organisms under investigation. Chapter 6 also reports on several family crises, the significant burdens of Müller’s external obligations, difficulties with the Brazilian bureaucracy, and his appointment as a traveling naturalist of the National Museum of Rio de Janeiro, a position offered in 1874, but delayed until 1876.

Complex adaptive systems, as conceived by John Holland, are groups of agents engaged in a process of coadaptation, in which adaptive moves by individuals have consequences for the group. Holland and others have shown that under certain circumstances simple models of this process show surprising abilities to self-organize (Holland 1993; Kauffman 1993). Complex adaptive systems have interesting mathematical properties, and the process of "anti-chaos"-—the spontaneous crystallization of ordered patterns in initially disordered networks— has become a new area of interdisciplinary research. But the question of whether these models can illuminate real world processes is still largely open. Not long ago John Maynard Smith described the study of complex adaptive systems as "fact-free science" (1995). This chapter has two purposes. First, in response to Maynard Smith, I will show how the concept of ecological feedback in complex adaptive systems provides a simple and powerful explanation for the structure and persistence of cooperative networks among Balinese rice farmers. Second, I will generalize this explanation to shed light on the emergence of cooperation in a class of social systems where interactions with the natural world create both rewards and punishments. But before turning to these examples, in line with the purposes of this volume I will comment on the ideas and assumptions that underlie the use of models in this analysis. "Society is a human product. Society is an objective reality. Man [sic] is a social product." With this epigram Peter Berger and Thomas Luckmann neatly encapsulated a fundamental problem in social theory (1967:61). In American anthropology today this paradox is often posed as a conflict between "structure" and "agency," where the former refers to ideational, economic, institutional, or psychological systems that are represented as generating social reality; and the latter to the ability of individual social actors to modify their own social worlds. The same paradox recurs in classical social theory, such as Jürgen Habermas' insistence on the need to somehow reconcile actor-focused and system-level social theories (Habermas 1985, 1987).

We accept many definitions for games, most not so grandiose as those of Napoleon treated by Byron. Often when I demonstrate the simulation of Anasazi settlement discussed in chapter 7 of this volume someone will say, "This is just a game isn't it?" I'm happy to admit that it is, so long as our definition of games encompasses child's play—which teaches about and prepares for reality—and not just those frivolous pastimes of adults, which release them from it. This volume is based on and made possible by recent developments in the field of agent-based simulation. More than some dry computer science technology or another corporate software gambit, this technology is in fact provoking great interest in the possibilities of simulating social, spatial, and evolutionary dynamics in human and primate societies in ways that have not previously been possible. What is agent-based modeling? Models of this sort are sometimes also called individual-oriented, or distributed artificial intelligence- based. Action in such models takes place through agents, which are processes, however simple, that collect information about their environment, make decisions about actions based on that information, and act (Doran et al. 1994:200). Artificial societies composed of interacting collections of such agents allow controlled experiments (of the sort impossible in traditional social research) on the effects of tuning one behavioral or environmental parameter at a time (Epstein and Axtell 1996:1-20). Research using these models emphasizes dynamics rather than equilibria, distributed processes rather than systems-level phenomena, and patterns of relationships among agents rather than relationships among variables. As a result visualization is an important part of analysis, affording these approaches a sometimes gamelike and often immediately engaging quality. OK, I admit it—they're fun. Despite our emphasis on agent-based modeling, we do not mean to imply that it should displace, or is always superior to, systems-level models based on, for example, differential equations. On the contrary: te Boekhorst and Hemelrijk nicely demonstrate how these approaches may be complementary. Even more strongly, we do not argue that these activities should become, ahead of empirical research, the principal tool of social science.