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This chapter explores three more examples that all arise in the context of fundamental ecological and evolutionary questions to further illustrate the diversifying force of frequency-dependent interactions. The first example concerns the dynamics of spatially structured populations and serves as an excellent case study for illustrating the feedback between ecological and evolutionary dynamics. The second example concerns the evolution of asymmetry in gamete size between the sexes, which sets the stage for the “paradox of sex.” Finally, the third example concerns the fundamental question of the evolution of trophic levels in food webs, that is, the evolution of complexity in ecosystems.

This chapter discusses the evolutionary dynamics of viruses. Preexisting variation in host phenotypes include variants with different levels of susceptibility to viruses, including complete resistance. Formative studies of the basis of the mutation rate relied upon virus–host interactions and the possibility of the evolution of resistance to infection. Viruses represent a strong selective pressure and can induce evolution among hosts. Host evolution, as induced by viruses, includes novel forms of ecological dynamics, including cryptic dynamics. Infection of hosts represents a strong selective pressure for viruses. Viruses that differ in their life history traits vary in their fitness and can invade and replace existing viral strains. The latent period represents a model trait for the further study of the evolution of intermediate phenotypes. Evolution among other traits is also possible, including who infects whom.

This introductory chapter discusses how when studying eco-evolutionary dynamics, one might focus on genotypes or phenotypes. Selection acts directly on phenotypes rather than on genotypes. Genotypes are affected by selection only indirectly through their association with phenotypes that influence fitness. Understanding the role of ecology in shaping evolution therefore requires a phenotypic perspective. Furthermore, the ecological effects of organisms are driven by their phenotypes rather than by their genotypes. In some cases, eco-evolutionary dynamics might be similar at the genetic and phenotypic levels, most obviously so when a key functional trait is mainly determined by a single gene. The chapter also explains how eco-evolutionary dynamics can be studied in theory or in real organisms.

This chapter evaluates various methods for inferring how phenotypes/genotypes influence population dynamics, including extensions of the year-by-year tracking approach used in analyzing the eco-to-evo side of eco-evolutionary dynamics. It provides a detailed outline of the various possibilities, including complexities that move beyond population dynamics. The chapter examines how maladaptation resulting from environmental change might decrease individual fitness and contribute to population declines, range contractions, and extirpations. It considers the extent to which contemporary evolution helps to recover individual fitness and population size, which might then make the difference between persistence versus extirpation and range expansion versus contraction. A final analysis asks how phenotypic variation within populations and species influences population dynamics.

This chapter presents a perspective on the types of developments, both technical and theoretical, that are forthcoming and the influence they are likely to have in shaping our view of the diversity and functions of environmental viruses. It focuses on theoretical challenges of two kinds: “forward” problems and “inverse” problems. It argues that for purely technological reasons, many measurements in viral ecology have not been comparable, in a quantitative sense. Moreover, there have been relatively few attempts to integrate what virus ecologists have measured into models of microbial communities. Whatever integration has occurred has focused almost exclusively on lab-based population and evolutionary dynamics. There is a pressing need to understand the ecological role of viruses of microbes, which requires an increasing number of theorists to investigate the interactions between microbes and the viruses that infect them.

This chapter begins by discussing fitness landscape, an idea first introduced by evolutionary geneticist Sewall Wright and later extended by several other authors. The fitness landscape is defined in terms of some particular traits that are implicit in the virus particle phenotype and are usually described in terms of replication rate or infectivity. The landscape appears in most textbook plots as a multi-peaked surface. Local maxima represent optimal fitness values, which can be reached through mutation from a subset of lower-fitness neighbors. Given an initial condition defined by a quasi-species distribution localized somewhere in the sequence space, the population will evolve by exploring nearest positions through mutation. The remainder of the chapter deals with symmetric competition, epistasis in RNA viruses, experimental virus landscapes, the survival of the flattest effect, and virus robustness.

Drosophila is one of the commonly used organisms in experimental evolution. One of the goals of experimental studies in Drosophila is to characterize the potential of populations to respond directly to selection. Studies of the evolutionary domestication of Drosophila are also of interest, as they allow the characterization of the evolutionary dynamics of local adaptation in populations with considerable genetic variation at the start of selection. In this chapter, the real-time studies of evolutionary domestication in Drosophila subobscura are discussed. The chapter outlines results for just two adult traits, early fecundity and female starvation resistance, and also discusses comparative methods used to study evolutionary patterns in laboratory adaptation as opposed to the analysis of evolutionary trajectories.

This concluding chapter evaluates the Gaia hypothesis. Based on the evidence studied in this book, the chapter argues that the Gaia hypothesis is not a reasonable picture of how Earth and life interact with each other. There is no single body of facts or line of unimpeachable reasoning that sways the debate conclusively in favor of Gaia. The lack of any established bottom-up mechanism that can explain how Gaia is produced also weighs against it. No one has been able to explain convincingly how Gaia could emerge out of evolutionary or ecological dynamics. It is therefore perhaps not surprising that major evolutionary advances, such as the evolution of oxygenic photosynthesis, or the first foresting of the land by sizeable trees, have been associated with environmental catastrophes.

Trait-based approaches focus on the functional traits that define how organisms interact with the environment and each other. They represent an efficient way to capture different aspects of diversity in ecological models, which is essential for understanding community structure and ecosystem functioning, both now and in the future. There is an extensive history of trait-based approaches in theoretical ecology, enriched by an expanding array of complementary frameworks. In this chapter, we give a pedagogical introduction to one such framework—adaptive dynamics—explaining how to both set up and analyse models and surveying a range of applications. Then we show how adaptive dynamics relates to other frameworks, including species sorting, ecological quantitative genetics, and moment methods, highlighting the differences and connections between them. We then consider how these basic theories can be extended to incorporate temporal and spatial heterogeneity and multiple traits. Finally, we outline some frontiers of trait-based theory, including connections with empirical systems, linking trait- and species-based approaches, and embedding trait-based approaches in Earth system models.

After having discussed how life might have originated in the early Earth (chapter 6), chapter 7 looks at how living organisms evolve by natural selection. It does that from a quantitative perspective, appropriate in the context of Artificial Chemistries. It starts with an introduction to evolutionary dynamics, the mathematical modelling of evolutionary processes. Basic concepts in evolutionary dynamics such as replication, death, selection, fitness landscapes, resource limitations, neutrality, drift and mutations are briefly explained. The classical Lotka-Volterra system is illustrated as a chemistry involving these basic concepts. An early artificial chemistry called random catalytic reaction network is then discussed, which links to the final part of the chapter, where various artificial chemistries that model evolutionary processes are reviewed, together with a brief overview of some algorithms in this area.

This chapter explores how the mathematical frameworks, empirical methods, and predictions introduced for community structure can be extended to ecosystem function. Also outlined is an alternative conceptual framework (biological stoichiometry) for evaluating eco-evolutionary dynamics at the ecosystem level. The key questions in this analysis include the importance of intraspecific diversity, the relative strength of the various effects, on what time scales do the effects play out, and to what extent are the effects direct or indirect. The chapter also addresses whether the effects of genotypes decrease toward higher levels of complexity (from phenotypes to communities to ecosystems), and to what extent feedbacks are evident-traits influence ecosystems which then influence traits.

This chapter examines whether recent advances in the theory of repeated games, as exemplified by the so-called folk theorem and related models, address the shortcomings of the self-interest based models in explaining human cooperation. It first provides an overview of folk theorems and their account of evolutionary dynamics before discussing the folk theorem with either imperfect public information or private information. It then considers evolutionarily irrelevant equilibrium as well as the link between social norms and the notion of correlated equilibrium. While the insight that repeated interactions provide opportunities for cooperative individuals to discipline defectors is correct, the chapter argues that none of the game-theoretic models mentioned above is successful. Except under implausible conditions, the cooperative outcomes identified by these models are neither accessible nor persistent, and are thus labeled evolutionarily irrelevant Nash equilibria.

This chapter describes the scope and limitation of what people know about eco-evolutionary dynamics. What they are striving to explain is the relationship between ecology and evolution, seeking to elucidate these relationships for a specific taxonomic group in a specific environment, such as Darwin's finches in Galapagos. In such cases, scholars need detailed studies of that particular group in that particular environment, ideally supplemented with theoretical models and laboratory studies that help to elucidate mechanisms. Alternatively, one might seek to discover generalities of relationships between ecology and evolution that transcend specific groups and environments. Ultimately, the chapter argues that no matter the goal, scholars need detailed studies of specific taxonomic groups in specific environments.

As urbanization leads to repeated, marked environmental gradients in space, it provides an ideal ‘natural’ experiment to study how evolving metacommunities, in which evolutionary and community ecological processes interact in a landscape context, respond to anthropogenic disturbances. An integrated approach that combines community data with data on genetic responses of focal taxa to urbanization is still lacking, notwithstanding the likely importance of eco-evolutionary feedbacks on urban ecosystem functions and services. Such a joint analysis is most easily achieved by focusing on shifts in trait values and their interspecific (cf. community ecology) and intraspecific components. The latter involves both non-genetic and genetic responses, and should be quantified for all dominant, abundant, or ecologically important species in the (meta)community. This chapter introduces the evolving metacommunity framework and discusses the use of cities to study how this framework can contribute to our insight into population and community responses to anthropogenic change. It discusses how this framework can enhance our capacity to predict responses to contemporary and future urbanization as well as its possible consequences for ecosystem functioning. It predicts that evolutionary trait change contributes substantially to observed trait shifts at the community level. Conversely, genetic adaptation might often be constrained by rapid changes in species composition. It explores eco-evolutionary partitioning metrics that quantify the evolutionary and ecological contributions to responses to urbanization. Finally, it provides guidelines for experimental studies on urban evolving metacommunities, and suggests directions on research that will build towards a fully integrated evolving metacommunity framework addressing biological responses to urbanization.

This chapter examines the influence of maternal effects on the evolutionary dynamics of wild small mammals. It analyzes work on a natural population of North American red squirrels and discusses some of the implications of genetic maternal effects and maternal-effect evolution for internally driven population cycles in small mammals. The analysis reveals the importance of the model of maternal-effect evolution in explaining the changes in nestling growth rates across generations and the observed changes in red squirrel growth rates in response to episodes of strong directional selection.

In the animal world, collective action to shelter, protect, and nourish requires the cooperation of group members. Among humans, many situations require the simultaneous cooperation of more than two individuals. Most of the relevant literature has focused on an extreme case, the N-person Prisoner’s Dilemma. Here we introduce a model in which a threshold less than the total group is required to produce benefits, with increasing participation leading to increasing productivity. It constitutes a generalization of the two-person Stag Hunt game to an N-person game. Finite and infinite population models are studied—in infinite populations a rich dynamics admits multiple equilibria. Scenarios of defector dominance, pure coordination, or coexistence may arise simultaneously. However, populations are finite and when their size is of the same order of magnitude as the group size, the evolutionary dynamics is profoundly affected: it may ultimately invert the direction of natural selection, compared with the infinite population limit.

This chapter outlines how to conceptualize and predict adaptive evolution based on information about selection and genetic variation. It introduces and explains adaptive landscapes, a concept that has proven useful in guiding the understanding of evolution. The chapter also reviews empirical data to answer fundamental questions about adaptation in nature, including to what extent short- and long-term evolution is predictable, how fast is phenotypic change, to what extent is adaptation constrained by genetic variation, and how well adapted natural populations are to their local environments. Moving beyond selection and adaptation within populations, the chapter shows how eco-evolutionary dynamics will be shaped by biological diversity: that is, different populations and species have different effects on their environment.

An individual’s phenotype may frequently be affected by the phenotypes (and hence genotypes) of other individuals with whom it interacts. Phenotypic effects that are caused by the genotype of another individual are referred to as indirect genetic effects, and these can have large and sometimes counterintuitive effects on evolutionary dynamics. Despite their potential importance, studies of indirect genetic effects in the wild are still rare. One class of indirect effect that has been investigated more commonly in natural populations is the effects of mothers on the phenotypes of their offspring. Maternal effects are defined as the contribution that a mother makes to the phenotypes of her offspring beyond the direct inheritance of genes from mother to offspring. Maternal effects have been widely studied phenotypically, and genetic variation in many important maternal traits has been quantified in the wild but rarely in the context of the indirect effects of this genetic variation on offspring traits. As a result, the importance of maternal genetic effects for evolutionary dynamics remains largely unexplored. This chapter provides conceptual background to the importance of maternal effects for evolution, and an overview of the various methods that can be employed to quantify maternal effects in the wild. Finally, this chapter provides some examples of important emerging questions in the field that could most rapidly advance our understanding of the importance of indirect genetic effects for evolutionary dynamics in the wild.