Evolutionary Branching and Sympatric Speciation Caused by Different Types of Ecological Interactions [Revised 18 September 2000]

Evolutionary branching occurs when frequency-dependent selection splits a phenotypically monomorphic population into two distinct phenotypic clusters. A prerequisite for evolutionary branching is that directional selection drives the population towards a fitness minimum in phenotype space. This paper demonstrates that selection regimes leading to evolutionary branching readily arise from a wide variety of different ecological interactions within and between species. We use classical ecological models for symmetric and asymmetric competition, for mutualism, and for predator-prey interactions to describe evolving populations with continuously varying characters. For these models, we investigate the ecological and evolutionary conditions that allow for evolutionary branching and establish that branching is a generic and robust phenomenon. Evolutionary branching becomes a model for sympatric speciation when population genetics and mating mechanisms are incorporated into ecological models. In sexual populations with random mating, the continual production of intermediate phenotypes from two incipient branches prevents evolutionary branching. In contrast, when mating is assortative for the ecological characters under study, evolutionary branching is possible in sexual populations and can lead to speciation. Therefore, we also study the evolution of assortative mating as a quantitative character. We show that evolution under branching conditions selects for assortativeness and thus allows sexual populations to escape from fitness minima. We conclude that evolutionary branching offers a general basis for understanding adaptive speciation and radiation under a wide range of different ecological conditions

Statistical mechanics of socio-economic systems with heterogeneous agents

We review the statistical mechanics approach to the study of the emerging collective behavior of systems of heterogeneous interacting agents. The general framework is presented through examples is such contexts as ecosystem dynamics and traffic modeling. We then focus on the analysis of the optimal properties of large random resource-allocation problems and on Minority Games and related models of speculative trading in financial markets, discussing a number of extensions including multi-asset models, Majority Games and models with asymmetric information. Finally, we summarize the main conclusions and outline the major open problems and limitations of the approach

Meta-Stability of Interacting Adaptive Agents

The adaptive process can be considered as being driven by two fundamental forces: exploitation and exploration. While the explorative process may be deterministic, the resultant effect may be stochastic. Stochastic effects may also exist in the expoitative process. This thesis considers the effects of stochastic fluctuations inherent in the adaptive process on the behavioural dynamics of a population of interacting agents. It is hypothesied that in such systems, one or more attractors in the population space exist; and that transitions between these attractors can occur; either as a result of internal shocks (sampling fluctuations) or external shocks (environmental changes). It is further postulated that such transitions in the (microscopic) population space may be observable as phase transitions in the behaviour of macroscopic observables. A simple model of a stock market, driven by asexual reproduction (selection plus mutation) is put forward as a testbed. A statistical dynamics analysis of the behaviour of this market is then developed. Fixed points in the space of agent behaviours are located, and market dynamics are compared to the analytic predictions. Additionally, an analysis of the relative importance of internal shocks(sampling fluctuations) and external shocks( the stock dividend sequence) across varying population size is presented

Evolutionary responses of fast adapting populations to opposing selection pressures

This thesis deals with the mathematical modeling of evolutionary processes that take place in heterogeneous populations. Its leitmotif is the response of complex ensembles of replicating entities to multiple (and often opposite) selection pressures. Even though the specific problems addressed in different chapters belong to different organizational levels—genome, population, and community—all of them can be conceptualized as the evolution of a heterogeneous population—let it be a population of genomic elements, viruses, or prokaryotic hosts and phages—facing a complex environment. As a result, the mathematical tools required for their study are quite similar. In contrast, the strategies that each population has discovered to perpetuate vary according to the different evolutionary challenges and environmental constraints that the population experiences. Along this thesis, there has been a special interest on connecting theoretical models with experimental results. To that end, most of the work presented here has been motivated either by laboratory findings or by the bioinformatic analysis of sequenced genomes. We strongly believe that such a multidisciplinary approach is necessary in order to improve our knowledge on how evolution works. Moreover, experiments are a must when it comes to propose antiviral strategies based on theoretical predictions, as we do in Chapter 3. This thesis is structured in two main blocks. The first one focuses on studying instances of viral evolution under the action of mutagenic drugs, paying particular attention to their possible application to the development of novel antiviral therapies. This block comprises chapters 2 and 3; the former dicussing the phenomenon of lethal defection and stochastic viral extinction; the latter dealing with the optimal way to combine mutagens and inhibitors in multidrug antiviral treatments. The second block is devoted to the study of the evolutionary forces underlying genome structure. In chapter 4, we propose a mechanism through which multipartite viruses could have originated. Interestingly, the pathway leading to genome segmentation shares some steps with lethal defection, but each outcome is reached at specific environmental conditions. Chapter 5 analyses the abundance distributions of transposable elements in prokaryotic genomes, with the aim of determining the key processes involved in their spreading. We explicitly explore the hypothesis that transposable elements follow a neutral dynamics, with a negligible fitness cost for their host genomes. A higher level of organization is studied in Chapter 6, where an agent based coevolutionary model based on Lotka-Volterra interactions is used to investigate the evolutionary dynamics of the prokaryotic antiviral immunity system CRISPR-Cas. This chapter also examines the environmental factors that are responsible for its maintenance or loss. Finally, Chapter 7 summarizes the main results obtained along the thesis and sketches possible lines of work based on them