Adaptive Dynamics of Pathogen-Host Interactions

This paper explains why the traditional approach of predicting evolutionary outcomes by maximizing the basic reproduction ratio of a disease is not always appropriate. Since pathogens tend to affect their host environment in radical ways, selection pressures usually depend on the types of pathogens and hosts that are established in an infected population. After outlining the theory of adaptive dynamics as a versatile toolbox for investigating the evolution and coevolution of pathogen-host interactions under conditions of frequency-dependent selection, examples illustrate how classic methods and the new models presented here result in different predictions about the evolution of infectious diseases

Spatial self-structuring accelerates adaptive speciation in sexual populations

Questions: How does spatial self-structuring influence the waiting time until adaptive speciation in a population with sexual reproduction? Which mechanisms underlie this effect? Model: Using a spatially explicit individual-based multi-locus model of adaptive speciation, we investigate the evolution of a sexually reproducing population, with different levels of spatial self-structuring induced by different distances of natal dispersal. We analyze how waiting times until speciation are affected by the mobility of individuals, the number of loci determining the phenotype under disruptive selection, and the mating costs for individuals preferring rare phenotypes. Conclusions: Spatial self-structuring facilitates the evolution of assortative mating and accelerates adaptive speciation. We identify three mechanisms that are responsible for this effect: (i) spatial self-structuring promotes the evolution of assortativity by providing assortative mating "for free," as individuals find phenotypically similar mates within their spatial clusters; (ii) it helps assortatively mating individuals with rare phenotypes to find mating partners even when the selected phenotype is determined by a large number of loci, so that strict assortativity is difficult; and (iii) it renders speciation less sensitive to costs of assortative mating, especially for individuals preferring rare phenotypes

Correlation Analysis of Fitness Landscapes

Fitness landscapes underlie the dynamics of evolutionary processes and are a key concept of evolutionary theory. Recent research on molecular folding and on evolutionary algorithms has demonstrated that such landscapes are also important for understanding problems of chemistry and of combinatorial optimization. In these cases free energy or cost functions are used instead of biological fitness functions defined on genotypes. However, the image of a three dimensional landscape with many peaks and valleys turns out to be misleading. Genotypes tend to differ in numerous characteristics, resulting in multidimensional fitness landscapes. Properties of these landscapes are very different from those of low dimensional ones. The main intention of this study is to investigate how these features affect the duration of adaptive walks on such landscapes. For this purpose we focus on the Traveling Salesman Problem (TSP), which amounts to finding the shortest tour visiting a given set of locations. By comparing theoretical predictions for the duration of adaptive walks to the actual waiting times observed for an evolutionary algorithm we demonstrate that a sufficiently fine-grained correlation matrix succeeds in capturing essential structural features of the TSP fitness landscape. To test the performance of correlation-based predictions for a class of fitness landscapes with varying degree of neutrality, we have analyzed evolutionary waiting times on NKp fitness landscapes. We show that for low degrees of neutrality, correlation statistics again prove to be an excellent basis for predicting waiting times, while for very high degrees of neutrality, a population's drift along neutral networks turns out to require incorporation of additional information on network topologies

Adaptive Dynamics and Evolving Biodiversity

Population viability is determined by the interplay of environmental influences and individual phenotypic traits that shape life histories and behavior. Only a few years ago the common wisdom in evolutionary ecology was that adaptive evolution would optimize a population’s phenotypic state in the sense of maximizing som

Pluralism in Evolutionary Theory

The review by Waxman and Gavrilets (Waxman and Gavrilets 2005) illustrates the collision of different mindsets in evolutionary theory. These differences originate from the awe-inspiring complexity of the evolutionary process itself: evolutionary understanding critically depends on processes at many biological levels. Starting out with base pairs and their sequences, scholars of evolution have to consider -- in the order of biological complexity -- alleles, quantitative allelic traits, physiological and morphological traits, life-history traits, demographic rates, fitness, changes in genotype frequencies, population population dynamics, trait substitution sequences, and population bifurcations, to eventually arrive at the levels of ecological communities and the biosphere. It would appear that no other field of contemporary science sports comparable ambitions

Evolutionary responses of communities to extinctions

Question: What are the evolutionary consequences of extinctions in ecological communities? Can evolution restore pre-extinction communities by replacing lost ecological strategies with similar ones, or will communities change in fundamental ways and never be the same again? Mathematical approach: We develop and explore a new framework based on evolutionary domains of attraction (EDAs), defined as sets of strategy combination from which a particular ESS community can be attained through gradual evolution. The latter dynamics may include three types of evolutionary processes: continuous strategy adaptation in response to directional selection, evolutionary branching in response to disruptive selection, and evolutionarily driven extinction. Key assumptions: We consider gradual frequency-dependent evolution in ecological communities, with evolutionary dynamics being fully determined by the strategy composition of a community's resident species. Results: The EDA approach distinguishes ESS communities that gradual evolution can restore after extinctions from ESS communities for which this option does not exist or is constrained. The EDA approach also offers a natural definition of 'evolutionary keystone species' as species whose removal causes a community to shift from one EDA to another. Our study highlights that environmentally driven extinctions can readily cause such shifts. We explain why the evolutionary attainability of an ESS community through gradual evolution from a single precursor species does not imply its evolutionary restorability after extinctions. This shows that evolution driven by frequency-dependent selection may lead to 'Humpty Dumpty' effects and community closure on an evolutionary time scale. By establishing EDAs for several example food webs, we discover that evolutionarily driven extinctions may be crucially involved in the evolutionary restoration of ESS communities