Evolution of pathogen specialisation in a host metapopulation: joint effects of host and pathogen dispersal

Metapopulation processes are important determinants of epidemiological and evolutionary dynamics in host-pathogen systems, and are therefore central to explaining observed patterns of disease or genetic diversity. In particular, the spatial scale of interactions between pathogens and their hosts is of primary importance because migration rates of one species can affect both spatial and temporal heterogeneity of selection on the other. In this study we developed a stochastic and discrete time simulation model to specifically examine the joint effects of host and pathogen dispersal on the evolution of pathogen specialisation in a spatially explicit metapopulation. We consider a plant-pathogen system in which the host metapopulation is composed of two plant genotypes. The pathogen is dispersed by air-borne spores on the host metapopulation. The pathogen population is characterised by a single life-history trait under selection, the infection efficacy. We found that restricted host dispersal can lead to high amount of pathogen diversity and that the extent of pathogen specialisation varied according to the spatial scale of host-pathogen dispersal. We also discuss the role of population asynchrony in determining pathogen evolutionary outcomes

When sinks become sources: Adaptive colonization in asexuals

International audienceThe establishment of a population into a new empty habitat outside of its initial niche is a phenomenon akin to evolutionary rescue in the presence of immigration. It underlies a wide range of processes, such as biological invasions by alien organisms, host shifts in pathogens, or the emergence of resistance to pesticides or antibiotics from untreated areas. We derive an analytically tractable framework to describe the evolutionary and demographic dynamics of asexual populations in a source-sink system. We analyze the influence of several factors on the establishment success in the sink, and on the time until establishment. To this aim, we use a classic phenotype-fitness landscape (Fisher's geometrical model in n dimensions) where the source and sink habitats have different phenotypic optima. In case of successful establishment, the mean fitness in the sink follows a typical four-phases trajectory. The waiting time to establishment is independent of the immigration rate and has a "U-shaped" dependence on the mutation rate, until some threshold where lethal mutagenesis impedes establishment and the sink population remains so. We use these results to get some insight into possible effects of several management strategies. K E Y W O R D S : Epistasis, establishment time, evolutionary rescue, Fisher's geometrical model, lethal mutagenesis

Distribution of the pathogen genetic clusters according to their proportion in each sub-population.

<p>This graph is issued from an example of simulation of the case-study A. The generalist cluster did not persist (<b>c</b>) and evolution led to the coexistence of the two fully (<b>a</b> and <b>d</b>) and two moderately (<b>b</b> and <b>e</b>) specialised clusters. Other parameters are: , and (see <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003633#pcbi-1003633-g001" target="_blank">Fig. 1a</a> for the global evolutionary trajectory).</p

Distribution of the pathogen genetic clusters according to their proportion in each sub-population.

<p>This graph is issued from an example of simulation of the case-study A. The generalist cluster did not persist (<b>c</b>) and evolution led to the coexistence of the two fully (<b>a</b> and <b>d</b>) and two moderately (<b>b</b> and <b>e</b>) specialised clusters. Other parameters are: , and (see <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003633#pcbi-1003633-g001" target="_blank">Fig. 1a</a> for the global evolutionary trajectory).</p

Stable coexistence among pathogen genetic clusters as a function of pathogen and host mean dispersal distances and for the case-study A.

<p><b>a</b>: ; <b>b</b>: . For clarity only the mean dispersal distances below 25% are displayed (see Fig. S4 in <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003633#pcbi.1003633.s001" target="_blank">Text S1</a> for more details).</p

Examples of pathogen evolutionary trajectories (a and b) and the corresponding host dynamics (c and d, respectively).

<p><b>a</b> and <b>c</b>: ; <b>b</b> and <b>d</b>: . Parameters are those of case-study A, and .</p

Infection efficacy (a) and efficacy range (b) of the moderately specialised genetic clusters as a function of host mean dispersal distance and for limited pathogen dispersal ().

<p>Parameters are those of the case-study A and . Only one genetic cluster is represented but two symmetric genetic clusters were present.</p

Parameter definition and values.

<p>Parameter definition and values.</p