Variation in infectivity and aggressiveness in space and time in wild host-pathogen systems: causes and consequences

Abstract

Variation in host resistance and in the ability of pathogens to infect and grow (i.e. pathogenicity) is important as it provides the raw material for antagonistic (co)evolution and therefore underlies risks of disease spread, disease evolution and host shifts. Moreover, the distribution of this variation in space and time may inform us about the mode of coevolutionary selection (arms race vs. fluctuating selection dynamics) and the relative roles of G × G interactions, gene flow, selection and genetic drift in shaping coevolutionary processes. Although variation in host resistance has recently been reviewed, little is known about overall patterns in the frequency and scale of variation in pathogenicity, particularly in natural systems. Using 48 studies from 30 distinct host-pathogen systems, this review demonstrates that variation in pathogenicity is ubiquitous across multiple spatial and temporal scales. Quantitative analysis of a subset of extensively studied plant-pathogen systems shows that the magnitude of within-population variation in pathogenicity is large relative to among-population variation and that the distribution of pathogenicity partly mirrors the distribution of host resistance. At least part of the variation in pathogenicity found at a given spatial scale is adaptive, as evidenced by studies that have examined local adaptation at scales ranging from single hosts through metapopulations to entire continents and - to a lesser extent - by comparisons of pathogenicity with neutral genetic variation. Together, these results support coevolutionary selection through fluctuating selection dynamics. We end by outlining several promising directions for future research.

Figure 1

The relative amount of variation in A) pathogenicity and B) resistance within and among populations for seven pathosystems. Multivariate analyses were conducted using pathogenicity and resistance (either binary or quantitative) as the response variable and the fixed variable Population as the explanatory variable (note that the residual error term reflects the within-population variation). Analyses were implemented with the function adonis in package vegan (version 1.17-6) in R (Oksanen et al., 2010), a method related to the AMOVA procedure implemented by Excoffier (1992). Data sources (and for more detailed information): Plantago lanceolata – Podosphaera plantaginis from Laine (2005); Linum marginale ~ Melampsora linifrom Barrett et al (2009b and unpublished data; data shown are from the ‘Plains’ metapopulation); Senecio vulgaris ~ Golovinomyces cichoracearum from Bevan et al.(1993a, 1993b); Plantago lanceolata ~ Phomopsis subordinaria from de Nooij and van Damme(1988a, 1988b); Betula pubescens ~ Melampsoridium betulinum from Ericson and Burdon (2009); Populus nigra ~ Melampsora larici-populina from the AMOVA table given in Gérard et al (2006); Phaseolus vulgaris ~ Colletotrichum lindemuthianum from the AMOVA table given in Sicard et al (1997a).

Figure 2

Within-population diversity in pathogen infectivity and host resistance diversity are positively associated for three study systems (ANCOVA; F_1,17= 4.37; P= 0.04, R²=0.09). Additional variation is explained by differences among study systems in the mean diversity in infectivity (F_2,17= 14.36; P= 0.002, R²=0.57).

Figure 3

Relative amount of spatial, temporal, and spatiotemporal variation in pathogenicity in the wild flax – flax rust pathosystem (Linum marginale ~ Melampsora lini). Data is based on pathogen samples (n=659) randomly collected from three populations across twelve years (1986–1997; Burdon et al., unpublished data). Populations are Kiandra, P1, and P2 in Kosciuszko National Park, Australia (see Jarosz & Burdon, 1991 for a map of the populations). Pathogens were tested on a standard set of eleven Linum marginale host lines described by Jarosz & Burdon (1991). A multivariate analysis explained the infectivity (0/1) of the pathogen across all host lines by the variables Population, Year, and the Population x Year interaction. Analyses were implemented with the function adonis in package Vegan (version 1.17-6) in R (Oksanen et al., 2010).

Figure 4

Spatial scale of variation in the pathogen: a) neutral genetic variation, b) pathogenicity, and c) genetic variation in avirulence genes. Multivariate analyses were conducted using the response variables pathogenicity (0/1), RAFLP markers (0/1), and avr-alleles (0/1). The explanatory variable was Population, and the residual error term reflects the within-population variation. Analyses were implemented with the function adonis in package Vegan (version 1.17-6) in R (Oksanen et al., 2010). Data sources(and for more detailed information): Phaseolus vulgaris ~ Colletotrichum lindemuthianum from AMOVA table given in Sicard et al (1997a); Populus nigra ~Melampsora larici-populina from AMOVA table given in Gérard et al (2006); Linum marginale ~ Melampsora lini from Barrett et al.(2009b).