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Keywords:

  • coevolution;
  • community;
  • disease;
  • epidemiology;
  • evolution;
  • host range;
  • life history;
  • parasite

Abstract

  1. Top of page
  2. Abstract
  3. I. Introduction
  4. II. Continua in plant host–pathogen interactions
  5. III. Mechanisms underlying continua in specificity and virulence
  6. IV. Ecological consequences
  7. V. Evolutionary patterns and processes
  8. VI. Conclusions
  9. Acknowledgements
  10. References

Contents

  • Summary 513

  • I. 
    Introduction 513
  • II. 
    Continua in plant host–pathogen interactions 514
  • III. 
    Mechanisms underlying continua in specificity and virulence 516
  • IV. 
    Ecological consequences 519
  • V. 
    Evolutionary patterns and processes 520
  • VI. 
    Conclusions 524
  • Acknowledgements 525

  • References 525

Summary

Ecological, evolutionary and molecular models of interactions between plant hosts and microbial pathogens are largely based around a concept of tightly coupled interactions between species pairs. However, highly pathogenic and obligate associations between host and pathogen species represent only a fraction of the diversity encountered in natural and managed systems. Instead, many pathogens can infect a wide range of hosts, and most hosts are exposed to more than one pathogen species, often simultaneously. Furthermore, outcomes of pathogen infection vary widely because host plants vary in resistance and tolerance to infection, while pathogens are also variable in their ability to grow on or within hosts. Environmental heterogeneity further increases the potential for variation in plant host–pathogen interactions by influencing the degree and fitness consequences of infection. Here, we describe these continua of specificity and virulence inherent within plant host–pathogen interactions. Using this framework, we describe and contrast the genetic and environmental mechanisms that underlie this variation, outline consequences for epidemiology and community structure, explore likely ecological and evolutionary drivers, and highlight several key areas for future research.


I. Introduction

  1. Top of page
  2. Abstract
  3. I. Introduction
  4. II. Continua in plant host–pathogen interactions
  5. III. Mechanisms underlying continua in specificity and virulence
  6. IV. Ecological consequences
  7. V. Evolutionary patterns and processes
  8. VI. Conclusions
  9. Acknowledgements
  10. References

The prevailing ecological, molecular and evolutionary models of interactions between hosts and pathogens are largely based around a notion of more or less tightly interacting species pairs (Frank, 1992; Thompson & Burdon, 1992; Bergelson et al., 2001; Woolhouse et al., 2001; Chisholm et al., 2006). Under such conditions, it is becoming increasingly clear that highly virulent pathogens can impose strong and dynamic selective pressures on host species, and hosts, in turn, can drive the adaptive evolution of pathogen species. However, highly specific associations between a single host species and a highly virulent pathogen represent only a fraction of the diversity encountered under natural conditions. Pathogenic microbes are rarely restricted to a single host. In fact, most infect several closely related species and some can parasitize a wide taxonomic range of hosts (Woolhouse et al., 2001; Parker & Gilbert, 2004; Power & Mitchell, 2004) (Fig. 1). Furthermore, plants typically host a community of pathogens that compete for limited resources (Kniskern et al., 2007; Lopez-Villavicencio et al., 2007). These pathogen communities are not uniformly virulent or harmful; many species/genotypes persist asymptomatically within their hosts (Redman et al., 2001), and recent reports show that even pathogenic species can sometimes have a positive fitness effect on their hosts in particular environmental or genetic contexts (Goss & Bergelson, 2007; Rouhier & Jacquot, 2008; Salvaudon et al., 2008b). Thus, interactions between plants and pathogens are arrayed along continua, varying in the degree of specificity and the net detriment to the host, and embedded within communities of varying complexity (Thrall et al., 2007).

image

Figure 1. (a) The number of plant families containing the described hosts of each of 1252 fungal pathogens; (b) the number of plant species containing the described hosts of 1252 fungal pathogens. Data were obtained as follows: a list of fungal pathogens was first compiled from the Common Names of Plant Diseases (http://www.apsnet.org/online/common/toc.asp) using all 96 host plants (American Phytopathological Society), and then the list of hosts for each fungal species was obtained using the Fungus-Host Distributions Database (Farr & Rossman, 2009; retrieved 27 January 2009). The data were sorted and the number of families and species for each fungal pathogen was calculated using a new Perl script. A long tail in the potential host range is truncated here for greater visibility.

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Until relatively recently, the full range of this complexity has been paid little attention. The purpose of this article is to characterize continua of specificity and detriment in plant–pathogen interactions and to evaluate their influence on the ecology and evolution of these interactions. The continua of specificity and symbiosis are described in Section II. In Section III we review mechanisms that determine variation in host range and pathogen impact. In Section IV we highlight the associated ecological consequences, and in Section V we discuss how these continua influence the evolution of host plant–pathogen interactions. Throughout these sections we highlight directions for future research. Finally, in Section VI we argue that this variation needs to be explicitly acknowledged in order to better understand the evolution and ecology of plant–pathogen interactions.

1. Definitions

For scientists generally working in the field of host–pathogen interactions, there is much inconsistency and confusion regarding the use and definitions of important terms describing the outcome of species interactions. Most confusion surrounds the word ‘virulence’. Here, in line with use of the term in the evolutionary and animal-host parasite literature, and more recently in the plant pathology literature (e.g. Sacristan & Garcia-Arenal, 2008), we define the term ‘virulence’ as the reduction in fitness of a host following infection with a pathogen, which may result from contributions of both host and pathogen (i.e. a consequence of microbial infection, and/or the defence response of the host). We use the term ‘pathogenicity’ to describe the ability of a pathogen to colonize a particular host – a qualitative property synonymous with host range. ‘Resistance’ refers to host traits that prevent or reduce pathogen growth on or in the host. ‘Tolerance’ refers to traits that do not limit or prevent infection, but instead reduce its fitness consequences.

II. Continua in plant host–pathogen interactions

  1. Top of page
  2. Abstract
  3. I. Introduction
  4. II. Continua in plant host–pathogen interactions
  5. III. Mechanisms underlying continua in specificity and virulence
  6. IV. Ecological consequences
  7. V. Evolutionary patterns and processes
  8. VI. Conclusions
  9. Acknowledgements
  10. References

1. The specialist[RIGHTWARDS ARROW]generalist continuum

While many pathogenic microbes are highly specialized as parasites of one species or genus, others attack a broad range of host species and families (Fig. 1). These interactions rarely occur in isolation; rather, plants and microbial pathogens interact within a diverse community of potential partners and competitors (Kniskern et al., 2007), within which individual interactions vary widely in terms of specificity (Jarosz & Davelos, 1995). Specialization may be qualitative, characterized by the complete inability of a pathogen species to infect many hosts, or quantitative, where pathogens have lower performance on certain hosts. Indeed, the occurrence of closely related species, subspecies and host-associated lineages of plant pathogens suggests that evolutionary and ecological divergence is commonly driven by host factors (Eshed & Dinoor, 1980; Shykoff et al., 1999; Douhan et al., 2008; Fournier & Giraud, 2008). An independent means whereby pathogens vary in specialization is the degree to which they depend on hosts at all. Some pathogens are obligately dependent upon living hosts for survival and reproduction while many others survive saprophytically in the soil or on plant debris and are only opportunistically pathogenic.

Characterizations of host specificity should consider how many host species are exploited, and how closely related these host species are to each other (Poulin & Mouillot, 2005). Perhaps some of the best indicators of specialization are the phylogenetic breadth of potential host species (Parker & Gilbert, 2004; Gilbert & Webb, 2007), and patterns of evolutionary diversification within pathogen species complexes. For example, the economically important fungal rust pathogen Puccinia graminis is considered a specialist with a restricted host range. Interestingly, this pathogen has been reported on more than 500 host species across 107 genera, but only two families are represented: Poaceae, the primary host, and Berberidaceae, the alternate host (Farr & Rossman, 2009). This tight association between a single primary and alternate host family suggests a strong and long-standing specialization. Within P. graminis are host-species specific lineages that account for much of the host range variation (Eriksson & Henning, 1896). By contrast, the bacterial plant pathogen Pseudomonas syringae has an extremely wide host range, causing disease on more than 100 plant families spanning eudicot and monocot groups (http://www.ncppb.com). However, the genetically complex P. syringae group has been further subdivided into approximately 50 pathovars that are distinguished based on the ability to cause disease on different hosts, within which the presence or absence of individual genes can determine host compatibility (Hirano & Upper, 1990). Unlike P. graminis, P. syringae pathovars do not follow any obvious co-phylogenetic pattern, suggesting that host specificity is labile and driven largely by intra-specific gene transfer and recombination (Sarkar & Guttman, 2004; Sarkar et al., 2006).

The multi-host potential inherent in both generalist and specialist interactions is well documented in wild populations. For example, Power & Mitchell (2004) showed that a generalist viral pathogen infects multiple co-occurring grasses, Goss et al. (2005) recorded that the generalist bacterial pathogen Pseudomonas viridiflava inhabits multiple unrelated herbs, and Kniskern & Rausher (2006b) observed a specialist fungal pathogen, Coleosporium ipomeoae, commonly infecting up to four co-occurring Ipomoea hosts. Plants, in turn, also harbour many different pathogen species. Triticum aestivum (common bread wheat), for example, has > 60 recognized fungal pathogens alone (Farr & Rossman, 2009). Similarly, in our laboratory's work on wild populations of Arabidopsis thaliana, coinfection of individual plants with multiple bacterial pathogens of varying relatedness is the norm (Kniskern et al., 2007; L. G. Barrett et al., unpublished data). Plants may also harbour multiple competing genotypes of the same species. In two experimental populations of Silene latifolia, 71% of the target plants were infected by multiple genotypes of the anther smut pathogen Microbotryum violaceum (Hood, 2003). As we discuss in the following review, the continuum in specialization and the multi-host nature of plant–pathogen interactions can influence the host response to infection, and pathogen colonization, growth and transmission (Read & Taylor, 2001; Malpica et al., 2006; Seabloom et al., 2009).

2. The parasitic continuum

Interactions between plants and pathogens are often detrimental to the host, and clearly important from both applied and basic perspectives. However, harmful parasitic interactions represent only one extreme of a continuum of life-history interactions that range from commensalism to parasitism depending on environmental conditions and the specific host and pathogen genotypes involved (Goss & Bergelson, 2007; Bradley et al., 2008; Salvaudon et al., 2008b). Considering the full range of these interactions is important to understanding ecological patterns of disease prevalence and evolutionary patterns manifested in traits such as plant resistance, tolerance, and pathogen virulence.

Pathogens vary both qualitatively and quantitatively in the effects they have on hosts. Burdon (1991) coined a classification system that distinguishes pathogens by their qualitative negative effects on hosts (termed ‘killers’, ‘castrators’ and ‘debilitators’) which are useful for highlighting variability in the ecological and evolutionary outcomes of pathogen infection. Interactions between hosts and the ‘killer’ class of pathogen typically result in seed and seedling mortality (e.g. Pythium spp.), although resistance to this class of pathogens does exist (e.g. Tewksbury et al., 2008). Pathogens that rapidly kill their hosts provide some of the best examples of how pathogens can drastically alter plant population dynamics and community structure (e.g. Smith, 2000; Shearer et al., 2007). The ‘castrator’ class of pathogens sterilize their plant hosts, representing a potentially strong selective agent in plant populations. The fungal pathogen M. violaceum is a classic example in that it systemically infects Silene plants and co-opts the flowers for spore transmission via its hawkmoth pollinators (Biere & Antonovics, 1996). Finally, ‘debilitating’ pathogens cause variable reductions in plant growth and fecundity through discrete lesions that individually have small effects on plant fitness (e.g. rusts and bacterial leaf endophytes) or through nonlethal systemic infections (e.g. many viruses). Debilitating pathogens are the most apparent, if not the most abundant, sources of plant disease, and are thus the most well studied class of pathogen.

Within these broader classes of plant–pathogen interactions exists more subtle variation in the effects of pathogens upon plants. Variation in the environment, as well as quantitative resistance and tolerance traits, can result in variable and often surprising outcomes. For example, Salvaudon et al. (2008b) demonstrated that highly fecund accessions of A. thaliana experience deleterious effects when infected with Hyaloperonospora arabidopsis, whereas the least fecund hosts actually produced more seed than uninfected controls. Community complexity can also influence the fitness effects of infection. Bradley et al. (2008) demonstrate that host species experiencing deleterious effects of pathogen infection in monoculture set significantly more seed than uninfected controls when grown in a more natural community context. As we show in Sections IV and V, the variable fitness effects that derive from varying environmental and genetic components of plant–pathogen interactions will govern the tempo and direction of evolutionary change.

III. Mechanisms underlying continua in specificity and virulence

  1. Top of page
  2. Abstract
  3. I. Introduction
  4. II. Continua in plant host–pathogen interactions
  5. III. Mechanisms underlying continua in specificity and virulence
  6. IV. Ecological consequences
  7. V. Evolutionary patterns and processes
  8. VI. Conclusions
  9. Acknowledgements
  10. References

1. Genetic mechanisms

Plants are continually exposed to a vast number of potential pathogens with widely varying life histories, and have consequently evolved a series of intricate mechanisms to control and resist pathogen attack. Elucidating the mechanisms underlying host resistance to microbial pathogens has been a major focus in plant pathology (for comprehensive and recent reviews see Nürnberger & Lipka, 2005; Bent & Mackey, 2007; Ellis et al., 2007; Hückelhoven, 2007; Friesen et al., 2008; Göhre & Robatzek, 2008). Our goal here is not to repeat the recent efforts at reviewing this complex body of work. Rather, we seek to establish a brief synthesis of mechanisms underlying the continua we have outlined above.

Most plant species are resistant to most potential pathogens, a phenomenon termed ‘nonhost resistance’ (Nürnberger & Lipka, 2005). This type of resistance relies on multiple layers that consist of both constitutive barriers and inducible responses (Heath, 2000; Mysore & Ryu, 2004; Nürnberger & Lipka, 2005). Preformed, passive (constitutive) defences are the most general means for resisting pathogen and herbivore attack; these defences generally involve physical barriers, such as waxy cuticles and the presence of trichomes, that inhibit epiphytic colonization and cell wall penetration (Hückelhoven, 2007). If passive defences are overcome, then general defence responses are induced by the presence of conserved microbial signals (termed ‘microbe-associated molecular patterns’ (MAMPs)) such as bacterial flagellin or lipopolysaccharides (Bent & Mackey, 2007). As MAMPs play an essential role in microbial growth, evasion of recognition through the loss or mutation of these elicitors should be very costly (Göhre & Robatzek, 2008). Similar mechanisms may also contribute to defence in compatible host–pathogen interactions, and are referred to as ‘basal resistance responses’.

Successful pathogens are thought to evade or suppress basal resistance through the acquisition of virulence factors. Many pathogens deliver into cells specialized proteins that suppress MAMP perception, signalling, and defence responses in hosts (Grant et al., 2006; Bent & Mackey, 2007; van der Does & Rep, 2007). Other pathogens achieve basic pathogenicity by secreting toxic proteins, metabolites and cell-wall-degrading enzymes that ultimately kill host cells (e.g. Botrytis and Alternaria spp.). These virulence factors are believed to play critical roles in determining the host range and virulence of plant pathogens. One particularly clear example involves the host specific virulence factor for gypsophila (HsvG) and host specific virulence factor for beet (HsvB) type III secreted effector proteins in the bacterial pathogen Pantoea agglomerans which determine the ability of two pathovars to induce galls on Gypsophila paniculata and Beta vulgaris, respectively (Barash & Manulis-Sasson, 2007). Similarly, many fungal pathogens with narrow host ranges produce host-specific toxins that promote disease in a single host species, and often only in specific genotypes of the host that express a susceptibility gene (Friesen et al., 2008). By contrast, generalist pathogens have been found to maintain multiple virulence factors to overcome the resistance mechanisms encountered among host species. The generalist fungal pathogen Botrytis cinerea (with a host range exceeding 100 plant families), for example, produces a broad spectrum of phytotoxic molecules and proteins, but only a subset of these virulence factors are important on any particular host species (Williamson et al., 2007).

Plants have evolved counter resistance mechanisms that target these pathogen virulence factors. The best understood of these mechanisms is broadly referred to as ‘gene-for-gene resistance’ (Flor, 1956), whereby recognition of a pathogen virulence effector by a plant resistance gene product elicits the hypersensitive response (HR) and associated programmed cell death (PCD), an induced response that kills pathogens along with plant cells at the immediate site of infection (Greenberg & Yao, 2004). This form of major gene defence is often associated with qualitative resistance to biotrophic pathogens reliant on living host tissue. In cereals (wheat (Triticum spp.), rice (Oryza sativa), maize (Zea mays) and barley (Hordeum vulgare)), major genes conferring qualitative resistance to fungal and bacterial pathogens have been cloned from 15 loci, which individually confer resistance to seven different biotrophic or hemibiotrophic pathogen species (Ayliffe & Lagudah, 2004). Not surprisingly, pathogens have counter-evolved specific virulence effectors that can suppress PCD, although, in a fantastic example of how specialization can evolve, these effectors have, in turn, become targets for new resistance (R) genes (Abramovitch & Martin, 2005).

The molecular basis of R gene recognition of specific pathogen strains has been a major focus in molecular plant pathology (Ellis et al., 2000). The simplest molecular interpretation of such specificity is that R genes encode receptors for the direct products of the targeted effector termed avirulence (Avr) genes (e.g. flax and flax rust: Dodds et al., 2006). In other cases, resistance proteins indirectly detect effectors through the changes they cause to host proteins. For example, the resistance to Pseudomonas syringae 2 (RPS2) and resistance to Pseudomonas syringae pv maculicola 1 (RPM1) resistance proteins in A. thaliana recognize corresponding Pseudomonas syringae effector gene products by detecting effector-mediated changes induced in the intermediary host protein RPM1 interacting protein 4 (RIN4) (Mackey et al., 2002; Axtell & Staskawicz, 2003; Mackey et al., 2003). In either case, a lack of matching R–effector gene pairs results in disease, meaning that hosts will recognize some, but not all, genotypes of the pathogen.

An alternative strategy for minimizing the impact of pathogens involves genetic tolerance, which can be defined as the ability of a susceptible plant to endure pathogen damage without suffering a loss in yield (i.e. fitness) (Caldwell et al., 1958). More generally, two or more plant genotypes differ in tolerance when similar levels of disease lead to different fitness reductions (Gaunt, 1995). While there are many reports of genetic variation for tolerance to disease (Simms & Triplett, 1994; Kover & Schaal, 2002; Schurch & Roy, 2004), little is known about the mechanisms mediating tolerance to pathogen infection. What is clear from past studies is that tolerance, in contrast to resistance, is exclusively polygenic in inheritance, and is likely to be influenced by a variety of traits.

2. Environmental influences

The probability of infection and the magnitude of the associated fitness effects vary as a consequence of environmental influences. Environmental heterogeneity influences both the pathogen, through changes in growth, transmission and survival (Parker & Gilbert, 2004), and the host, through changes in the effectiveness of host resistance and tolerance mechanisms (Islam et al., 1989; Lively, 2006; Springer et al., 2007). Indeed, the influence of environmental heterogeneity on plant–pathogen interactions is a central tenet in plant pathology (Fig. 2). Two of the most relevant environmental variables in this regard are moisture and temperature (Burdon, 1987). Many fungal, oomycete and bacterial pathogens experience enhanced growth and transmission in cool and humid conditions (Schnathorst, 1965; Duniway, 1979), whereas the growth of other pathogens such as powdery mildews can be inhibited by free moisture (Schnathorst, 1965). High humidity also favours pathogen infection, in part by inhibiting the closure of stomata and thus providing easy entry into the leaf (Melotto et al., 2008). Similarly, temperature has been shown to impact the probability and strength of pathogen infection (Schnathorst, 1965; Colhoun, 1973; Laine, 2008). These two environmental variables are so critical that they are thought to determine the geographic distribution of many plant pathogens (Colhoun, 1973; Burdon, 1987).

image

Figure 2. Hypothetical, quantitative disease triangle in plant–pathogen interactions. The area within the dashed lines reflects potential parameter space in a hypothetical interaction. Solid lines (inner triangle) delineate the realized limits under which disease will develop, with the shading representing relative intensity resulting from interacting host, pathogen and environmental parameters. In this example the intensity of disease (i.e. pathogen growth) is high because of favourable environmental conditions and high pathogen growth and is limited only by the absence of completely susceptible hosts.

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Host plant resistance and tolerance are also sensitive to environmental variation. Indeed, even when under strong gene-for-gene control, variable temperature conditions can alter the outcome of plant–pathogen interactions. For example, the wheat resistance gene Yr36 confers temperature-dependent resistance to the fungal pathogen Puccinia striiformis; plants with Yr36 are resistant to rust infection at relatively high temperatures (25–35°C) but susceptible at lower temperatures (15°C) (Uauy et al., 2005). Edaphic factors may also influence outcomes of plant–pathogen interactions. In glasshouse experiments, Springer et al. (2007) demonstrated a negative relationship between infection rates and soil calcium concentrations in interactions between Hesperolinon californicum and Melampsora lini. Environmental variation has also been shown to mediate plant tolerance to disease (Paul et al., 1990); Ipomoea purpurea plants in poor-quality microenvironments suffer greater fitness reductions from infection by Coleosporium ipomoeae than plants in high-quality microenvironments, even though plants actually experience more damage in high-quality microenvironments (Kniskern & Rausher, 2006a). Alternatively, Thrall & Jarosz (1994) showed that mortality of Silene alba induced by the pathogen Microbotryum violceum is more severe in mild winters than in harsh winters because when absolute mortality is low (mild winters), pathogen-induced mortality can exceed the higher background mortality observed in harsh conditions. Finally, variation in the biotic environment can further influence outcomes of infection: susceptible A. thaliana lines infected with P. syringae produced 40% fewer seeds in the presence of intra-specific competition, but 10% more seeds than uninfected controls in the absence of competition (Korves & Bergelson, 2004).

3. Coinfection

Coinfection with multiple pathogens can have important consequences for the outcomes of infection, as a result of changes in both host and pathogen responses (Read & Taylor, 2001; Malpica et al., 2006; Seabloom et al., 2009). The simplest way that this can occur is through direct interactions among microbes within the plant. For example, many bacterial Pseudomonads produce antifungal compounds such as 2,4-diacetylphloroglucinol and other antibiotics (Walsh et al., 2001) that negatively impact competing pathogenic and nonpathogenic species. Many such compounds are also phytotoxic, and consequently have negative effects on the host plant (Maurhofer et al., 1992). Conversely, some nonpathogenic micro-organisms can suppress the growth of disease-causing micro-organisms (e.g. Redman et al., 2001; Compant et al., 2005). This impact can be so profound that plants play an active role in promoting the growth of disease-suppressive microbes; for example, in a pioneering study, Smith et al. (1999) demonstrated quantitative genetic variation in tomato (Solanum lycopersicum) governing growth of the disease-suppressing bacterium Bacillus cereus.

More generally, plant-associated microbes impact each other through modification of the host environment. One way this occurs is through the induction of systemic defence responses. Plant defence is largely regulated by three signalling pathways (salicylic acid (SA), jasmonic acid (JA) and ethylene (ET)) that interact together in a complex signalling network. Upon pathogen attack, these signals accumulate and activate defence gene expression. Generally, SA-dependent defence provides resistance against biotrophic pathogens whereas JA/ET-dependent defences act again necrotrophic pathogens and insect herbivores (Glazebrook, 2005). Induction of either of these pathways can have subsequent effects on pathogens that are negatively impacted by the same defence response. For example, HR activation by a nonpathogenic strain of P. syringae inhibits the growth of a pathogenic strain of this species (Govrin & Levine, 2000). More generally, systemic acquired resistance (SAR) can be induced by and offers long-lasting protection against a broad range of pathogens including viruses, bacteria, fungi, oomycetes and even insects (Sticher et al., 1997). Similarly, root-associated bacteria such as Pseudomonas fluorescens activate induced systemic resistance (ISR), which offers protection against a wide variety of microbes and insects (Van Oosten et al., 2008; Van Wees et al., 2008). Conversely, infection by some pathogens may decrease resistance to subsequent infection, a phenomenon called ‘systemic induced susceptibility or facilitation’ (e.g. Cui et al., 2005).

While the SA and JA pathways can be induced separately, there is also negative regulation, or cross-talk, between them (Kunkel & Brooks, 2002). This raises the possibility that infection by one pathogen may enhance growth of a second, as a result of down-regulation of the alternative pathway. The fungal necrotroph Botrytis cinerea grows better in plants infected by nonpathogenic P. syringae (Govrin & Levine, 2000) and conidia production by the facultative saprophyte Pyrenophora tritici-repentis is increased in wheat plants that are pre-infected with the biotroph Puccinia triticina (Al-Naimi et al., 2005). Similarly, Spoel et al. (2007) showed that plants inoculated with the biotroph P. syringae are more susceptible to the necrotroph Alternaria brassicicola.

Induction of these defence-related pathways can broadly impact entire plant-associated microbial communities. Kniskern et al. (2007) showed that the JA and SA pathways influence bacterial community composition in A. thaliana. The diversity of epiphytes (residing on the surface of leaves) is increased on JA-deficient plants, whereas plants treated with SA harbour a reduced diversity of endophytes (residing in the leaf). SA-induced defences reduce the abundance of the most common members of the community, demonstrating that SA induction confers broad-spectrum bacterial resistance (Kniskern et al., 2007; Traw et al., 2007). As a consequence of this broad-spectrum resistance, host plant fitness was shown to increase (Traw et al., 2007). While the growth of P. syringae was strongly suppressed by SA-mediated defences in growth chamber experiments utilizing a single pathogen species, P. syringae was not affected by SA treatment in the above field experiments (Uknes et al., 1992; Lawton et al., 1996; Traw et al., 2003). This difference between field and growth chamber experiments may reflect an important influence of the environment, and/or interactions among the many different species of bacteria inhabiting the leaves of A. thaliana under field conditions, on the interaction between host defence and pathogen growth.

Although beyond the scope of this review, it is important to note that the co-occurrence of microbes is also important for the evolution of pathogenicity and virulence. Competition for resources among different pathogenic strains may favour the faster growing strain, leading to an increase in the virulence of the pathogenic community, with associated negative consequences for the host (Read, 1994; Read & Taylor, 2001). In addition, the evolution of resistance to one natural enemy can be influenced by genetic correlations in resistance to a second enemy (Iwao & Rausher, 1997). Finally, gene exchange among even distantly related pathogenic species within hosts is probably an important (albeit infrequent) driver of genetic change and host range variation in pathogenic microbes. For example, within-species recombination is likely to have played an important role in the evolution of specialized strains of bean yellow mosaic virus adapted to infect a range of plant species (Wylie & Jones, 2009), while Friesen et al. (2006) demonstrated that gene transfer among different fungal species led to the emergence of a new disease of wheat.

IV. Ecological consequences

  1. Top of page
  2. Abstract
  3. I. Introduction
  4. II. Continua in plant host–pathogen interactions
  5. III. Mechanisms underlying continua in specificity and virulence
  6. IV. Ecological consequences
  7. V. Evolutionary patterns and processes
  8. VI. Conclusions
  9. Acknowledgements
  10. References

Understanding in what manner, to what degree and under what conditions pathogenic microbes harm their hosts is critical to developing an understanding of disease threats to biodiversity, and the role of pathogens as agents of selection in natural populations. Host specificity, for example, is an important predictor of the capacity of an organism to emerge as a new agent of infectious disease, or to switch hosts (Cleaveland et al., 2000; Dobson & Foufopoulos, 2001; Parker & Gilbert, 2004). In biological invasions, pathogenic organisms are more likely to spread into new areas if they are able to persist as saprophytes, or infect a phylogenetically broad range of hosts (Parker & Gilbert, 2004). Similarly, the sensitivity of pathogen virulence to environmental variation can condition the effectiveness of pathogens as biocontrol agents (Gomez et al., 2008).

1. Disease dynamics and epidemiology

The epidemiology of plant pathogens, and thus levels of disease within populations, are strongly influenced by variation in virulence and host range. Pathogen growth, virulence and transmission rates are typically assumed to be positively correlated – by extracting more resources from the host, the pathogen grows and makes more transmissible propagules per unit of time (Frank, 1996). Relationships between virulence and transmission have been shown to be positively correlated in some animal host–pathogen systems; laboratory data for the rodent malaria model Plasmodium chabaudi and field data on the human malaria parasite P. falciparum show strong positive correlations between asexual multiplication and transmission rate (Mackinnon & Read, 2004).

Rates of encounter between host and pathogen are also inevitably influenced by changes in the number of available hosts and the distance and probability of transmission between susceptible hosts (Woolhouse et al., 2001). At the community level, one likely outcome of increasing host specificity is a decline in the density of susceptible hosts. For directly transmitted pathogens, such a decline is predicted to be associated with a decrease in rates of transmission, epidemic development, and overall levels of disease (Leonard, 1969; Burdon & Chilvers, 1982; Dwyer et al., 1997; Garrett & Mundt, 1999; Gilbert, 2002).

Indirect evidence supporting a role of host range in influencing plant disease dynamics has been found in numerous tests with highly specialized pathogens and varietal mixtures of crop plants that differ in resistance specificities (Mundt, 2002). There is also some evidence that host range influences disease dynamics in more natural settings. In a survey of disease incidence and prevalence in natural populations of the wild flax species Linum marginale in Australia, Thrall & Burdon (2000) found that disease prevalence in populations decreases with increasing resistance diversity and decreasing host susceptibility. Similarly, in two field experiments with perennial grassland species in the Midwestern USA, Mitchell et al. (2002, 2003) reported that the percentage of leaf area infected by specialized foliar fungal pathogens was nearly three times higher in monocultures than in communities comprising 16–24 host species. Thus, given variability in population genetic diversity, species diversity and spatial structure, host specificity helps determine the abundance and distribution of particular pathogens (Garrett & Mundt, 1999; Power & Mitchell, 2004). Nevertheless, there remains a lack of direct experimental evidence comparing the performance and epidemiology of pathogens that explicitly vary in their host range, whether in a race-specific or species-level manner.

2. Host community structure

Plant pathogens can be a powerful force acting to structure natural communities (Jarosz & Davelos, 1995), facilitating or preventing species coexistence depending on the nature of the interaction. In particular, it has been proposed that pathogen host range and virulence are important variables influencing the diversity and structure of plant communities and populations. At the community level, the Janzen–Connell hypothesis (Janzen, 1970; Connell, 1971) suggests that highly virulent host-specific pathogens can maintain species coexistence and promote increasing plant diversity. A greater incidence of seed and seedling mortality occurs near the parent host because host-specific pathogens respond to high densities of seedlings in the neighbourhood of the parent. Consequently, probabilities of recruitment increase with increasing distance from the parent plant. There is growing evidence that host-specific pathogens can influence the structure and diversity of plant communities in predictable ways (Packer & Clay, 2000; Bell et al., 2006; Grewell, 2008; Seiwa et al., 2008), although it is not generally clear whether pathogens have the requisite specificity to drive Janzen–Connell type dynamics (Gilbert, 2005). Indeed, some of the clearest examples of pathogens causing very high host mortality, and thus broadly influencing community structure, can be found among more generalist root pathogens capable of infecting multiple species (Jarosz & Davelos, 1995).

Infection with less virulent pathogens may also alter the outcome of competitive interactions among plant species in more subtle ways, conferring advantage to both host and nonhost species depending on the context (Gibson & Watkinson, 1991; Fenton & Brockhurst, 2008). In plant communities, potential host species almost invariably vary in key traits such as prevalence, resistance and tolerance (Power & Mitchell, 2004), and this variation can be important even to generalist pathogens. Such heterogeneities will generate asymmetries in disease prevalence, leading to variation in transmission rates and fitness impacts on hosts (Jarosz & Davelos, 1995). Furthermore, variation in response to shared pathogens can influence the outcomes of competitive interactions among co-occurring host species in either of two ways (Hatcher et al., 2006). Direct interactions between species that are influenced by a shared pathogen are referred to as ‘parasite-mediated competition’. If the pathogen has differential effects on the fitness of competing species, relative competitive strengths and competitive outcomes can be altered (Gibson & Watkinson, 1991; Hudson & Greenman, 1998). ‘Apparent competition’ refers to a situation where co-occurring species do not compete directly for resources, but share a pathogen that differentially impacts host fitness (Holt, 1977). Unlike the Janzen–Connell model, such parasite-mediated or apparent competition can lead to either coexistence or species exclusion depending on the host species affected and their dynamics before infection (Hatcher et al., 2006).

Plant community disease dynamics and competition mediated by pathogens have been considered explicitly by Power & Mitchell (2004), who revealed that the prevalence of a generalist virus in experimental plant communities is strongly influenced by the presence of a highly susceptible host species (Avena fatua). Communities containing A. fatua were over 10 times more heavily infected than communities lacking this species. Interestingly, high virus prevalence resulted not only from the high rate of infections in the population of A. fatua itself, but also from an associated increase in virus prevalence in more resistant host species, a phenomenon termed ‘pathogen spillover’. Moreover, pathogen spillover from A. fatua resulted in negative effects on the aboveground biomass of two out of three species in the experimental community, seemingly as a result of either parasite-mediated or apparent competition. Thus, by initiating positive feedback on its abundance, a generalist pathogen influenced competitive dynamics so as to maintain A. fatua as a dominant species in the community, despite being the most heavily infected. In a related fashion, but under more natural conditions, Malmstrom et al. (2005b) showed that the presence of invasive A. fatua in native grassland communities in California more than doubled the incidence of virus prevalence in native, perennial bunchgrass species. Furthermore, viral infection results in strong declines in fitness and competitive ability in several native species, suggesting that virus-mediated competitive dynamics can contribute to shifts in plant community composition and promote the invasion of exotic species (Malmstrom et al., 2005a; Malmstrom et al., 2006).

V. Evolutionary patterns and processes

  1. Top of page
  2. Abstract
  3. I. Introduction
  4. II. Continua in plant host–pathogen interactions
  5. III. Mechanisms underlying continua in specificity and virulence
  6. IV. Ecological consequences
  7. V. Evolutionary patterns and processes
  8. VI. Conclusions
  9. Acknowledgements
  10. References

1. The evolution of host range

The mechanisms that drive variation in specificity are elements of broader processes driving the evolution of specialization and the maintenance of biological diversity (Falconer, 1952; Levins, 1968; Price, 1980; Jaenike, 1990). In the absence of evolutionary constraints, specialization acts only to reduce the number of potential species with which both hosts and symbionts can successfully interact; a seeming disadvantage to the pathogen. However, specialization is a common and highly successful strategy (Fig. 1), suggesting that generalist strategies must be selected against under some ecological conditions. From the pathogen perspective, biotic heterogeneity, competition arising from multiple infection, disruptive selection and genetic trade-offs in adaptation to different hosts are all likely to be important in driving the emergence and maintenance of specialized lineages (Levins, 1968; Jaenike, 1990; Kawecki, 1998; Woolhouse et al., 2001) (Table 1).

Table 1.  Ecological, evolutionary and mechanistic factors that have the potential to influence the evolution of host range
 SpecialistGeneralist
EcologicalHost monocultures, high population/community relatednessHost community complexity, low population/community relatedness
High host density, large populations, wide rangeEphemeral, rare or over-dispersed hosts
High transmissionLow transmission
Multiple infection, competitionLittle direct competition for host resources
GeneticNegative correlations between performances on different hostsLack of constraint, positive correlation between performances on different hosts
CoevolutionStrong effect of environment on performance
MechanisticObligate parasitismOpportunistic

Biotic heterogeneity (i.e. variation in community composition, diversity and spatial structure) is probably a key ecological driver of pathogen host range evolution (Thrall et al., 2007). As we have discussed above, epidemiological models and experimental work have demonstrated that host plant diversity plays an important role in the dynamics of infectious disease (e.g. the importance of pathogen spillover), but the consequences of such biotic complexity for the evolution of specialization within plant host–pathogen interactions remain largely unknown (Gandon, 2004; Thrall et al., 2007). Jaenike (1990) suggests that wide host ranges may be expected to evolve in pathogens when suitable hosts are rare, highly dispersed, or ephemeral. By contrast, specialists should be favoured when host diversity is restricted and hosts are abundant. General support for these predictions can be found in analysis of data from fish–metazoan and mammal–flea host–parasite interaction networks, which suggest that increased host abundance correlates directly with the proportion of specialist parasites found infecting that host (Vazquez et al., 2005). More recently, Thrall et al. (2007) developed a series of expectations for the evolution of specificity, based on the outcome (i.e. parasitism vs mutualism) of symbiotic interactions, evolutionary constraints and environmental quality. However, to our knowledge, no empirical studies have explicitly addressed the broader role of host community structure in driving host range evolution (or other traits such as transmission and virulence) in plant–pathogen interactions.

The evolution of pathogen specialization obviously depends in part on whether traits required to infect alternative hosts are positively or negatively correlated. One of the central hypotheses concerning the evolution of host range is that genetic trade-offs in adaptation to different hosts can drive the emergence of specialization (Falconer, 1952; Jaenike, 1990; Kawecki, 1998). Such trade-offs in components of fitness are typically thought to be driven by antagonistic pleiotropy – one or more genes that improve performance in one host or habitat but impair performance in another (Futuyma & Moreno, 1988; Jaenike, 1990). Theory predicts that, in the absence of genetic constraints, a generalist lineage should evolve to be as well adapted to each habitat as respective specialist lineages. Thus, in order for specialists to be favoured, the evolution of generalization necessarily entails a cost (Jaenike, 1990; Fry, 1996; Kawecki, 1998; Palaima, 2007).

While the generality of the trade-off hypothesis has been widely questioned (e.g. Kassen & Bell, 1998; Kawecki, 1998; Krasnov et al., 2004), empirical studies suggest that these mechanisms can be important for the evolution of specialization in plant–pathogen interactions. Agudelo-Romero et al. (2008) evolved replicate populations of the plant pathogen tobacco etch potyvirus, allowing viral populations to evolve for 15 generations on both a novel host and the native host species. In agreement with a pleiotropic cost of host specialization, lineages that evolved increases in virulence and growth in the novel host suffered negative effects for both these traits on the original host species. Similarly, Wallis et al. (2007) demonstrated that serial passaging of the plum pox potyvirus increased infectivity, growth rate and virulence in a novel host (Pisum sativum) with a concomitant reduction in transmission efficiency on the original peach (Prunus persica) host. Even so, there remains a lack of direct evidence that pleiotropic costs are responsible for these patterns.

Antagonistic pleiotropy is not the only genetic mechanism that can influence the evolution of host range. Disruptive selection, where different trait values are favoured on different hosts, combined with host–pathogen coevolution, can promote the formation of specialist lineages via assortative mating, even in the absence of trade-offs (Kawecki, 1998; Duffy et al., 2007). If hosts evolve new resistance following exposure to a pathogen, the impact of this host-induced selection will be weaker for a generalist than a specialist lineage, because only a subset of a generalist lineage is exposed to any given host, and gene flow will consistently counteract selection-driven changes in gene frequency (Fry, 1996; Whitlock, 1996; Kawecki, 1998). For similar reasons, specialization may also be promoted as a result of assortative mating followed by the accumulation of mutations and/or linkage between genes that reduce fitness in interactions with some hosts, but have little effect in others (Kawecki, 1994; Bergelson & Purrington, 1996; Fournier & Giraud, 2008), particularly for hosts that are encountered infrequently or irregularly.

From the host perspective, trade-offs in resistance to different pathogen species may also limit the generality of defence mechanisms. Loiseau et al. (2008) found for the bird species Passer domesticus (the common European house sparrow) that a single major histocompatibility complex (Mhc) class I allelic variant was associated with a 2.5-fold increase in susceptibility to a pathogenic Plasmodium strain, but with a 6.4-fold reduction in susceptibility to a Haemoproteus strain. Similar trade-offs in susceptibility and resistance may drive population-level maintenance of variants of the LOV1 gene in A. thaliana, which confers susceptibility to the fungal pathogen Cochliobolus victoriae (Sweat et al., 2008). Interestingly, loss-of-function mutations in this member of the nucleotide-binding-site leucine-rich-repeat (NBS-LRR) resistance gene family confer resistance to C. victoriae (Lorang et al., 2007), suggesting that the susceptible variant must perform some other important function, probably associated with resistance to an unidentified pathogen species (Sweat et al., 2008). Indeed, many R gene loci segregate for multiple allelic variants providing alternative specificities and susceptibilities (Ellis et al., 2000), limiting the capacity of a single host genotype to resist attack from multiple pathogen strains. This constraint to the evolution of plant defence could be removed if diversification for the same set of specificities had instead occurred at the level of the gene family, because different resistance specificities could be carried simultaneously, providing a greater range of resistance. However, fitness costs associated with carrying multiple genes or specificities simultaneously are one possible explanation for this apparent limitation (Bergelson & Purrington, 1996). Tian et al. (2003) demonstrated a 9% reduction in the fitness of A. thaliana plants carrying the R gene RPM1 relative to isolines lacking RPM1. Other context-dependent constraints may also limit the diversification of R gene families and the evolution of multiple resistance specificities. As one example, Bomblies et al. (2007) demonstrated a negative epistatic interaction in A. thaliana between an allele of an NBS-LRR disease resistance gene homologue and a specific allele at a second locus, which when combined prevent viable hybrids from forming.

2. Specialization and patterns of genetic variation within species

The influence of host–pathogen interactions on the maintenance of genetic variation within (Hamilton, 1980; Clay & Kover, 1996) and among (Lively, 1989; Gandon et al., 1996) interacting populations is well documented (for recent and comprehensive reviews see Greischar & Koskella, 2007; Sacristan & Garcia-Arenal, 2008; Salvaudon et al., 2008a). Here, we limit our discussion to some recent advances in understanding how variation in host range can interact with mechanisms of resistance to influence patterns of genetic variation within species.

Hosts and pathogens interact within spatially variable environments. Even at relatively local scales, these can vary from single, largely homogeneous populations, to fragmented, spatially structured metapopulations. Within this framework, variation in different micro-evolutionary forces among demes may act to promote diversity and population divergence in different host and pathogen traits (e.g. Parker, 1985; Jarosz & Burdon, 1991; Laine, 2004; Thrall et al., 2005; Barrett et al., 2007). Local adaptation of pathogens to their hosts has been demonstrated as a strong driver of genetic structure in a number of host–pathogen interactions (see Greischar & Koskella, 2007 for review), and complex interactions between various host and pathogen traits in different populations may also arise. For example, in the interaction between the wild host plant Linum marginale and its rust pathogen, Melampsora lini, Thrall & Burdon (2003) demonstrated an intraspecific trade-off between host range and the mean number of infective spores produced by the pathogen, such that strains infecting a wider range of hosts are generally less fecund. Importantly, at the metapopulation scale, these patterns are implicated in maintaining diversity in both host range and virulence among local pathogen populations; selection favours pathogens that have a wide host range in resistant host populations, and more fecund, narrow host range pathogens in susceptible host populations. It is also likely that nonselective factors, such as random genetic drift and selection on linked traits, will influence host and pathogen evolution and drive genetic divergence among demes (Parker, 1991; Salathéet al., 2005; Barrett et al., 2008). Within this framework, rates of genetic recombination (Barrett et al., 2008), and the degree to which pathogen dispersal occurs at local scales relative to the metapopulation as a whole (Thrall & Burdon, 1999; Greischar & Koskella, 2007), will further influence how variation is maintained and distributed within the metapopulation.

The potential importance of coevolution and negative frequency-dependent selection in maintaining genetic diversity within host–pathogen interactions is well recognized (Hamilton, 1980; Clay & Kover, 1996). Theory underlying such dynamics is based upon assumptions of high levels of fidelity and genotype specificity within the interaction. However, multiple recognition specificities have been demonstrated in several gene-for-gene interactions. For example, dual recognition specificity has been shown for the Pseudomonas syringae pv. tomato (Pto) R gene in tomato (Martin et al., 1993), which independently recognizes two P. syringae effector proteins with little amino acid similarity, AvrPto (Ronald et al., 1992) and AvrPtoB (Kim et al., 2002). Similarly, convergent evolution underlies shared R gene specificities in A. thaliana (RPM1) and Glycine max (resistance to Pseudomonas syringae pv. glycinea (Rpg1-b)) to the type III effector protein AvrB from P. syringae (Ashfield et al., 2004). The role that more complex interactions such as these might play in the generation and maintenance of genetic variation within demes is currently unclear.

For more generalist pathogens, the generation and maintenance of adaptively significant genetic variation at local scales may be driven by adaptation among rather than within host species (e.g. Fournier & Giraud, 2008). Variation in pathogen specificity and virulence can further influence the development and maintenance of patterns of genetic diversity among pathogen demes. For example, Lajeunesse & Forbes (2002), using a meta-analysis approach, demonstrated that pathogens with narrow host ranges are more likely to be locally adapted to their hosts. Similarly, using a theoretical approach, Gandon (2002) demonstrated that both increasing specificity and virulence lead to greater local adaptation in pathogen species.

Mechanistic variation underlying resistance specificities may also influence intra-species patterns of genetic variation in both host and pathogen. As discussed in Section II, R genes sometimes interact directly with effectors, but in other cases interact with an intermediary host protein that is modified by the effector. Differences in the mode of recognition (direct or indirect) have been suggested to lead to qualitatively different outcomes in the diversity and specificity of host–pathogen interactions (Van der Hoorn et al., 2002). Specifically, in direct interactions, continual changes in pathogen Avr genes may be matched by changes in host R genes with a resultant increase in diversity and specificity of R and Avr allelic series (e.g. flax and flax rust: Dodds et al., 2006; Ellis et al., 2007). By contrast, indirect recognition appears to be often associated with simple, presence/absence polymorphisms for host resistance/susceptibility alleles and pathogen virulence/avirulence alleles that are maintained for long periods of time by balancing selection, as observed for the RPM1 and RPS5 loci in A. thaliana (Stahl et al., 1999; Tian et al., 2002).

3. The evolution of pathogen virulence

Complex interrelationships can develop between host and pathogen fitness, influenced by the effects that a pathogen has on its host and the specialization of the pathogen in question. Pathogen infection is often assumed to reduce host fitness as an inevitable consequence of pathogen growth within the host (Frank, 1996). Although growth is a critical element of pathogen fitness, increased host mortality resulting from pathogen growth will negatively impact the longevity and productivity of the pathogen's resource base, and thus influence the potential for persistence within populations and opportunities for among host transmission (Read, 1994). It is perhaps not surprising, then, that relationships between host and parasite fitness can change within interactions, depending on the identity of the partners (Salvaudon et al., 2005), and that general support for a positive relationship between virulence and pathogen growth in plant–pathogen systems is equivocal (Sacristan & Garcia-Arenal, 2008). Clearly, natural selection should strike a balance between the costs and benefits of harming hosts, and pathogens should be expected to vary in their impact on the host, depending on the context (Lenski & May, 1994).

Expectations for the evolution of pathogen virulence are dependent in part on the qualitative (e.g. killer, castrator or debilitator) and quantitative effects that a pathogen has on its host. Theory predicts that castrating, florally transmitted pathogens should be under strong selection to completely sterilize their hosts. By forcing hosts to invest all resources into vegetative growth rather than seed production, host resources are funnelled into clonal reproduction and flower production, thereby promoting pathogen transmission (Sloan et al., 2008). While the associated fitness effects of completely preventing seed production can be very high, this need not always be the case. The castrating fungal pathogen Epichloe glyceriae prompts its grass host Glyceria striata to invest more resources into clonal growth than uninfected plants. This promotes vertical transmission of the pathogen to new clones, but paradoxically increases host fitness under some ecological conditions (Pan & Clay, 2002; Pan & Clay, 2003).

The evolution of virulence can also depend on host range. Theory based on tightly coupled, obligate pathogens and their hosts suggests that natural selection will favour the maintenance of high levels of virulence whenever increased virulence is positively associated with increased growth and among-host transmission (May & Anderson, 1983; Frank, 1996). A generalist strategy affords the parasite with more opportunities for transmission and persistence, so that, in theory, decreasing specificity can be associated with increasing virulence (Kirchner & Roy, 2002). The same should be true for opportunistic pathogens and those with long-lived resting or dormant propagules (e.g. Bacillus anthracis), where survival for long periods in the absence of a host should enhance chances of transmission and permit high levels of virulence to evolve (Gandon, 1998; Caraco & Wang, 2008).

4. Environmentally mediated evolution of plant–pathogen interactions

The physical environments within which plants and pathogens interact are highly variable across space and time (Burdon, 1987). As we describe in Section III, environmental variation mediates the amount of pathogen growth, and the fitness effect of pathogen damage. Both phenomena can, in theory, influence the evolution of plant resistance and/or tolerance to microbial pathogens. First, environmental variation may alter the strength or direction of pathogen-mediated selection on plant resistance or tolerance (Burdon & Thrall, 1999). Secondly, environmental variation can reduce the proportion of phenotypic variation in resistance or tolerance that is genetic in nature (Levins, 1968; Reboud & Bell, 1997). One likely consequence of such spatial and temporal heterogeneity is that patterns and rates of evolutionary change will be highly variable, contributing to what Thompson (1994) has coined ‘the geographic mosaic of coevolution’. While important differences are typically envisioned to be most pronounced across large geographic regions (e.g. Barrett et al., 2008), micro-environmental differences can influence the dynamics and evolutionary outcomes of plant–pathogen interactions even at very small spatial scales (Laine, 2006).

Appreciation of such heterogeneity will be critical to developing a deeper understanding of important ecological and evolutionary phenomena including the stable coexistence of highly deleterious pathogens and their plant hosts, and the maintenance of genetic variation in plant resistance and related pathogen traits. Consider, for example, the killer class of pathogens, which are thought to be more strongly influenced by environmental variation than others (Jarosz & Davelos, 1995). While these pathogens can generate strong selection on plant resistance or tolerance, they also tend to have large host ranges and often require very specific environmental conditions to cause plant mortality (e.g. high host density, shade and humidity). Our understanding of how these factors trade off is extremely limited. However, it seems likely that, in a system such as this, local patterns of selection will depend on host plant diversity and the environment, and thus be highly heterogeneous.

The variable impact that infection has on host fitness means that it is important to pay attention not only to disease, but also to host fitness. Here we propose to extend the concept of the disease triangle to include a related plant fitness triangle (Figs 2, 3). From a fitness perspective, the disease triangle describes pathogen fitness, particularly when disease is quantified through some measure of pathogen growth or fecundity. The plant fitness triangle is not simply a mirror of the disease triangle (Fig. 2), and should be considered when the environment fundamentally alters the relationship between plant and pathogen fitness. In the plant fitness triangle, we consider the same three phenomena that influence the disease triangle: host genetic variation in resistance, pathogen genetic variation in growth, and environmental variation.

image

Figure 3. Corresponding, hypothetical plant disease (a) and fitness (b) triangles in plant–pathogen interactions. In (a), the dashed lines represent the potential parameter space where a pathogen could grow. Solid lines (inner triangle) delineate realized limits under which disease will develop given limited water availability (in this example, 50% of maximum water availability is the limiting parameter). Shading represents the relative intensity of disease resulting from interacting host, pathogen and environmental parameters. In (b), the dashed lines represent the potential parameter space within which a pathogen could reduce plant fitness. Solid lines again reflect realized limits given limited water availability. Shading represents the relative reduction in plant fitness resulting from interacting host, pathogen and environmental parameters. In this example, fitness effects and pathogen growth have become partially uncoupled, so that pathogen growth is reduced in environments with less water, but fitness effects of infection are more severe as a result of the direct effects of water limitations on the plant, interacting with water loss caused by infection.

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Consider the following hypothetical disease plant fitness triangles. In an environment that is not conducive to growth, only the most well-adapted pathogen genotypes will grow, and only within the most susceptible plant genotypes. The disease triangle – the area of which describes the quantity of disease that develops – will be small relative to its potential (Fig. 3a). In an environment highly conducive to pathogen growth, pathogen genotypes will be able to infect all but the most resistant host genotypes, and the area of the triangle will be large relative to its maximum (Fig. 2). Now, consider a pathogen whose growth depends on an environment with abundant moisture, and whose negative fitness effects derive from rupturing the leaf surface and increasing water loss in the plant host. In a low-rainfall environment, pathogen growth is low, but even moderate growth reduces host fitness because water is scarce. When rain is plentiful, there may be more pathogen growth, but plant fitness is not impacted as strongly on a per-unit-damage basis. Thus, in this hypothetical example, the plant fitness triangle has become decoupled from the disease triangle, because in high-water environments, the interaction is inherently less pathogenic (Fig. 3).

In summary, we expect that environmentally induced variation in disease and the fitness effects of disease will create heterogeneities in the rate of evolution within and among natural populations. These heterogeneities will affect the evolution of resistance traits that reduce damage as well as tolerance traits that reduce the fitness effects of damage. However, it is not clear how environmentally induced variation is partitioned within and among populations, and whether it will tend to primarily slow plant–pathogen evolution within populations or make evolution more heterogeneous among populations. Given the widely recognized importance of the environment in plant disease, the potential influence of environmental variation on rates of evolutionary change in different plant–pathogen interactions is probably understudied.

VI. Conclusions

  1. Top of page
  2. Abstract
  3. I. Introduction
  4. II. Continua in plant host–pathogen interactions
  5. III. Mechanisms underlying continua in specificity and virulence
  6. IV. Ecological consequences
  7. V. Evolutionary patterns and processes
  8. VI. Conclusions
  9. Acknowledgements
  10. References

We argue that host range and traits associated with the fitness consequences of pathogen infection vary along continua, making broad ecological and evolutionary predictions difficult. Indeed, full consideration of these continua would require a new conceptual framework; models based on highly virulent, closely coupled interactions capture only a fraction of the diversity of host–pathogen interactions encountered in nature, yet models in which pathogens are only loosely associated with hosts are almost nonexistent. While individual elements of a framework capable of describing plant–pathogen interactions exist, these need to be integrated with information regarding the specificity and fitness outcomes of the full gamut of host and pathogens, especially as they occur in a natural community context. Further complicating these relationships is the pervasive role that the environment plays mediating plant–pathogen interactions, the complexity of microbial species assemblages and interrelationships among the plant species that serve as hosts for these microbes. It is essential to embrace this complexity as it is the driving force of ecological and evolutionary dynamics and provides a means of understanding differences among systems. We hope that the review highlights that ecological and evolutionary theory can both set directions for future experimental work across molecular and population scales, and be informed by empirical research at these scales. Determining what ecological factors and mechanistic constraints influence the direction of evolution will remain a fundamental area for research into plant host–pathogen interactions.

Acknowledgements

  1. Top of page
  2. Abstract
  3. I. Introduction
  4. II. Continua in plant host–pathogen interactions
  5. III. Mechanisms underlying continua in specificity and virulence
  6. IV. Ecological consequences
  7. V. Evolutionary patterns and processes
  8. VI. Conclusions
  9. Acknowledgements
  10. References

This research was supported by grants from the National Institute of Health and National Science Foundation to JB, Dropkin Foundation Postdoctoral Fellowships to LGB and JMK, a Swiss National Science Foundation Postdoctoral Fellowship to NB and a China Scholarship Council Fellowship to WZ.

References

  1. Top of page
  2. Abstract
  3. I. Introduction
  4. II. Continua in plant host–pathogen interactions
  5. III. Mechanisms underlying continua in specificity and virulence
  6. IV. Ecological consequences
  7. V. Evolutionary patterns and processes
  8. VI. Conclusions
  9. Acknowledgements
  10. References