Population Biology of Plant Pathogens

Abstract

Population change in plant pathogens is driven by environmental factors, especially wetness and temperature, and by intrinsic and induced host resistance. For pathogens except those of woody tissue, turnover of host tissue, and decline in pathogen population during periods when the host is absent or conditions unsuitable for infection produce intrinsic fluctuations in populations. Movement of propagules to new hosts is much more efficient in denser host stands, introducing density dependence at a larger scale. The reduction of host populations by a successful pathogen both allows space for other plants to grow, and introduces selective forces increasing resistance in the host. This gives rise to a coevolutionary race between host and pathogen, resulting in both diversity and turnover of genetic elements involved in defence signalling. Rare movement of pathogens between subpopulations of a metapopulation introduces a further birth–death process causing fluctuations in disease and selection pressures on relevant genetic elements.

Key Concepts:

  • Population densities of plant pathogens are set by the balance between pathogen infection and host turnover.

  • Populations of plant pathogens are rarely stable, but cycle from high to low across seasons.

  • Environmental factors modulate or limit but do not regulate pathogen abundance.

  • Asexual reproduction is often associated with shorter distance dispersal mechanisms and sexual reproduction with longer distance mechanisms.

  • Density‐dependence acts at the level of the individual host, the host population and within metapopulations.

  • Pathogens increase biodiversity by disadvantaging common hosts.

  • Coevolution between host and pathogen leads to continuing change and diversity in pathogenesis related parts of the genome.

  • When a pathogen encounters a susceptible host not coevolved with it – for example, through trade – a dramatic drop in host density may occur.

Keywords: coevolution; red queen; density dependence; epidemiology; metapopulation; dispersal

Figure 1.

Pattern of buildup of pathogen infection during a growing season in a host population of fixed size. Proportion of host units infected is denoted y, ranging from 0 to 1. (a) Simple logistic growth in a host population which rapidly becomes more resistant as the season progresses (dy/dt=ry(1−y)(1−t)2). Over the whole season, the population trajectory is often well‐fit by a Gompertz curve (dy/dt=ry(−ln y)). (b) Logistic growth to an equilibrium in a susceptible–infected–susceptible differential equation based model of a host population in which individuals are either infected or healthy and infected host tissue dies and is replaced by new healthy tissue at some rate; the equilibrium is a balance between the death of infected tissue and new infections (dS/dt=αS–βS I−vS; dI/dt=βS I–νI; y=I/(S+I)).

Figure 2.

Annual cycles of pathogen abundance in a seasonal environment. (a) Variability is introduced by varying average rates of increase, decrease, and length of season all produced by varying weather conditions. In year 1 a slow winter decline after a year with high peak pathogen abundance is followed by a very favourable summer. This leads to high pathogen abundance. Starting from this peak, in year 2 a moderately unfavourable winter is followed by an unfavourable summer and peak abundance is moderate. In year 3 a similar abundance follows a harsh winter but a moderately favourable summer. The very favourable summer in year 4 then follows a severe winter, so peak abundance is still only moderate. In year 5 a very unfavourable summer follows an unfavourable winter after the moderate peak in year 4, and peak abundance is low. (b) In the absence of negative density dependence at low population density, the time during which a pathogen could persist in the face of environmental variation would be limited, because sooner or later a consecutive run of bad off‐season conditions would eliminate the pathogen. (c) If the rate of increase during the growing season is such that random extinction is unlikely, infection will usually be limited by the upper density limits in a particular season, and there will be little correlation of levels across years.

Figure 3.

Idealised coevolutionary patterns in a plant–pathogen system in which successful attack depends on matching at specific loci. (a) Arms race: innovation in defence or attack leads to complete substitution of the older variant. (b) Red queen or trench warfare: rare alleles are favoured; selection on common alleles does not often lead to extinction, so there is constant recycling of variants. The pathogen has reused the alleles at loci coloured red, green and blue; the host has reused a resistance allele at the red locus.

Figure 4.

Birth and death processes and coevolution in a metapopulation. The metapopulation is shown at two times; the combination of resistance and virulence characters present is shown by colour and the size of the population by radius. Some populations are unchanged between the two times (e.g. arrows); others have died out (e.g. |) or been founded (e.g. __) or have changed the genetic composition of the populations (e.g. circled).

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Further Reading

Burdon JJ (1987) Diseases and Plant Population Biology. Cambridge: Cambridge University Press.

Madden L, Hughes G and van den Bosch F (2007) The Study of Plant Disease Epidemics. St. Paul, Minnesota: APS Press.

Zadoks JC and Schein RD (1979) Epidemiology and Plant Disease Management. New York: Oxford University Press.

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Shaw, Michael W(Jun 2014) Population Biology of Plant Pathogens. In: eLS. John Wiley & Sons Ltd, Chichester. http://www.els.net [doi: 10.1002/9780470015902.a0022337]