Population Biology of Plant Pathogens


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).



Aylor DE (1990) The role of intermittent wind in the dispersal of fungal pathogens. Annual Review of Phytopathology 28: 73–92.

Bagchi R, Gallery RE, Gripenberg S et al. (2014) Pathogens and insect herbivores drive rainforest plant diversity and composition. Nature 506: 85–88.

te Beest DE, Paveley ND, Shaw MW and van den Bosch F (2008) Disease‐weather relationships for powdery mildew and yellow rust on winter wheat. Phytopathology 98: 609–617.

Burdon JJ (1993) The structure of pathogen populations in natural plant communities. Annual Review of Phytopathology 31: 305–323.

Burdon JJ and Thrall PH (2013) What have we learned from studies of wild plant‐pathogen associations? – The dynamic interplay of time, space and life‐history. European Journal of Plant Pathology 138: 417–429.

Fitt B, McCartney H and Walklate P (1989) The role of rain in dispersal of pathogen inoculum. Annual Review of Phytopathology 27: 241–270.

Gómez‐Alpizar L, Carbone I and Ristaino JB (2007) An Andean origin of Phytophthora infestans inferred from mitochondrial and nuclear gene genealogies. Proceedings of the National Academy of Sciences of the USA 104: 3306–3311.

Heye CC and Andrews JH (1983) Antagonism of Athelia bombacina and Chaetomium globosum to the apple scab pathogen, Venturia inaequalis. Phytopathology 73: 650–654.

Hirano SS and Upper CD (2000) Bacteria in the leaf ecosystem with emphasis on Pseudomonas syringae – A pathogen, ice nucleus, and epiphyte. Microbiology and Molecular Biology Reviews 64: 624–653.

Hunter T, Royle DJ and Arnold GM (1996) Variation in the occurrence of rust (Melampsora spp) and other diseases and pests, in short‐rotation coppice plantations of Salix in the British Isles. Annals of Applied Biology 129: 1–12.

McRoberts N, Hughes G and Madden LV (2003) The theoretical basis and practical application of relationships between different disease intensity measurements in plants. Annals of Applied Biology 142: 191–211.

Milgroom MG (1995) Population biology of the chestnut blight fungus, Cryphonectria parasitica. Canadian Journal of Botany 73: S311–S319.

Oliver RP and Solomon PS (2008) Recent fungal diseases of crop plants: is lateral gene transfer a common theme? Molecular Plant‐Microbe Interactions 21: 287–293.

Peters JC and Shaw MW (1996) Effect of artificial exclusion and augmentation of fungal plant pathogens on a regenerating grassland. New Phytologist 134: 295–307.

Schieber E (1972) Economic impact of coffee rust in Latin America. Annual Review of Phytopathology 10: 491–510.

Schneider DJ and Collmer A (2010) Studying plant‐pathogen interactions in the genomics era: beyond molecular Koch's postulates to systems biology. Annual Review of Phytopathology 48: 457–479.

Schumacher CFA, Steiner U, Dehne H‐W and Oerke E‐C (2008) Localized adhesion of nongerminated Venturia inaequalis conidia to leaves and artificial surfaces. Phytopathology 98: 760–768.

Segarra J, Jeger MJ and van den Bosch F (2001) Epidemic dynamics and patterns of plant diseases. Phytopathology 91: 1001–1010.

Shaw MW (1994) Seasonally induced chaotic dynamics and their implications in models of plant disease. Plant Pathology 43: 790–801.

Soubeyrand S, Laine AL, Hanski I and Penttinen A (2009) Spatiotemporal structure of host‐pathogen interactions in a metapopulation. American Naturalist 174: 308–320.

Spanu PD (2012) The genomics of obligate (and nonobligate) biotrophs. Annual Review of Phytopathology 50: 91–109.

Stukenbrock EH and McDonald BA (2009) Population genetics of fungal and oomycete effectors involved in gene‐for‐gene interactions. Molecular Plant Microbe Interactions 22: 371–380.

Tauger MB (2003) Entitlement, shortage and the 1943 Bengal Famine: another look. Journal of Peasant Studies 31: 45–72.

Tomley AJ and Evans HC (2004) Establishment of, and preliminary impact studies on, the rust, Maravalia cryptostegiae, of the invasive alien weed, Cryptostegia grandiflora in Queensland, Australia. Plant Pathology 53: 475–484.

Yoshida K, Schuenemann VJ, Cano LM et al. (2013) The rise and fall of the Phytophthora infestans lineage that triggered the Irish potato famine. eLife 2: e00731.

Zaffarano PL, McDonald BA and Linde CC (2008) Rapid speciation following recent host shifts in the plant pathogenic fungus Rhynchosporium. Evolution 62: 1418–1436.

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]