epidemiological principles in plant pathology. 3. Epidemiology in 1970s →
epidemiology sensu stricto a. What does an “epidemiologist” (sensu stricto) do
today?
Pl Pa 601, Spring 2004, Lecture 19 Introduction to population biology of plant pathogens I. Population biology and epidemiology A. Levels of biological organization Simple Molecules Cells Individuals ↓ Populations Communities Complex Ecosystems B. What is “epidemiology”? 1. Epidemiology sensu lato → biology/ecology of pathogens a. Disease management depends on knowledge of pathogen biology under field condition (=ecology) b. How many Cornell faculty do research on epidemiology? How many claim to be “epidemiologists”? 2. Vanderplank’s (1963) seminal book = first formal compilation of epidemiological principles in plant pathology 3. Epidemiology in 1970s → epidemiology sensu stricto a. What does an “epidemiologist” (sensu stricto) do today? Mathematical modeling Statistical analyses Environmental sensing (e.g, micrometeorology) Spore-trapping GPS etc… b. Campbell and Madden’s (1990) book; Introduction to plant disease epidemiology = a compilation of mathematical tools for use in plant pathology NO host plant resistance NO genetics NO evolution 4. Emergence of genetics and evolution from epidemiology in the 1970s & 1980s a. few “epidemiologists” were concerned with race evolution and host resistance genes b. attempts were made to integrate epidemiology and pathogen evolution Fungicide resistance theory and management Cultivar mixtures; resistance gene deployment Key books from this era: Wolfe and Caten (1987); Leonard and Fry (1989) 5. Molecular genetic markers in late 1980s & 1990s → population genetics a. descriptive studies on genetic variation, “population structure”, etc. b. emphasis on evolutionary processes c. almost complete divergence from epidemiology; occasional impact on disease management C. What is “population biology”? 1. Ideally, the synthesis epidemiology, genetics and evolution (Milgroom and Peever, 2003); (could also be included with epidemiology sensu lato, but with a distinctive emphasis on evolution) (see figure)
2. Epidemiology and population genetics often address similar issues, but from different perspectives (Milgroom and Fry, 1997) (see table) NOTE: there are numerous overlapping concepts in these two lists! Examples: dispersal and gene flow host specialization and selection sources/types of inoculum and mating systems etc. 3. What is NOT population biology? Common misconception is that anything dealing with genetic variation is population biology, for example: a. diagnostic tools • based on genetic markers (e.g., diagnostic PCR products) • similar technology (genetic markers), but NOT ecology, evolution b. systematics/taxonomy o based on genetic markers (e.g., sequencing) o similar technology (genetic markers), usually NOT ecology, evolution on a longer time scale o recent efforts have been made merging population biology and phylogenetics II. Epidemiology A. Review of basic concepts from introductory plant pathology 1. disease triangle; epidemic 2. stages of pathogenesis → epidemiological terms: incubation period = time from inoculation to appearance of symptoms latent period = time from inoculation to reproduction (e.g., sporulation) infectious period = time during which secondary inoculum is produced 3. monocyclic epidemics; primary inoculum only 4. polycyclic epidemics; primary and secondary inoculum; secondary disease cycles 5. disease severity and incidence 6. disease progress curves (Fry, 1982) B. Disease progress models (Vanderplank, 1963) 1. We will only look at the two simplest models; many more elaborate models exist 2. Model assumptions: • constant environment • constant host (population size, no growth, susceptibility) • constant pathogen virulence • spatially random 3. Simple models: a. monomolecular model (“simple interest” model) for monocyclic process: dx/dt = QR(1 – x) linear form: ln[1/(1-xt)] = ln[1/(1-x0)] + QRt x = proportion of infected plants or tissue diseased (1 – x) = proportion of healthy plants or tissue Q = inoculum density R = basic infection rate
Note: disease increase is not dependent on the amount of disease present; only primary inoculum is effective because there are no secondary cycles b. logistic model (“compound interest” model) for polycyclic process: dx/dt = rx(1 – x) linear form: ln[(xt/(1-xt)] = ln[x0/(1-x0)] + rt x = proportion of infected plants or tissue diseased (1 – x) = proportion of healthy plants or tissue r = apparent infection rate Note: disease increase is proportional to the amount of disease present; assumes (unrealistically) that all diseased tissue is instantly producing secondary inoculum 4. What do these models tell us? a. Monomolecular model: i. QR: inoculum (density and “potential”) and effects of environment combined ii. increase in Q → increase dx/dt (Pullman and DeVay, 1982) iii. increase in R (e.g., more favorable environment) → increase dx/dt iv. management directed at reducing Q sanitation, rotation, fumigation, clean seed, cultural practices iv. management directed at reducing R host plant resistance, fungicides, cultural practices b. Logistic model: i. apparent infection is most important ii. x0 has generally smaller effect (delays epidemic) iii. management directed at reducing r 5. Interpretations of disease progress curves a. What does it tell us if the observed curve fits the one of these models? b. Cannot infer biology from observed curve! (Pfender, 1982) c. examples: black shank of tobacco (Jacobi et al., 1983): monocyclic or polycyclic? Douglas fir root growth (Bloomberg, 1979) d. alternative explanations? Hypothesis testing? e. some other shapes for disease progress curves (Jeger, 1987) f. other models…which model fits best? 6. Why use models? What good are these models? Valuable for providing a conceptual framework and suggesting testable hypotheses, but need empirical data 7. Common use of models: comparing field epidemics; problems fitting field data to models: a. there is no “correct” model: we do not know the underlying relationships; many different models might fit b. some models are completely inappropriate, e.g., trying to fit a monomolecular model to a polycyclic pathogen is illogical c. alternative to models (NO assumptions) = compare experimental treatments by measuring area under the disease progress curve (AUDPC); accounts for onset and rate of epidemic development
References Bloomberg, W. J. 1979. A model of damping-off and root rot of Douglas-fir seedlings caused by Fusarium oxysporum. Phytopathology 69:74-81. Campbell, C. L., and Madden, L. V. 1990. Introduction to plant disease epidemiology. J. Wiley and Sons, New York. Fry, W. E. 1982. Principles of plant disease management. Academic Press, New York. Jacobi, W. R., Main, C. E., and Powell, N. T. 1983. Influence of temperature and rainfall on the development of tobacco black shank. Phytopathology 73:139-143. Jeger, M. J. 1987. Modelling the dynamics of pathogen populations In Populations of plant pathogens: their dynamics and genetics (M. S. Wolfe and C. E. Caten, eds), pp. 91-107. Blackwell Scientific Publ, Oxford. Leonard, K. J., and Fry, W. E. 1989. Plant disease epidemiology: genetics, resistance, and management. Pp. 377. McGraw-Hill Publ. Co., New York. Milgroom, M. G., and Fry, W. E. 1997. Contributions of population genetics to plant disease epidemiology and management. Adv. Bot. Res. 24:1-30. Milgroom, M. G., and Peever, T. L. 2003. Population biology of plant pathogens: the synthesis of plant disease epidemiology and population genetics. Plant Dis 87:608-617. Pfender, W. F. 1982. Monocyclic and polycyclic root diseases: distinguishing between the nature of the disease cycle and the shape of the disease progress curve. Phytopathology 72:31-32. Pullman, G. S., and DeVay, J. E. 1982. Epidemiology of Verticillium wilt of cotton: relationship between inoculum density and disease progression. Phytopathology 72:549-554. Vanderplank, J. E. 1963. Plant diseases: epidemics and control. Academic Press, New York. Wolfe, M. S., and Caten, C. E. 1987. Populations of plant pathogens: their dynamics and genetics. Blackwell Scientific Publ, Oxford, UK.
Population genetics
Epidemiology
Evolution
Ecology
Genetics
Population biology
Epidemiology • • • • • • • • • •
Source of inoculum Dispersal Types of inoculum Host specialization Gene deployment Fungicide resistance Competition Disease progress Forecasting Crop losses
Population genetics • • • • • • • • • •
Population structure Gene flow Recombination Mating systems Selection Fitness Genetic drift Mutation Coevolution Phylogenetics
Milgroom and Peever (2003)
Monocyclic epidemic:
Fry (1982), Principles of Plant Disease Management
Polycyclic epidemics:
Fry (1982), Principles of Plant Disease Management
Polyetic epidemic (multiple years)
Fry (1982), Principles of Plant Disease Management
What kind of epidemic is this? Monocyclic? Polycyclic? Polyetic?
Fry (1982), Principles of Plant Disease Management
Disease progress models and linear transformations
Fry (1982), Principles of Plant Disease Management
Prediction from monomolecular model: rate of disease increase is proportional to inoculum density
Pullman and DeVay (1982)
Monocyclic or polycyclic?
Jacobi et al. (1983)
What else can cause a disease progress curve to be sigmoidal?
Bloomberg (1979)
Possible shapes of disease progress curves:
Jeger (1987)
