Abstract
Resistance to chemical control is a major impediment to combating many socially and economically important diseases. Theoretical and experimental studies have shown that reducing the intensity of treatment can slow, or even prevent, the invasion of resistance, yet reducing treatment levels also results in a net increase in disease severity. Clearly there is a need to identify control strategies that balance the conflicting aims of resistance management and disease suppression. Using a mathematical model for the dynamics of fungicide resistance in crop pathogens, we present a broadly applicable measure of the performance of chemical control in the presence of resistant pathogen strains. We illustrate how to optimise fungicide performance with respect to the intensity of treatment as a function of the duration of treatment and the fitness of the resistant strain. We find that in the short term, fungicide performance is optimised at high levels of treatment despite rapid selection for resistance, while the long-term optimum performance is achieved when treatment renders the fungicide-sensitive and fungicide-resistant pathogens equally fit. We further present evidence that under prescribed conditions, the ratio of dose size and frequency, and the fungicide mode of action, can have a significant effect on fungicide performance.
Similar content being viewed by others
References
Anderson, R.M., May, R.M., 1986. The invasion, persistence and spread of infectious diseases within animal and plant communities. Phil. Trans. R. Soc. Lond. B 314, 533–570.
Austin, D.J., Anderson, R.M., 1999. Studies of antibiotic resistance within the patient, hospitals and the community using simple mathematical models. Phil. Trans. R. Soc. Lond. B 354, 721–738.
Bierman, S.M., Fitt, B.D.L., van den Bosch, F., Bateman, G.L., Jenkyn, J.F., Welham, S.J., 2002. Changes in populations of the eyespot fungi Tapesia yallundae and T. acuformis under different fungicide regimes in successive crops of winter wheat, 1984–2000. Plant Pathol. 51, 191–201.
Blower, S.M., Porco, T.C., Darby, G., 1998. Predicting and preventing the emergence of antiviral drug resistance in HSV-2. Nat. Med. 4, 673–678.
Bonhoeffer, S., Barbour, A.D., De Boer, R.J., 2002. Procedures for the reliable estimation of viral fitness from time-series data. Proc. R. Soc. Lond. B 269, 1887–1893.
Bonhoeffer, S., Lipsitch, M., Levin, B.R., 1997. Evaluating treatment protocols to prevent antibiotic resistance. Proc. Natl. Acad. Sci. USA 94, 12106–12111.
Bonhoeffer, S., Nowak, M.A., 1997. Pre-existence and emergence of drug resistance in HIV-1 infection. Proc. R. Soc. Lond. B 264, 631–637.
Brent, K.J., 1995. Fungicide resistance in crop pathogens: How can it be managed? FRAC monograph no. 1. GCPF (now Crop Life International), Brussels (available online from www.FRAC.info).
Brent, K.J., 2000. UK fungicide resistance research: Risk of resistance development in cereal pathogens to Qo inhibitor fungicides. MAFF, London.
Clark, C.W., 1990. Mathematical Bioeconomics: The optimal management of renewable resources. Wiley, New York.
Comins, H.N., 1977. The management of pesticide resistance. J. Theor. Biol. 65, 399–420.
De Waard, M.A., Georgopoulos, S.G., Hollomon, D.W., Ishii, H., Leroux, P., Ragsdale, N.N., Schwinn, F.J., 1993. Chemical control of plant diseases: Problems and prospects. Ann. Rev. Phytopathol. 31, 403–421.
Gubbins, S., Gilligan, C.A., 1997. Persistence of host-parasite interactions in a disturbed environment. J. Theor. Biol. 188, 241–258.
Gubbins, S., Gilligan, C.A., 1999. Invasion thresholds for fungicide resistance: Deterministic and stochastic analyses. Proc. Roy. Soc. Lond. B 266, 2539–2549.
Hall, R.J., Gubbins, S., Gilligan, C.A., 2004. Invasion of drug and pesticide resistance is determined by a trade-off between relative fitness and treatment efficacy. Bull. Math. Biol. 66, 825–840.
Hastings, A., 2004. Transients: The key to long-term ecological understanding? Trends Ecol. Evol. 19, 39–45.
Hunter, T., Brent, K.J., Carter, G.A., 1984. Effects of fungicide regimes on sensitivity and control of barley mildews. Proceedings of the 1984 Crop Protection Conference—Pests and diseases, pp. 471–476.
Laxminarayan, R., Brown, G.M., 2001. Economics of antibiotic resistance: A theory of optimal use. J. Environ. Econ. Manage. 42, 183–206.
Little, S.J., McLean, A.R., Spina, C.A., Richman, D.D., Havlir, D.V., 1999. Viral dynamics of acute HIV-1 infection. J. Exp. Med. 190, 841–850.
Madden, L.V., Hughes, G., Irwin, M.E., 2000. Coupling disease-progress-curve and time-of-infection functions for predicting yield loss of crops. Phytopathol. 90, 788–800.
Marin, D.H., Romero, R.A., Guzman, M., Sutton, T.B., 2003. Black Sigatoka: An increasing threat to banana cultivation. Plant Dis. 87, 208–222.
Metcalfe, R.J., Shaw, M.W., Russell, P.E., 2000. The effect of dose and mobility on the strength of selection for DMI fungicide resistance in inoculated field experiments. Plant Pathol. 49, 546–557.
O'Hara, R.B., Nielsen, B.J., Ostergard, H., 2000. The effect of fungicide dose on the composition of laboratory populations of barley powdery mildew. Plant Pathol. 49, 558–566.
Parry, D.W., 1990. Plant pathology in agriculture. Cambridge University Press, Cambridge.
Paveley, N.D., Sylvester-Bradley, R., Scott, R.K., Craigon, S.J., Day, W., 2001. Steps in predicting the relationship of yield on fungicide dose. Phytopathol. 91, 708–716.
Paveley, N.D., Thomas, J.M., Vaughan, T.B., Havis, N.D., Jones, D.R., 2003. Predicting effective doses for the joint action of two fungicide applications. Plant Pathol. 52, 638–647.
Pearson, H., 2002. ‘Superbug’ hurdles key drug barrier. Nature 418, 469.
Porras, L., Gisi, U., Staele-Csech, U., 1990. Selection dynamics in triazole-treated populations of Erysiphe graminis. Proceedings of the 1990 Brighton Crop Protection Conference, pp. 1163–1168.
Schulz, U., 1994. Evaluating anti-resistance strategies for control of Erysiphe graminis f.sp. tritici. In: Heaney, S., Slawson, D., Hollomon, D.W., Smith, M., Russell, P.E., Parry, D.W. (Eds.), Fungicide resistance. BCPC, Farnham, UK, pp. 55–58.
Shaw, M.W., 1993. Theoretical analysis of the effect of interacting activities on the rate of selection for combined resistance to fungicide mixtures. Crop Prot. 12, 120–126.
Shaw, M.W., 2000. Models of the effects of dose heterogeneity and escape on selection pressure for pesticide resistance. Phytopathology 90, 333–339.
Swinton, J., Anderson, R.M., 1995. Model frameworks for plant-pathogen interactions. In: Grenfell, B.T., Dobson, A.P. (Eds.), Ecology of infectious diseases in natural populations. Cambridge University Press, Cambridge, pp. 280–294.
van den Bosch, F., Gilligan, C.A., 2003. Measures of durability of resistance. Phytopathology 93, 616–625.
van der Plank, J.E., 1963. Plant diseases: Epidemics and control. Academic Press, New York and London.
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Hall, R.J., Gubbins, S. & Gilligan, C.A. Evaluating the Performance of Chemical Control in the Presence of Resistant Pathogens. Bull. Math. Biol. 69, 525–537 (2007). https://doi.org/10.1007/s11538-006-9139-z
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11538-006-9139-z