Advertisement

European Journal of Plant Pathology

, Volume 152, Issue 3, pp 635–651 | Cite as

Environmental and inoculum effects on epidemiology of bacterial spot disease of stone fruits and development of a disease forecasting system

  • G. Morales
  • C. Moragrega
  • E. Montesinos
  • I. Llorente
Article

Abstract

Bacterial spot disease of stone fruits, caused by Xanthomonas arboricola pv. pruni, is of high economic importance in the major stone-fruit-producing areas worldwide. A better understanding of disease epidemiology can be valuable in developing disease management strategies. The effects of weather variables (temperature and wet/dry period) on epiphytic growth of X. arboricola pv. pruni on Prunus leaves were analyzed, and the relationship between inoculum density and temperature on disease development was determined and modeled. The information generated in this study, performed under controlled environmental conditions, will be useful to develop a forecasting system for X. arboricola pv. pruni. Optimal temperature for growth of epiphytic populations ranged from 20 to 30 °C under leaf wetness. In contrast, multiplication of epiphytic populations was not only interrupted under low relative humidity (RH) (< 40%) at 25 °C, but also resulted in cell inactivation, with only 0.001% initial cells recovered after 72 h incubation. A significant effect of inoculum density on disease severity was observed and 106 CFU/ml was determined as the minimal infective dose for X. arboricola pv. pruni on Prunus. Infections occurred at temperatures from 15 to 35 °C, but incubation at 25 and 30 °C gave the shortest incubation periods (7.7 and 5.9 days respectively). A model for predicting disease symptom development was generated and successfully evaluated, based on the relationship between disease severity and the accumulated heat expressed in cumulative degree day (CDD). Incubation periods of 150, 175 and 280 CDD were required for 5, 10 and 50% of disease severity, respectively.

Keywords

Epiphytic growth Incubation period Inoculum potential Growth rate Leaf wetness Temperature 

Notes

Acknowledgements

We are grateful to Agromillora Catalana for supplying plant material (GF677 plants). We thank Marc Nicolàs and Josep Pereda for helpful collaboration, and Shirley Burgess for assistance in language editing.

Funding

This research was supported, in part, by grants from the Ministerio de Educación, Ciencia y Deporte (AGL2013–41405-R) of Spain, from the University of Girona (SING12/13 and MPCUdG2016/085) and from the European Union’s Seventh Framework Programme for research, technological development and demonstration under grant agreement number 613678 (DROPSA). Gerard Morales was the recipient of predocotoral fellowships from the University of Girona (BR 2013/31) and from MECD (FPU13/04123) from Spain.

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.

References

  1. Agrios, G. N. (2005). Plant pathology (5th ed.). San Diego: Elsevier Academic Press.Google Scholar
  2. Anonymous. (2006). EPPO standards PM7/64. Diagnostics protocols for regulated pests. Xanthomonas arboricola pv. pruni. Bulletin OEPP/EPPO Bulletin, 36, 129–133.CrossRefGoogle Scholar
  3. Battilani, P., Rossi, V., & Saccardi, A. (1999). Development of Xanthomonas arboricola pv. pruni epidemics on peaches. Journal of Plant Pathology, 81(3), 161–171.Google Scholar
  4. Baty, F., Ritz, C., Charles, S., Brutsche, M., Flandrois, J. P., & Delignette-Muller, M. L. (2015). A toolbox for nonlinear regression in R: the package nlstools. Journal of Statistical Software, 66(5), 1–21.CrossRefGoogle Scholar
  5. Beresford, R. M., Tyson, J. L., & Henshall, W. R. (2017). Development and validation of an infection risk model for bacterial canker of kiwifruit, using a multiplication and dispersal concept for forecasting bacterial diseases. Phytopathology, 107(2), 184–191.CrossRefPubMedGoogle Scholar
  6. Billing, E. (1984). Principles and applications of fire blight risk assessment systems. Acta Horticulturae, 151, 15–22.CrossRefGoogle Scholar
  7. Billing, E. (1999). Fire blight risk assessment: Billing’s integrated system (BIS) and its evaluation. Acta Horticulturae, 489, 399–406.CrossRefGoogle Scholar
  8. Boudon, S., Manceau, C., & Nottéghem, J. L. (2005). Structure and origin of Xanthomonas arboricola pv. pruni populations causing bacterial spot of stone fruit trees in western Europe. Phytopathology, 95(9), 1081–1088.CrossRefPubMedGoogle Scholar
  9. Campbell, C., & Madden, L. (1990). Introduction to plant disease epidemiology. New York: Wiley.Google Scholar
  10. Cerf, O. (1977). Tailing of survival curves of bacterial spores. Journal of Applied Bacteriology, 42(1), 1–19.CrossRefPubMedGoogle Scholar
  11. Civerolo, E. (1975). Quantitative aspects of pathogenesis of Xanthomonas pruni in peach leaves. Phytopathology, 65, 258–264.CrossRefGoogle Scholar
  12. Crossman, L., & Dow, J. M. (2004). Biofilm formation and dispersal in Xanthomonas campestris. Microbes and Infection, 6, 623–629.CrossRefPubMedGoogle Scholar
  13. Dhingra, O. D., & Sinclair, J. B. (1985). Basic plant pathology methods (2nd ed.). Boca Raton: CRC Press.Google Scholar
  14. Dickson, J., & Holbert, J. (1928). The relation of temperature to the development of disease in plants. The American Naturalist, 62(681), 311–333.CrossRefGoogle Scholar
  15. Dow, J. M., Crossman, L., Findlay, K., He, Y. Q., Feng, J. X., & Tang, J. L. (2003). Biofilm dispersal in Xanthomonas campestris is controlled by cell-cell signaling and is required for full virulence to plants. Proceedings of the National Academy of Sciences, 100(19), 10995–11000.CrossRefGoogle Scholar
  16. EPPO. (2017). Xanthomonas arboricola pv. pruni (XANTPR). EPPO Global Database. Retrieved from https://gd.eppo.int.
  17. EPPO/CABI. (1997). Xanthomonas arboricola pv. pruni. In I. M. Smith, D. G. McNamara, P. R. Scott, & M. Holderness (Eds.), Quarantine pests for Europe (2nd ed., pp. 1096–1100). Wallingford: CAB International.Google Scholar
  18. Garcin, A., Neyrand, S., & Fabresse, M. (2007). Fruits á noyau: Sensibilité variétale au Xanthomonas. L’arboriculture fruitière, 612, 28–32.Google Scholar
  19. Garcin, A., Vibert, J., & Cellier, M. (2011a). Xanthomonas sur pêcher: étude des conditions d’infection. Fonctionnement du modèle et résultats d’essais (2e partie). Infos CTIFL, 272, 30–39.Google Scholar
  20. Garcin, A., Vibert, J., & Leclerc, A. (2011b). Xanthomonas sur pêcher: étude des conditions d’infection. Développement de l’outil (1re partie). Infos CTIFL, 268, 26–39.Google Scholar
  21. Giovanardi, D., Dallai, D., & Stefani, E. (2016). Population features of Xanthomonas arboricola pv. pruni from Prunus spp. orchards in northern Italy. European Journal of Plant Pathology, 147, 761–771.CrossRefGoogle Scholar
  22. Gnanamanickam, S. S., & Immanuel, J. E. (2007). Epiphytic bacteria, their ecology and functions. In S. S. Gnanamanickam (Ed.), Plant-associated Bacteria. Dordrecht: Springer Netherlands.Google Scholar
  23. Goodman, R. N. (1976). Physiological and cytological aspects of the bacterial infection process. In Heitefuss, R., & Williams, P. H. (Eds.), Physiological plant pathology. Encyclopedia of plant physiology (New Series, vol.4, pp. 172–196). Berlin: Springer.Google Scholar
  24. He, Y. W., & Zhang, L. H. (2008). Quorum sensing and virulence regulation in Xanthomonas campestris. FEMS Microbiology Reviews, 32(5), 842–857.CrossRefPubMedGoogle Scholar
  25. Janse, J. D. (2012). Bacterial diseases that may or do emerge, with (possible) economic damage for Europe and the Mediterranean basin: Notes on epidemiology, risks, prevention and management on first occurrence. Journal of Plant Pathology, 94(Supplement 4), S4.5–S4.29.Google Scholar
  26. Kim, J., Kang, W., & Yun, S. (2014). Development of a model to predict the primary infection date of bacterial spot (Xanthomonas campestris pv. vesicatoria) on hot pepper. The Plant Pathology Journal, 30(2), 125–135.CrossRefPubMedPubMedCentralGoogle Scholar
  27. Lalancette, N., & McFarland, K. (2007). Phytotoxicity of copper-based bactericides to peach and nectarine. Plant Disease, 91(9), 1122–1130.CrossRefGoogle Scholar
  28. Lebeaux, D., Chauhan, A., Rendueles, O., & Beloin, C. (2013). From in vitro to in vivo models of bacterial biofilm-related infections. Pathogens, 2(2), 288–356.CrossRefPubMedPubMedCentralGoogle Scholar
  29. Lightner, G. W., & Steiner, P. W. (1992). Maryblyt™: a computer model for predicting of fire blight disease in apples and pears. Computers and Electronics in Agriculture, 7(3), 249–260.CrossRefGoogle Scholar
  30. Lindemann, J. (1984). Use of an apparent infection threshold population of Pseudomonas syringae to predict incidence and severity of brown spot of bean. Phytopathology, 74(11), 1334–1339.CrossRefGoogle Scholar
  31. Magarey, R. D., & Sutton, T. B. (2007). How to create and deploy infection models for plant pathogens. In A. Ciancio & K. G. Mukerji (Eds.), Integrated management of plants pests and diseases (Vol. 1, pp. 3–25). Dordrecht: Springer Netherlands.Google Scholar
  32. Mercier, J., & Lindow, S. E. (2000). Role of leaf surface sugars in colonization of plants by bacterial epiphytes. Applied and Environmental Microbiology, 66(1), 369–374.CrossRefPubMedPubMedCentralGoogle Scholar
  33. Moh, A., Massart, S., & Lahlali, R. (2011). Predictive modelling of the combined effect of temperature and water activity on the in vitro growth of Erwinia spp. infecting potato tubers in Belgium. Biotechnology, Agronomy, Society and Environment, 15(3), 379–386.Google Scholar
  34. Moragrega, C., Manceau, C., & Montesinos, E. (1998). Evaluation of drench treatments with phosphonate derivatives against Pseudomonas syringae pv. syringae on pear under controlled environment conditions. European Journal of Plant Pathology, 104(2), 171–180.CrossRefGoogle Scholar
  35. Morales, G., Llorente, I., Montesinos, E., & Moragrega, C. (2016). Basis for a predictive model of Xanthomonas arboricola pv. pruni growth and infections in host plants. Acta Horticulturae, 1149, 1–8.CrossRefGoogle Scholar
  36. Morales, G., Llorente, I., Montesinos, E., & Moragrega, C. (2017). A model for predicting Xanthomonas arboricola pv. pruni growth as a function of temperature. PLoS ONE, 12(5), e0177583.  https://doi.org/10.1371/journal.pone.0177583.CrossRefPubMedPubMedCentralGoogle Scholar
  37. Morales, G., Moragrega, C., Montesinos, E., & Llorente, I. (2018). Effects of leaf wetness duration and temperature on infection of Prunus by Xanthomonas arboricola pv. pruni. PLoS ONE, 13(3), e0193813.  https://doi.org/10.1371/journal.pone.0193813.CrossRefPubMedPubMedCentralGoogle Scholar
  38. Palacio-Bielsa, A., Roselló, M., Cambra, M. A., & López, M. M. (2010). First report on almond in Europe of bacterial spot disease of stone fruits caused by Xanthomonas arboricola pv. pruni. Plant Disease, 94(6), 786.CrossRefGoogle Scholar
  39. Palacio-Bielsa, A., Cubero, J., Cambra, M. a., Collados, R., Berruete, I. M., & Lopez, M. M. (2011). Development of an efficient real-time quantitative PCR protocol for detection of Xanthomonas arboricola pv. pruni in Prunus species. Applied and Environmental Microbiology, 77(1), 89–97.CrossRefPubMedGoogle Scholar
  40. R Core Team (2015). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. https://www.R-project.org/.
  41. Randhawa, P. S., & Civerolo, E. (1985). A detached-leaf bioassay for Xanthomonas campestris pv. pruni. Phytopathology, 75(9), 1060–1063.CrossRefGoogle Scholar
  42. Ritchie, D. F. (1995). Bacterial spot. In J. M. Ogawa, E. I. Zehr, G. W. Bird, D. F. Ritchie, K. Uriu, & J. K. Uyemoto (Eds.), Compendium of stone fruit diseases. St. Paul: APS Press.Google Scholar
  43. Ritchie, D. F. (2004). Copper-containing fungicides/bactericides and their use in management of bacterial spot on peaches. Southeast Regional Newsletter, 4(1).Google Scholar
  44. Ruz, L., Moragrega, C., & Montesinos, E. (2008). Evaluation of four whole-plant inoculation methods to analyze the pathogenicity of Erwinia amylovora under quarantine conditions. International Microbiology, 11(2), 111–119.PubMedGoogle Scholar
  45. Ryan, R. P., Vorhölter, F.-J., Potnis, N., Jones, J. B., Van Sluys, M.-A., Bogdanove, A. J., & Dow, J. M. (2011). Pathogenomics of Xanthomonas: understanding bacterium-plant interactions. Nature Reviews Microbiology, 9(5), 344–355.CrossRefPubMedGoogle Scholar
  46. Schubert, T., Rizvi, S., Sun, X., Gottwald, T., Graham, J., & Dixon, W. (2001). Meeting the challenge of eradicating citrus canker in Florida - again. Plant Disease, 85(4), 340–356.CrossRefGoogle Scholar
  47. Scortichini, M. (2010). Epidemiology and predisposing factors of some major bacterial diseases of stone and nut fruit trees species. Journal of Plant Pathology, 92(Supplement 1), S1.73–S1.78.Google Scholar
  48. Shepard, D., & Zehr, E. (1994). Epiphytic persistence of Xanthomonas campestris pv. pruni on peach and plum. Plant Disease, 78(6), 627–629.CrossRefGoogle Scholar
  49. Smith, E. (1903). Observation on a hitherto unreported bacterial disease the cause of which enters the plant through ordinary stomata. Science, 17, 456–457.CrossRefGoogle Scholar
  50. Smith, T. (1993). A predictive model for forecasting fire blight of pear and apple in Washington State. Acta Horticulturae, 338, 153–160.CrossRefGoogle Scholar
  51. Socquet-Juglard, D., Patocchi, A., Pothier, J. F., Christen, D., & Duffy, B. (2012). Evaluation of Xanthomonas arboricola pv. pruni inoculation techniques to screen for bacterial spot resistance in peach and apricot. Journal of Plant Pathology, 94(Supplement 1), S1.91–S1.96.Google Scholar
  52. Stefani, E. (2010). Economic significance and control of bacterial spot/canker of stone fruits caused by Xanthomonas arboricola pv. pruni. Journal of Plant Pathology, 92(Supplement 1), 99–104.Google Scholar
  53. Stockwell, V. O., & Duffy, B. (2012). Use of antibiotics in plant agriculture. Scientific and Technical Review of the Office International des Epizooties, 31(1), 199–210.CrossRefGoogle Scholar
  54. van der Wal, A., Tecon, R., Kreft, J.-U., Mooij, W. M., & Leveau, J. H. J. (2013). Explaining bacterial dispersion on leaf surfaces with an individual-based model (PHYLLOSIM). PLoS ONE, 8(10), e75633.  https://doi.org/10.1371/journal.pone.0075633.CrossRefPubMedPubMedCentralGoogle Scholar
  55. Vanneste, J., McLaren, G., & Yu, J. (2005). Copper and streptomycin resistance in bacterial strains isolated from stone fruit orchards in New Zealand. New Zealand Plant Protection, 58, 101–105.Google Scholar
  56. Vauterin, L., Hoste, B., Kersters, K., & Swings, J. (1995). Reclassification of Xanthomonas. International Journal of Systematic Bacteriology, 45(3), 472–489.CrossRefGoogle Scholar
  57. von Bodman, S. B., Bauer, W. D., & Coplin, D. L. (2003). Quorum sensing in plant-pathogenic bacteria. Annual Review of Phytopathology, 41, 455–482.CrossRefGoogle Scholar
  58. Wert, T. W., Miller, P., Williamson, J. G., & Rouse, R. E. (2006). Preliminary studies for controlling bacterial spot in low-chill peaches. Proceedings of the Florida State Horticultural Society, 119, 32–33.Google Scholar
  59. Whitehead, N. A., Barnard, A. M. L., Slater, H., Simpson, N. J. L., & Salmond, G. P. C. (2001). Quorum-sensing in Gram-negative bacteria. FEMS Microbiology Reviews, 25(4), 365–404.CrossRefPubMedGoogle Scholar
  60. Xiong, R., Xie, G., Edmondson, A. E., & Sheard, M. A. (1999). A mathematical model for bacterial inactivation. International Journal of Food Microbiology, 46(1), 45–55.CrossRefPubMedGoogle Scholar
  61. Young, J., Luketina, R., & Marshall, A. (1977). The effects on temperature on growth in vitro of Pseudomonas syringae and Xanthomonas pruni. Journal of Applied Bacteriology, 42(3), 345–354.CrossRefPubMedGoogle Scholar
  62. Zehr, E. I., Shepard, D. P., & Bridges Jr., W. C. (1996). Bacterial spot of peach as influenced by water congestion, leaf wetness duration, and temperature. Plant Disease, 80(3), 339–341.CrossRefGoogle Scholar
  63. Zwietering, M., Jongenburger, I., Rombouts, F. M., & van’t Riet, K. (1990). Modeling of the bacterial growth curve. Applied and Environmental Microbiology, 56(6), 1875–1881.PubMedPubMedCentralGoogle Scholar

Copyright information

© Koninklijke Nederlandse Planteziektenkundige Vereniging 2018

Authors and Affiliations

  • G. Morales
    • 1
  • C. Moragrega
    • 1
  • E. Montesinos
    • 1
  • I. Llorente
    • 1
  1. 1.Institute of Food and Agricultural Technology-XaRTA-CIDSAVUniversity of GironaGironaSpain

Personalised recommendations