Ecology and Epidemiology
A Multivariate Analysis of Pathogenic Variation in Colletotrichum gloeosporioides Infecting the Tropical Pasture Legume, Stylosanthes scabra. Sukumar Chakraborty, Division of Tropical Crops and Pastures, Commonwealth Scientific and Industrial Research Organization (CSIRO), 306 Carmody Road, St. Lucia, Queensland 4067, Australia; Mervyn R. Thomas(2), and Nick Ellis(3). (2) (3)CSIRO, Institute of Plant Production and Processing Biometrics Unit, St. Lucia, Queensland 4067, Australia. Phytopathology 86:283-289. Accepted for publication 7 November 1995. Copyright 1996 The American Phytopathological Society. DOI: 10.1094/Phyto-86-283.
Multivariate statistical analysis was used to characterize and classify pathogenic variation in isolates of Colletotrichum gloeosporioides that cause anthracnose disease of the tropical pasture legume. Stylosanthes scabra. A total of 182 isolates collected from field sites in Queensland, Australia, over the past 15 years were tested for pathogenic variation on six differential genotypes of S. scabra using a seedling bioassay. Four reference isolates, representing the four pathogenic races, were included in the bioassay for comparison. Data on the disease severity of 172 field and four reference isolates (set 1) were used to classify the reference isolates into races and to determine if the field isolates belonged to an existing or new race. Linear discriminant functions were developed to classify the four reference isolates, and a cross-validation procedure was used to test the classification success of placing these isolates into the four races. Isolate sr4 was classified 76% of the time as race 1 and 17% of the time as race 4, isolate sr24 was classified 88% of the time as race 3, and isolates wrs20 and wrs32 were mainly classified as either race 4 or 4a. With one small cluster of weakly virulent isolates and the prior expectation of the four races, the field isolates were classified into five virulence groups using cluster analysis. Three of these clusters were associated with the existing races: race 1 in cluster 3, race 3 in cluster 1, and races 4 and 4a jointly in cluster 2. Cluster 1 isolates were avirulent on the differential cultivar Seca, cluster 3 isolates were virulent on ‘Seca’, and isolates in clusters 2 and 4 were virulent on accessions 36260 and Q10042. For an independent evaluation of the discriminant analysis, additional data were obtained on 14 isolates (set 2), of which eight had been previously classified. The five set 1 clusters were used to develop linear discriminant functions to classify the isolates in set 2. Five of the eight isolates common to both data sets were correctly classified; while isolates wrs20 and wrs32, previously in cluster 2, were classified in cluster 4 in set 2. However, clusters 2 and 4 were close neighbors with no striking differences in the overall disease severity levels on the six differentials for the isolates. In future analyses, three races, represented by the reference isolates sr4, sr24, and wrs20 and/or wrs32, may be used to account for the existing range of pathogenic variation. The usefulness of the multivariate approach to classify field isolates into races, in order to ascertain if isolates belong to an existing or novel race, was discussed.
Additional keywords: pathogen diversity, race composition, virulence analysis.