Emergence of wheat blast in Bangladesh was caused by a South American lineage of Magnaporthe oryzae.

BACKGROUND: In February 2016, a new fungal disease was spotted in wheat fields across eight districts in Bangladesh. The epidemic spread to an estimated 15,000 hectares, about 16 % of the cultivated wheat area in Bangladesh, with yield losses reaching up to 100 %. Within weeks of the onset of the epidemic, we performed transcriptome sequencing of symptomatic leaf samples collected directly from Bangladeshi fields. RESULTS: Reinoculation of seedlings with strains isolated from infected wheat grains showed wheat blast symptoms on leaves of wheat but not rice. Our phylogenomic and population genomic analyses revealed that the wheat blast outbreak in Bangladesh was most likely caused by a wheat-infecting South American lineage of the blast fungus Magnaporthe oryzae. CONCLUSION: Our findings suggest that genomic surveillance can be rapidly applied to monitor plant disease outbreaks and provide valuable information regarding the identity and origin of the infectious agent.

Temporal patterns of ascospore release in Leptosphaeria maculans vary depending on geographic region and time of observation.

Diurnal patterns of spore release have been observed in a number of fungal pathogens that undergo wind-assisted dispersal. The mechanisms that drive these patterns, while not well understood, are thought to relate to the ability of dispersing spores to survive their journey and infect new hosts. In this paper, we characterise the diurnal pattern of ascospore release by a Western Australian population of Leptosphaeria maculans. Although L. maculans has been previously shown to exhibit diurnal patterns of ascospore release, these patterns appear to vary from region to region. In order to characterise the pattern of release in the Mediterranean climate of Western Australia, we analysed historical data describing the bi-hourly count of airborne ascospores at Mt Barker, Western Australia. Results of this analysis showed diurnal patterns that differ from those previously observed in other countries, with ascospore release in our study most likely to occur in the afternoon. Furthermore, we found that the time of peak release can shift from month to month within any one season, and from year to year. In explaining the hourly pattern of spore release over an entire season, time since rainfall, time since last release, temperature, hour and month were all shown to be significant variables.

Seasonal and diurnal patterns of spore release can significantly affect the proportion of spores expected to undergo long-distance dispersal.

Many of the fungal pathogens that threaten agricultural and natural systems undergo wind-assisted dispersal. During turbulent wind conditions, long-distance dispersal can occur, and airborne spores are carried over distances greater than the mean. The occurrence of long-distance dispersal is an important ecological process, as it can drastically increase the extent to which pathogen epidemics spread across a landscape, result in rapid transmission of disease to previously uninfected areas, and influence the spatial structure of pathogen populations in fragmented landscapes. Since the timing of spore release determines the wind conditions that prevail over a dispersal event, this timing is likely to affect the probability of long-distance dispersal occurring. Using a Lagrangian stochastic model, we test the effect of seasonal and diurnal variation in the release of spores on wind-assisted dispersal. Spores released during the hottest part of the day are shown to be more likely to undergo long-distance dispersal than those released at other times. Furthermore, interactions are shown to occur between seasonal and diurnal patterns of release. These results have important consequences for further modelling of wind-assisted dispersal and the use of models to predict the spread of fungal pathogens and resulting population and epidemic dynamics.

Principles of predicting plant virus disease epidemics.

Predicting epidemics of plant virus disease constitutes a challenging undertaking due to the complexity of the three-cornered pathosystems (virus, vector, and host) involved and their interactions with the environment. A complicated nomenclature is used to describe virus epidemiological models. This review explains how the nomenclature evolved and provides a historical account of the development of such models. The process and steps involved in devising models that incorporate weather variables and data retrieval and are able to forecast plant virus epidemics effectively are explained. Their application to provide user-friendly, Internet-based decision support systems (DSSs) that determine when and where control measures are needed is described. Finally, case studies are provided of eight pathosystems representing different scenarios in which modeling approaches have been used with varying degrees of effectiveness to forecast virus epidemics in parts of the world with temperate, Mediterranean, subtropical, and tropical climates.