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Geographical and seasonal dynamics and air trajectory modeling of air-borne cereal rust fungi: a case study in western Canada
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  • Wen Chen,
  • Nathaniel Newlands,
  • Sarah Hambleton,
  • Andre Laroche,
  • Devon Radford,
  • Guus Bakkeren
Wen Chen
Agriculture and Agri-Food Canada
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Nathaniel Newlands
Agriculture and Agri-Food Canada
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Sarah Hambleton
Agriculture and Agri-Food Canada
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Andre Laroche
Agriculture and Agri-Food Canada
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Devon Radford
Agriculture and Agri-Food Canada
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Guus Bakkeren
Agriculture and Agri-Food Canada

Corresponding Author:[email protected]

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Abstract

Cereal rust diseases are caused by Puccinia spp. urediniospores that travel by air currents to cause losses in wheat, oat, barley, and rye. In western Canada, inoculum can arrive from the USA through the Great Plains or via a Pacific corridor west of the Rocky Mountains. Moreover, climate and land use are known factors in Puccinia dissemination, but their relative effects remain poorly understood. We investigated aeromycobiota in western Canada by weekly air-sampling over four growing seasons (2015-2018) three mixed-crop sites in British Columbia (BC) and one season (2018) at five sites in southern Alberta (AB). ITS2-based metabarcoding and novel bioinformatic comparative analyses to known, especially curated cereal rust fungal sequences, was used for species identification. The overall aeromycobiota and rust fungal community diversity was higher west than east of the Canadian Rockies. This mountain range delineates climate and land use and also creates a barrier to wind flow to prevent the spread of rusts and other plant pathogens (e.g., Bipolaris, Blumeria). We recovered seven major cereal rusts and revealed their geographic and seasonal dynamics at the eight sampling sites. Forward and reverse trajectory HYSPLIT model simulations predicted the potential sources of rust urediniospores and their pathways of movement modulated by air currents, through which some pronounced changes in the abundance of wheat rust pathogens were explained. This study paves the way for a potential application for pathogen monitoring and disease risk forecasting as part of the design and development of an early-warning system for enhanced biovigilance against crop diseases.