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Anthropogenic activities have a major impact on the global environment. Canada's natural resources are threatened by the spread of fungal pathogens, which is facilitated by agricultural practices and international trade. Fungi are introduced to new environments and sometimes become established, in which case they can cause disease outbreaks resulting in extensive forest decline. Here, we describe how a nationwide sample collection strategy coupled to next-generation sequencing (NGS) (i.e., metagenomics) can achieve fast and comprehensive screening for exotic invasive species. This methodology can help provide guidance to phytopathology stakeholders such as regulatory agencies. Several regulated invasive species were monitored by processing field samples collected over 3 years (2013 to 2015) near high-risk areas across Canada. Fifteen sequencing runs were required on the Ion Torrent platform to process 398 samples that yielded 45 million reads. High-throughput screening of fungal and oomycete operational taxonomic units using customized fungi-specific ribosomal internal transcribed spacer 1 barcoded primers was performed. Likewise, Phytophthora-specific barcoded primers were used to amplify the adenosine triphosphate synthase subunit 9-nicotinamide adenine dinucleotide dehydrogenase subunit 9 spacer. Several Phytophthora spp. were detected by NGS and confirmed by species-specific quantitative polymerase chain reaction (qPCR) assays. The target species Heterobasidion annosum sensu stricto could be detected only through metagenomics. We demonstrated that screening target species using a variety of sampling techniques and NGS-the results of which were validated by qPCR-has the potential to increase survey capacity and detection sensitivity, reduce hands-on time and costs, and assist regulatory agencies to identify ports of entry. Considering that early detection and prevention are the keys in mitigating invasive species damage, our method represents a substantial asset in plant pathology management.

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The Canadian beekeeping industry is spread across the country, with the greatest proportion of managed honey bee colonies occurring in the Prairie Provinces. Nationally, the number of beekeepers has recently been trending upwards. Simultaneously, agronomic and environmental plant pest incidents are increasing due to a number of factors, including the introduction of exotic organisms through international trade, which is a major pathway for the introduction of potentially invasive alien species and quarantine pests. Therefore, regulatory agencies are interested in developing high‐throughput tools to achieve earlier detection of unwanted species in order to expedite application of mitigating measures to limit the impacts of their introduction. This study evaluates the potential of pollen pellet contents collected by honey bees to monitor plant pests using metabarcoding, a high‐throughput sequencing (HTS) approach for monitoring complex environmental samples. The study used the ITS1 intergenic region to target oomycetes and fungi, the ATP9‐NAD9 spacer to specifically target Phytophthora species, and the ITS2 region to target plant species. From the HTS results, a number of plants that were detected corresponded to known hosts of certain pathogens or species closely related to potentially invasive plant species. Genera including phytopathogenic species found in the pollen samples comprised Fusarium sp., Ophiostoma sp., Peronospora sp., Phytophthora sp., and Pythium sp. Correlations, high entropy, and co‐occurrences between certain plants and oomycetes or fungi were observed. The potential for using honey bee‐collected pollen pellets to study phytopathogens in a given environment is demonstrated here, and this concept could represent a promising complementary tool for the surveillance of phytopathogens or unwanted plants with previously described air and insect sampling methods if the protocol was applied with additional genetic markers.

MotivationCorrect taxonomic identification of DNA sequences is central to studies of biodiversity using both shotgun metagenomic and metabarcoding approaches. However, no genetic marker gives sufficient performance across all the biological kingdoms, hampering studies of taxonomic diversity in many groups of organisms. This has led to the adoption of a range of genetic markers for DNA metabarcoding. While many taxonomic classification software tools can be re-trained on these genetic markers, they are often designed with assumptions that impair their utility on genes other than the SSU and LSU rRNA. Here, we present an update to Metaxa2 that enables the use of any genetic marker for taxonomic classification of metagenome and amplicon sequence data.ResultsWe evaluated the Metaxa2 Database Builder on 11 commonly used barcoding regions and found that while there are wide differences in performance between different genetic markers, our software performs satisfactorily provided that the input taxonomy and sequence data are of high quality.Availability and implementationFreely available on the web as part of the Metaxa2 package at http://microbiology.se/software/metaxa2/.Supplementary information Supplementary data are available at Bioinformatics online.