PHYTO-THREATS: Addressing Threats to UK Forests and Woodlands from Phytophthora; Identifying Risks of Spread in Trade and Methods for Mitigation

Utility of decision tools for assessing plant health risks from management strategies in natural environments

Increased imports of plants and timber through global trade networks provide frequent opportunities for the introduction of novel plant pathogens that can cross-over from commercial to natural environments, threatening native species and ecosystem functioning. Prevention or management of such outbreaks relies on a diversity of cross-sectoral stakeholders acting along the invasion pathway. Yet, guidelines are often only produced for a small number of stakeholders, missing opportunities to consider ways to control outbreaks in other parts of the pathway. We used the infection of common juniper with the invasive pathogen Phytophthora austrocedri as a case study to explore the utility of decision tools for managing outbreaks of plant pathogens in the wider environment. We invited stakeholders who manage or monitor juniper populations or supply plants or management advice to participate in a survey exploring their awareness of, and ability to use, an existing decision tree produced by a coalition of statutory agencies augmented with new distribution maps designed by the authors. Awareness of the decision tree was low across all stakeholder groups including those planting juniper for restoration purposes. Stakeholders requested that decision tools contain greater detail about environmental conditions that increase host vulnerability to the pathogen, and clearer examples of when management practices implicated in pathogen introduction or spread should not be adopted. The results demonstrate the need to set clear objectives for the purpose of decision tools and to frame and co-produce them with many different stakeholders, including overlooked groups, such as growers and advisory agents, to improve management of pathogens in the wider environment.

THAPBI PICT-a fast, cautious, and accurate metabarcoding analysis pipeline

THAPBI PICT is an open source software pipeline for metabarcoding analysis of Illumina paired-end reads, including cases of multiplexing where more than one amplicon is amplified per DNA sample. Initially a Phytophthora ITS1 Classification Tool (PICT), we demonstrate using worked examples with our own and public data sets how, with appropriate primer settings and a custom database, it can be applied to other amplicons and organisms, and used for reanalysis of existing datasets. The core dataflow of the implementation is (i) data reduction to unique marker sequences, often called amplicon sequence variants (ASVs), (ii) dynamic thresholds for discarding low abundance sequences to remove noise and artifacts (rather than error correction by default), before (iii) classification using a curated reference database. The default classifier assigns a label to each query sequence based on a database match that is either perfect, or a single base pair edit away (substitution, deletion or insertion). Abundance thresholds for inclusion can be set by the user or automatically using per-batch negative or synthetic control samples. Output is designed for practical interpretation by non-specialists and includes a read report (ASVs with classification and counts per sample), sample report (samples with counts per species classification), and a topological graph of ASVs as nodes with short edit distances as edges. Source code available from https://github.com/peterjc/thapbi-pict/ with documentation including installation instructions.

On a handshake: business-to-business trust in the biosecurity behaviours of the UK live plant trade

The movement of plants through the ornamental plant trade presents a major source of risk for the introduction and spread of plant pests and pathogens. To minimise the likelihood of infested or infected plants moving through the value chain, individual businesses can adopt a range of biosecurity practices to prevent introduction on site, as well as detecting and then containing or eradicating any plant pests or pathogens present. However, a major additional source of risk is the arrival of unhealthy plants sourced from a supplier. Using the example of bacterial plant pathogen Xylella fastidiosa which has a large host range and potentially devastating economic and environmental impacts, we highlight the importance of trust when businesses navigate the risks of sourcing plants. Through interviews and a survey with a range of plant businesses, we show (i) how two general types of risk-relational risk associated with suppliers acting in good faith, and performance risk associated with suppliers having the ability to perform as expected-can be applied to the challenge of sourcing healthy plants, (ii) how businesses respond to these risks through behaviours based on trust and control, and (iii) the potential outcomes of trust-based and control-based behaviours in the presence of a hard to detect pathogen such as Xylella fastidiosa. We conclude that trust is a significant component in decision-making in the live plant trade, and as such any behavioural interventions designed to encourage better biosecurity practices in the industry should capitalise on this understanding to strengthen responses and avoid undermining of effort.

An open-access T-BAS phylogeny for emerging Phytophthora species

Phytophthora species cause severe diseases on food, forest, and ornamental crops. Since the genus was described in 1876, it has expanded to comprise over 190 formally described species. There is a need for an open access phylogenetic tool that centralizes diverse streams of sequence data and metadata to facilitate research and identification of Phytophthora species. We used the Tree-Based Alignment Selector Toolkit (T-BAS) to develop a phylogeny of 192 formally described species and 33 informal taxa in the genus Phytophthora using sequences of eight nuclear genes. The phylogenetic tree was inferred using the RAxML maximum likelihood program. A search engine was also developed to identify microsatellite genotypes of P. infestans based on genetic distance to known lineages. The T-BAS tool provides a visualization framework allowing users to place unknown isolates on a curated phylogeny of all Phytophthora species. Critically, the tree can be updated in real-time as new species are described. The tool contains metadata including clade, host species, substrate, sexual characteristics, distribution, and reference literature, which can be visualized on the tree and downloaded for other uses. This phylogenetic resource will allow data sharing among research groups and the database will enable the global Phytophthora community to upload sequences and determine the phylogenetic placement of an isolate within the larger phylogeny and to download sequence data and metadata. The database will be curated by a community of Phytophthora researchers and housed on the T-BAS web portal in the Center for Integrated Fungal Research at NC State. The T-BAS web tool can be leveraged to create similar metadata enhanced phylogenies for other Oomycete, bacterial or fungal pathogens.

Comparison of Primers for the Detection of Phytophthora (and Other Oomycetes) from Environmental Samples

Many oomycetes are important plant pathogens that cause devastating diseases in agricultural fields, orchards, urban areas, and natural ecosystems. Limitations and difficulties associated with isolating these pathogens have led to a strong uptake of DNA metabarcoding and mass parallel sequencing. At least 21 primer combinations have been designed to amplify oomycetes, or more specifically, Phytophthora species, from environmental samples. We used the Illumina sequencing platform to compare 13 primer combinations on mock communities and environmental samples. The primer combinations tested varied significantly in their ability to amplify Phytophthora species in a mock community and from environmental samples; this was due to either low sensitivity (unable to detect species present in low concentrations) or a lack of specificity (an inability to amplify some species even if they were present in high concentrations). Primers designed for oomycetes underestimated the Phytophthora community compared to Phytophthora-specific primers. We recommend using technical replicates, primer combinations, internal controls, and a phylogenetic approach for assigning a species identity to OTUs or ASVs. Particular care must be taken if sampling substrates where hybrid species could be expected. Overall, the choice of primers should depend upon the hypothesis being tested.

DNA Metabarcoding and Isolation by Baiting Complement Each Other in Revealing Phytophthora Diversity in Anthropized and Natural Ecosystems

Isolation techniques supplemented by sequencing of DNA from axenic cultures have provided a robust methodology for the study of Phytophthora communities in agricultural and natural ecosystems. Recently, metabarcoding approaches have emerged as new paradigms for the detection of Phytophthora species in environmental samples. In this study, Illumina DNA metabarcoding and a conventional leaf baiting isolation technique were compared to unravel the variability of Phytophthora communities in different environments. Overall, 39 rhizosphere soil samples from a natural, a semi-natural and a horticultural small-scale ecosystem, respectively, were processed by both baiting and metabarcoding. Using both detection techniques, 28 out of 39 samples tested positive for Phytophthora. Overall, 1,406,613 Phytophthora internal transcribed spacer 1 (ITS1) sequences and 155 Phytophthora isolates were obtained, which grouped into 21 taxa, five retrieved exclusively by baiting (P. bilorbang; P. cryptogea; P. gonapodyides; P. parvispora and P. pseudocryptogea), 12 exclusively by metabarcoding (P. asparagi; P. occultans; P. psycrophila; P. syringae; P. aleatoria/P. cactorum; P. castanetorum/P. quercina; P. iranica-like; P. unknown sp. 1; P. unknown sp. 2; P. unknown sp. 3; P. unknown sp. 4; P. unknown sp. 5) and four with both techniques (P. citrophthora, P. multivora, P. nicotianae and P. plurivora). Both techniques complemented each other in describing the variability of Phytophthora communities from natural and managed ecosystems and revealing the presence of rare or undescribed Phytophthora taxa.