Beth Purse wp3 13 Nov 19
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The document summarizes research from Work Package 3 of the Phytothreats project. The research analyzed pathogen traits and global distribution data to identify and rank threats from Phytophthora species to the UK. Key findings include: (1) Species that are cold-adapted are more likely to be introduced through the live plant trade; (2) Thermal tolerance traits influence a species' ability to establish at higher latitudes; (3) Models using global occurrence data and environmental factors accurately predict UK habitat suitability. Traits like cold tolerance and symptoms modulated a species' global spread and host range. The research aims to develop tools to inform policies and practices around Phytophthora importation, establishment, and
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Beth Purse wp3 13 Nov 19
- 1. Beth Purse beth@ceh.ac.uk, Louise Barwell, Dan Chapman, Ana Perez-Sierra, David Cooke, Sarah Green, Mariella Marzano, Michael Dunn, Phytothreats: WP3 highlights Linking global spread and impact of Phytophthoras to biological traits, trade and travel, suitable habitat and climate
- 2. Pathogens as Invaders: Invasion Stages & Barriers Which Phytophthora species can overcome barriers to cause impact? Does this depend on their biological characteristics or traits?
- 3. Phytophthoras spread by trade and travel, sensitive to climatic and habitat conditions Agricultural trade flows into Europe (source) Growth rate at different temperatures
- 4. WP3: Analyse pathogen behaviour and traits to identify and rank global Phytophthora threats to UK What range of hosts and sectors could be impacted? How are Phytophthoras introduced into new areas, from which key source areas, by which trade pathways? Which Phytophthoras establish in the wider environment, does this depend on traits? How much of UK is at risk? Which Phytophthoras arrive? Does this depend on traits? Could tourism provide a potential mechanism for spread and how could this be investigated?***
- 5. Culture collections Citizen Science Projects Governmental bodies Global distribution databases Global Phytophthora distribution: data sources Published records Diagnostic laboratories Sequence data repositoriesConsultants Researchers Agricultural Forest / forestry Nursery / ornamental
- 6. Global Phytophthora distribution database 39 888 country level records 1585 species x country new detections 27 706 site level records 179 countries 173 species 107 expert contributors
- 7. • 179 species (8 hybrids, 13 provisionally named) • Phylogeny included Phytophthora trait database Peter Scott Nari Williams Giles Hardy Treena Burgess Ana Perez-Sierra Sarah Green Beatrice Henricot David Cooke (JHI)
- 8. Phytophthora traits included in the database • Arrival / establishment o Oospores or not o Oospore wall width o chlamydospores o hyphal swellings o proliferating sporangia • Establishment o thermal tolerance for growth o growth rate at optimum temperature o minimum temperature for growth • Spread o caducous sporangia (aerial spread) • Impact o root disease (below-ground) o foliar disease (above ground)
- 9. Risk of introduction: countries where arrived 75 countries with ≥ 1 documented Phytophthora arrival since 2000 How are Phytophthoras introduced into new areas, from which key source areas, by which trade pathways? Which Phytophthoras arrive? Does this depend on traits? species X country arrival
- 10. Risk of introduction: plant trade networks
- 11. Risk of introduction: species arriving 35 Phytophthora species with ≥ 1 documented arrival since 2000 (with trait data): species X country arrival Phytophthora asparagi (5) Phytophthora drechsleri (5) Phytophthora nicotianae (36) Phytophthora austrocedri (3) Phytophthora fragariae (15) Phytophthora palmivora (21) Phytophthora boehmeriae (4) Phytophthora gregata (5) Phytophthora porri (6) Phytophthora cactorum (11) Phytophthora hedraiandra (8) Phytophthora pseudosyringae (8) Phytophthora cambivora (17) Phytophthora heveae (8) Phytophthora quercina (11) Phytophthora capsici (14) Phytophthora hibernalis (17) Phytophthora ramorum (18) Phytophthora cinnamomi (12) Phytophthora infestans (11) Phytophthora richardiae (4) Phytophthora citricola (4) Phytophthora lateralis (9) Phytophthora rubi (17) Phytophthora citrophthora (5) Phytophthora meadii (11) Phytophthora sojae (20) Phytophthora colocasiae (5) Phytophthora megasperma (12) Phytophthora syringae (9) Phytophthora cryptogea (11) Phytophthora melonis (3) Phytophthora tentaculata (6) Phytophthora x alni (9) Phytophthora uniformis (6)
- 12. National recording and biosecurity effort Early, R., Bradley, B., Dukes, J. et al. Nat Commun 7, 12485 (2016)
- 13. Introductions more likely where countries are connected to source regions by the live plant trade Estimate z P Total imports -0.188 -0.952 0.341 Network connectivity 1.330 7.516 <0.001 Network connectivity explains 21% of the variation in arrival
- 14. Some species better able to exploit trade pathways than others Graph of key interactions between trade and traits please!
- 15. Risk of introduction: key findings How are Phytophthoras introduced into new areas, from which key source areas, by which trade pathways? Which Phytophthoras arrive? Does this depend on traits? Arrival All commodities Agricultural Horticultural Live plants Species that are cold-adapted (lower min. temp for growth) more likely to arrive through live plant trade Logtradevolumes
- 16. Risk of Phytophthora importation to UK: Key source countries and pathogens Excerpt from your interactive map for the UK please!
- 17. Risk of establishment: escape from nurseries UK Key data sources THDAS (268 records) SASA (820 records) eDOMERO P type (12,596) SOD inspection (28,515) RHS (1595) Premise types: sites of interception: ports, nurseries, garden centres, shops, warehouses sites of onward spread: private and public gardens, amenity planting, woodland and heathland, landscaping Which Phytophthoras establish in the wider environment, does this depend on traits?
- 18. Risk of establishment: escape from nurseries UK Which Phytophthoras establish in the wider environment? S Species in forests: subset of those in nurseries and gardens Species Numberofrecords
- 19. Risk of establishment: escape from nurseries UK Species thermal traits seem to affect the latitudinal range of spread Cold-tolerance – linked to establishment at higher latitudes elsewhere in Europe (Redondo et al. 2018)
- 20. Risk of establishment: global niche models Global niche models How much of UK is at risk of establishment?
- 21. Risk of establishment: global niche models How much of UK is at risk of establishment? Non-UK records (training) UK records (validation) First UK record Phytophthora ramorum 3401 12037 2002 Phytophthora cinnamomi 3921 221 1937 Phytophthora lacustris 962 6 1972 Phytophthora plurivora 259 334 1999 Phytophthora cryptogea 144 302 1915 Phytophthora gonapodyides 375 62 1900 Phytophthora x alni 382 16 1993 Phytophthora cactorum 127 190 1959 Phytophthora cambivora 219 55 1938 • All 9 species for which we have sufficient records to make a niche model are already present in the UK • Very few records for focal species not already in the UK
- 22. Risk of establishment: global niche models Models based on environmental factors predicted global distributions well Species Modelaccuracy
- 23. Risk of establishment: global niche models Models with environmental factors and global occurrence data give accurate predictions of UK distribution in the wider environment (UK data excluded from model training)
- 24. Risk of establishment: global niche models
- 25. Risk of establishment: global niche models Varying role of environmental drivers between species 0% 10% 20% 30% 40% 50% 60% P. cactorum P. cambivora P. cinnamomi P. cryptogea P. gonapodyides P. lacustris P. plurivora P. ramorum P. x alni Variableimportance Summer temperature (Bio10) Winter temperature (Bio6) Precipitation seasonality (Bio15) Moisture index Forest cover Urban cover Agricultural cover
- 26. Risk of establishment: global climate limits linked to traits y = 0.4889x + 10.18 R² = 0.272 8 10 12 14 16 1 3 5 7 9 Minwintertemperature atoccurrences(C) Minimum growing temperature (C) y = 0.3112x + 16.354 R² = 0.362 20 22 24 26 28 30 24 29 34 39 Maxsummer temperatureat occurrences(C) Max growing temperature (C) Thermal traits >>> Climaticlimits>>
- 27. Risk of establishment: global niche models • Global niche models: valid methodology for identifying suitable habitat for Phytophthoras in the UK • But lots of occurrence data needed to develop them, not available for many species that are yet to arrive in the UK • Many Phytophthoras unknown to science when 1st emerge in invaded range • Centralised occurrence and trait databases, integrating across sectors and enhanced sampling in source regions needed to be able to develop models and understand species behaviour when invade temperate areas
- 28. Risk of spread: do traits explain variation in global spread and host range of Phytophthora species range = 1 to 132 countries median = 2.5 countries range = 0 to 90 host plant families median = 2 Geographical extent: Number of countries “occupied” by species Host range: Number of host families recorded for species
- 29. Risk of spread: do traits explain variation in global spread and host range of Phytophthora species Fitness Trait A Trait B Trait C Growth rate Survival Reproduction Global impact SpreadTrait D Assumed indirect effects
- 30. Do individual traits explain geographic extent? • Cold-tolerance • Root symptoms – soil- borne transport with ornamentals, better buffered from dessication, temp extremes • Foliar symptoms – sporulation and local spread
- 31. Do individual traits explain host range? • Optimum growth rate • Thicker oospore walls • Root symptoms – soil- borne transport with ornamentals, better buffered from dessication, temp extremes • Foliar symptoms – sporulation and local spread
- 32. Do thermal traits especially cold-tolerance modulate invasion of Phytophthora into temperate regions? • Biological basis of cold-tolerance in Phytophthoras? • Thermal traits and oospore wall width that we have linked to invasion success also very variable across the phylogeny, suggesting recent adaptation Min temp for growth values across family tree of Phytophthoras
- 33. Risk of spread and impact : findings and horizon scanning • Phylogenetic relatedness: a strong a predictor of impact than biological traits • Years known to science • Cold-tolerance and disease symptoms predict geographic extent • Oospore wall width, disease symptoms and optimal growth rates predict host range ! • Phylogeny + traits accounted for 50-60% of variation we see in host range and global extent, potential for horizon scanning • Traits measured routinely for species descriptions, are we missing key traits linked to invasion?
- 34. Conclusions and future work Invasion success of Phytophthoras modulated by biological traits at each invasion stage, live plant trade from source areas is key pathway Transport/Introduction • Connectivity to source countries through live plant trade (which products/hosts?) • Some species, that are cold-adapted, better able to exploit live plant trade pathways Establishment • UK: cold-adapted species established further north in wider environment • Predictable from global niche models • Global climate limits linked to traits Global spread and impact • Cold-tolerant species, cause root and foliar symtoms - wider global extent • Sp. with higher growth rates, more resistant oospores, cause root and foliar symtoms - wider host range • Closely-related species are similar in impact • horizon-scanning
- 35. Global Phytophthora databases • Data pooled across sectors is valuable for understanding and predicting pathogen behaviour - traits and environment • Available to end-users and researchers, updated in future
- 36. Model outputs into policy & practice: How are Phytophthoras introduced into new areas, from which key source areas, by which trade pathways? Which Phytophthoras arrive? Does this depend on traits? Phytophthora importation tool
- 37. Model outputs into policy & practice: Phytophthora establishment
- 38. Model outputs into policy & practice: Hosts and sectors impacted Demonstrate host-phytophthora relationship part of tool
- 39. Model outputs into policy & practice: your feedback Would you use any of these tools to inform your practices? If yes, how could your practices change as a result of using these tools? What modifications or additional information would you like to see in the tools or models to improve their usefulness? How would you like to access them? Website, phone app
- 40. Louise and I would like to thank: Daniel Chapman, University of Stirling > 100 global contributors to the trait, host and occurrence data-bases Ana Perez-Sierra, Treena Burgess Beatrice Henricot, Anna Harris, Giles Hardy, Peter Scott, Nari Williams, David Cooke, Sarah Green, Paul Sharp Jane Barbrook, Alexandra Schlenzig, Royal Horticultural Society for sharing UK data Mike Dunn and Mariella Marzano Funders of Phytothreats Acknowledgements
Editor's Notes
- We’ve targeted These organisations are a selection of those we have contacted representing the different Beth works a lot with zoonotic diseases where there is a movement towards a One Health approach. This is where emerging diseases are best understood when human health, wildlife health and livestock health are dealt with together. And there is a similar argument for mapping the global distribution of Phytophthora species in all of these sectors, so we can properly understand the factors driving their distributions. Because we know that horticultural hosts are a potential reservoir for Phytophthora and there’s some agricultural pests that are shared with forestry species too. So I think it’s important that we try to get as much data as possible from all of these sectors, even if it’s mainly forest and forestry disease that is our focus.
- We’ve targeted These organisations are a selection of those we have contacted representing the different Beth works a lot with zoonotic diseases where there is a movement towards a One Health approach. This is where emerging diseases are best understood when human health, wildlife health and livestock health are dealt with together. And there is a similar argument for mapping the global distribution of Phytophthora species in all of these sectors, so we can properly understand the factors driving their distributions. Because we know that horticultural hosts are a potential reservoir for Phytophthora and there’s some agricultural pests that are shared with forestry species too. So I think it’s important that we try to get as much data as possible from all of these sectors, even if it’s mainly forest and forestry disease that is our focus.
- Reproductive strategies can be more flexible than homo-heterothallism distinction would suggest, so Treena suggested modifying to just consider oospores versus no oospores, minimum temperature included because most labile. Look up biological significance of proliferating sporangia and check terminology We hope that the trait-based framework can be used as a predictive tool, when a pathogen is first described, when only a few morphological or biological traits are known or measured. The root and foliar disease traits may only be fully described after a species has been known for quite some time, so we compared the performance of models with and without these impact traits. Because information on other trait or impact values like host range may also take time to accumulate, we included time known to science as a potential explanatory variable in our analysis. This should eventually let us predict for recently described species, the eventual host range for example.
- This is a quick overview of the prevalence of different Phytophthora species in nurseries, gardens and forest sites. It’s difficult to unpick these patterns from the sampling effects as we clearly have lots more records from gardens because of the huge amount of data in the RHS dataset. But it does seem that the species in forests are subset of those found in nurseries and gardens. So it be that we
- Next is the results from the global niche models. Dan Chapman has been working on these very recently and he’s made loads of progress really quickly. So I apologise if I don’t do these results justice as I haven’t really digested them yet, but the results look really exciting.
- Methods: Available species data How models account unsuitable habitat, dispersal distances and recording effort Environmental drivers considered (expert-elicitation)
- This summarises the performance of the niche models for all nine species. It’s based on a 80:20 split of the non-UK data. The model performance is based on the area under the receiver operating characteristic, which is a measure of how well the model classifies the presences and absences at different thresholds of probability of occurrence predicted by the model. A model that predicts perfectly would get a value of one, which is represented by the blue dotted line at the top. So it seems like within the non-UK data the model is performing pretty well at discriminating presences and absences.
- Looking at the traits which are significantly linked to host range in blue here, Phytophthora species are recorded as using more host families if they have been known to science for longer, if they cause foliar or root disease, and again if they have a low minimum temperature for growth. In addition, closely related species are likely to be similar in the number of host species they are found on.
- Looking at the traits which are significantly linked to geographical extent in blue here, Phytophthora species are recorded as using more host families if they have been known to science for longer, if they cause foliar or root disease, and again if they have a low minimum temperature for growth. In addition, closely related species are likely to be similar in the number of host species they are found on.
- cold-tolerance may be labile than heat-tolerance? There’s evidence for this among other taxa (e.g. Araujo et al. 2013). Implications for colonising novel regions? Are higher latitudes more prone to new arrivals? Are recent emerging infectious diseases at higher latitudes associated with adaptation to cold-tolerance? Do we know (have a feeling about) the origin of any of these (e.g. P. ramorum, cinnamomi, austrocedri) that might corroborate this? Treena’s pictures Pr versus Pk Look at with spread models in Europe and Global Biological basis of thermal tolerance, how thermal traits co-vary with others?
- Root disease more likely to kill the plant than foliar disease – related to disease severity? soil / root disease pathogens more buffered from external environmental fluctuations (protected from dessication, temperature extremes) compared to foliar pathogens? Soil-borne /root disease pathogens more likely to arrive with live plants?
- This summarises the performance of the niche models for all nine species. It’s based on a 80:20 split of the non-UK data. The model performance is based on the area under the receiver operating characteristic, which is a measure of how well the model classifies the presences and absences at different thresholds of probability of occurrence predicted by the model. A model that predicts perfectly would get a value of one, which is represented by the blue dotted line at the top. So it seems like within the non-UK data the model is performing pretty well at discriminating presences and absences.