Beth purse wp3 april 2018
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Beth purse wp3 april 2018
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Beth purse wp3 april 2018
- 1. Beth Purse beth@ceh.ac.uk WP3 Core Team: Louise Barwell, Dan Chapman, Ana Perez- Sierra, Beatrice Henricot, Mariella Marzano, Michael Dunn, David Cooke, Sarah Green Phytothreats: WP 3 overview
- 2. WP3 objectives –identify and rank global Phytophthora threats to the UK WP3.1 Risk of introduction WP3.3 Horizon-scanning for emerging pathogens: scoping of knowledge gaps WP3.2 Risk of establishment and spread Trait-based frameworks to inform risk register • Identify the most important trade pathways • Link introduction risk to ecological traits • Map global environmental niches of Phytophthora species • Link establishment in Europe to social factors and ecological traits • Map risk areas in the UK • identify research priorities for horizon scanning for emerging pathogens (supply chains, tourism pathways)
- 3. WP3.1 Risk of introduction WP3 Milestones • WP3 Compile European database of country level occurrence/arrival of Phytophthoras in nursery and wider environment and associated trade and environmental data (year 1) • WP3 Compile global database of fine scale occurrence data for ~40 target Phytophthora species and associated environmental data (year 1) • WP3 Compile traits database for Phytophthora species (including all species found in Europe plus selected others worldwide) (year 2) • WP3 Complete models relating to patterns of introduction and establishment in Europe to species traits, trade pathways and local environmental conditions and global niche models for ~ 40 target Phytophthora species worldwide (year 3) • WP3 Develop policy brief for UK risk register board and other stakeholders on improved risk ranking for Phytophthoras (year 3)
- 4. WP3.1 - Risk of introduction Aims: • Identify the most important trade and recreational pathways linking Phytophthora source regions to the UK • Model introduction risk based on transport networks, source and destination characteristics • Test links between introduction risk and traits Agricultural trade flows into Europe
- 5. Milestone: Global country-level database of occurrence/arrival of Phytophthoras (year 1) • 17371 country-level records • 1417 species x country combinations • Source / recipient country • Year of first record – arrival • Invasion status WP3.1 - Risk of introduction
- 6. WP3.1 – National occurrence database
- 7. WP3.1 – National recording and biosecurity effort • National pest reporting activity (IPPC) • Legal instruments (ECOLEX) • Biosecurity investment (FAO) • descriptors of Plant Health Inspector or Research activity Turbelin et al. 2017 Global Ecology and Biogeography, 26, 78–92 Legal instruments relevant to invasive species
- 8. WP3.1 – preliminary analyses: defining arrivals source distribution pre-2000 reports # new(?) arrivals post-2000 reports
- 9. WP3.1 – preliminary analyses: first models Response # Phytophthora arrivals per country since 2000 Predictors trade connectivity to pest source countries (total imports of live plants) Biosecurity effort (# pieces of invasive species legislation) Surveillance effort (# official pest reports - IPPC)
- 10. WP3.1 – Live plant imports Live plant imports + recording effort together explained 59.4% of deviance in the number of new Phytophthoras arriving in a country since 2000
- 11. https://resourcetrade.earth WP3.1 - Risk of introduction
- 12. Key data sources THDAS 268 records (to 2016, update?) SASA 820 records eDOMERO 12,596 (P type) , 28,515 (SOD inspection) RHS 1595 records Premise types: ports, airports, nursery, private and public gardens, garden centres and shops, woodland and heathland WP3.1 –Interception/escape in the UK
- 13. WP3.1 –Interception/escape in the UK • From premise types, possible to separate sites of interception from sites of onward spread? • Relate species (i) extent of interception and (ii) extent of onward spread to traits • Define source pool as those species present in countries that UK trades in plants/substrates with (threshold level) • Relate geographical pattern of spread to environmental characteristics and traits
- 14. WP3.2 Risk of establishment and spread WP3 Milestones • WP3 Compile European database of country level occurrence/arrival of Phytophthoras in nursery and wider environment and associated trade and environmental data (year 1) • WP3 Compile global database of fine scale occurrence data for ~40 target Phytophthora species and associated environmental data (year 1) • WP3 Compile traits database for Phytophthora species (including all species found in Europe plus selected others worldwide) (year 2) • WP3 Complete models relating to patterns of introduction and establishment in Europe to species traits, trade pathways and local environmental conditions and global niche models for ~ 40 target Phytophthora species worldwide (year 3) • WP3 Develop policy brief for UK risk register board and other stakeholders on improved risk ranking for Phytophthoras (year 3)
- 15. Global site-level database Collation has continued: Data to come from Australia, New Zealand, South Africa, RGBE, Canada (meta-barcoding) • 11407 records (1 - 10 km precision) • 82 species • 38 countries
- 16. Prioritise regions climatically similar to UK regions
- 17. Culture collections Citizen Science Projects Governmental bodies Global distribution databases Data sources Published records Diagnostic laboratories Sequence data repositoriesConsultants Researchers Agricultural Forest / forestry Nursery / ornamental
- 18. Niche models: potential global impact, establishment and spread in UK Burgess, T.I., Scott, J.K., McDougall, K.L., Stukely, M.J.C., Crane, C., Dunstan, W.A., Brigg, F., Andjic, V., White, D., Rudman, T., Arentz, F., Ota, N. & Hardy, G.E.S.J. (2016) Current and projected global distribution of Phytophthora cinnamomi , one of the world’s worst plant pathogens. Global Change Biology • Matching past patterns in occurrence with environmental data • E.g. over 15000 presences from 11 countries • 5 CLIMEX meteorological variables
- 19. Focal species (see Huddle for list) Known / potential impacts on UK tree species Exclude: • species known only from water/soil • species with only non-woody hosts • Non-relevant woody hosts: e.g. Eucalyptus spp. Crossover species Risk maps (Absent from UK) Model validation (Present in UK) N = 24N = 30
- 20. WP3 – Accounting for biases in reporting effort Predicted relative risk of emerging infectious disease events Reporting bias not accounted for Reporting bias factored into analysis Allen et al. 2017 Nature Communications DOI: 10.1038/s41467-017- 00923-8
- 21. WP3 – Accounting for biases from recording effort • Mine scientific literature for locations where a wider taxonomic group have been reported • e.g. Oomycetes or fungal plant pathogens • e.g. CAB abstracts? Genbank? • Map recording intensity at 5km or 10km grid scale and include as layer or down-weight records for species in highly recorded areas Where do plant pathologists, plant health practitioners and researchers publish their data?
- 22. Potential environmental drivers of Phytophthora distributions: break out groups Which environmental factors do you think influence the establishment and spread of Phytophthoras in particular locations? Rank factors RANK Establishment Why do you think this is important? Most important 1 2 3 4 Least important 5 RANK Spread Why do you think this is important? Most important 1 2 3 4 Least important 5
- 23. WP3.1 & WP3.2 A global Phytophthora trait database WP3 Milestones • WP3 Compile European database of country level occurrence/arrival of Phytophthoras in nursery and wider environment and associated trade and environmental data (year 1) • WP3 Compile global database of fine scale occurrence data for ~40 target Phytophthora species and associated environmental data (year 1) • WP3 Compile traits database for Phytophthora species (including all species found in Europe plus selected others worldwide) (year 2) • WP3 Complete models relating to patterns of introduction and establishment in Europe to species traits, trade pathways and local environmental conditions and global niche models for ~ 40 target Phytophthora species worldwide (year 3) • WP3 Develop policy brief for UK risk register board and other stakeholders on improved risk ranking for Phytophthoras (year 3)
- 24. • Merged Phytophthora database (June 2017) • 179 species (8 hybrids, 13 provisionally named) • Maintained by Scion Research • Phylogeny included Phytophthora trait database Peter Scott Nari Williams Giles Hardy Treena Burgess Ana Perez-SierraBeatrice Henricot
- 25. Phytophthora trait database publication Conceptual framework for links between traits and invasion success of Phytophthora Do closely related species share similar values for traits or groups of traits or have traits linked to invasion evolved independently in several places in the phylogeny? Are some traits more labile (weak phylogenetic signal) whilst other traits are more constrained or gradual in evolutionary trajectory (strong phylogenetic signal)?
- 26. Phytophthora trait database publication How do traits co-vary across species and how much of this is down to phylogenetic history rather than convergent evolution of trait syndromes? Can the traits of hybrids be predicted from those of the parent taxa?
- 27. Conceptual framework – traits hypothesised to increase invasion success
- 28. Phytophthora phylogenies Treena Burgess Also multi-gene phylogenies, resolve deeper nodes: 78 species: Martin and Coffey (2014) 133 species: Yang et al. (2018) 179 species, ITS-based
- 29. Phylogenetic signal: sporangial traits • All medium to strong phylogenetic structure (λ = 0.798 to 1) • Strongest signal - proliferating versus non-proliferating sporangia • Non-papillate sporangia almost exclusively in clades 6, 7 and 9 • Caducous sporangia most frequently in clades 1 to 3.
- 30. Phylogenetic signal: reproductive traits • Moderate phylogenetic structure (λ = 0.744 to 0.836) in reproductive strategy and survival structures (oospores, chlamydospores, hyphal swellings) • Morphology of oospores and oogonia weakest phylogenetic signal (λ = 0.471 to 0.748)
- 31. Phylogenetic signal: temperature traits • medium to strong phylogenetic structure (λ = 0.617 to 0.981) • Strongest signal – growth rate at optimum temperature, fastest in sub-clade 6a • Optimum and maximum temperatures also highly conserved, higher trait values clustering in clades 6a and 9
- 32. Phylogenetic signal: temperature traits • Thermal tolerance range and minimum growth temperatures less phylogenetically conserved
- 33. Tempo of trait diversification over time - within versus between clade trait variation High average within-clade disparity at given point in time (compared to that expected from Brownian model, grey) suggests rapid diversification and independent evolution to share common traits between clades • Minimum growth temperature and temperature tolerance range more labile (greater within-clade disparity) than other temperature traits • All thermal traits - greater within-clade disparity in recent evolutionary history • Oospore traits also more labile than expected from Brownian model of evolution Averagesub-cladedisparity Relative time Oospore wall thickness Oospore wall index Growth rate – opt T Min. growth temp. Max. growth temp.Opt. growth temp. Temp. tolerance range
- 34. Do thermal traits especially cold-tolerance modulate invasion of Phytophthora into temperate regions? • Emerging infections at higher latitudes linked to cold tolerance? Test with occurrence data • Biological basis of cold-tolerance in Phytophthoras?
- 35. Trait syndromes - how do traits co-vary with each other and how much of this is driven by phylogeny?
- 36. Global impact of Phytophthora • Can species traits explain the global impact of Phytophthora species? - Phytophthora trait database (170 species depending on phylogeny) - Global impact metrics • Do Phytophthora trait syndromes outperform individual traits as predictors of global impact?
- 37. Trait-based analysis: impact metrics • Geographical extent (# countries reported) • Host range (# host families) • Regulation (# NPPO/ RPPO lists) • Reports of new geographic regions / hosts (EPPO Reporting Service archives) • Legislation (ECOLEX database)
- 38. Trait-based analysis of Phytophthora impacts Fitness Trait A Trait B Trait C Growth rate Survival Reproduction Global impact SpreadTrait D Traits Invasion fi Performance Response Assumed indirect effects
- 39. Conceptual framework – traits hypothesised to increase invasion success
- 40. Global impact of Phytophthora • Can species traits explain the global impact of Phytophthora species? - Phytophthora trait database (170 species) - Global impact metrics • Do Phytophthora trait syndromes outperform individual traits as predictors of global impact?
- 41. Traits versus trait syndromes Phytophthora trait data: Trait database (102 species) ordination of trait-space trait syndromesindividual traits Compare model performance Impact ~ individual traits Impact ~ trait syndromes Phytophthora impact metrics: - host range - geographic extent
- 42. Do individual traits explain global impact?
- 43. Can trait syndromes explain global impact? • Highest impact species at greater values of axes 1 and 2 cause root disease + heterothallic + multiple survival structures + broad thermal tolerances + rapid growth at optimum • Mostly consistent with trait effects in individual trait models
- 44. Predicting host range: traits versus trait syndromes Better model performance Predictiveerror
- 45. Predicting host range: traits versus trait syndromes • Root (and foliar) disease symptoms predict broad host range Phytophthora • Trait syndromes are more ecologically informative about their global impact • A trait-based early-warning system for pathogens (which have the similar traits but no impact yet?) ! heterothallism + alternative survival structures disease symptoms impact?
- 46. Phytophthora traits – added new traits 10 traits • Arrival / establishment o Oospores or not o chlamydospores o hyphal swellings o proliferating sporangia • Establishment o mating strategy 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)
- 47. Tested impact-trait relationships using different phylogenies Treena Burgess Also multi-gene phylogenies, resolve deeper nodes: 78 species: Martin and Coffey (2014) 133 species: Yang et al. (2018) 179 species, ITS-based
- 48. Milestones: Plan for next year • Submit papers on (i) trait database and phylogenetic analyses (ii) linking traits and global impact Global occurrence and environmental databases (ongoing) • Fine-tune species-specific trade models (December 2018) • UK and European spread models (March 2019) WP3 Milestones • WP3 Compile European database of country level occurrence/arrival of Phytophthoras in nursery and wider environment and associated trade and environmental data (year 1) • WP3 Compile global database of fine scale occurrence data for ~40 target Phytophthora species and associated environmental data (year 1) • WP3 Compile traits database for Phytophthora species (including all species found in Europe plus selected others worldwide) (year 2) • WP3 Complete models relating to patterns of introduction and establishment in Europe to species traits, trade pathways and local environmental conditions and global niche models for ~ 40 target Phytophthora species worldwide (year 3) • WP3 Develop policy brief for UK risk register board and other stakeholders on improved risk ranking for Phytophthoras (year 3)
- 49. Milestones: Plan for next year • Finalise niche models (April 2019) • Finalise tourism review and questionnaire analysis • Co-develop final model outputs with policy makers and practitioners e.g. interactive source maps, list of Phytophthoras associated with key forestry species WP3 Milestones • WP3 Compile European database of country level occurrence/arrival of Phytophthoras in nursery and wider environment and associated trade and environmental data (year 1) • WP3 Compile global database of fine scale occurrence data for ~40 target Phytophthora species and associated environmental data (year 1) • WP3 Compile traits database for Phytophthora species (including all species found in Europe plus selected others worldwide) (year 2) • WP3 Complete models relating to patterns of introduction and establishment in Europe to species traits, trade pathways and local environmental conditions and global niche models for ~ 40 target Phytophthora species worldwide (year 3) • WP3 Develop policy brief for UK risk register board and other stakeholders on improved risk ranking for Phytophthoras (year 3)
- 50. Tailoring model outputs to policy & practice
- 51. A thank you to our funders and all those who have kindly shared their data Acknowledgements
Editor's Notes
- Do hybrids share traits with parent taxa Therefore we report λ as a measure of the phylogenetic signal in each trait and use it to infer the extent to which different traits appear to be more labile (weak phylogenetic signal) and which traits appear to be either constrained or gradual in their evolutionary trajectory (strong phylogenetic signal). ?
- Do hybrids share traits with parent taxa Therefore we report λ as a measure of the phylogenetic signal in each trait and use it to infer the extent to which different traits appear to be more labile (weak phylogenetic signal) and which traits appear to be either constrained or gradual in their evolutionary trajectory (strong phylogenetic signal). ?
- Give an example
- For the three phylogenies, it looks like the number of species we can match is 179 (Treena’s ITS-only) 78 (Martin and Coffey, 2014 - multi-gene) 133 (Yang 2018 – multi-gene) - I had a another look at this when I got home and I was totally wrong about the species matches. There are actually plenty. I was looking at the mismatched list (~40), not the matched list like an idiot. So it is still a contender, although perhaps the ML, rather than the Bayesian version because of the outgroup problem.
- Check values against latest version Which traits have high and low phylogenetic signal Lambda robust even if not Brownian evolution When λ or κ = 1, trait divergence is proportional to branch lengths (Brownian Motion). When λ = 0 the trait value has no phylogenetic structure and is distributed randomly across the tree. All sporangial features (Fig. 1A) showed medium to strong phylogenetic structure (λ = 0.798 to 1). The strongest phylogenetic signal was in the production of proliferating (the production of secondary sporangia either below or within primary sporangia or on a new sporangiophore) or non-proliferating sporangia. The distribution of papillate, semi-papillate or non-papillate sporangia was also strongly correlated with phylogenetic relatedness. Sporangia of species in clades 6, 7 and 9 were almost exclusively non-papillate, while papillate or semi-papillate sporangia pre-dominated in the remaining clades. Sporangiophore form was also (more weakly) phylogenetically structured, with simple sympodial morphology more abundant in clades 1 to 5 and clade 11 and unbranched morphology predominating in the remaining clades. Caducous sporangia were also phylogenetically structured occurring most frequently in clades 1 to 3. Pedicle length is applicable only to species with caducous sporangia, but among those species the trait showed strong phylogenetic signal with all pedicle lengths short in clade 1 and medium to long pedicle lengths more frequent in clade 2. g. 1A) showed medium to strong phylogenetic structure (λ = 0.798 to 1).
- There was moderate phylogenetic signal (λ = 0.744 to 0.836) in reproductive strategy and the production of different types of survival structures (oospores, chlamydospores and hyphal swellings). The morphology of reproductive structures (oospores and oogonia) tended to show the weakest phylogenetic signal of all Phytophthora traits (λ = 0.471 to 0.748), although phylogenetic signal in antheridial attachment was strong (λ = 0.968).
- Among temperature features (λ = 0.617 to 0.981), the strongest phylogenetic signal was observed for growth rate at optimum temperature, with the fastest growth rates clustering in sub-clade 6a. Optimum and maximum temperatures for growth also highly conserved with species with preferences for higher optimum and maximum temperatures tending to cluster in clades 6a and clade 9. Thermal tolerance range and minimum temperatures for growth were less phylogenetically conserved, but still showed some phylogenetic signal.
- Among temperature features (λ = 0.617 to 0.981), the strongest phylogenetic signal was observed for growth rate at optimum temperature, with the fastest growth rates clustering in sub-clade 6a. Optimum and maximum temperatures for growth also highly conserved with species with preferences for higher optimum and maximum temperatures tending to cluster in clades 6a and clade 9. Thermal tolerance range and minimum temperatures for growth were less phylogenetically conserved, but still showed some phylogenetic signal.
- Partitioning trait variation within and between clades Amongst temperature features, for optimum and maximum temperatures for growth, the proportion of trait diversity contained within clades versus between clades, was not significantly greater than would be expected under a Brownian Motion null model. In contrast, disparity in minimum temperatures for growth and thermal tolerance range within clades were consistently significantly higher than expected. There was a tendency for all temperature traits to show increased partitioning trait disparity within clades in more recent phylogenetic history. relative disparity, as implemented in the R package geiger (Harmon et al., 2008). For each quantitative trait, at each node, average sub-clade disparity was measured as pairwise Euclidean distance among trait values. This disparity within sub-clades was expressed as a proportion of trait disparity in the entire clade (relative trait disparity: Harmon et al. 2003) in relative time along the phylogeny. By comparing the observed relative disparity to the expected relative disparity under Brownian motion, we can visualise the tempo of trait diversification through time. High average within-clade disparity at a given time in phylogenetic history would suggest that sub-clades have diversified rapidly and independently evolved to share common traits between clades. Relative disparity through time can be summarised into a morphological disparity index (MDI) representing the overall difference in average within-clade disparity compared with that expected under Brownian motion. High MDI can be interpreted as evidence that a trait is more labile through evolutionary time. This method is only applicable to continuous traits currently. Adapting it to categorical traits could be quite difficult. Perhaps we could just focus on the thermal tolerance traits for this part of the analysis to illustrate how minimum temp tolerance has tended to be more labile through evolutionary time (and is therefore more likely to respond to current selection pressures)?
- 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?
- With and without phylogeny Significant pairwise trait correlations for 24 traits among 179 Phytophthora species. Significance is defined as correlations with 95% posterior distributions that do not overlap zero. Traits are arranged in functional groups of sporangial features, temperature features, survival structures and oospore features. Trait correlations are derived from pure latent variable models with (A) unconstrained latent variables, so that trait correlations reflect all evolutionary processes driving trait covariance (B) phylogenetically constrained latent variables, so that only trait correlations due to shared phylogenetic history are captured. Where trait correlations are stronger in (A) than in (B), there may be an additional non-phylogenetic component to relationships between traits, for instant convergent co-evolution of traits in more distant parts of the phylogeny. There was a strong association between all sporangial morphology traits (Fig. 2). Species with non-proliferating sporangia tended also to be caducous, have either papillate or semi-papillate sporangia, with simple sympodial sporangiophore form. Proliferating sporangia tended to also be non-caducous, non-papillate and with unbranched sporangiophore form
- e.g Phytophthora riparia? saprotrophic strategy thermophilic: high thermal profile, opt 30c, max 35c sterile proliferating, non-caducous sporangia Low impact: no known hosts, known only from forest streams and rivers in the USA Give an example
- Update with louise’s new figure with and without the impact traits.
- Update with latest findings
- 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.
- For the three phylogenies, it looks like the number of species we can match is 179 (Treena’s ITS-only) 78 (Martin and Coffey, 2014 - multi-gene) 133 (Yang 2018 – multi-gene) - I had a another look at this when I got home and I was totally wrong about the species matches. There are actually plenty. I was looking at the mismatched list (~40), not the matched list like an idiot. So it is still a contender, although perhaps the ML, rather than the Bayesian version because of the outgroup problem.