The landscape pathology and network epidemiology of Phytophthora ramorum

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Sudden Oak Death, landscape ecology and forest pathology, interdisciplinary approaches, network of co-occurrences, Phytophthora and climate change, small-world networks, epidemic final size, network structure, degree distribution, spatial autocorrelation, pathogen dispersal, disease spread

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The landscape pathology and network epidemiology of Phytophthora ramorum

  1. 1. Photo: Dave Rizzo, Univ. of California, Davis Landscape pathology and network epidemiology of Phytophthora ramorum Marco Pautasso Imperial College, Silwood Park York University, 19 June 2008
  2. 2. From: Hufnagel, Brockmann & Geisel (2004) Forecast and control of epidemics in a globalized world. PNAS 101: 15124-15129 number of passengers per day Disease spread in a globalized world
  3. 3. Photo: Marin County Fire Department Marin County, CA, US (north of San Francisco) Sudden Oak Death 1. Phytophthora ramorum and related species 3. The relevance of network epidemiology 2. The Sudden Oak Death outbreak in the US and the situation in the UK 4. Conclusions
  4. 4. Photos: UC Davis & UC Berkeley Phytophthora ramorum in culture Sporangia releasing zoospores Phytophthora ramorum Chlamydospores
  5. 5. P. ramorum: an aggressive AND generalist pathogen Modified from: Pautasso, Holdenrieder & Stenlid (2005) In: Forest Diversity and Function: Temperate and Boreal Systems. Ecological Studies Acer macrophyllum, Aesculus californica, Lithocarpus densiflorus, Quercus agrifolia, Quercus kelloggii, Quercus chrysolepis, Quercus parvula, Pseudotsuga menziesii, Sequoia sempervirens
  6. 6. Super-connected nodes in the network of co-occurrences at infected sites (England & Wales, 2003-2005) of genera susceptible to P. ramorum from: Pautasso, Harwood, Xu, Shaw & Jeger (2008) Proceedings SOD Science Symposium III
  7. 7. Phytophthora is a Stramenopile/Straminipile from: James et al. (2006) Nature
  8. 8. Other Phytophthoras: P. infestans Photo: William Fry, Cornell University
  9. 9. Jarrah forest dieback due to Phytophthora cinnamomi from: http://www.cmis.csiro.au/rsm/casestudies/flyers/dieback/bluffdie2.jpg
  10. 10. from: Jeger & Pautasso (2008) New Phytologist
  11. 11. Picture courtesy of Thomas Jung, http://www.baumkrankheiten.com/ Alnus dieback in Bavaria due to Phytophthora alni
  12. 12. Phytophthora alni along water courses in Bavaria Modified from: Holdenrieder et al. (2004) Trends in Ecology & Evolution From: Jung & Blaschke (2004) Plant Pathology 10 km
  13. 13. P. ramorum confirmations on the US West Coast vs. national risk Map from www.suddenoakdeath.org Kelly, UC-Berkeley Hazard map: Frank Koch & Bill Smith, 3rd SOD Science Symposium (2007)
  14. 14. from: Rizzo et al. (2005) Annual Reviews of Phytopathology, Photo: Susan Frankel P. ramorum in Monterey County, California
  15. 15. The Phytophthora ramorum outbreak in Curry Count, Oregon; from: Hansen (2007) SOD Science Symposium III 2001 2002 2003 2004 2005 2006
  16. 16. from: Rizzo et al. (2005) Annual Reviews of Phytopathology, Photo: Clive Brasier P. ramorum eradication in Oregon
  17. 17. Phytophthora ramorum in England & Wales (2003-2006) Outbreak maps courtesy of David Slawson, PHSI, DEFRA, UK Climatic match courtesy of Richard Baker, CSL, UK 511 nurseries/ garden centres 85 426 168 historic gardens/ woodlands 46 122
  18. 18. Dec/02 Jun/03 Dec/03 Jun/04 Dec/04 Jun/05 Dec/05 Jun/06 Dec/06 Numberofcasesmonthly(site) 0 10 20 30 40 50 60 Garden/Nursery Other lcoations Phytophthora ramorum in the UK (2003-2006) garden/nursery semi-natural environment
  19. 19. Spatial analysis of P. ramorum reported cases in the UK: garden/nurseries vs. semi-natural environment O12 (r)values 0.00 0.06 0.12 0.18 0.24 0.30 f: Garden/Nursery - SNE 04 - 05 Distance (km) 0 2 4 6 8 10 0.0 0.1 0.2 0.3 0.4 g: Garden/Nursery - SNE 05 - 06 0.00 0.05 0.10 0.15 0.20 Distance (km) 0 2 4 6 8 10 0.00 0.05 0.10 0.15 0.20 d: SNE - Garden/Nursery 05 - 06 c: SNE - Garden/Nursery 04 - 05
  20. 20. from: McKelvey, Koch & Smith (2007) SOD Science Symposium III
  21. 21. NATURAL TECHNOLOGICAL SOCIAL food webs airport networks cell metabolism neural networks railway networks ant nests WWW Internet electrical power grids software maps computing grids E-mail patterns innovation flows telephone calls co-authorship nets family networks committees sexual partnerships DISEASE SPREAD Food web of Little Rock Lake, Wisconsin, US Internet structure Network pictures from: Newman (2003) SIAM Review HIV spread network Epidemiology is just one of the many applications of network theory urban road networks modified from: Jeger, Pautasso, Holdenrieder & Shaw (2007) New Phytologist
  22. 22. step 1 step 2 step 3 step n … Simple model of infection spread (e.g. P. ramorum) in a network pt probability of infection transmission pp probability of infection persistence … 100node 1 2 3 4 5 6 7 8
  23. 23. The four basic types of network structure used local random small- world scale-free SIS Model, 100 Nodes, directed networks, p [i (x, t)] = Σ {p [s] * p [i (y, t-1)] + p [p] * p [i (x, t-1)]}
  24. 24. 0.0 0.2 0.4 0.6 0.8 1.0 1.2 1 26 51 76 0 10 20 30 40 50 60 70 80 0.0 0.2 0.4 0.6 0.8 1.0 1.2 1 26 51 76 0 5 10 15 20 25 0.0 0.2 0.4 0.6 0.8 1.0 1.2 1.4 1.6 1 26 51 76 0 10 20 30 40 50 60 0.0 0.2 0.4 0.6 0.8 1.0 1.2 1 51 101 151 201 0 5 10 15 20 25 30 35 40 Examples of epidemic development in four kinds of directed networks of small size (at threshold conditions) local sumprobabilityofinfectionacrossallnodes random scale-free %nodeswithprobabilityofinfection>0.01 from: Pautasso & Jeger (2008) Ecological Complexity small-world
  25. 25. 0.00 0.25 0.50 0.75 1.00 0.00 0.05 0.10 0.15 0.20 0.25 0.30 probability of transmission probabilityofpersistence local small-world random scale-free Lower epidemic threshold for scale-free networks from: Pautasso & Jeger (2008) Ecological Complexity Epidemic does not develop Epidemic develops
  26. 26. Lower epidemic threshold for two-way scale-free networks (unless networks are sparsely connected) N replicates = 100; error bars are St. Dev.; different letters show sign. different means at p < 0.05
  27. 27. probability of persistence = 0 Lower epidemic threshold for higher correlation coefficient between out- and in-degree N= 100, links = 369, pp = 0 0.000 0.200 0.400 0.600 0.800 1.000 -0.4 -0.2 0.0 0.2 0.4 0.6 0.8 1.0 correlation coefficient between links in and links out thresholdpoftransmission local random small-world scale-free 2 scale-free 0 scale-free 1
  28. 28. scale-free network nr 8 0 25 50 75 100 0 25 50 75 100 local network nr 2 0 25 50 75 100 0 25 50 75 100 starting node %nodesatequilibriumwithprobabilityofinfection>0.01 starting node random network nr 9 0 25 50 75 100 0 25 50 75 100 small world network nr 6 0 25 50 75 100 0 25 50 75 100 Marked variations in the final size of the epidemic at threshold conditions depending on the starting node a b dc
  29. 29. -1.0 0.0 1.0 -1 0 1 2 3 0.0 0.5 1.0 1.5 2.0 2.5 3.0 0 2 4 6 8 10 12 0.0 0.5 1.0 1.5 2.0 0 1 2 3 4 5 6 sumatequilibriumofprobability ofinfectionacrossallnodes Variations in epidemic final size at threshold conditions are well explained by the number of links from the starting node local random scale-free (log-log scale) n of links from starting node n of links from starting node 0.0 0.5 1.0 1.5 2.0 2.5 3.0 0 2 4 6 8 small-world
  30. 30. Correlation of epidemic final size with out-degree of starting node increases with network connectivity N replicates = 100; error bars are St. Dev.; different letters show sign. different means at p < 0.05
  31. 31. Connectivity loss in the North American power grid due to the removal of transmission substations from: Albert, Albert & Nakarado (2004) Physical Review E transmission nodes removed (%)
  32. 32. Three main results: 1. lower epidemic threshold for scale-free networks compared with random, small-world and local networks even if networks are of small size 2. but: targeting control towards super-connected nodes is potentially a more effective and efficient eradication or management strategy 3. importance of trace-forward/-back data for better characterizing the structure of the UK horticultural trade network
  33. 33. Super-connected nodes in the network of co-occurrences at infected sites (England & Wales, 2003-2005) of genera susceptible to P. ramorum from: Pautasso, Harwood, Xu, Shaw & Jeger (2008) Proc SOD Science Symposium III
  34. 34. Source: Wikimedia Commons Back to the P. ramorum epidemic in the US West Coast
  35. 35. from: Prospero et al. (2007) Molecular Ecology
  36. 36. from: Anacker et al. (2008) New Phytologist Environmental parameters related to SOD disease expression
  37. 37. Effect of landscape heterogeneity on sudden oak death from: Condeso & Meentemeyer (2007) Journal of Ecology and: Mascheretti et al. (2008) Molecular Ecology
  38. 38. from: Cushman & Meentemeyer (in press) Journal of Ecology Multi-scale correlation of human presence and Phytophthora ramorum disease incidence
  39. 39. Source: United States Department of Agriculture, 2004 Animal and Plant Health Inspection Service, Plant Protection and Quarantine Trace forward/back zipcode Positive (Phytophthora ramorum) site Hold released Effect of nursery presence on likelihood of introduction
  40. 40. Conclusions: 1. landscape pathology approach 2. disease spread in networks 3. implications for emerging diseases/ invasive species/climate change
  41. 41. What about horse chestnut bleeding canker? (not due to Pythophthora ramorum but to Pseudomonas syringae) From: Report on the National Survey to Assess the Presence of Bleeding Canker of Horse Chestnut Trees in Great Britain, Forestry Commission (March 2008) Bleeding canker ≠ Cameraria ohridella rural urban
  42. 42. Acknowledgements Ottmar Holdenrieder, ETHZ, CH Mike Shaw, University of Reading Alan Inman, DEFRA Joan Webber, Forest Research, Farnham Tom Harwood, CEP, Imperial College Mike Jeger, Wye & Silwood, Imperial College Jennifer Parke, Univ. of Oregon Xiangming Xu, East Malling Research Mathieu Moslonka- Lefebvre, Univ. Orsay & ENS Cachan, France Richard Baker, CSL
  43. 43. References Dehnen-Schmutz K, Holdenrieder O, Jeger MJ & Pautasso M (2010) Structural change in the international horticultural industry: some implications for plant health. Scientia Horticulturae 125: 1-15 Harwood TD, Xu XM, Pautasso M, Jeger MJ & Shaw M (2009) Epidemiological risk assessment using linked network and grid based modelling: Phytophthora ramorum and P. kernoviae in the UK. Ecological Modelling 220: 3353-3361 Jeger MJ & Pautasso M (2008) Comparative epidemiology of zoosporic plant pathogens. European Journal of Plant Pathology 122: 111-126 MacLeod A, Pautasso M, Jeger MJ & Haines-Young R (2010) Evolution of the international regulation of plant pests and challenges for future plant health. Food Security 2: 49-70 Moslonka-Lefebvre M, Pautasso M & Jeger MJ (2009) Disease spread in small-size directed networks: epidemic threshold, correlation between links to and from nodes, and clustering. Journal of Theoretical Biology 260: 402-411 Moslonka-Lefebvre M, Finley A, Dorigatti I, Dehnen-Schmutz K, Harwood T, Jeger MJ, Xu XM, Holdenrieder O & Pautasso M (2011) Networks in plant epidemiology: from genes to landscapes, countries and continents. Phytopathology 101: 392-403 Pautasso M (2009) Geographical genetics and the conservation of forest trees. Perspectives in Plant Ecology, Systematics and Evolution 11: 157-189 Pautasso M, Dehnen-Schmutz K, Holdenrieder O, Pietravalle S, Salama N, Jeger MJ, Lange E & Hehl-Lange S (2010) Plant health and global change – some implications for landscape management. Biological Reviews 85: 729-755 Pautasso M, Moslonka-Lefebvre M & Jeger MJ (2010) The number of links to and from the starting node as a predictor of epidemic size in small-size directed networks. Ecological Complexity 7: 424-432 Pautasso M, Xu XM, Jeger MJ, Harwood T, Moslonka-Lefebvre M & Pellis L (2010) Disease spread in small-size directed trade networks: the role of hierarchical categories. Journal of Applied Ecology 47: 1300-1309 Xu XM, Harwood TD, Pautasso M & Jeger MJ (2009) Spatio-temporal analysis of an invasive plant pathogen (Phytophthora ramorum) in England and Wales. Ecography 32: 504-516

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