Photo: Dave Rizzo, Univ. of California, Davis

Landscape pathology and network epidemiology of Phytophthora ramorum

Marco PautassoImperial College,

Silwood Park

York University, 19 June 2008

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

Photo: Marin County Fire DepartmentMarin County, CA, US (north of San Francisco)

Sudden Oak Death1. 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

Photos: UC Davis & UC Berkeley

Phytophthora ramorum in culture

Sporangia releasing zoospores

Phytophthora ramorum

Chlamydospores

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, Aesculuscalifornica, Lithocarpus densiflorus, Quercus agrifolia, Quercus kelloggii, Quercus chrysolepis, Quercus parvula,

Pseudotsuga menziesii, Sequoia sempervirens

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

Phytophthora is a Stramenopile/Straminipile

from: James et al. (2006) Nature

Other Phytophthoras: P. infestans

Photo: William Fry, Cornell University

Jarrah forest dieback due to Phytophthora cinnamomi

from: http://www.cmis.csiro.au/rsm/casestudies/flyers/dieback/bluffdie2.jpg

from: Jeger & Pautasso (2008) New Phytologist

Picture courtesy of Thomas Jung, http://www.baumkrankheiten.com/

Alnus dieback in Bavaria

due to Phytophthora

alni

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

P. ramorumconfirmations on

the US West Coast vs. national risk

Map from www.suddenoakdeath.orgKelly, UC-Berkeley

Hazard map: Frank Koch & Bill Smith, 3rd SOD Science

Symposium (2007)

from: Rizzo et al. (2005) Annual Reviews of Phytopathology, Photo: Susan Frankel

P. ramorum in Monterey County, California

The Phytophthora ramorum outbreak in Curry Count, Oregon; from: Hansen (2007) SOD Science Symposium III

2001 2002 2003

2004 2005 2006

from: Rizzo et al. (2005) Annual Reviews of Phytopathology, Photo: Clive Brasier

P. ramorum eradication in Oregon

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

Dec/02 Jun/03 Dec/03 Jun/04 Dec/04 Jun/05 Dec/05 Jun/06 Dec/06

Num

ber o

f cas

es m

onth

ly (s

ite)

0

10

20

30

40

50

60Garden/Nursery Other lcoations

Phytophthora ramorum in the UK (2003-2006)

garden/nurserysemi-natural environment

Spatial analysis of P. ramorum reported cases in the UK: garden/nurseries vs. semi-natural environment

O12(r) v

alue

s

0.00

0.06

0.12

0.18

0.24

0.30f: Garden/Nursery - SNE 04 - 05

Distance (km)0 2 4 6 8 10

0.0

0.1

0.2

0.3

0.4g: 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

0.20d: SNE - Garden/Nursery 05 - 06

c: SNE - Garden/Nursery 04 - 05

from: McKelvey, Koch & Smith (2007) SOD Science Symposium III

NATURAL

TECHNOLOGICAL SOCIAL

food webs

airport networks

cell metabolism

neural networks

railway networks

ant nests

WWWInternet

electrical power grids

software mapscomputing

gridsE-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

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

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)]}

0.0

0.2

0.4

0.6

0.8

1.0

1.2

1 26 51 760

10

20

30

40

50

60

70

80

0.0

0.2

0.4

0.6

0.8

1.0

1.2

1 26 51 760

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 51 760

10

20

30

40

50

60

0.0

0.2

0.4

0.6

0.8

1.0

1.2

1 51 101 151 2010

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

sum

pro

babi

lity

of in

fect

ion

acro

ss a

ll no

des

randomscale-free

% n

odes

with

pro

babi

lity

of in

fect

ion

> 0.

01

from: Pautasso & Jeger (2008) Ecological Complexity

small-world

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

prob

abili

ty o

f per

sist

ence 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

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

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

thre

shol

d p

of tr

ansm

issi

on

localrandomsmall-worldscale-free 2scale-free 0scale-free 1

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

% n

odes

at e

quili

briu

m w

ith p

roba

bilit

y of

infe

ctio

n >

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

-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

sum

at e

quili

briu

m o

f pro

babi

lity

of in

fect

ion

acro

ss a

ll no

des

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

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

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 (%)

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

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

Source: Wikimedia Commons

Back to the P. ramorum epidemic in the US West Coast

from: Prospero et al. (2007) Molecular Ecology

from: Anacker et al. (2008) New Phytologist

Environmental parameters related to SOD disease expression

Effect of landscape heterogeneity on sudden oak death

from: Condeso & Meentemeyer (2007) Journal of Ecology and: Mascheretti et al. (2008) Molecular Ecology

from: Cushman & Meentemeyer (in press) Journal of Ecology

Multi-scale correlation of human presence and Phytophthora ramorum disease incidence

Source: United States Department of Agriculture, 2004Animal 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

Conclusions:

1. landscape pathology approach

2. disease spread in networks

3. implications for emerging diseases/invasive species/climate change

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

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

ReferencesDehnen-Schmutz K, Holdenrieder O, Jeger MJ & Pautasso M (2010) Structural change in the international horticultural industry: some implications for plant health. Scientia Horticulturae 125: 1-15Harwood 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: 111-126MacLeod 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: 402-411Moslonka-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: 392-403Pautasso M (2009) Geographical genetics and the conservation of forest trees. Perspectives in Plant Ecology, Systematics and Evolution 11: 157-189Pautasso 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: 729-755Pautasso 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: 1300-1309Xu 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