Modelling and experimentation of the spatio-temporal spread of soilborne pathogens: Rhizoctonia solani on sugar beet as an

example pathosystem

Leclerc Melen

PhD defence

1st February 2013 – Agrocampus Ouest

UMR – IGEPP

Cifre I.T.B

Reporters: Joël Chadoeuf, Christian Lannou

Examiners: Yannick Outreman, Marc Richard-Molard

Supervisors: Philippe Lucas, Thierry Doré, João Filipe

2

General context – current problemsIntroduction

• 50% reduction in pesticide use (Grenelle de l’environnement Ecophyto)

• Find alternatives to pesticide use

• Keep crop production levels and growers’ incomes

3

Proposed approachesIntroduction

• Necessity of considering the complexity of agro-ecosystems (e.g. using system approaches)

• Understand ecological and epidemiological processes involved in the dynamics of pathogens

• Use ecological and epidemiological knowledge to improve pest management and design efficient crop protection strategies

• Combine several controls with partial effects (there is no ‘one fits all’ solution)

• SysPID Casdar Project: Reduce the impact of soilborne diseases in crop systems towards an integrated and sustainable pest management

• Action n°2: Epidemiological processes in field crop systems

4

Soilborne disease epidemicsIntroduction

Soilborne diseases

• Wide range of pathogenic organisms (fungi, bacteria, viruses, nematodes, protozoa)

• Cause substantial damage to crops worldwide (up to 50 % of crop loss in the US (Lewis & Papaizas, 1991) )

• Pathogens often survive for many years in soils (5-7 years for Pythium)

• Difficult to detect, predict and control

Epidemiology

• External source of inoculum X(t) : dynamic pathogen population

• Primary infections : external inoculum epidemic initiation

• Secondary infections : spread of the pathogen within the crop/population

S

X

ISusceptible

hostsInfected/Infectious

hosts

5The host The pathogen

Rhizoctonia solani on sugar beet as an example pathosystemIntroduction

6

The host : sugar beetIntroduction

The plant

• Cultivated Beta vulgaris

• High production of sucrose

The crop

• Grown for sugar production

• France is one of the largest producer (33 Mt in 2009)

7

The pathogen : Rhizoctonia solaniIntroduction

R. solani fungi

• Basidiomycetes

• Polyphagous saprotrophic fungi

• Anastomisis Groups (AG)

R. solani AG2-2 IIIB

• Parasites maize, rice, sugar beet, ginger …

• Important optimal temperature range for growth

• Develops late in the growing season

• Infects mostly mature plants

Aoyagi et al., 1998

8

The disease : the brown root rot disease of sugar beetIntroduction

9

Rhizoctonia root rot disease control Introduction

Current management strategies

• Think crop rotations (host & non-host crops)

• Resistant varieties

• Biological controls ?

Antagonists (e.g. Trichoderma fungi)

Biofumigation

10

Our systemIntroduction

Hidden epidemic:

Cryptic infections

Source of inoculum :

R. solani

Temporal scale : growing season of sugar beet

Spatial scale : field

Visible epidemic:

symptomatic plants

Belowground

Above-ground

11

Research questions and structure of the presentationIntroduction

1. How does R. solani spread in field conditions ? (Understand epidemiology of R. solani in field conditions)

2. How to infer hidden infections from observations of the disease ?

3. How does biofumigation affect epidemic development ?

Study 3 : Effects of biofumigation

2007 data (Motisi, 2009)Modelling

Study 2 : Incubation period

Experimentation Modelling

Study 1 : R. solani spread

Experimentation Modelling

12

Part 1

How does R. solani spread in field conditions ?

13

Pathozone concept and spread of soilborne pathogensPart 1

• Difficult to asses R. solani growth in soils use of pathozone concept

• “Pathozone means the region of soil surrounding a host unit within which the centre of a propagule must lie for infection of the host unit to be possible” (Gilligan, 1985)

Contact distance Tim

e ex

pose

d

to in

ocul

um

Probability of infection

Placement experiment

Inoculum(donor)-host(recipient)

n replicates at distance x

ni number of infected recipients at time t

P(x,t) = ni / n

14

Results of the experiments (Pathozone profiles)Part 1

• Field experiments in 2011 (Le Rheu)

Secondary inoculum

(an infected plant)

• Localised spread (nearest neigbour plants) (Filipe et al., 2004, Gibson et al.,2006)

• Infections occurs further with secondary inoculum ( Kleczkowski et al.,1997)

The fungus translocates nutrients from the parasited host to other parts of the mycelium

Primary inoculum

(5 infested barley seeds)

15

Host growth may increase pathogen transmission at individual levelPart 1

Host-plant growth decreases the contact distance between neighbouring plants

Host growth may increase pathogen transmission at individual level

16

Host growth can trigger the development of epidemicsPart 1

xcc= 11 cm

xcc= 14 cm

xcc= 17 cm

Static contact distance Dynamic contact distance

Host growth can cause a switch from non-invasive to invasive behaviourNon-invasive behaviour (linear trend)

Invasive behaviour (non-linear trend)

17

Conclusion (Part 1)Part 1

• First Pathozone profiles measured in a real soil

• Locality of pathogen spread in field conditions

Importance of considering space : mean-field approximation/homogeneous mixing assumption may fail when predicting the spread of soilborne pathogens (Dieckmann et al., 2000 ; Filipe & Gibson, 2001)

• Host growth can trigger the development of epidemics by decreasing contact distances

Need to take into account host growth for epidemic thresholds – conditions for invasive spread of plant population by pathogens (Grassberger, 1983 ; Brown & Bolker, 2004)

18

Part 2

How to infer hidden infections from observations ?

19

ProblemPart 2

20

A problem of incubation period Part 2

• Time between hidden infection and appearance of detectable symptoms of pathology

Incubation period (Kern, 1956 ; Keeling & Rohani, 2008)

• Incubation period distributions are often described by non-negative probability distributions with a pronounced mode (Keeling & Rohani, 2008 ; Chan & Johansson, 2012)

• Incubation period data are rare …

21

Compartmental models an incubation period distributionsPart 2

• How to describe cryptic infections with compartmental models ?

• Compartmental Markovian models

The time spent in each state is exponentially distributed

In a simple SID model the incubation period is exponentially distributed

• How to introduce realistic distributions in classical compartmental models ?

A tractable way: by subdividing compartments (i.e. introducing transient states)

Sum of exponentialy distributed random variables =

Erlang (or Gamma) distributed random variable

Susceptible hosts Infected/infectious hosts Detectable/symptomatic hosts

Realistic distribution

Distribution in classical Markovian

models

22

Working hypothesis and methodology Part 2

• Hypothesis: the distribution (e.g. mean and range) of the incubation period is age-dependent

• Methodology:

Experimental measures for various ages of infection

Statistical analysis (is Gamma distribution robust enough ?)

Build a model for age-varying distribution of the incubation period

Incorporate it into an SID compartmental model

23

Experimental measures of the incubation periodPart 2

Experiments

• Plant inoculated with 3 infested barley seeds (inoculum)

• 9 ages of plants (14, 32, 46, 60, 74, 88, 102, 116, 130 days)

• For each individual the time of first above-ground symptom was recorded

• At least 45 individual observations for each age distributions of the incubation period

24

Raw data (results of the experiments)Part 2

Inub

atio

n pe

riod

calc

ulat

ed in

deg

ree-

day

s

25

Age-varying model of the incubation period distributionPart 2

• Age by age distribution analysis Gamma distribution (general case of Erlang) can reasonably describe incubation period distributions

• Age-varying model of the incubation period T(t)

( ) ~ [ , ( )] with an integer and ( ) btT t Erlang k t k t ae c

• Compartmental model with realistic incubation distribution (19 transient non-symptomatic states)

k : shape parameter =

number of transient states (=19)

λ: time dependent rate parameter

26

Hidden infections and observationsPart 2

• Simulations of cryptic epidemics (individual-based spatial model with stochastic continuous time)

Infected and detectable/symptomatic individuals have different dynamics

27

Conclusion (Part 2)Part 2

• One of the first epidemiological model for soilborne disease with data-supported incubation period

• Link hidden processes and observations of disease

Estimate rates of infections and cryptic infections from observations

Test management strategies based on the detection of symptomatic individuals

• This end of the incubation period corresponds to a visual detectability

May change with other detection/survey methods, e.g. molecular techniques, remote sensing

• Variability ? (soils, human error, strains, environmental conditions …)

28

Part 3

How does biofumigation affect epidemic development?

29

Background Part 3

Previous work

• The effect of biofumigation on the root rot disease has been analysed using a simple epidemiological model (Motisi, 2009 ; Motisi et al., 2012)

• Observations of symptomatic plants for 3 treatments :

1) without control, 2) with complete biofumigation, 3) with partial biofumigation

Biofumigation affects mostly primary infections

Biofumigation can affect secondary infections with a variable pattern

( ) ( ) ( ) ( )

Susceptible

dSt X t I t S t

dt

Cryptic infections

( ) ( ) ( ) ( )dI

t X t I t S tdt

Detectable plants

D I

1 2

21 3 2

Rates of infection

( ) exp( )

( ) exp( 0.5[log( / ) / ] )

t t

t t

For

ce o

f inf

ectio

n

30

Aim of the current studyPart 3

1. Integrate new epidemiological knowledge and data

2. Improve existing epidemiological models

3. Re-analyse the effects of biofumigation

4. Investigate the variability of epidemics to estimate uncertainty in the outcome of treatments

31

Improved epidemiological model : epidemic predictionsPart 3

• Spatial individual-based model with stochastic spread of the pathogen

Spatial component : better description of epidemics

Stochastic model : introduce variability in outcomes predictions of uncertainty

1 2 0 0

0

( ) exp( ( )) if

( ) 0 if

t t t t t

t t t

1 3 2exp( 0.5[log( / ) / ]²)t

( ) [ ( ) ( ) ].t t dt IP S I t t n dt

Stochastic infections

Rate of primary infection

Rate of secondary infection

32

Estimate parameter for each treatment from observations of diseasePart 3

• Introduce a more realistic incubation period for inferring epidemiological parameters

• Statistical inferrence of spatio-temporal can be difficult and time consuming…

Estimate spatial rates of infection using a semi-spatial model (Filipe et al., 2004)

• Localized spread of infections (see Part 1)

Pair approximation (Matsuda et al., 1992 ; Filipe & Gibson, 1998 ; van Baalen, 2000)

• Need to describe the dynamics of all pairs of the system (i.e. SS, SI II for an SI model)

Tractability : necessity to simplify the incubation period…

33

Model fitting and estimated rates of infectionPart 3

Biofumigation reduced rates of primary and secondary infection in this trial (2007)

1 2 0 0

0

( ) exp( ( )) if

( ) 0 if

t t t t t

t t t

1 3 2exp( 0.5[log( / ) / ]²)t

Rate of primary infection

Rate of secondary infection

Symptomatic plants (2007 data)

34

Spatial model predictionsPart 3

• Biofumigation allows a partial control of epidemics

• Biofumigation seems to reduce the uncertainty in epidemic outcome

• Marginal differences between partial and complete biofumigation in 2007

Distributions of infected plants at harvest (%)

35

Conclusion (Part 3)Part 3

• Analyses are consistent with previous results on the effect of biofumigation on the spread of R. solani, but

• We predict less primary infections and more secondary infections than in the previous study

New vision of epidemic : different disease progress curves

• Biofumigation seems to reduce the uncertainty in epidemic outcome

• Take these results with care

More statistical analyses are required to assess model fitting and conclude on the effects of treatments on epidemic development

Assess the effects of incubation period simplification – Pairwise vs temporal model …

Isotropic space (may overestimate epidemics ?)

• Re-analyse 2008 data

36

General conclusion

1

1 1 1

2

1 2 2 2

2

1 1

1 1 1

1

1 2

2

1

1( 1)[ ]

2

( ) ( 1)[ ]

( ) ( 1)[ ]

( ) ( 1)[ ]

1( ) (

2

SSSS SS SSS

SISS SI SI SSI SS SSS

SISI SI SI SSI

SDSI SD SD SSD

I ISI I I

dPP z P P

dtdP

P P z P P P PdtdP

P P z P PdtdP

P P z P PdtdP

P Pdt

1 1

1 2

2 1 1 1 2 2 2

1

1 2 1

2 2

1 2 2 2

2

2 2 1 2

2

1 1 2

2 1

1 2

2 1 2

2

1)[ ]

( ) ( ) ( 1)[ ]

( ) ( 1)[ ]

1

2

SI SSI

I ISI I I I I SI SSI

I DSD I I I D SD SSD

I II I I I

I DI I I D I D

DDI D

z P P

dPP P P z P P

dtdP

P P P z P PdtdP

P Pdt

dPP P P

dtdP

Pdt

4

t t+dt s,k inf,k 1

Prob( ) ( , ) kS I t t x dt

37

Soilborne disease epidemicsGeneral conclusion

• This work provide insights into root rot disease epidemics

spread of R. solani

incubation period

• Data-supported studies – field experiments

• We still need to improve knowledge on the epidemiology of this disease

• May apply to others pathosystems : perennial and non-perennial plants

38

Control of soilborne disease epidemicsGeneral conclusion

• Biofumigation

partial control of the root rot disease (Motisi et al., 2009 , 2010, 2012)

can reduce the uncertainty in epidemic outcome

• This work points out important epidemiological parameters for disease management

Design and test new strategies

Plant growth use crop mixing, precise key phenological stages to select for resistances

Incubation period improve disease survey

Locality of pathogen spread optimize the effects of treatments, use local treatments ?

• Combine partial controls (new and conventional) improve the control of epidemics

• Models may help to test disease management strategies

39

PerspectivesGeneral conclusion

• Mutltiple perspectives (theoretical, applied, epidemiological, ecological…)

• Consider main environmental parameters (temperature, moisture)

• Investigate pathogen dynamic at the crop rotation scale

• Understand ecological functionning of soils (pathogenic and non pathogenic communities)

Merci…• Doug Bailey

• Philipe Lucas – Thierry Doré – João Filipe

• Françoise Montfort

• Les anciens membres de l’équipe EPSOS

• UMR IGEPP

• Les Unités Expérimentales de Dijon et de Le Rheu

• L’Institut Technique de la Betterave

• Christian Lannou – Joël Chadoeuf – Marc Richard Molard – Yannick Outreman (jury)

• Pauline Ezanno – Marie Gosme – Christian Steinberg – Agnès Champeil – Étienne Rivot (comité de thèse)

• Chris Gilligan et l’Epidemiology and Modelling Group

• Les membres du projet Casdar SysPID

• Le portakabin (qui a eu chaud…)

• Et tous ceux qui m’ont supporté…

Mon bureau…avant-hier

41

Evolution of symptomsIntroduction

42

The pathogen: R. solaniIntroduction

p

p p

Spatial decline due to Time decline due to Delay inMaximum rate

location of inoculum away source of nutrients onset ofof infection ( )

from host ( ) decline ( ) infection

pa

( )

pp p

ss s

( , )( , )[1 ( , )]

( , )( , )[1 ( , )]

dP x tx t P x t

dtdP x t

x t P x tdt

inf ( , ) ~ ( , ( , ))totn x t Binomial n P x t

s

Spatial decline due to Delay inMaximum rate

location of inoculum away onset ofof infection ( )

from host ( ) infection ( )

s

s

a

Rate of primary infection

Rate of secondary infection

Rates of infection and pathozones

Infer parameters from pair experiment data

Pathozones P(x,t)

x: contact distance

t: time of exposure

ninf: number of infected recipients

ntot: number of replicates (25)

43

Incubation period ?Part 2

• Incubation period:

"time required for multiplication of a parasitic organism within a host organism up to the threshold point at which the parasite population is large enough to produce detectable symptoms of pathology“ (Kern, 1956)

Susceptible Latent/Exposed Infectious Recovered

Incubation Disease

Incubation

Incubation

Disease

Disease

Infectiousstatus

Pathologicalstatus

time of infection time sinceinfection

Susceptible Latent/Exposed Infectious Recovered

Incubation Disease

Incubation

Incubation

Disease

Disease

Infectiousstatus

Pathologicalstatus

time of infection time sinceinfection

• Periods in Natural history of disease in a host – these are incorporated as compartments in epidemiological models

44

Host growth and dynamic contact distancesIntroduction

0.4( ) 5 / [1 1000exp( 1.18* )]h t t

• Increase in the radius h(t)

• Radial growth measured with Pepista tools (ITB)

• Simple empirical model

• Dynamic of the contact distance between nearest neighbours xee(t)

• Static centre-centre distance xcc

• Spatial population model

• 30*30 square lattice

• t0= 30 days

• 5% infected

ee cc inf

ee cc inf

ee cc inf

if 30 < < 70

, if 70 < 90

if 90

( ) ( ) ,

( ) 1.5 ( )

( ) 2 ( ) ,

t

t

t

x t x h t

x t x h t

x t x h t

4

t t+dt s,k inf,k 1

Prob( ) ( , ) ( ) ( )

cck k

ee

x xS I t t x t dt

x x t

45

First results Part 3

Previous epidemiological model

Detectable/

symptomatic

Infected

Rate of primary infection

1 2

21 3 2

( ) ( ) ( ) ( )

( ) ( ) ( ) ( )

( ) exp( )

( ) exp( 0.5[log( / ) / ] )

dSt X t I t S t

dtdI

t X t I t S tdt

D I

t t

t t

Assumptions

• Mean field mass action/homogeneous mixing assumption

• Epidemics initiated too soon

• Pre-emergence damping off

• Unrealistic incubation periodMotisi et al., 2012

For

ce o

f inf

ectio

n