1st February 2013 – Agrocampus Ouest 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

General context – current problems Introduction 50% reduction in pesticide use (Grenelle de l’environnement  Ecophyto) Find alternatives to pesticide use Keep crop production levels and growers’ incomes

Proposed approaches Introduction 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

Soilborne disease epidemics Introduction Soilborne disease epidemics 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 General points on soilborne disease and epidemiology of these disease Susceptible hosts S I Infected/Infectious hosts X

Rhizoctonia solani on sugar beet as an example pathosystem Introduction The host The pathogen

The host : sugar beet The plant Cultivated Beta vulgaris Introduction 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) Introduce briefly sugar beet: plant and crop

The pathogen : Rhizoctonia solani Introduction The pathogen : Rhizoctonia solani 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 Introduce R. solani fungi, characteristics of R. solani AG2-2IIIB Aoyagi et al., 1998

The disease : the brown root rot disease of sugar beet Introduction Present the root rot disease at different levels: field, individual, above-ground and belowground symptoms

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

Our system Visible epidemic: symptomatic plants Hidden epidemic: Introduction Visible epidemic: symptomatic plants Above-ground Hidden epidemic: Cryptic infections Source of inoculum : R. solani Temporal scale : growing season of sugar beet  Spatial scale : field Belowground

Research questions and structure of the presentation Introduction Research questions and structure of the presentation 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 1 : R. solani spread Experimentation Modelling Study 2 : Incubation period Experimentation Modelling Study 3 : Effects of biofumigation 2007 data (Motisi, 2009) Modelling

How does R. solani spread in field conditions ? Part 1 How does R. solani spread in field conditions ?

Pathozone concept and spread of soilborne pathogens Part 1 Pathozone concept and spread of soilborne pathogens 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) 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 Time exposed to inoculum Contact distance

Results of the experiments (Pathozone profiles) Part 1 Results of the experiments (Pathozone profiles) Field experiments in 2011 (Le Rheu) Primary inoculum (5 infested barley seeds) 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

 Host growth may increase pathogen transmission at individual level Part 1  Host-plant growth decreases the contact distance between neighbouring plants  Host growth may increase pathogen transmission at individual level

Host growth can trigger the development of epidemics Part 1 Static contact distance Dynamic contact distance xcc= 11 cm  Host growth can cause a switch from non-invasive to invasive behaviour Non-invasive behaviour (linear trend) xcc= 14 cm xcc= 17 cm Invasive behaviour (non-linear trend)

Conclusion (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)

How to infer hidden infections from observations ? Part 2 How to infer hidden infections from observations ?

Problem Part 2 From belowground infections to above-ground detection of the disease

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 …

Compartmental models an incubation period distributions 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 Introduce the issue of realistic nonexponential distribution in compartmental models One solution  Erlang distribution

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

Experimental measures of the incubation period Part 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

Raw data (results of the experiments) Part 2 Inubation period calculated in degree- days Present raw data and general tendencies

number of transient states (=19) Part 2 Age-varying model of the incubation period distribution 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) k : shape parameter = number of transient states (=19) λ: time dependent rate parameter Compartmental model with realistic incubation distribution (19 transient non-symptomatic states) Present our model and how we can introduce it in a simple compartmental model

Hidden infections and observations Part 2 Hidden infections and observations Simulations of cryptic epidemics (individual-based spatial model with stochastic continuous time) Illustrate i) difference in infected and detected dynamics, ii) importance of considering realistic distributions (Exp vs Erlang)  Infected and detectable/symptomatic individuals have different dynamics

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 …)

How does biofumigation affect epidemic development? Part 3 How does biofumigation affect epidemic development?

Background Previous work 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 Force of infection

Aim of the current study Part 3 Integrate new epidemiological knowledge and data Improve existing epidemiological models Re-analyse the effects of biofumigation Investigate the variability of epidemics to estimate uncertainty in the outcome of treatments

Improved epidemiological model : epidemic predictions Part 3 Improved epidemiological model : epidemic predictions 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 Stochastic infections Rate of primary infection Rate of secondary infection

Estimate parameter for each treatment from observations of disease Part 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…

Model fitting and estimated rates of infection Part 3 Model fitting and estimated rates of infection Rate of primary infection Symptomatic plants (2007 data) Rate of secondary infection  Biofumigation reduced rates of primary and secondary infection in this trial (2007)

Distributions of infected plants at harvest (%) Spatial model predictions Part 3 Distributions of infected plants at harvest (%) 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

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

General conclusion

Soilborne disease epidemics General 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

Incubation period  improve disease survey Control of soilborne disease epidemics General 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

Perspectives General 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

Introduction Evolution of symptoms

The pathogen: R. solani Rate of primary infection Introduction Rate of primary infection Rate of secondary infection Rates of infection and pathozones Pathozones P(x,t) x: contact distance t: time of exposure Infer parameters from pair experiment data ninf: number of infected recipients ntot: number of replicates (25)

Incubation period ? Part 2 Periods in Natural history of disease in a host – these are incorporated as compartments in epidemiological models Introduce period in epidemiology : infectious statess vs pathological states Link between infections and detection  incubation period 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)

Host growth and dynamic contact distances Introduction Host growth and dynamic contact distances 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

Rate of primary infection Part 3 First results Previous epidemiological model Detectable/ symptomatic Force of infection Infected Assumptions Mean field mass action/homogeneous mixing assumption Epidemics initiated too soon Pre-emergence damping off Unrealistic incubation period Rate of primary infection Motisi et al., 2012