1. Use of ecophysiological approaches and biophysic plant modelling in determination of complex phenotypic traits and analysis of interactions GxE Pr. Jérémie LECOEUR Professor of Plant Biology Director of Plant Science Department Montpellier SupAgro

2. 1. Context

3. Context : a need to understand the building of the plant phenotype The plant phenotype is always a complex object resulting from the spatial and temporal integration of various biological processes  « Integrated Plant Phenotype » Integrated plant phenotype: Plant traits resulting of the integration of the major plant functions in response to environment. An example of an « Integrated Plant Phenotype »: The architecture of the At rosette Corresponding Virtual plant Picture Col se rot This integrated phenotype results from: organogenesis morphogenesis carbon metabolism… in interaction with the environment

4. x = Phenotype Genotype Environment  Responses x = = Response Environment genotype 1 genotype 2 = Context : a need to understand the building of the plant phenotype The plant phenotype is always a complex object resulting from the spatial and temporal integration of various biological processes  « Integrated Plant Phenotype » Integrated plant phenotype: Plant traits resulting of the integration of the major plant functions in response to environment.

5. Choice of the plant representation Process based models (crop models) Leaves fruits roots Genetic modelling phenotype = G + E + GxE +  Mainly statistical approaches Ecophysiological modelling Organ populations in relation with environment through correlative relationships =  Response Environment géno 1 géno 2 « Virtual plants » Set of phytomeres with topological connections with matter flows Context : a need to understand the building of the plant phenotype

6. The plant is a complex system = a large number of sub-units with the same organisation and topological connection resulting in a network The same level of complexity could be find at organelle, cell, tissue… Cell protein tree (d’après Jeong, 2003) Context : a need to understand the building of the plant phenotype Purslane plant

7. A postulate ? «The only way to make significant progress in understanding the genotype - environment interaction is to associate several scientific disciplines» The needed scientific disciplines would be: - genetic and genomic, - plant biology and plant physiology, - ecophysiology and biophysic - applied mathematics, Theory of the increase in scientific progress through combinatories of conceptual and technic artefacts (Lebeau, 2005) Context : a need to understand the building of the plant phenotype

8. 2. Advances in Ecophysiology

9. Step 0 : Characterization of the physical environment at plant boundaries

10. The absolute necessary to take into account the physical environment Systematic characterization of plant microclimate Advances in Ecophysiology To allow the comparison between experiments and the establishment of trial network typologies or a future use of models In field In growth chamber The minimum data set includes temperature, radiation and atmospheric humidity, wind speed and rainfall

11. First use of modelling: to estimate the environmental variables instead of measuring them. To model the energy, radiative and water balances…. (from Rey, 2003; Lhomme and Guilioni, 2004 and 2006; Chenu et al., 2005 and 2007; Louarn et al., 2007) To be as close as possible to the microclimate sensed by the plant or by its organs Advances in Ecophysiology

12. To identify the environmental variables quantitatively related to plant development and growth. For instance, what is the radiative variable well related to the organogenesis on At? Incident PARLight quality (R/FR - Blue) Absorbed PAR To be as close as possible to the microclimate sensed by the plant or by its organs (from Chenu et al., 2005) Phytomere production rate (CDD-1) Incident PAR (mol m-2 d-1) Absorbed PAR (mmol plt-1 d-1) Absorbed PAR (log scale) Advances in Ecophysiology

13. To be as close as possible to the microclimate sensed by the plant or by its organs Advances in Ecophysiology A lot can be done by using standard bioclimatological indicators… Thermal time, Cumulative solar radiation, Photothermal coefficient, Climatic water balance…

14. Step 1 : Ecophysiologic diagnosis of the phenotypic variability To dissect the genotype – environment interaction

15. Second use of modelling: formalization of plant – environment interaction to identify unknown phenotypes (from Chenu et al, 2007) Analysis of a panel of wild types and their mutants in At Advances in Ecophysiology Col ron se rot 3.5 WsLerDij Wild type mutants

16. Second use of modelling: formalization of plant – environment interaction to identify unknown phenotypes Col 0.00 0.05 0.10 0.15 se 0.00 0.05 0.10 0.0010.010.1110 0.00 0.05 0.10 Ws Ler/ron 0.010.1110 Génotypes 0.0010.010.1110 0.00 0.05 0.10 0.15 All wild type All genotypes Comparison wild types vs corresponding mutants (from Chenu et al, 2007) Advances in Ecophysiology

17. Response curve families For instance, leaf expansion… Establishment of consistent relatioship betwen plant and environment variables

18. (from Chenu et al., 2007) Vini = a ini log(PARa) + b ini GGG x E Columbia Serrate This approach allowed to identify a new involvement of the Serrate gene in plant organogenesis. Second use of modelling: formalization of plant – environment interaction to identify unknown phenotypes Advances in Ecophysiology

19. Time consuming ecophysiological measurements require « industrial phenotyping » or a large field trail network It will be necessary to increase by 10 to 100 the number of characterized experimental situations (From Joined Unit LEPSE – INRA / SupAgro, 2006 report) Advances in Ecophysiology

20. Step 2 : To quantify the impact of the observed phenotypic differences

21. Third use of modelling: to analyse the consequences of multi-trait differences on integrated plant phenotypes The sensitivity analyses allow to rank the traits in term of their quantitative effects on the integrated phenotype. An example: phenotypic variability in light interception in sunflower during seed development. Among a panel of 20 genotypes, the following phenotypic differences were observed: - plant leaf area, - individual leaf area, - leaf number, - leaf size distribution along the stem, - blade angle, - duration of leaf life. Advances in Ecophysiology

22. Virtual sensitivity analysis of light interception to various phenotypic traits Average virtual plant Changes in position of the largest leaf on the stem Changes in plant leaf area Changes in leaf number (from Casadebaig, 2004) Third use of modelling: to analyse the consequences of multi-trait differences on integrated plant phenotypes Advances in Ecophysiology

23. Virtual plot at flowering (6.6 plants m -2 cv Heliasol) Sunflower virtual plant cv Heliasol Estimation of light interception Days 0.0 0.2 0.4 0.6 0.8 1.0 Fraction of radiation intercepted Ei ii (from Rey, 2003; Casadebaig, 2004)

24. -400-2000200400 50 100 150 200 Evaluated ranges of variation in observed traits (in % of the average value) Changes in light interception (in % of average plant) Plant leaf area Leaf number Position of the largest leaf on the stem Plant heigth Duration of leaf life Blade angle Sensitivity analysis A hidden trait affecting the light interception was identified: the distribution of leaf sizes along the stem (from Casadebaig, 2004) Third use of modelling: to analyse the consequences of multi-trait differences on integrated plant phenotypes Advances in Ecophysiology

25. (adapted from Chenu et al., 2005) Emerging properties at plant level in At? The changes in organogenesis, organ expansion and morphology lead to unexpected property: the life irradiance is improved in response to reductions in incident light Advances in Ecophysiology

26. 3 phases 1 1- decrease in trophic competition due to the increase in sources 1 11 2- Increase in trophic competition due to rapid production of new sinks 2 22 2 3-(0C)- Decreasein trophic competition due to the end of secondary axes development 3a 3-(6C)- Increase trophic competition due the second growth phasis of clusters 3b Change with time in trophic competition inside the grapevine shoot F FV V

27. Relationship between axis development and trophic competition 0.000.050.100.150.200.25 0.0 0.2 0.4 0.6 0.8 1.0 0.000.050.100.150.200.250.000.050.100.150.200.25 Sigmoidial adjustment Syr 0C Syr 6C Gre 0C Gre 6C Q/D ratio (arbitrary units) Probability to maintain the development Primary axes P0 secondary axes P1- P2 secondary axes A B C Relationship between Q/D values and the probability of end of secondary axes development Primary axes are not affected by the trophic competition Secondary axis are affected by the trophic competition A single sigmoidal relationship P=f(Q/D). A difference in sensitivity according to the type of axes

28. 0.000.050.100.150.200.25 0.0 0.2 0.4 0.6 0.8 1.0 0.0 0.2 0.4 0.6 0.8 1.0 0.0 0.2 0.4 0.6 0.8 1.0 P0 secondary axes P1-P2 secondary axes P1-P2 sigmoid adjustment P0 sigmoid adjustment Probability to maintain the development Q/D ratio (arbitrary units) A 1-5 B 6-10 C 11-16 Relationship between Q/D values and the probability of end of secondary axes development according to their type and size 1-5 leaves (0.31g) 6-10 leaves (2.87g) 11-16 leaves (10.21g) Relationship between axis development and trophic competition

29. 3. The front of « modelling experiences »

30. Step 3 : To model the impact of genotypic variability on the plant phenotypic plasticity To associate various kind of models to predict the integrated plant phenotypes

31. The front of modelling experiences To evaluate the genotype performances The biophysical modelling approaches are now enough tried and tested to be revisited to predict the genotype – environment interaction. The available modelling approaches (not exhaustive): - biophysical balances, - crop models, - ecophysiological descriptions of regulations and signals in plants, - 3D architectural plant and canopy models, - mathematical models to estimate parameters in complex systems…

32. Construction of dedicated models (adapted from Lecoeur et al., 2008) Flow chart of potential yield estimation in sunflower Input data Phenology Architecture (3D) Light interception (3D) Biomass production Biomass partitioning To evaluate the genotype performances The front of modelling experiences

33. Construction of dedicated models (adapted from Lecoeur et al., 2008) Flow chart of potential yield estimation in sunflower Input data Phenology Architecture (3D) Light interception (3D) Biomass production Biomass partitioning To evaluate the genotype performances The front of modelling experiences

34. Estimation of a productivity index from the genotypic traits A simple biophysic model allows to take into account from 80 to 90% of the observed phenotypic variability in potential yield among a panel of 30 genotypes. (from Lecoeur et al., 2008) To evaluate the genotype performances The front of modelling experiences

35. A sensitivity analysis allowed to quantify the impact on plant productivity of the genotypic traits (from Lecoeur et al., 2008) All the major functions contributed to the productivity variability. Classical ANOVA detected only the contribution of the harvest index To evaluate the genotype performances The front of modelling experiences

36. Reminder : first setting of the biomass partitioning model (Greenlab) Objective : to understand the genotype variability of harvest index (d’après Rey et al., 2006) Fitting on experimental data on 4 genotypes Leaf area Leaf biomass Leaf sink strength Sink strengths : petiole < leaf < stem < capitulum 0,45 < 1,00 < 1,07 < 3000 Actually, we are combining SunFlo (crop model) with GreenLab (FSPM) in order to analyse the genotypic variability of harvest index

37. Sunflo, a crop model including : A description of plant compartiments (vegetative parts, reproductive parts, roots), A description of main processes (organogenesis, morphogenesis, photosynthesis, biomass partitioning), Responses to temperature, solar radiation and water availability. Each genotype is described by a set of 15 to 20 traits Quantitative Genetics Modules : Estimation of genetic correlation between phenotypic traits, Estimation of heritabilities, Choice of selection pressure on the traits according the target environnement, Applying several selection cycles resulting in population with new phenotypic characterics. The performance of each new genotype is tested in various environnement. This leads to estimate the potential genetic progress. The front of modelling experiences First attempt in combining genetics modules and crop model to test the potentialities of a virtual breeding on index

38. 3. Potentialities and present limitations

39. Potentialities The past 10-20 years plant modelling could be now an effective tool to analyse and model the genotype – environment interaction: Estimations of microclimate variables Modelling plant responses to environment Ranking plant traits in term of quantitative impact on phenotypic variability Predictions of integrated plant phenotypic The links between concepts and methologies from various disciplines may increase the progress in understanding integrated plant phenotypes. Conclusions

40. Present limitations Low spreading of the biophysical modelling culture. Heavy cost of phenotypic information. Lack of applied mathematic adapted to complex systems. Conclusions