Projet de maîtrise en biologie de Giancarlo MARINO Prévoir la capacité photosynthétique d’une feuille à partir d’une combinaison de ses traits fonctionnels : le test en milieu naturel.     The objective of our project is: Predicting photosynthetic capacity of a leaf at different light intensities from functional attributes under natural conditions. Projet de maîtrise en biologie de Giancarlo MARINO Directeur de recherche Bill SHIPLEY

Eau + Lumière + CO2 -> Oxygène + Glucose L’assimilation de C est la fonction la plus importante dans la majorité des feuilles. Un excellent indicateur pour l’évaluer : taux de photosynthèse de la feuille + L’intensité de lumière est le patron fondamental du taux de photosynthèse = La réponse photosynthétique (A) vs la radiation lumineuse (I) est un bon instrument pour prédire la productivité des plantes As you know, photosynthesis is the process by which plants transform water and carbon dioxide into carbohydrates, using the energy of sunlight. Carbon assimilation is the most important function in the majority of leaves. An excellent indicator for evaluating carbon assimilation is the rate of photosynthesis in the leaf. Moreover, one of the most critical factors influencing the efficiency of photosynthesis is the intensity of light which hits a leaf. Therefore, photosynthetic response (A) vs irradiance (I) is a good tool for predicting plant productivity.

Influence de l'éclairage sur la photosynthèse q() Amax Θ L.C.P. Rd Here, we see a graph of the photosynthetic production as a function of irradiance. Am = the increasing rate of net assimilation when light is not a limiting factor LCP = the light compensation point Rd = the leaf respiration rate q() = the rate at the light compensation point (when =LCP) or at the leaf respiration point (when I=0). Θ = a control parameter describing the curve at intermediate levels of light L'éclairage saturante ou optimale (Amax) : c'est l'éclairage pour laquelle la courbe atteint un plateau. Le rapport quantique (q()) : une mesure de l’efficience avec laquelle les plantes utilisent l’énergie absorbée pour produire des sucres. Le point de compensation de la lumière (L.C.P.). Le taux de respiration en absence de lumière (Rd). La convexité (Θ).

Comparaison de la photosynthèse des plantes de lumières et des plantes d'ombres Generally, the more light that strikes a leaf, the greater the rate of photosynthesis in that leaf. For example, a leaf that is exposed to direct sunlight will photosynthesize at the highest rate, while a leaf in shadow will photosynthesize at a much lower rate. Because of this, plants living under different light exposures are characterized by different photosynthetic curves.

Contexte et problématiques de la recherche Existent-ils des contraintes générales sur les différentes organisations structurelles et fonctionnelles des plantes de différentes espèces? Besoin : développer des méthodes pour comprendre s’il existe des façons de «construire» les plantes et comment ces façons varient en fonction de l’environnement. 2548 espèces In the ten last years, researchers have begun to study the existence of general constraints in the organization of leaves, with regard to their structure and function. One worldwide data base of variables measured on leaves from two-thousand-five-hundred-forty-eight species located in one-hundred-seventy-five sites showed strong correlations between them. Two of these variables (maximum net photosynthetic rate and dark respiration) are parameters describing the photosynthesis-light relationship. Other variables are attributes obtained by derivation of simple measurable characteristics on leaves, such as mass, surface area, leaf lifespan and nitrogen content 175 sites 6 variables (LMA, Amax, N, P, LL, Rd) Wright et al. 2004. The worldwide leaf economics spectrum. Nature 428:821-827.

Le 82% de la variation de Amass, LMA et Nmass entre les espèces se distribue autour d’une droite dans l’espace à trois dimensions. A remarkable 82% of variation in Amass, LMA (Leaf mass per area) and Nmass across species was accounted by the first principal axis in three-trait space.

Recherches antérieures du laboratoire Le projet de M.Sc. de Marouane Aqil SLA Amax azote q(Ф)  Rd chlorophylle l’étude de M. Aqil est basée sur seulement 25 espèces herbacées, cultivées dans conditions constantes d’intensité lumineuse, disponibilité des nutriments, température, etc. An analysis of photosynthetic curves from several species of plants under controlled conditions showed that it is possible to predict photosynthesis-light curves using these simple leaf attributes. The present study is to test such equations on leaves of various tree species in the field. prévision interspécifique possible sur le terrain où les conditions de culture sont beaucoup plus variables?

Mes variables Paramètres Attributs Masse fraîche Surface spécifique des feuilles (SLA) ou masse des feuilles pour unité de surface (LMA=1/SLA) Contenu en azote des feuilles (N) Chlorophylle (Chl) Leaf dry matter content (LDMC) Épaisseur photosynthèse lorsque la feuille est saturée de lumière (Amax/Wmax) taux de respiration en absence de lumière (Rd) Le point de compensation de la lumière (L.C.P.) Le rapport quantique (q() ) Θ = convexité Here are the variables in my project. We distinguish between parameters that describe photosynthetic curves and leaf traits. For the parameters: Net photosynthesis when leaves are light-saturated, The Dark Respiration rate, The Light Compensation Point, The rate (dA/dI) at the light compensation point or at respiration point, The parameter for control of curve at intermediate level of light. For the leaf traits: Leaf fresh mass, Leaf Surface Area, Nitrogen and Chlorophyll concentration in the leaf, leaf dry matter content, stomate density and leaf thickness.

Puissance de prédiction Trouver une méthode pour prévoir les paramètres Amax, LCP, q() et Rd à partir des quelques traits des feuilles, peut nous permettre d’extrapoler les données pour construire des courbes de photosynthèse a partir de la base de données mondiale. The prediction of parameters Amax, LCP, q() and Rd using simple leaf attributes may allow us to extrapolate data from the worldwide data base and quickly describe new photosynthetic curves. Such an objective is important in order to improve models in forestry and agriculture so that differences in photosynthetic response vs irradiance can be included. For example, photosynthesis curves are utilized for LIGNUM, an individual-based model of forestry dynamics that describe metabolic activities and crown architecture of trees. La réalisation d’un tel objectif est importante pour améliorer les modèles en foresterie et en agriculture en tenant compte de la réponse photosynthèse – lumière dans les projets de gestion.

Dispositif expérimental Base de données: Site1 Espèce1 Individu1 feuille soleil 0 50 100 200 400 800 1600 feuille ombre Individu2 Espèce2 Espèce3 Espèce4 Espèce5 Site2 Site3 Site4 Site5 Site6 Site7 Site8 Data were collected during summer 2007 in eight sites. For each site we chose five species of deciduous trees. Two undamaged leaves were sampled per plant and 2 plants per species. Whenever possible, 1 leaf was sampled from the outside of the canopy (light) and 1 leaf from within the canopy (shadow). A photosynthetic light-response curve was estimated for each leaf, based on 7 levels of PAR (Photosynthetically Active Radiation), the spectral range of solar light from 400 to 700 nanometers ( in terms of μmol/m2/s). The leaf was allowed to habituate at each irradiance level for 5 minutes before measurements began. We then took 3 measurements over a 5 minute period at each level. 1 mesure de photosynthèse à la seconde (moyenne sur 15s, 3 répliques).

Plan des sites Plants were sampled randomly within a 5 km radius of the city of Sherbrooke. Generally, we chose a forested area in a large park.

Espèces 1. Acer negundo 2. Acer pensylvanicum 3. Acer rubrum 4.   Acer saccharinum 5.   Acer saccharum 6.   Alnus rugosa 7.   Aesculus hippocastanum 8.   Betula alleghaniensis 9.  Betula papyrifera 10. Betula populifolia 11. Castanea sativa 12. Celtis occidentalis 13. Cornus alternifolia 14. Cornus stolonifera 15. Crataegus sp. 16. Fagus grandifolia 17. Fraxinus americana 18. Fraxinus pennsylvanica lanceolata 19. Juglans cinerea 20. Malus pumila 21. Ostrya virginiana 22. Parthenocissus quinquefolia 23. Polygonum cuspidatum 24. Populus balsamifera 25. Populus deltoides 26. Populus tremuloides 27. Prunus serotina 28. Quercus macrocarpa 29. Quercus robur 30. Quercus rubra 31. Rhamnus frangula 32. Rhus typhina 33. Rosa rugosa 34. Salix nigra 35. Syringa vulgaris 36. Tilia americana 37. Tilia cordata 38. Ulmus americana 39. Ulmus rubra 40. Vitis riparia A large number of species are deciduous trees, but when the diversity of trees in the site was limited, we chose shrubs in the genera Cornus, Rosa, Vitis, as well as others.

Méthodologie : Mesures de photosynthèse Les mesures des échanges gazeux pour la détermination de l’activité photosynthétique des feuilles sur le terrain ont été effectuées avec le nouvel appareil CI-340 Portable Photosynthesis System qui permet en même temps de contrôler plusieurs variables environnementales pendant la prise de mesures. Photosynthetic measurements were taken using a a lightweight portable photosynthesis system. Display, keypad, computer, data memory, CO2/H2O (carbon dioxide and water) gas analyzer, flow control system and battery is contained in a single, hand-held case. With the CO2/H2O generator module, the instrument can precisely control gases concentrations in the leaf chamber. The system can also control temperature and light intensity with two other modules. Concentration de CO2 Intensité de lumière Humidité Température Débit de CO2 Surface de la feuille Nous avons commencé au mois de mai, après que les feuilles soient sorties et aient atteintes leurs tailles normales.

Méthodologie : Mesures de photosynthèse Here is a schematic representation of the system. A pump pulls CO2 (carbon dioxide) from the generator module. A first precise measure of concentration is taken before passage to the leaf chamber. A second measure of CO2 is taken at the exit of leaf chamber. The difference in concentration between the two measurements give information near the process of photosynthesis or respiration of leaf. Finally information is analyzed and stored in memory. Ambient air temperature in the leaf chamber was 20°C, relative humidity was 60% and CO2 concentration of the incoming air was 400ppm. [CO2]=400ppm Humidité=60% T=20°C Débit de CO2

Méthodologie: Mesures des attributs de la feuille Immediately following photosynthetic measurements, 2 branches were taken from a single plant with a minimum of 2 other intact leaves. These branches contained the leaves used for photosynthetic measurements. The branches were transported back to the laboratory in a cooler and with the cut stem submerged in a tube filled with water. Upon arrival at the laboratory the end of the branch stem was again cut under water to eliminate possible absorption of air in the xylem and stored overnight in this way in the dark in order to allow for evacuation of non-structural carbon. The following day, 2 intact leaves were chosen from each branch. One leaf was separated into lamina and petiole. The two pieces were separately weighed and the thickness of the lamina was measured. Then the lamina was scanned and its projected surface area was calculated using the software WinFolia. The lamina and petiole were reweighed after oven-drying at 50°C for 48h and LDMC was estimated as ratio of dry mass to fresh mass. Finally, this first leaf was ground and N concentration determined by Macro Elemental Analyzer. The second leaf was utilized for extraction of chlorophyll with DMSO (dimethyl sulphoxide). One other, non destructive method of measuring chlorophyll content was tested with a PlantPen (like a SPAD meter, but calculating a reflectance index).

Analyses statistiques préliminaires Outils mathématiques l’équation de Mitscherlich : (photosynthèse nette) l’équation de l’hyperbole non rectangulaire : (photosynthèse brute) Two non-linear statistic models were employed to extrapolate net photosynthesis vs light intensity curve from the variables in the worldwide leaf economic spectrum: (1) the Mitscherlich equation, and (2) the non-rectangular hyperbola equation. The first model gives information about: the increasing rate of net assimilation when light is not a limiting factor, together with the light compensation point, and the rate at the light compensation point. The second model gives information about the value of carbon fixation when I is maximal, the maximuml production rate when I=0, the leaf respiration rate, and curve convexity . Am, Wm = le taux maximal de photosynthèse nette quand la lumière n’est plus limitante (I)  = le point de compensation de la lumière (L.C.P.) q(…) = le rapport quantique, taux de variation instantané de la photosynthèse par rapport à la variation de l’intensité de lumière (dA / dI). = convexité, paramètre qui contrôle les comportements de la fonction aux niveaux intermédiaires de lumière Rd = le taux de respiration de la feuille

Reg. Non Lin. – Prévisions des paramètres Les paramètres de la courbe photosynthétique de Mitscherlich ont été calculés en utilisant la fonction « nls » de R (régression non linéaire). Une fois les points tracées, on trouve la meilleure interpolante et on détermine les valeurs de: Amax LCP q(Φ) Function “nls” in the statistical language R was utilized to calculate the parameters of Mitscherlich equation. In this process, the best interpolation of data points estimates the increasing rate of net assimilation when light is not a limiting factor, the light compensation point and the quantum at the light compensation point. fit<-nls(photo~Am*(1-exp(q*(LCP-lumiere)/Am))), data=gcphoto,start=list(Am=8, q=0.08.LCP=5, suset=sel,na.action=na.omit, trace=T)

Reg. Non Lin. – Prévisions des paramètres Les paramètres de la courbe d’hyperbole non rectangulaire ont été calculés en utilisant le logiciel « Photosynthesis Assistant »: Wmax q(Φ) Rd Θ The non-rectangular hyperbola equation is integrated into the software Photosyn Assistant to likewise calculate the rate of carbon fixation when I is maximal, the maximum production rate when I=0, the leaf respiration rate, and curve convexity. Light response curve analysis

Détermination des attributs des feuilles - Mitscherlich SLAo, Chlo, No Amaxo, q(Φ)o, LCPo SLA, Chl, N, Amax, q(Φ), LCP SLA●, Chl●, No● Amax●, q(Φ)●, LCP● In this way the parameters of every photosynthetic curve can be related to the leaf traits in the first model… Chaque courbe correspond à une feuille avec ses attributs spécifiques.

Détermination des attributs des feuilles - hyperbole non rectangulaire SLAo, Chlo, No Θo, q(0)o, Amaxo,R0o SLA, Chl, N, Θ, q(0), Amax, R0, SLA●, Chl●, No●, Θ●, q(0)●, Amax●, R0●, As well as in the second. Chaque courbe correspond à une feuille avec ses attributs spécifiques.

Construction de la base de données pour la prévision F Sla Chl N 1 A 357.99 1.95 2.20 2 358.38 2.18 1.96 3 364.42 1.77 3.01 4 401.89 2.40 1.73 5 B 433.92 2.13 1.91 6 659.93 1.71 3.92 : 160 Z 536.96 2.09 1.64 Thus, a leaf can be transformed into a series of measurements for the data base.

Extrait de la base de données The result of this work is synthesized in this data base. Here, for each leaf, we know site and species, date of sampling, light exposure (sun or shade), and all measured variables.

Analyses avancées Les relations entres les paramètres et les attributs morphologiques et chimiques des feuilles peuvent être développées en utilisant les régressions linéaires multiples et les corrélations. In the second step of statistical analysis we used linear regression and correlation to determine relationships between the parameters of photosynthetic curves and leaf traits.

Linear Mixed-Effects models Prévision de Amax, Wmax, Rd, θ sur la base des attributs des feuilles: Am ln(Amax) = 4.4538 – 0.4124 * ln(SLA) r2 =0.59 (espèce) (0.5363) (0.0984) Wm ln(Wmax) = 4.8993 – 0.4507 * ln(SLA) r2 =0.55 (espèce) (0.4825) (0.0885) Rd ln(Rd) = 0.8220 + 0.8149 * ln(épaisseur) r2 =0.20 (espèce) (0.6808) (0.3728) θ θ = – 0.6408 – 0.2857 * ln(N) r2 =0.08 (feuille) (0.1051) (0.2548) Linear Mixed-Effects Models allowed us to express each parameter as a algebraic combination of leaf traits. We obtained four empirical equations to predict the parameters Amax, Wmax, Rd and θ as a function of the leaf attributes SLA (Specific leaf area) and N.

Finally, in this slide, it is possible to evaluate the level of agreement between the predicted value of parameters and the corresponding values measured in the field. Our results confirm the validity of the Mitscherlich and Non-Rectangular Hyperbola equations. However, estimations from leaf traits could be improved, particularly for high levels of photosynthesis.

Thank you all!