Presentation on theme: "Toward a mechanistic modeling of nitrogen limitation on vegetation dynamics Chonggang Xu 1, Rosie Fisher 2, Cathy Wilson 1, Stan Wullschleger 3, Michael."— Presentation transcript:
Toward a mechanistic modeling of nitrogen limitation on vegetation dynamics Chonggang Xu 1, Rosie Fisher 2, Cathy Wilson 1, Stan Wullschleger 3, Michael Cai 1, Nate McDowell 1 1: Los Alamos National Laboratory, Los Alamos, NM; 2: National Center for Atmospheric Research, Boulder, CO; 3: Oak Ridge National Laboratory, Oak Ridge, TN. The nitrogen limitation is an important regulator of vegetation growth and the global carbon cycle (Thornton et al., 2009; Xu et al 2011). Most ecosystem models simulate the nitrogen effects on photosynthesis based on a prescribed relationship between leaf nitrogen and photosynthesis; however, this relationship may vary with different light, temperature, nitrogen availability and CO 2 conditions (Friend, 1991; Reich et al., 1995; Ripullone et al., 2003). Using a constant relationship can thus reduce the reliability of photosynthesis prediction under different climate conditions in the future. In order to improve the prediction accuracy of nitrogen limitation on photosynthesis, it is important that we build models that account for key factors contributing to this variability. Previous studies have pointed out that the altered nitrogen investment in photosynthesis enzymes (mainly ribulose-1,5-bisphosphate carboxylase oxygenase, Rubisco) and in light capturing proteins of thylakoid (responsible for light absorption and electron transport) under different light conditions is one of the key factors contributing to the variability in the relationship between leaf nitrogen and photosynthesis (Evans, 1989). In this paper, we propose two additional types of nitrogen investment that can impact photosynthesis: the storage nitrogen and nitrogen allocated for respiration. The storage nitrogen can be stored into tissues in the form of inorganic nitrogen, amino acid and proteins (Millard, 1988), which can be used to grow new plant organs (i.e., carbon sink) and produce metabolic enzymes, and can thus sustain plant growth and survival under environmental stresses (Chapin et al., 1990). The respiratory nitrogen is invested in enzymes of mitochondria generate energies to support the carbon sink and maintenance. Importantly to this study, nitrogen allocated for storage and respiration can impact photosynthesis rate because it reduces the total amount of nitrogen available to photosynthetic tissues, while simultaneously enabling growth and increasing sink capacity. This study is supported by DOE Office of Science. Please contact email@example.com for questions and comments. 1. BACKGROUND 2. PURPOSES AND ASSUMPTIONS 3. MATERIALS AND METHODS4. APPLICATIONS AND FINDINGS 5. ACKNOWLEDGEMENTS In this study, we developed a complete nitrogen allocation model that incorporates nitrogen trade-offs among five major biological processes including light absorption, electron transport, carboxylation, respiration and carbon sink (Fig. 1). The developed model is based on three key assumptions. First, plants will balance the nitrogen allocations so that the photosynthesis is co-limited by light capture (including light absorption and electron transport), carboxylation and carbon sink. Second, plants have different strategies of the trade-off between plant persistence and growth, which will cause differences in the amount of nitrogen allocated to storage and thus variability of the relationship between leaf nitrogen and photosynthesis. Third, plants are able to acclimate their nitrogen allocations to different environment and climate conditions. The model is applied in four test cases with changes in nitrogen availability, CO 2 concentration, growing temperature and radiation, which demonstrates the model’s capability to investigate nitrogen allocation patterns and to predict nitrogen limitation upon photosynthesis at different environmental conditions. Finally, the nitrogen allocation model is coupled with a Ecosystem Demography model to test the effects of nitrogen fertilization on species composition in the Arctic. As far as we know, the developed model is the first model of nitrogen allocation that considers storage and respiratory nitrogen under different environmental conditions. It can help us better understand the photosynthetic acclimation under future climate and also provide a more mechanistic prediction of nitrogen limitation upon photosynthesis. Storage nitrogen Light absorption Carbo- xylation N in Calvin Cycle enzymes N in chlorophyll Structural nitrogen N in thylakoid except for chl Electron transport Photosynthesis Respiration New tissue production Carbon sink Respiratory nitrogen Photosynthetic nitrogen Plant nitrogen Fig.1 Schemes of nitrogen allocation model for plants based on nitrogen trade-offs among light absorption, electron transport, carboxylation, and carbon sink. Rectangles indicate pools and ovals represent processes. Dashed arrows indicate feedback effects. In our model, we propose to balance the allocation of nitrogen among light absorption, electron transport, carboxylation and storage to maximize plant growth given their strategies of trade-off between growth and persistence. We calculate the nitrogen allocation based on the environmental conditions (temperature, radiation, water, and nitrogen availability) that plants have experienced. 2 Fig. 2 Hierarchical nitrogen allocations at the individual-plant level. The nitrogen allocation starts from the bottom to top. The whole plant nitrogen is first divided into functional nitrogen and structural nitrogen. Functional nitrogen is then divided into growth nitrogen and storage nitrogen. The growth nitrogen is further divided into photosynthetic nitrogen and respiratory nitrogen, with the photosynthetic nitrogen divided into nitrogen for light harvesting and nitrogen for carboxylation. Finally, nitrogen allocated for light harvesting is divided into nitrogen for light absorption and nitrogen for electron transport. The parameter in the parenthesis indicates the proportion of nitrogen invested for its category in the same row. 3.1 Nitrogen allocation between storage and growth We assume that the plant will store a certain amount of nitrogen so that nitrogen is always available for the production of additional photosynthetic, respiratory and defense enzymes in old and new plant tissues, which can be important for plant growth and survival (Chapin et al., 1990; Herms and Mattson, 1992). The amount of storage nitrogen is determined by a nitrogen storage duration parameter (D ns ), which defines the duration of time that the nitrogen storage can support the current rate of carbon sink. Specifically, given a certain level of FNC a, the proportion of growth nitrogen is determined by solving the equation as follows, 3.2 Nitrogen allocation between photosynthesis and respiration The nitrogen allocation coefficient between photosynthesis and respiration is determined based on nitrogen cost for growth respiration and maintenance respiration. Specifically, 3.3 Nitrogen allocation between carboxylation and light capture To maximize photosynthetic nitrogen use efficiency, we assume that plants will able to balance the nitrogen allocation so that electron-transport-limited carboxylation rate is equal to Rubisco-limited carboxylation rate. The maximum carboxylation rate (V cmax ) is set to be proportional to the amount of Rubisco, which is determined by the amount of nitrogen allocated for Calvin cycle enzymes. 3.4 Nitrogen allocation between light absorption and electron transport The electron transport rate (J) depends on both light absorption and maximum electron transport rate and can be described by the Smith's equation (Niinemets and Tenhunen, 1997; Tenhunen et al., 1976), where J max and [Chl] is linked to the amount of allocated nitrogen (Niinemets and Tenhunen, 1997). The nitrogen allocation is optimized to maximize the electron transport rate J. We define the total nitrogen for growth and storage as functional nitrogen. The ratio of functional nitrogen allocated for a specific leaf layer to its leaf area [FNC a, g functional N/ m 2 leaf] or to its leaf biomass [FNC m, g functional N/ g leaf] are used as indicators of functional nitrogen availability for the leaf layer. The nitrogen is hierarchically allocated for five major processes (see Fig. 2). Fig. 5 Growing temperature effects on nitrogen allocations. In panel (a), open and filled circles indicate observed V cmax for a Japanese plantain (Plantago asiatica) growing at temperatures of 15 o C and 30 o C, respectively, both of which are scaled to the reference temperature of 25 o C. Solid lines are estimates of V cmax by the nitrogen allocation model tuned to data at the high growing temperature (30 o C), while dashed lines are predictions of V cmax by the tuned nitrogen allocation model using the low growing temperature (15 o C). Lower growing temperature increases nitrogen allocation for carboxylation (c) and respiration (f), but decreases allocation for light absorption (d) and electron transport (e). There is only a small change for nitrogen storage (b). The higher nitrogen allocation for carboxylation at the low growing temperature leads to a stronger relationship between leaf nitrogen and maximum carboxylation rate (a). Fig.4 CO 2 fertilization effects on nitrogen allocations. Based on the Farquhar photosynthesis model (Farquhar et al., 1980), elevated CO 2 will result in a higher photosynthesis rate for the same amount of Rubisco by inhibiting photorespiration and increasing Rubisco activities. To support a higher rate of carboxylation rate, based on our model more nitrogen is needed to be allocated for storage, light capture and electron transport with the same amount of Calvin Cycle enzymes. Given the same level of functional nitrogen availability, the CO 2 fertilization will cause higher nitrogen allocation for storage (b) but a lower proportion of nitrogen allocated for carboxylation (c). Data are from Crous et al. (2008) with 8-9 years of CO 2 fertilization. Fig.3 Nitrogen fertilization effects on nitrogen allocations for a poplar species (Populus euroamericana) and douglas-fir (Pseudotsuga menziesii). Data are from Ripullone et al. (2003). The open and filled circles represent observed V cmax for poplar and douglas-fir, respectively, with the dashed and solid lines representing the fitted V cmax by the tuned nitrogen allocation model for poplar and for douglas-fir. Nitrogen fertilization increased leaf nitrogen content and subsequently cause longer storage duration for the coniferous tree but no change in storage duration for the deciduous tree (b). This indicates that the deciduous species adjust its nitrogen allocation with increased nitrogen availability so that it keeps the nitrogen storage duration relative stable. Compared with poplar, the nitrogen allocation for douglas-fir is less responsive to increased nitrogen availability since it mainly keep the increased function nitrogen in storage, which results in a longer nitrogen storage duration. Higher nitrogen allocation to storage for douglas-fir results in lower nitrogen allocation to carboxylation (d) and thus a weaker relationship between V cmax and leaf nitrogen content (a). Fig. 5 Effects of nitrogen fertilization on vegetation response in the arctic by coupling the nitrogen allocation model with a Ecosystem Demography (ED) model in Toolik lake (Shaver et al 2001). Panels (a)-(d) shows the nitrogen allocation fed into ED and panels (e) and (f) shows the simulated aboveground biomass for deciduous (dashed lines) and evergreen shrub (solid lines), respectively. Our model simulations showed that higher nitrogen allocation to carboxylation (b) and fast responses of nitrogen allocation to soil nitrogen availability change (a) are the key drivers for the dominance of deciduous shrubs (e) with nitrogen fertilization. Meanwhile, the light shading on evergreen shrubs cause a higher rate of mortality and reduce its abundance (f). (a)(b)(c) (d)(e) (f) (a)(b)(c) (a)(b)(c) (d)(e) (f) (a)(b)(e) (c)(d) (f)
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