Responses of a C 3 and C4 perennial grass enrichment and ... et al 1996.pdf · (Bouteloua gracilis), Ca, were selected at the Central Plains Experimental Range (CPER) in N.E. Colorado

ELSEVIER Ecological Modelling 87 (1996) 11-27

E(OLOGI(DL mODELLInG

Responses of a C 3 and C4 perennial grass to C O 2 enrichment and climate change: Comparison between model predictions and

experimental data

De-Xing Chen a,., H.W. Hunt a,b, J.A. M o r g a n c

a Natural Resource Ecology Laboratory, Colorado State UniL,ersity, Fort Collins, CO 80523, USA b Department of Rangeland Ecosystem Science, Colorado State University, Fort Collins, CO 80523, USA

c USDA-ARS, Crops Research Laboratory, Fort Collins, CO 80526, USA

Received 15 August 1994; accepted 14 December 1994

Abstract

Ecological responses to C O 2 enrichment and climate change are expressed at several interacting levels: photosynthesis and stomatal movement at the leaf level, energy and gas exchanges at the canopy level, photosynthate allocation and plant growth at the plant level, and water budget and nitrogen cycling at the ecosystem level. Predictions of these ecosystem responses require coupling of ecophysiological and ecosystem processes. Version GEM2 of the grassland ecosystem model linked biochemical, ecophysiological and ecosystem processes in a hierarchical approach. The model included biochemical level mechanisms of C 3 and C4 photosynthetic pathways to represent direct effects of CO 2 on plant growth, mechanistically simulated biophysical processes which control interactions between the ecosystem and the atmosphere, and linked with detailed biogeochemical process submodels. The model was tested using two-year full factorial (CO2, temperature and precipitation) growth chamber data for the grasses Pascopyrum smithii (C 3) and Bouteloua gracilis (C4). The C3-C 4 photosynthesis submodels fitted the measured photosynthesis data from both the C 3 and the C 4 species subjected to different CO2, temperature and precipitation conditions. The whole GEM2 model accurately fitted plant biomass dynamics and plant N content data over a wide range of temperature, precipitation and atmospheric CO 2 concentration. Both data and simulation results showed that elevated CO 2 enhanced plant biomass production in both P. smithii (C 3) and B. gracilis (C4). The enhancement of shoot production by elevated CO 2 varied with temperature and precipitation.

Doubling CO 2 increased modeled annual net primary production (NPP) of P. smithii by 36% and 43% under normal and elevated temperature regimes, respectively, and increased NPP of B. gracilis by 29% and 24%. Doubling CO 2 decreased modeled net N mineralization rate (N min) of soil associated with P. smithii by 3% and 2% at normal and high temperatures, respectively. N min of B. gracilis soil decreased with doubled CO 2 by 5% and 6% at normal and high temperatures. NPP increased with precipitation. The average NPP and N min of P. smithii across the treatments was greater than that of B. gracilis. In the C 3 species the response of NPP to increased temperatures was negative under dry conditions with ambient CO2, but was positive under wet conditions or doubled CO 2. The

* Corresponding author.

0304-3800/96/$15.00 © 1996 Elsevier Science B.V. All rights reserved SSDI 0304-3800(94)00199-5

12 D.-X. Chen et al. / Ecological Modelling 87 (1996) 11-27

responses of NPP to elevated C O 2 in the C 4 species were positive under all temperature and precipitation treatments. N min increased with precipitation in both the C 3 and C 4 species. Elevated CO 2 decreased N min in the C4 system. The effects of elevated CO 2 on N_min in the C 3 system varied with precipitation and temperature. Elevated temperature decreased N rain under dry conditions, but increased it under wet conditions. Thus, there are strong interactions among the ef~cts of CO 2 enrichment, precipitation, temperature and species on NPP and N min.

Interactions between ecophysiological processes and ecosystem processes were strong. GEM2 coupled these processes, and was able to represent the interactions and feedbacks that mediate ecological responses to CO 2 enrichment and climate change. More information about the feedbacks between water and N cycling is required to further validate the model. More experimental and modeling efforts are needed to address the possible effects of CO 2 enrichment and climate change on the competitive balance between different species in a plant community and the feedbacks to ecosystem function.

Keywords: Carbon; Climate; Photosynthesis; Production, primary; Nitrogen

1. Introduction

Knowledge of how ecosystems respond t o C O 2

enrichment and climate change is incomplete. CO 2 enrichment experiments may show different responses in different systems (Drake, 1992a,b; Owensby et al., 1993). In tussock tundra, the stimulation of carbon assimilation of the entire system by elevated CO 2 was appreciable in year 1 but declined in year 2 and was unchanged or slightly reduced in year 3 (Oechel and Riechers, 1986; Grulke et al., 1990). Despite the small effects on assimilation, significant species shifts were observed in the third year (Oechel et al., 1991). Limited field data involving C3-C 4 competition under elevated CO 2 have shown different outcomes for different ecosystems. In estuary marsh, elevated CO 2 strongly stimulated C 3 species photosynthesis and growth, and no photosynthetic homeostasis was observed, but C 4 species responded only slightly (Curtis et al., 1989; Drake, 1992a,b; Arp et al., 1993). In a tallgrass prairie, however, the production of a C 4 grass (Andropogon gerardi) showed a greater response to elevated CO z than that of C 3 plants (Owensby et al., 1993).

Different responses to elevated CO 2 among different ecosystems might be due to differences in temperature, soil water and nutrient availability. Simulation modeling indicated that the interactions among environmental factors, as well as the main effects of CO 2 enrichment, were important in determining whole ecosystem responses to

CO z enrichment and climate change (Hunt et al., 1991). The long-term effects of changes in atmospheric CO 2 concentration, temperature and precipitation may differ from the short-term effects because of feedbacks involving nutrient cycling. Interactions among environmental factors and feedbacks such as photosynthetic acclimation and nutrient cycling make it difficult to predict ecosystem responses. A more mechanistic under- standing of interacting effects at both the ecophysiology and ecosystem levels is necessary to understand why different ecosystems respond so differently to elevated COz and climate change.

Ecosystem models have become effective tools for synthesizing isolated experimental and theo- retical results from parts of ecosystems, and for predicting the responses of ecosystems to the environment (Agren et al., 1991). Process-ori- ented models may be grouped into three main categories: ecophysiological process-based models, e.g. GRASS (Coughenour, 1984), GEMTM (Chen and Coughenour, 1994); population models, e.g. gap models (Shugart, 1984; Coffin and Lauenroth, 1990); and biogeochemistry models, e.g. CENTURY (Parton et al., 1987) and TEM (Raich et al., 1991)o Ecophysiological models describe many important biophysical and physiological processes in great detail, but often inade- quately treat interactions with nutrient cycles. These models can predict the interactive effects of environmental factors on plant growth and related processes in relatively short time scales. Both biogeochemistry models and population

D.-X. Chen et at/Ecological Modelling 87 (1996) 11-27 13

models encapsulate relationships between plants and the soil system, but they rely heavily on empirical functions describing aggregated processes and their responses to the environment. For instance, the CENTURY model simulates annual production based on an empirical rela- tionship with annual precipitation, which is then decomposed to monthly values and adjusted for nutrient availability (Parton et al., 1987). Popula- tion models simulate plant demographic processes and the competition between individual plants. A major current limitation of population models is the lack of physiological detail in the simulation of plant growth, which is modeled as a simple function of annual climate indices such as growing degree-days and some water stress indices (Coffin and Lauenroth, 1990). These functions are empirical relationships estimated for current climates, and will likely require re- calibration if the models are applied to predict the responses of ecosystems to CO 2 enrichment and climate change. Both population models and biogeochemistry models presently oversimplify interactions among the physiological processes and the environment which determine plant growth and nutrient cycling (Bonan, 1993). They also lack the mechanisms to simulate the interaction between C O 2 enrichment and plant water balance (Leadly and Reynolds, 1992). Thus, the pre- dictive capability of these current models is limited, particularly regarding CO 2 enrichment and climate change.

Most of the above models were calibrated using relatively short-term and limited data. Ideally, model validation requires long-term experimental data, particularly regarding interactions between the effects of CO2 enrichment and climate factors. In this paper, we describe a revision (GEM2) to the grassland ecosystem model (Hunt et al., 1991), which simulates both short-term biophysical-physiological processes and long-term biogeochemical processes, and mechanistically ac- counts for differences between C3 and C4 photosynthesis. To quantitatively characterize ecosystem responses to CO 2 enrichment and climate change, the model was tested using data from a full factorial experiment (CO2, temperature and precipitation) on the whole system response of

intact cores of two native perennial grasses. The model was then applied to investigate the possible long-term effects of CO 2 enrichment and climate change on annual net primary production and nitrogen cycling.

2. Materials and methods

2.1. Phytotron experiment

Nearly pure stands of western wheatgrass (Pascopyrum smithii), C3, and blue grama (Bouteloua gracilis), Ca, were selected at the Central Plains Experimental Range (CPER) in N.E. Colorado. Steel cylinders (25 cm diameter by 45 cm long) were driven into soil and immedi- ately extracted with intact soil cores. The surface soil (~ 10 cm) associated with B. gracilis was a sandy loam with 1.6% organic matter, and that with P. smithii a sandy clay loam with 3.5% organic matter. The intact cores were placed in growth chambers side by side at the Duke Uni- versity Phytotron and subjected to daily and seasonal variation in temperature, photoperiod and precipitation. Seasonal distribution of precipitation approximated that at the CPER for three water level treatments. Water was added periodi- cally in 25 mm events to approximate the seasonal distribution in the field. Temperatures for an ambient temperature treatment matched long-term averages at the CPER, while temperatures were shifted up 4°C for a high temperature treatment (see Morgan et al., 1994 for details about growth chamber conditions). Atmospheric CO 2 concentration was regulated to either 350 + 15 or 700 + 35/~mol/mol day and night, all year. Light intensity was maintained at 550 ~mol m -2 s-l photosynthetic photo flux density at the soil surface using mixed fluorescent and incandescent lights. Leaf gas exchange experiments were car- ried out at 25 + 2°C at a time corresponding to early July in the field in the second growing season using an ADC portable photosynthesis system (Analytical Development Co., Hoddesdon, UK) (see Morgan et al., 1994). Shoot biomass and N contents were sampled at the end of each growing season, and root biomass and N content

14 D.-X. Chen et aL /Ecological Modelling 87 (1996) 11-27

~'/Atmosphere ~/4//~__ _

andln ~ st ' gL, I Liveshoots ,,ead r 1 s"°°ts /

"ver°°ts I -

Labile/Res~tant ]!r.~.~..~ Myco.rrh,zal] . . . . . . . t [ lung,

t M'crobes So"°rgan'c ma**er Fauna Stable/Refractile

' I

Inorganic

T Fig. 1. Simplified mass flow diagram of the grassland ecosystem model GEM (Hunt et al., 1991).

after the second season. Soil water content and soil inorganic N concentration were measured on three dates. Air temperature and relative humidity were monitored every hour. Hunt et al. (in prep.) described the experimental methods and results in greater detail.

2.2. GEM model structure

The grassland ecosystem model GEM links ecophysiology and ecosystem processes to quantitatively assess ecosystem level effects of CO z enrichment and climate change (Hunt et al., 1991). It mechanistically incorporates key features of soil, climate, and physiological ecology of plants, microbes and fauna. The model includes mechanisms to represent the effects of CO 2 on primary production, soil and plant water balances, and nutrient cycling feedbacks involving substrate quality and nitrogen mineralization. The C and N flows among the main components of grassland

ecosystems are shown in Fig. 1. Inorganic N variables include ammonium and nitrate. Organic residues are divided into the labile and resistant portions of fresh material, and transfer to two fractions of soil organic matter with turnover times of 50 and 1000 years.

The decomposition rates of residues (resistant and labile) are functions of bacterial and fungal biomass and N, soil water, temperature and the effects of interference among microbes. Organic nitrogen is released from residues through decomposition in the same ratio as exists in the residues. The decomposition rate of soil organic matter is proportional to the level of soil organic matter and, via a priming mechanism, to the rate of decomposition of fresh residues.

Ammonium and nitrate uptake by plant roots, mycorrhizal fungi and microbes is simulated using Michaelis-Menten functions. Uptake rates de- crease as the nitrogen concentrations (N:C ratios) in roots, mycorrhizal fungi and microbes increase. The uptake rates are also affected by soil water and temperature. Mycorrhizal fungi are assumed to take up inorganic N and to transfer N to the plant in return for C. Plants have control over the C transfer, and the fungi have control over N transfer.

Carbon fixed through photosynthesis is allo- cated between shoots and roots with variable partition coefficients, which are functions of shoot C:N ratio and soil water levels. The fraction of photosynthate used for root growth increases with increasing N or water stress. A portion of fixed carbon is respired for growth and maintenance. Growth respiration is proportional to net biomass synthesized. Maintenance respiration is proportional to existing biomass and responds to temperature. Death rates of shoots and roots increase with water stress and freezing. Retranslo- cation of nitrogen from dying plant tissue varies with tissue N content.

Soil water dynamics are simulated using a two-layer tipping-bucket soil water model. The model simulates the dynamics of total available soil water in each layer based on mass balance. Root water uptake from each layer is based on root biomass, soil temperature and available water (Hunt et al., 1991).

D.-X. Chen et al. / Ecological Modelling 87 (1996) 11-27 15

2.3. Structure of GEM2

GEM2 incorporates an energy and gas exchange submodel to simulate latent (transpiration and evaporation) and sensible heat exchanges among soil-surface, canopy and the atmosphere using an electrical analogy (Goudriaan, 1977). Hence, energy exchange and water transfer are tightly linked. The submodel also predicts canopy temperature and soil temperatures (Chen and Coughenour, 1994). GEM2 simulates light pene- tration within the plant canopy using an exponen- tial function. The photosynthesis submodel was revised to better distinguish the C 3 and C 4 photosynthetic pathways (Chen et al., 1994a). In GEM2, maintenance respiration of shoots is also affected by atmospheric CO z (Drake, 1990; ldso and Kimball, 1993) using an empirical linear function (Coughenour and Chen, 1994).

an / / - - ~ ", /

/ Ta e a Reference / • / / l / height air

ral Rncp i r a 1 / . . . . i~//

/ rbl r cp rbl /

/ / T c ~ p e c p • ~ / / Canopy

ra2 ra2

rbs rbs Rnss

i ~ rss /- . . . . . . / / Tss ess /

/ k / I ~" -" I

/ I I / So i l

/ I I / / _ _ . . . . . . . . . . _ /

/ /

L J

Fig. 2. Resistance network of soil-surface and canopy energy balance, e a is water vapor pressure of air. ecp and ess are saturated water vapor pressures at canopy temperature and soil surface temperature, respectively. R., Rnc p and Rns s are net radiation at the reference height, the canopy and the soil surface, respectively. See text for other symbols.

Energy and gas exchanges at soil-surface and canopy

Many mass and energy transfer models have been developed in micrometeorology, climatology and plant ecophysiology (e.g. Goudriaan, 1977; Sellers et al., 1986; Choudhury and Monteith, 1988; Chen and Coughenour, 1994). Although the links among canopy physiology, surface energy and gas exchanges have long been recognized, few ecosystem models (e.g. Coughenour, 1984) explicitly include this linkage. The model for energy and gas exchanges presented here is based on the work of Chen and Coughenour (1994) and Choudhury and Monteith (1988). Energy and gas exchanges at the soil surface and canopy are represented as a resistance network as in Fig. 2. Differences of saturated vapor pressures are approximated as linear functions of the corresponding temperature differences (Choudhury and Monteith, 1988). Analogously to Ohm's law, latent and sensible heat exchanges between the soil surface, canopy and the atmosphere can be described with the following equations:

(ral + rbl + rcp)LEcp + ral L E ~

-- PaCp A(Tcp - Ta) + PaCp(es(Ta) - ea) (1) Y Y

ral LEcp + ( rbs + rss + ral + ra2)LEss

PaCp A(Tss Ta) + PaCp( es( Ta) ~ e a )

- - ( 2 )

Y Y

(ral +rb l )Hcv+ralHss=paCo(Tco- Ta) (3)

ral Hop + (ral + ra2 + rbs)Hss = paCo( Tss - T.~)

(4)

where Ecv and E~s (g H 2 0 m -2 s -1) are canopy transpiration and soil surface evaporation, and L (2450 J g-1 H 2 0 ) is specific latent heat of water. Hcp and Hs~ (J m -2 s - l ) are sensible heat fluxes at the canopy and the soil surface, respectively. ral and ra2 (s m - l ) are aerodynamic resistances from the mid-canopy to the reference height and from the bottom to the mid-canopy, respectively. rcp and rss (s m -1) are surface resistances for the canopy and the soil surface, rbl and rbs (s m-1)

16 D.-X. Chen et aL /Ecological Modelling 87 (1996) 11-27

are boundary layer resistances of the canopy and the soil surface (here the resistances to water vapor and heat are assumed the same). These aerodynamic resistances and surface resistances were simulated using the approach of Chen and Coughenour (1994, eqs. 21-32). Tcp, Tss and T a (°C) are canopy, soil surface and air temperature, respectively. A (mbar °C-1) is the slope of saturated water vapor pressure against temperature. Cp (1.012 J g-1 oC-1 ) is the specific heat of air; Pa (1.183 kg m -3) is air density; y (0.66 mbar °C -1) is the psychrometric constant; and es(T a) and e a (mbar) are saturated and actual water vapor pressure of air.

According to the energy balance at the canopy and the soil surface, we have:

L +/4op = e,n p (5) LEss +H,s + Gss =Rnss (6)

where Rncr , and Rnss (J m -2 s -1) are net radiation at the canopy and the soil surface. Gss (J m -2 s -1) is the soil heat flux which is calculated based on the soil temperature gradient and soil heat resistance (Choudhury and Monteith, 1988).

There are six real unknown variables in the above six equations, LEap, LEss, H~ and Hss, T~p and Tss, when the relevant resistances are given. These six simultaneous equations are solved using the Gauss-Newton numerical method.

Photosynthesis and stomatal conductance Plants with the C 3 and C 4 photosynthetic

pathway respond differently to CO 2, temperature and light intensity (Pearcy and Ehleringer, 1984). CO z is finally assimilated via the enzyme ribulose bisphosphate (RuBP) carboxylase-oxygenase (Rubisco) in both C 3 and Ca photosynthetic pathways (Fig. 3). Rubisco has both carboxylase and oxygenase functions, with the latter resulting in photorespiration. C 3 photosynthesis is subject to 0 2 inhibition through photorespiration at present atmospheric levels of CO 2 and 0 2. The C a pathway of photosynthesis couples the C3-cycle with the C4-cycle which acts as a CO 2 pump, concentrating CO 2 in the bundle sheath cells where Rubisco and the photosynthetic carbon reduction cycle (PCR) are located, thereby in-

a. C3 Photosynthesis

Mesophyll cell .hv

gs Ca

CH20 1~ RuB# T I,,ght roac,,oo,

Calvin ] ATP I

,~Crn ~ 1 ~ PGIAI NADPH

Rubisco

b. C4 Photosynthesis

Mesophyll . Bundle sheath hv

g~, Ca--

PEP• CH20

R u B P ' I t - ~ Calvin 1

,Cm IPEPcase' I k,,~Yc'e pdA'~

I rbs

ATP I NADPH

Fig. 3. Diagrams of (a) C 3 photosynthesis and (b) C 4 photosynthesis.

hibiting photorespiration. C 4 photosynthesis is generally considered to be CO 2 saturated at the present atmospheric level of CO 2 (however see Morgan et al., 1994). In order to address these differences between C 3 and C 4 photosynthesis, subroutines were developed to represent both C 3 and C 4 pathways.

C 3 photosynthesis. The submodel of C 3 photosynthesis is based on the model of Farquhar et al. (1980). CO 2 assimilation is determined by the relative limitations of rates of Rubisco fixation of CO 2 in the mesophyll, RuBP regeneration, and RuBP oxygenation (Fig. 3a). Net photosynthesis rate is expressed as:

( 0"50m)min{Wc,Wi}-Ra (7) A,--- 1 "/'Cm

where min{} denotes ' the minimum of'. W~ and W i are carboxylation rates limited by Rubisco activity and RuBP regeneration, respectively. C m and Om are CO 2 and 0 2 concentrations in the


mesophyll. ~- is the specificity factor for Rubisco (Jordan and Ogren, 1984). R a is dark respiration rate. When Rubisco activity is limiting, the assimilation rate, We, is simulated with a Michaelis- Menten equation:

Vcmfm Wc = (8 )

C m Jr- k c ( l + Om//ko)

where Vcm is the maximum rate of carboxylation at saturated CO 2. k c and k o are Michaelis con- stants of carboxylation and oxygenation, respectively. When photosynthetic electron transport controls ribulose-l,5-bisphosphate (RuBP) regeneration, assimilation rate is described by:

J C m

Wj ~-- Cm -4- Orn//T (9)

where J is the maximum rate of photosynthesis at saturated C m at a given irradiance. J is described in terms of incident PAR, I o, as:

= ( 1 0 ) _2r2 /r2~ 1/2 J (1 + Ot l p / d m ]

where Jm is the potential rate of electron transport, and a is quantum efficiency.

Net CO 2 exchange rate between surrounding atmosphere and the intercellular space through stomata is equal to An:

An = gs(Ca - Cm)

where gs is stomatal conductance, and C a is atmospheric CO 2 concentration.

Leaf photosynthesis decreases with increasing water stress (decreasing water potential) and nitrogen stress (decreasing N content in leaves). Plant respiration is also influenced by water stress. The effects of soil water potential and leaf nitrogen content on photosynthesis were described by zero-to-one factor functions as in Coughenour (1984). The similar factor function was used to describe the response of plant respiration to water potential.

C 4 photosynthesis. The C 4 photosynthetic pathway consists of the phosphoenolpyruvate carboxylase (PEPcase) dependent Ca-cycle and the Ru- bisco-dependent C3-cycle (Fig. 3b). The C a pho-

tosynthesis submodel is described in detail in Chen et al. (1994a).

The Ca-cycle of C a photosynthesis is assumed to be controlled solely by PEP carboxylase. The dependence of the rate of the Ca-cycle , 1/4, upon CO 2 concentration is modeled by Michaelis- Menten kinetics:

Vamfm I / 4 = - -

C m + k p

where kp is the Michaelis constant. V4m, the maximum velocity at a given incident radiation, is modeled in terms of incident PAR with the same equation as used above for J:

a p l p

V4m (1 tO~plp /Vpm ) - 2 r2 / r z2 xl/2 (13)

where ap is a parameter, and Vom is the potential activity of PEPcase.

The diffusion flux of CO 2 between the bundle sheath and the mesophyll, Vu, is a function of the difference in CO a concentration and the resistance between them, rbs:

V b -~- ( C i - C m ) / r b s

Similarly, the outward diffusion flux of O 2 from the bundle sheath is equal to the production of O 2 inside the bundle sheath and is simulated with the following equation after Berry and Far- quhar (1978) and Collatz et al. (1992):

f A n = (O i - Om)/rbs o (15)

where f is the proportion of total net 0 2 production occurring inside the bundle sheath. O i is the oxygen concentration inside the bundle sheath and Om that in the mesophyll, rbs o is the resistance to 0 2 diffusion between the bundle sheath and the mesophyll.

The C3-cycle of the C a photosynthetic pathway is simulated using the same equations in the C 3 photosynthetic submodel (Eqs. 7-10), replac- ing C m with C i and O m with Oi, respectively (see Chen et al., 1994a).

Dependence on temperature. The kinetic parameters k c, ko, k o, specificity factor ~-, and dark respiration R d depend on temperature according

18 D.-X. Chen et al. /Ecological Modelling 87 (1996) 11-27

to Q10 factors. The temperature dependence of Jm, Vcm and Vpm is described by an Arrhenius function with a 25°C reference temperature (Farquhar et al., 1980; Johnson and Thornley, 1985):

Parameter ( Jm,Vcm,Vpm)

= P25 exp - ~- T

))

where E, H and S are parameters, R is the gas constant, and T is temperature in Kelvin. T25 is the 25°C reference temperature in Kelvin. P25 denotes the value of any of these three parameters at 25°C.

Stomatal conductance. Ball et al. (1987) proposed an empirical model to describe the response of gs to An, the relative humidity and CO z concentration at the leaf surface. This model was modified by using humidity, H a, and CO 2 concentration, Ca, in the air outside the leaf boundary layer:

A n n a g s = g b +gm (17)

Ca

where gb and grn are parameters. A simultaneous solution for photosynthesis rate and stomatal conductance is then obtained using numerical methods.

3. Model calibration and validation

The photosynthesis submodel was calibrated using measurements of photosynthetic responses to intercellular CO 2 concentration, incident photosynthetic active radiation (PAR), and leaf temperature (Monson et al., 1982,1983; see Coughenour and Chen, 1994). The estimation of key parameters Jm, Vcm, Vprn, a and ap in the photosynthesis submodels was achieved by nest- ing photosynthesis simulation routines in the non-

linear regression procedure of SAS (1987; see Chen et al., 1994a). The zero-to-one factor functions for the effects of soil water potential on photosynthesis and respiration were parameter- ized based on the report of Brown and Trlica (1977). The whole photosynthesis submodels were validated by comparing the predicted photosynthetic rates with measured rates for both B. gracilis and P. smithff from our Phytotron experiment (Morgan et al., 1994).

Parameters in the energy and gas exchange submodel are mostly basic physical and biological characteristics, such as leaf reflectance for different spectra (Chen and Coughenour, 1994). The reflectance of single leaves was estimated according to Gates (1980) and Monteith and Unsworth (1990). The light extinction coefficient was estimated as 0.25 (LAI-1). Soil hydraulic characteris- tic parameters such as saturated water content and saturated soil water conductivity were estimated based on soil texture data after Clapp and Hornberger (1978). The main subroutines in the energy and gas exchange submodel were previ- ously evaluated in Chen and Coughenour (1994).

The values of all parameters involved in decomposition processes in GEM were adopted here, which were derived from data from two perennial grassland sites -a B. gracilis (C a) domi- nated site, and an Agropyron cristatum (C 3) dom- inated site (Hunt et al., 1991). Different values of soil organic C and N contents, and soil hydraulic parameters (soil water holding capacity, thermal and water conductivity) were used in GEM2 to represent different soil texture and physical properties of the B. gracilis and P. smithii systems.

The biomass data from both P. smithii and B. gracilis at normal temperature, ambient atmospheric CO z and all three water levels (Hunt et al., in prep.) were used to calibrate the whole model. Several model parameters, in addition to those in the photosynthesis and energy balance submodels, were adjusted to achieve a fit to the ambient CO z growth chamber data; i.e., the threshold temperature at which freezing starts to kill shoots was increased because the temperature in the growth chambers never fell below 0°C, and soil water potential was used in place of stomatal resistance to control the allocation of


carbon between shoots and roots. Neither the biomass data for elevated CO 2 or elevated temperatures, nor the plant N data from any of these treatments, were used in model calibration. Thus the fit of the model to these data is a test of model validity.

4. Results

The photosynthesis submodel satisfactorily fitted both C 3 and C 4 photosynthesis data from different CO 2 and temperature treatments (Fig. 4). The intercellular CO z (C m) compensation point of photosynthesis was higher in P. smithii (C 3) (~ 80 /~mol/mol) than in B. gracilis (C 4) (~ 0.0 /~mol/mol), and photosynthesis ap-

proached saturation at higher C m in P. smithii (~ 600 /zmol/mol) than in B. gracilis (~ 300 /zmol/mol), as expected because of the CO 2 concentrating mechanism of C 4 photosynthesis (Pearcy and Ehleringer, 1984) (Fig. 4). The maximum photosynthesis rate decreased with decreasing leaf N concentration. Decreased leaf nitrogen concentration could account for the acclimation of photosynthetic capacity to elevated CO 2 in these grasses (Morgan et al., 1994). The photosynthesis model accurately fitted the data of photosynthetic responses to temperature for both species, despite the different shapes of the curves (Fig. 5). P. smithii, a cool season grass, has a lower optimal temperature for photosynthesis than B. gracE&, a warm season grass, (~ 25 vs. 38°C). B. gracilis (C 4) assimilation performed bet-

16 ̧

12

8'

.~. 4-

04 E o -~ 16-

~ 12-

~ 4"

2 O -X o

16-

Z 12.

Pascopyrum smithii

0 0 lxC ~ 1 . 0 7

0

0

2xC 0 1.05

2xC, +4°C

O0

0 o

200 400 600

Intercelluor C02 ( /zmol /mol)

Bouteloua gracilis

800

S N~=1 .81 / o lxC

0 0

o ~ O o ~ O °

~ o 0 2xC O0

N~= 1.04 0 0 0 0 0

~ ~xc. ÷,oo 0

260 46o 66o Intercellulor C02 (p.mol/mol)

800

Fig. 4. Responses of measured (circles) and simulated (curves) photosynthesis to intercellular CO 2 in P. smithii and B. gracilis, respectively, in the second season. PAR = 1000/.~mol/m2/s, RH = 50% and T~ = 25°C. Growth conditions were ambient CO 2 and current temperature regime (1 × C); twice ambient CO z concentration and current temperature regime (2 x C); and twice ambient CO 2 concentration and current temperatures + 4°C (2 x C, + 4°C). Leaf N concentrations given for each growth conditions. Data from Morgan et al. (1994).

20 D,-X. Chen et aL / Ecological Modelling 87 (1996) 11-27

40-

Pascopyrum smithii

"~ ' 30- cq E

-5 2O- E :&

~ J

.~ 10-

0 o

40

0

lb ~ 2'o 2'~ 3'o ~'~ 4'0 4'~ 5'0 ~ T e m p e r a t u r e ( ° C )

B o u t e l o u a g r a c i l i s

~ " 30 -

c,4 E "~ 2 0 - E :&

10-

ol

0 -

t~ l o 1's 2'o 2'5 3'0 3'~ 4'0 4's 5'o 55 T e m p e r a t u r e ( ° C )

Fig. 5. Responses of simulated (curves) and observed (circles) photosynthesis to temperature in ambient CO 2. Data from Monson et al. (1983), PAR = 2000 i~mol/me/s, RH = 80%.

ter at higher tempera ture , while P. smith# (C 3) assimilation was inhibited at 50°C (Fig. 5). The optimal t empera ture shifted upward under elevated CO 2 (about 3 -5°C in the C3 and 1-2°C in the C 4 species), which might partly be due to different t empera tu re sensitivities of solubilities and Rubisco affinities for C O 2 and O 2 (Long, 1991; Chen et al., 1994a; Coughenour and Chen, 1994).

The model simulated shoot biomass dynamics across a wide range of precipi tat ion (150, 250 and 360 m m y e a r - l ) , t empera ture ( + 0 and +4°C) and C O 2 (350 and 700 / z m o l / m o l ) (Figs. 6 and 7). Both data and simulation results showed that peak shoot biomass product ion of the two species increased with precipitation. P. smithii produced a larger peak shoot biomass than did B. gracilis

in all t reatments . This may partly be ascribed to the richer soil associated with P. smithii (Hunt et al., in prep.). Elevated CO z enhanced shoot biomass product ion in both species. The CO 2 enhancement effect on shoot biomass product ion varied with tempera ture and precipitation.

3 0 0 - Pascopyrum smithii - - l x ¢ , 150mm

2 5 0 " - .... lxc, 250mm - - - lxc, 360mm

200- . 4 , 150- . z ~ / , / ~ ~"

100- ~ x ~

50-

0 I I I I f I - - lxC,+4o0150mm

2 5 0 - " . . . . lxC,'l'4"°,250mm _ _ 1xC,+4o,36Omm

.-. 200" } ^ #"

f" %

0 , , , ~ , , .-v - - 2xC, 150rnm .D 250- - .... 2xC, 250ram 0 -- -- 2xC, 360ram

° oo t k U) \ i i "~

15o-

lOO-

0 I I I I I I - - ~ , + 4 o , 1 5 0 m m

2 5 0 - " . . . . 2xC,+4°,250mm ~,~ ~ - - - - 2xC.+4°,360mm

200- .~.~, f 150- ~:~'~,, / . . - '~ ' #/ " ~:, / / ~ \ '

d~ "~, ~ ~ / / " ~ ~ 100- / / ~3 / / / - \ \~

5o- t % 0

o 160 260 360 460 560 660

Time (days) Fig. 6. Simulated (curves) and measured (symbols) shoot biomass of P. smithii in different treatments. CO 2 was at 350 (1 x C) or 700 (2 x C) ppm. Precipitation was at 150, 250 or 360 mm year -1. Temperature was either a normal yearly regime or elevated by 4°C (+4°C). Vertical bars are 95% confidence intervals. Data from Hunt et al. (in prep.).


Model performance was further evaluated by comparing simulated with observed biomass data and N content data of shoots and roots (Figs. 8-11). Simulated and observed shoot biomass and root biomass compared favorably across all treatments (Figs. 8 and 9). The regression of observed against simulated shoot biomass and root biomass resulted in a r 2 of 0.86 (P < 0.01) and 0.78 (P < 0.01) respectively, in P. smithii, and 0.76 (P < 0.01) and 0.63 (P < 0.05) in B. gracilis. Simulated N concentrations of senescent shoots and live roots were in general agreement with the

B o u t e l o u a grac i l is 250-

- - lxC, 150ram 200- --,... lxC, 250rnm

- - lxC, 360rnm

150- / ~. /~t2~. '

/ \

50- \

0 . . . . - - l x & + 4 O . l ~ & r . . . . . lxC,+4o,250rnrn

200- I - - 1xC*+4o,360mm

E 150- r ~" //-i ~ ,oo- v / \

c~ f \ \~ / / y \ \ >

~0 0 . . . . - - 2X& 150ram' .a 200" - . . . . 2xC, 250mm

- - 2xC, 360mm

,>-° 100" //~" ~ ",~ / . .,)~

" 5o- S,"~. ,'//-~,

o - - 2x&+4O.lS&~ 200- "--- 2xC'+4°'250mm

~T-. - - 2xO'+4°'360mm

lOO4

0 I O0 200 300 400 500 66o T i m e ( d a y s )

Fig. 7. Simulated (curves) and measured (symbols) shoot biomass of B. gracilis in different treatments. CO 2 was at 350 ( I × C ) or 700 ( 2 x C ) ppm. Precipitation was at 150, 250 or 360 mm year -~. Temperature was either a normal yearly regime or elevated by 4°C ( + 4°C).

250- Pascopy rum smi th i i

o E o

-~o,1 ooE Un

n ~ v 0) >

.D 0

200-

150-

100-

5O 5O

45°i__

4001

- - 1:1 correspondence ~ / • ~o~!bra}io. T ~ 2 L /

16o 1~0 260 250

Simu la ted s h o o t b i o m a s s ( g C / m 2 )

1:1 correspondence • calibration --I--

o ! [: 0 validation o 350- "8.- .

o4 *8 E 30o- o ~ t_(_)

o~ 250-

~" 200.

.13 0 150.

i 100

15o 260 2~o ~6o 25o

Simu la ted roo t b iomass ( g C / m 2 )

Fig. 8. Observed (with 95% confidence intervals) vs. simulated live shoot biomass and root biomass of P. smithii.

observed data (Figs. 10 and 11), although the model tended to slightly underestimate plant N concentrations, particularly senescent shoot N concentration of P. smithii. The r 2 are 0.62 (P < 0.01) for shoot N, and 0.69 (P < 0.05) for root N in P. smithii, and 0.57 (P < 0.01) and 0.33 (P < 0.1) in B. gracilis. N concentration of roots in P. smithii was significantly greater than that in B. gracilis (P < 0.05). Plant N concentration was decreased by doubling CO 2 in both data (Hunt et al., in prep.) and simulation.

The model was run for 20 years to investigate long-term effects of these different atmospheric conditions on annual net primary production (NPP) and net mineralization rate (N min) under nearly real field conditions except f'or targeted variables (CO2, temperature and precipitation, respectively). In general, NPP of the two species responded similarly to precipitation, temperature

22 D.-X. Chen et al. / Ecological Modelling 87 (1996) 11-27

and CO 2, and in parallel to the production of shoot biomass (Fig. 12). NPP increases with precipitation and elevated CO 2. The greatest NPP of P. smithii (about 345 g C m -2 year - l ) and B. gracilis (about 240 g C m -2 year -1) was reached under elevated CO2, 360 mm precipitation and elevated temperature (Fig. 12). The average NPP of P. smithii across the treatments was greater than that of B. gracilis, which is consistent with the shoot biomass data. In P. smithii, responses of NPP to elevated temperature were negative with 150 mm yr-~ and 250 mm yr-~ precipitation under ambient CO2, while the responses were positive at 360 mm yr -~ and elevated CO 2. Re- sponses of NPP to elevated CO 2 in B. gracilis were positive under all temperature and precipitation treatments.

N min increased with precipitation in both P. smithii and B. gracilis ecosystems (Fig. 13). Ele-

o

E .£ ..Q ~ C q

~ ( . ~

o

200

150~

100"

50"

0

Bouteloua gracilis

1:1 correspondence T-T-J • cal!brofion .~-~

s'0 16o 1 ~o 200 Simulated shoot b iomass ( g C / m 2 )

400 / / _ _ 1:1 correspondence 350 J • calibration

o

E 0

o ~ L( D

-(3 o

300"

250-

200-

150-

100 150

0 validation

26o 2~o 3(50 35o

Simulated root b iomoss ( g C / m 2 )

Fig. 9. Observed (with 95% confidence intervals) vs. simulated live shoot biomass and root biomass of B. gracilis.

o 1 . 6 -

=~ 1.4-

Poscopy rum smithi i

"6

E

E

8 z

.o 0

- - 1:1 correspondence

1.2-

1.0-

0.8-

0.6-

0.4--

0.2-

0.0 , , ,

o.0 o.2 o.4 o.6 018 110 1.2 Simulated N concentration of shoots (%)

2 . 0 ] _ 1:1 correspondence /

1.8

1.6

1.4-

1.2

1.0

0.8

ca

*6 2 "6

. t 3

O

1.1 1.2 1.3 1.4 1.5 1.6

Simulated N concentration of roots (%)

Fig. 10. Observed (with 95% confidence intervals) vs. simulated N concentrations of senescent shoots and live roots in P. smithii.

vated CO 2 decreased N min except in the P. smithii system at high precipitation. Elevated temperature decreased N rain except for a slight increase with low water and low CO 2 in the P. smithii system (Fig. 13). The average N min

m

across the treatments of the P. smithii system was greater than that of the B. gracilis system in parallel to the NPP. N min of the P. smithii system was greatest (abo~t 11 g N m -2 year -1) with elevated CO2, ambient temperature and high precipitation, while N min of the B. gracilis system was greatest (abo~t 5.6 g N m -z year -1) at ambient CO2, ambient temperature and high precipitation (Fig. 13).

The ratios at elevated vs. ambient CO 2 of NPP and N min are given in Table 1. Doubling CO 2 increased NPP of P. srnithii by 36% and 43% at


2.0 B o u t e l o u a g rac i l i s

8

-5

E

8 z

..Q O

-5 o ~6

8

C ~ ~ N

8 z

.gl

O

1.6 /

1.2-

0.8-

0.4-

0.0


1.1

'0 0.2 0:4 o:6 0:8 1. Simuloted N concentration of shoots (%)

1.0

0.9-

0.8-

0.7 0.7 o'.g


1.0 i

0.9

Simulated N concentrotion of roots (%)

Fig. 11. Observed (with 95% confidence intervals) vs. simulated N concentrations of senescent shoots and live roots in B. gracilis.

normal and elevated temperatures, respectively, and by 29% and 24% in B. gracilis. The stimulus of NPP by elevated CO 2 in P. smithii did not vary

400

~ 350

i v ,

250

-~ 200 o

~ 150 ¢u

E -= 1 O0 {3_

~ 5o

i ~ 1 5 0 8 2 5 0 1 3 6 0 m m i

+0 +4 +0 +4 +0 +4 +0 +4 °C 350 ppm 700 ppm 350 ppm 700 ppm

P. smithii (C3) B. gracilis (C4)

Fig. 12. Simulated annual net primary production of P. smithii and B. gracilis subjected to three levels of precipitation, two levels of temperature and CO 2 as in Figs. 7 and 8. The results are means over 20 years.

much with precipitation or temperature at medium or high water levels; however, the stimulus increased with temperature at low precipitation level. In B. gracilis, the stimulus of NPP by elevated CO 2 was usually less than in P. smithii, and decreased slightly with elevated temperatures, especially with low precipitation. Doubling CO 2 reduced the N rnin of the B. gracilis system by 5% and 6% in normal and high temperature, respectively. In the P. smithii system, doubling CO 2 decreased N_min by 3% and 2% at normal and high temperature, respectively. However,

Table 1 Ratios of annual net primary production ( N P P 2 × c / N P P 1 ×¢) and net N mineralization rate (N m i n 2 × c / N _ m i n 1 xc ) at elevated CO 2 vs. ambient CO z for combinations of precipitation and temperature regimes

Species Precipitation ( m m / y r ) + O°C + 4°C

NPP2 × c / N min 2 × c / NPP2 × c / N min 2× c / NPP1 xc N_min I ×c NPP1 ×c N_min I ×c

P. smithii (C 3) 150 1.27 250 1.41 360 1.40

Average across precipitation 1.36 B. gracilis (C 4) 150 1.31

250 1.28 360 1.27

Average across precipitation 1.29

0.88 1.48 0.76 0.99 1.41 1.03 1.05 1.39 1.15 0.97 1.43 0.98 0.92 1.23 0.94 0.96 1.24 0.96 0.96 1.24 0.93 0.95 1.24 0.94


12 ) I~1.~o ~250 m360r~m~l

E

6

• -- 4 E

Z ',

0 +0 +4 +0 +4 +0 +4 +0 +4 °C

350 ppm 700 ppm 350 ppm 700 ppm

P. smithii (C3) B. gracilis (C4)

Fig. 13. Simulated annual net N mineralization in the P.

smithii system and the B. gracilis system subjected to three

levels of precipitation, two levels of temperature and CO z as in Figs. 6 and 7. The results are means over 20 years.

N min in the P. smithii system was increased by 15% by doubling CO 2 with high precipitation (360 mm) plus high temperature. These results indicate strong interactions between the effects of CO2, precipitation, temperature and species on NPP and N min.

5. Discussion

The present model (GEM2) successfully coupled biochemical, ecophysiological and ecosystem processes in a hierarchical approach. The model included biochemical level mechanisms of photosynthesis to represent both direct and indirect effects of CO 2 on plant growth, and also mechanistically simulated biophysical processes which control the interactions between the ecosystem and the atmosphere. The C3-C 4 photosynthesis submodels fitted the measured photosynthesis data from both C 3 and C a species subjected to different CO2, temperature and precipitation conditions (Figs. 4 and 5). The acclimation of photosynthetic capacity to elevated CO 2 could be accounted by decreasing N concentration in the model. The model accurately fitted plant biomass dynamics and plant N concentration data over a

wide range of temperature, precipitation and atmospheric CO 2 (Figs. 6-11). The CO 2 enhancement effect on shoot biomass production varied with temperature, precipitation and species. Both data and simulation results showed that doubling CO 2 increased shoot biomass production in both the C 4 and the C a species, which in general is consistent with the report of Owensby et al. (1993).

Ecosystem responses to CO 2 enrichment and climate change are expressed at different levels: biochemical and stomatal processes at the leaf level, growth processes at the plant level, and water budget and carbon and nitrogen cycling at the ecosystem level. Predicted responses of NPP and N min to CO z enrichment and climate changeare, therefore, complex. Responses to individual climate factors were often modified by responses to other factors, through interaction among processes at the same and different levels of organization (Coughenour and Chen, 1994). The final outcome of ecosystem responses to CO 2 enrichment and climate change are dependent on the relative importance of these different processes.

The relative enhancement of NPP by CO z enrichment was greater at high temperature in the C 3 species, and greater under ambient temperature conditions in the C4 (Table 1). Most previous studies revealed that a COe-doubling generally caused greater increases in growth at higher temperatures (see Cure, 1985; Eamus, 1991; Kimball et al., 1993), although Sionit et al. (1981) reported that the CO 2 response of okra had stimulatory effects on growth at lower temperature, and Coleman and Bazzaz (1992) reported that the effects of CO 2 enrichment on final biomass of a Ca annual forb was positive at low temperature and negative at high temperature. Chen et al. (1994b) found that the relative enhancement of CO 2 enrichment on an aquatic plant was strongly temperature dependent, and that the maximum relative effects of CO 2 were reached at a low temperature (25°C). These different responses suggest that the effects of elevated CO2 and temperature may interact in a seasonal environment, and that this interaction is species specific (Hunt et al., in prep.).


The responses of N mineralization to elevated C O 2 w e r e negative or near neutral except for the C 3 species system at high precipitation. Reduced N mineralization under elevated CO 2 may be ascribed to reduced litter quality (Strain and Baz- zaz, 1983). However, N min in the P. smithii system was increased by doubling CO 2 under wet conditions. Increased N_min under elevated CO 2 was also observed in an open top chamber study of Populus granditenta (Zak et al., 1993). They found up to five-fold increases in N min. They suggested that elevated CO 2 would increase be- lowground carbon inputs, which would increase microbial biomass and their activity and thus decomposition and mineralization rate. Decomposi- tion and mineralization could be greater under elevated CO2, in spite of decreased residue quality, because decomposition and mineralization, being donor-controlled processes, tend to increased with NPP (Hunt et al., 1991; Coughenour and Chen, 1995).

Global warming without CO 2 enrichment would have negative effects on primary production of the C 3 species except under wet conditions. In the C 4 species, global warming itself would increase NPP at all precipitation levels, particularly at high precipitation. This suggests that models must consider the direct effects of CO 2 enrichment and its interactions with other factors to predict ecosystem responses to climate change. It should be noted that the responses of peak shoot biomass and NPP to elevated CO 2 and climate factors are different since peak shoot biomass is dependent upon not only NPP but also tissue death processes. NPP and N min increased with precipitation in both species. Pri- mary production and decomposition in semiarid grasslands are water-limited. Thus, any changes in precipitation resulting from greenhouse warming should significantly affect grassland productivity.

Interactions among biochemical, ecophysiological and ecosystem processes appear to be significant. To reasonably estimate ecological effects of CO 2 enrichment and climate change, a model must couple biochemical, ecophysiological and ecosystem processes. Ecosystem models that ig- nore physiological and ecophysiological responses

are incapable of predicting responses to elevated CO z and climate change (Coughenour and Chen, 1995). On the other hand, ecophysiological models that do not couple soil water and nitrogen cycling would be not able to represent the feedbacks of biogeochemical processes such as N mineralization.

In natural ecosystems, many plant species co- exist competitively. There are hundreds of plant species representing C3, C 4 and CAM photosynthetic pathways in Colorado shortgrass steppe. CO 2 enrichment and climate change differently affect many plant and ecosystem processes. It has been hypothesized that different responses of C 3 and C 4 species to CO 2 enrichment and climate change could change the competitive balance between these species in a future CO2-enriched atmosphere (Bazzaz et al., 1989; Arp et al., 1993; Johnson et al., 1993). Changes in species composition may lead to changes in ecosystem level processes such as gas exchange, C and N cycling. The present model did not include the possible effects of CO z enrichment and climate change on plant demographic processes and intraspecific competition, although the model was originally designed to simulate multi-species systems. Fur- ther modeling efforts are needed to couple ecophysiological-ecosystem models with plant population models, and more empirical studies are needed to address how changes in the composition of C3-C 4 mixtures affect primary production, N cycling and the atmosphere in a high CO z world.

Acknowledgements

E.T. Elliott, J.K. Detling, T.G.F. Kittel, D.E. Walter, and D.W. Freckman participated in plan- ning and executing the experimental effort. The experimental work was coordinated by D.E. Reuss and C.A. Monz. M.S. Engel was responsible for data management and contributed to computer programming. Discussions with M.B. Coughenour during the work were valuable. Dr. M.B. Coughenour, J. Hanson and W.J. Parton re- viewed the manuscript. Research supported by NSF grant BSR-8818269.


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Responses of a C 3 and C4 perennial grass enrichment and ... et al 1996.pdf · (Bouteloua gracilis), Ca, were selected at the Central Plains Experimental Range (CPER) in N.E. Colorado