co2浓度和辐射强度变化对核桃、酸枣光合作用速率及水分

co2浓度和辐射强度变化对核桃、酸枣光合作用速率及水分 1 Seasonal Variation and Correlation with 2 Environmental Factors of Photosynthesis 3 and Water Use Efficiency of Juglans regia 4 and Ziziphus jujuba 11*25 Hai-Bo Yang, Shu-Qing An, Osbert-Jianxin Sun, 3456 Zuo-Min Shi, Xin-Song She, Qing-Ye Sun and Shi-Rong 3Liu 7 8 (1. Laboratory of Forest Ecology and Global Changes, School of Life Science, Nanjing University, 9 Nanjing 210093, China; 2. Laboratory of Quantitative Vegetation Ecology, Institute of Botany, the 10 Chinese Academy of Sciences, Beijing 100093, China; 3. Institute of Forest Ecology Environment 11 and Protection, Chinese Academy of Forestry, Beijing 100091, China; 4. Department of Resources 12 and Environment, Huangshan University, Huangshan 245021, China; 5. School of Life Sciences, 13 Anhui University, Hefei 230039, China) 14 Received 8 May 2006 Accepted 15 Jul. 2006 15 Handling editor: Da-Yong Zhang 16 *Author for correspondence. Tel: +86 (0)25 8359 4560; Fax: +86 (0)25 8359 4560; Email: . Seasonal Variation & Correlation with Environment of Photosynthesis and WUE 17 Abstract 18 Both the photosynthetic light curves and COcurves of Juglans regia L. and Ziziphus jujuba 2 Mill. var. spinosa in three seasons were measured using a LI-6400 portable photosynthesis 19 20 system. The maximal net photosynthetic rate (A), apparent quantum efficiency(φ), max 21 maximal carboxylation rate (V) and water use efficiency (WUE) of the two species were cmax 22 calculated based on the curves. The results showed that A of J. regia reached its max maximum at the late-season, while the highest values of A of Z. jujuba occurred at the 23 max 24 mid-season. The A of J. regia was more affected by relative humidity (RH) of the max 25 atmosphere, while that of Z. jujuba was more affected by the air temperature. Light 26 saturation point (LSP) and Light compensation point (LCP) of J. regia had a higher 27 correlation with RH of the atmosphere, those of Z. jujuba, however, had a higher correlation with air temperature. V28 of both J. regia and Z. jujuba had negative correlation with RH cmax 29 of the atmosphere. WUE of J. regia would decrease with the rise of the air temperature while 30 that of Z. jujuba increased. Thus it could be seen that RH, temperature and soil moisture had 31 main effect on photosynthesis and WUE of J. regia and Z. jujuba. Incorporating data on the 32 physiological differences among tree species into forest carbon models will greatly improve 33 our ability to predict alterations to the forest carbon budgets under various environmental 34 scenarios such as global climate change, or with differing species composition. 35 Key words: Apparent quantum efficiency (φ); Maximal carboxylation rate (V); Net photosynthetic cmax 36 rate (P); Returning cropland to forest or grassland; Seasonal variation; Water use efficiency (WUE). n 37 2 Seasonal Variation & Correlation with Environment of Photosynthesis and WUE 38 The research on plant photosynthetic physiology is always the hot issue in ecology, especially at present, because the temperature has been getting higher and the concentration of CO of the 39 2 40 atmosphere has been increasing all around the globe, which could greatly affect plant 41 photosynthesis (Cure and Acock 1986; Tissue et al. 1995; Jiang et al. 1997; Heath et al. 2005). In 42 addition, understanding the relationships between the traits of plant photosynthesis and their 43 large-scale patterns is essential for simulating carbon cycle in ecosystems and predicting 44 ecosystem functioning in response to environmental changes (Mellilo et al. 1993; Nemry et al. 45 1996; Norby and Luo 2004). The tree photosynthesis was the most important physiological process that determined the productivity and ecohydrology of forests (Raich et al. 1991; Warnant 46 47 et al. 1994; Imhoff et al. 2004), and photosynthetic performance might be a diagnostic feature of 48 the successional and ecological restoration status of forest species (Eschenbach et al. 1998). 49 Drought and semiarid lands occupied more than one third of the total land area on the earth, 50 thus researching and cultivating the plant species that were adaptive to grow on the drought and 51 semiarid lands was very important for the survival of human beings in the future (Swindale and 52 Bidinger 1981; Boyer 1982; Levitt 1972). Water use efficiency (WUE) of plants would rise with 53 the decrease of soil moisture within a certain scope, which had been reported in many experiments 54 (Dickmann et al. 1992; Seiler et al. 1988; Smit et al. 1992; Damesin et al. 1998; Korol et al. 1999). 55 WUE was treated as a key functional trait under drought conditions and study on that had been 56 another hot issue in drought and semiarid areas in recent years (Dewar 1997). The natural native 57 arbores or shrubs which had higher WUE should be chosen as possible to restore and reconstruct 58 the steady vegetation system (Hu and Wang 1998; Xue et al. 2003). 59 Seasonality profoundly influences leaf physiology in deciduous forests (Dougherty et al. 1979). 3 Seasonal Variation & Correlation with Environment of Photosynthesis and WUE 60 Large seasonal variation in physiological activity exists in part because temperature, humidity, light levels, soil moisture and CO concentration all vary through the course of the season. 61 2 62 Physiological response to environmental conditions may also shift seasonally (Dougherty et al. 63 1979). The research on seasonal variations of photosynthetic characteristics and WUE could be 64 helpful for us to identify the relative contribution of various environmental factors on 65 photosynthesis and WUE, and further revealed that species-specific sensitivities to various 66 environmental conditions shifted through the course of the season (Bassow and Bazzaz 1998), 67 which will facilitate choose and cultivation of various species under various environmental conditions in the management of forestry. We focused on two tree species: Juglans regia L. and 68 69 Ziziphus jujuba Mill. var. spinosa (Bunge) Hu ex. H.F. Chou. The two species are widely 70 distributed in China, and they are usually grown in drought and semiarid lands (Wu et al. 1998). 71 Mature individuals of both species are frequently found together at our study site, Beichuan county, 72 Sichuan Province, China, where was a demonstration spot of the project of returning cropland to 73 forest or grassland in China. There J. regia and Z. jujuba were chosen in the project after actively 74 searching in many patterns and ecological restoration based on native species. 75 The goals of this paper are to address the following questions: (1) What is the essentiality of the 76 seasonal differences in the photosynthesis rates and WUE? (2) Can we explain these differences 77 based on the seasonally shifting environmental conditions? Does the relative importance of the 78 environmental conditions to photosynthesis and WUE shift through the course of the season? 79 Answers to these questions will help improve understanding of atmosphere-biosphere interactions 80 and facilitate incorporating biological processes in forest carbon-cycle models (Bassow and 81 Bazzaz 1998). 4 Seasonal Variation & Correlation with Environment of Photosynthesis and WUE 82 Results 83 Differences in physiological characteristics in three seasons 84 Seasonal Differences in light utilization of J. regia and Z. jujuba 85 Maximal net photosynthetic rate (A) was equal to the mean photosynthesis rate of observations max 86 taken with incident light levels above light saturation point (LSP). There were sufficient light-saturated observations of leaves for both species in our study, and A estimates were 87 max 88 calculated for the leaf populations. A of J. regia growing at the early-season was 7.51 max -2-1-2-1μmol?m?s, which hadn’t significant difference from that at mid-season, 7.68 μmol89 ?m?s. And -2-190 at the late-season, Aof J. regia reached its peak value, 9.45 μmol?m?s, which had significant max 91 difference from the early-season and mid-season (P < 0.05, n = 9). Unlike J. regia, the maximum 92 of A of Z. jujuba occurred at the mid-season and late-season. max 93 Apparent quantum efficiency (φ) of J. regia was highest at the late-season and that at the 94 early-season was as low as at the mid-season (P > 0.05, n = 9). Apparent quantum efficiency of Z. 95 jujuba achieved its maximum at the mid-season and the minimum occurred at the early-season. 96 The figures also showed that the seasonal variation of apparent quantum of efficiency presented 97 the same trend as A of both J. regia and Z. jujuba. max 98 Both LSP and LCP of the two species had significant differences between the early-season, 99 mid-season and late-season. LSP of J. regia reached the maximum at the mid-season while LSP of 100 Z. jujuba achieved the maximum at the mid-season. LCP of J. regia reached the minimum at the 101 late-season while LCP of Z. jujuba reached its maximum at the late-season (Figure 1). 102 Figure 1 103 104 5 Seasonal Variation & Correlation with Environment of Photosynthesis and WUE 105 Seasonal Differences in CO utilization of J. regia and Z. jujuba 2 106 Vof J. regia and Z. jujuba had significant differences (P < 0.05, n = 9) between the cmax 107 early-season, mid-season and late-season. And Vof J. regia reached its maximum at the cmax 108 mid-season and reached the minimum at the late-season. V of Z. jujuba decreased as the cmax 109 processing of the season. 110 CO saturation point (CSP) of J. regia had significant differences (P < 0.05, n = 9) between the 2 111 three seasons, and the value increased as the processing of the season. CSP of J. regia reached its -1112 maximum, 1235 μmol?mol, at the late-season. Similarly, CSP of Z. jujuba reached its maximum -1at the late-season, 1101 μmol?mol, and CSP hadn’t significant differences between the 113 114 early-season and late-season (P > 0.05, n = 9). CO compensation point (CCP) of both J. regia and 2 115 Z. jujuba had significant differences between the three seasons (P < 0.05, n = 9) and CCP of J. 116 regia reached its maximum at the mid-season while the maximum of Z. jujuba occurred at the 117 late-season (Figure 2). Figure 2 118 119 Seasonal variation in water use efficiency (WUE) and transpiration rate (T) of J. regia and r 120 Z. jujuba 121 WUE of J. regia and Z. jujuba reached its maximum at the late-season，and WUE of J. regia was the lowest at the mid-season while WUE of Z. jujuba had its minimum at the early-season. T122 of J. r123 regia was the lowest at the early-season, and T of Z. jujuba reached its minimum at the r 124 late-season (Figure 3). 125 Figure 3 126 6 Seasonal Variation & Correlation with Environment of Photosynthesis and WUE 127 Correlation of photosynthesis of J. regia and Z. jujuba with environmental factors 128 The relationships between A, φ, LSP or LCP and the environmental factors max 129 A and φ of J. regia had apparent correlation with atmospheric relative humidity and soil max 22130 moisture (r = 0.950 and r = 0.681, respectively for A and φ). And A of Z. jujuba had maxmax 2significant correlation with atmospheric temperature and soil moisture (r = 0.331), whereas 131 2132 apparent quantum efficiency of Z. jujuba had significant correlation only with air temperature (r = 0.402) (Table 1). 133 134 Table 1 135 T 136 Correlation of V, CSP and CCP with the environmental factors cmax 137 V of J. regia and Z. jujuba presented negative correlation with atmospheric relative humidity cmax 22(r138 = 0.932). In addition, V of Z. jujuba was significantly affected by PAR and RH (r = 0.745). cmax 2139 CSP of J. regia was significantly correlated with soil moisture (SM) (r = 0.965) while CCP was 2140 correlated with atmospheric temperature (r = 0.916). CSP of Z. jujuba not only had significant 2141 positive correlation with SM but also had negative correlation with atmospheric temperature (r = 20.513). And CCP was just significantly negatively correlated with the temperature (r142 = 0.357) 143 (Table 2). 144 Table 2 145 146 Regressions of WUE and T to the environmental factors r 2WUE of J. regia presented a significant negative correlation with atmospheric temperature (r147 = 2148 0.814), but WUE of Z. jujuba had a significant positive correlation with RH (r = 0.479). T of J. r 7 Seasonal Variation & Correlation with Environment of Photosynthesis and WUE 2149 regia had a significant positive correlation with SM and atmospheric temperature (r = 0.870), however, T of Z. jujuba presented negative correlation with RH and atmospheric CO 150 r2 2151 concentration (r = 0.412) (Table 3). 152 Table 3 153 Discussion 154 Seasonal and species differences of photosynthesis and WUE 155 A is an important photosynthetic parameter that represents the maximal photon utilization max capability of the plants and reflects the net primary productivity and the accumulation of biomass 156 157 at the subtropical forests where light level was near to or higher than LSP of the tree species on 158 sunlit days (Bassow 1995). In our present study we found that species’ photosynthetic responses 159 differed over the growing season. Light-saturated photosynthesis reached maximum values at 160 different points through the growing season among the different species. Notably, J. regia did not 161 reach maximal rates until late in the season in October, in contrast, Z. jujuba reached near 162 maximal A rates in mid-season in July. For a number of deciduous tree species, Jurik (1986) max found that all species he measured reached their maximal A163 in June, and he considered that the max 164 highest maximal photosynthesis rate of temperate deciduous tree species was positively correlated 165 with the extent of the leaf expansion. However, in our study, the leaves of J. regia and Z. jujuba 166 growing at subtropics had completed 100% leaf expansion in the early season, then their A both max 167 reached the minimum. At the experiment species differed in their light-saturated photosynthesis, A168 : J. regia had higher A values than Z. jujuba. Apparent quantum efficiency is correlative maxmax 169 with photosynthetic electron transport rate in course of the photosynthetic phosphorylation and the 8 Seasonal Variation & Correlation with Environment of Photosynthesis and WUE 170 regeneration of RuBP in course of the CO assimilation (Farquhar et al. 2001). LSP and LCP 2 reflect the adaptability of the plants to the light condition of the natural environment. In our study 171 172 J. regia had lower apparent quantum efficiency than Z. jujuba, which showed that Z. jujuba had a 173 higher light use efficiency than J. regia. But LCP of Z. jujuba was higher than J. regia and its LSP 174 was lower than J. regia, it revealed that Z. jujuba only made use of less narrow range of PAR than 175 J. regia. V, CSP and CCP are the important parameters that reflect the intrinsic CO cmax2 176 assimilation capability of the plants. V is maximal carboxylation rate when RuBP is saturated, cmax 177 and V has a positive correlation with the quantity and activity of Rubisco (Farquhar et al. cmax 1980). In this article V of J. regia reached the maximum at the mid-season while V of Z. 178 cmaxcmax 179 jujuba reached the maximum at the early-season, which reflected the variational status of the 180 quantity and activity of Rubisco of J. regia and Z. jujuba, respectively, in one year. Photosynthetic 181 CSP could denote the maximal CO capacity fixed in course of photosynthesis. In the article all of 2 182 COsaturation points were measured under saturated light condition, and the photosynthesis of 2 183 plants under saturated light was limited mostly by the quantity and activity of photosynthetic 184 enzymes (von Caemmerer et al. 1994), especially by the quantity and activity of Rubisco under the 185 condition of saturated CO and saturated light. Transpiration and WUE denote the consumption 2 186 and utilization of water for the plants. WUE is the quantity of photosynthetic production when per 187 unit water is transpired; it lies on the ratio between net photosynthetic rate and transpiration rate 188 and denotes the drought resistance of the plants. In the article WUE of J. regia and Z. jujuba 189 reached the minimum at the mid-season when it contributes 86% of the annual rainfall in 190 Beichuan county from May to September, which is accordant with some research results (Jiang 191 and He 1999; Yan et al. 2001; Chen et al. 2003). 9 Seasonal Variation & Correlation with Environment of Photosynthesis and WUE 192 The relative importance of the environmental conditions to photosynthesis and WUE shift 193 through the course of the season 194 In situ patterns of leaf-level photosynthesis consist of interactions between the suite of ambient 195 environmental conditions and the species-specific sensitivity to the combination of those factors (Bassow and Bazzaz 1998). In other words, the environmental conditions may have been similar 196 197 surrounding the trees studied here, but the trees’ photosynthetic responses were different. The results of stepwise regressions demonstrated that environmental conditions had some similar and 198 199 some different effects on the two species. These different environmental effects on species, while 200 relatively subtle, may have led to the significant variation among years in forest carbon and water 201 exchange (Goulden et al. 1996). 202 Our results showed that atmospheric relative humidity and soil moisture had more effects on A203 of J. regia. And at the late-season in October when the rainy season had ended in Beichuan, max 204 there was higher soil moisture, lower average radiation intensity, lower atmospheric temperature 205 and higher atmospheric relative humidity. Higher atmospheric relative humidity would be 206 favorable for increasing stomatic conductance of the leaves (Tezara et al. 1999), therefore 207 photosynthesis would rise and A increased as a result. Unlike J. regia, Aof Z. jujuba maxmax 208 presented a significant positive correlation with atmospheric temperature. There is a significant 209 positive correlation between photosynthetic capability of the leaves and the activity of 210 photosynthetic enzymes too, and one of the most important environmental factors that affect the 211 activity of the enzymes is the temperature. At the mid-season, there was higher atmospheric 212 temperature that promoted photosynthesis of Z. jujuba. It was concluded that Z. jujuba was more 213 adaptable to high temperature than J. regia. The leaves of Z. jujuba was less sensitive to 10 Seasonal Variation & Correlation with Environment of Photosynthesis and WUE 214 atmospheric humidity than J. regia, which could prove that the leaves of Z. jujuba was more adaptable to dry atmosphere than J. regia. Soil moisture had apparent effect on Aof J. regia 215 max 216 and Z. jujuba and lower water content in the soil would inhibit physiological activities including 217 photosynthesis. Difference between the phases of J. regia and Z. jujuba when the maximum of 218 A occurred reflected that the maximal accumulation of productivity of the two species at max 219 different seasons, which was beneficial to the management of choosing tree species in the project 220 of “returning cropland to forest or grassland”. 221 Apparent quantum efficiency of J. regia is mostly influenced by atmospheric relative humidity and soil moisture, while apparent quantum efficiency of Z. jujuba is mainly correlative with air 222 223 temperature. In the article LSP and LCP of J. regia was mainly correlative with atmospheric 224 humidity while LSP and LCP of Z. jujuba was mostly correlative with atmospheric temperature, 225 which accorded with the opinion of some articles published (Hu and Wang 1998). 226 In this article CSP of J. regia and Z. jujuba had a positive correlation with soil moisture, which 227 reflected the important effect of soil water content on the activity and quantity of photosynthetic 228 enzymes. CCP had a significant relation with dark respiration and respiration in light (Farquhar et 229 al. 2001). In this article CCP of J. regia had a positive correlation with atmospheric temperature 230 while CCP of Z. jujuba had a negative correlation with air temperature, which showed the activity 231 of enzymes in different plant species had a different correlation with air temperature. Respiration 232 of J. regia reached the maximum at the mid-season when the air temperature was the highest, 233 while Z. jujuba presented higher respiration intensity at the late-season when the atmospheric 234 temperature was lower. 235 The results in the article indicated that WUE of J. regia was mainly affected by air temperature 11 Seasonal Variation & Correlation with Environment of Photosynthesis and WUE 236 while WUE of Z. jujuba was mostly influenced by atmospheric humidity. In the project of returning cropland to forest or grassland, different managements should be taken to improve WUE 237 238 of different tree species. 239 Most forests are mixtures of some different tree species. Global climate change or other major 240 disturbance may lead to substantial shifts in species composition, which in turn will have 241 implications for forest carbon cycles (Bazzaz et al. 1996). Incorporating data on the physiological 242 differences among tree species into forest carbon models will greatly improve our ability to 243 predict alterations to the forest carbon budgets under various environmental scenarios or with differing species composition. The clarity of the dynamic parameters in photosynthesis and water 244 245 uses of the important cultivated species will provide baseline information to forecasting carbon 246 storage of the artificial forests in China. 247 Materials and Methods 248 Study Area 249 The study was carried out in Southwestern China (Beichuan county, province of Sichuan, 250 31?58.439′N， 104?36.233′E, 808 m a.s.l.). Environmental parameters as photosynthetic active 251 radiation (PAR), atmospheric relative humidity (RH), soil moisture (SM), air CO concentration 2 (C252 ), air temperature (T) were obtained (Table 4). aa 253 Table 4 254 2,,，,,,，,,,,,,QAQAQkA4，，maxmaxmax255 Beichuan county is at the west-northern edge of Sichuan basin, located at the up reach of AR,,day 2k 2256 Peijiang river, 2829 km of total area. It was the transition zone of west-southern alpine gorge at 12 Seasonal Variation & Correlation with Environment of Photosynthesis and WUE 257 Hubei-Chongqing-Sichuan mountainous region in geology, which was affected by Longmen rupture and cut by Peijiang water system. Mountains were freely located across the area and 258 259 ravines interlaced. The highest altitude is 4796 m and the lowest is 540 m, which makes 4229 m of 260 relative difference. Beichuan county belongs to humid subtropical monsoon climatic region, thus 261 has a mild whether all-year. The mean annual temperature was 15.6? and the light period was 262 1100 h a year. Although Sichuan is a famous rainstorm collecting area, which is enriched in 263 precipitation rain fall and has an average of 1400 mm, it was distributed unevenly in temporal and 264 spatial scale. Most of the rain fall occurs in May to September, which occupies 86 percent of a year. The land surface was eroded severely, and so was the soil loss. Flood and mud-rock flow 265 266 happen frequently. The complex physiognomy characteristics and climate conditions contribute to 267 the formation of the catastrophic weather in this area, including winter dry, spring drought, 268 summer flood and autumn waterlogging. The type of soil in this area is mainly consisted of the 269 yellow soil, and PH value of the soil is around 5.4-8.4 and the soil water capacity is around 270 5.5%-13.7%. But, northeastern Beichuan, where the study site located in, has a drought 271 environment since the steep slope and low soil capacity for effective water storage existed. And it 272 is disadvantaged for the growth of the trees and the crops. 273 Plants materials 274 The two plant species studied in this paper were Juglans regia L. and Ziziphus jujuba Mill. var. 275 spinosa (Bunge) Hu ex. H.F. Chou, which was chosen for the project of returning cropland to 276 forest in Beichuan county both to protect the soil and advance the local farmers’ living level. J. 277 regia is a species of deciduous arbor or shrub, which is an economic cultivated species with strong 13 Seasonal Variation & Correlation with Environment of Photosynthesis and WUE 278 adaptabilities, wide distribution range and wide utilization. It usually grows in humid fertile soil and is often planted in plains or foothills. Z. jujuba is a deciduous shrub or small arbor, which 279 280 usually grows on natural hillside, roadside or in the field. And it is mainly distributed in Liaoning, 281 Inner-Mongolia, Hebei, Shanxi, Shandong, Anhui, Henan, Hubei, Gansu, Shanxi, Sichuan 282 provinces. It adapts to chill or dry habitat and is commonly used for the conservation of soil and 283 water. Both J. regia and Z. jujuba are C plants (Le Roux et al. 1999; Zheng and Shangguan 2005). 3 284 One yr old tree seedlings of J. regia and Z. jujuba were chosen in the present study and during 285 each measuring period, we measured the photosynthesis rate of three randomly selected leaves of each of three target trees in no particular order for each species. 286 287 Gas exchange measurement 288 Measurements were taken in April, July and October, 2004, respectively and only uniformly 289 sunlit days were selected to minimize sources of diurnal heterogeneity. Three seedlings of each 290 species were randomly selected, and the third to fifth fully expanded leaves from the apical 291 meristem of each selected seedling were used for gas exchange measurement (Loik and Holl 292 1999). LI-6400P portable photosynthesis system was used at the experiment and the gas analyzer 293 was calibrated daily and checked periodically throughout the measurement days. Photosynthetic 294 light saturation points of the two species were calculated by light curve which was measured at the 295 free relatively constant CO concentration in atmosphere. Then CO curve was measured at 22 296 photosynthetic active radiation (PAR) that was slightly higher than light saturation point. The -2-1297 value of PAR was reflected by photon flux density in the unit of μmol?m?s. It was controlled by 298 red-blue light source of 6400-02B, and several light treatments was set at the range of 0-2500 14 Seasonal Variation & Correlation with Environment of Photosynthesis and WUE -2-1299 μmol?m?s. The concentration of CO was controlled by 6400-01 CO injector during the 22 -1measurement of A-C curve, and the unit was μmol?mol. Several CO concentration levels were 300 i2 -1301 set at the range of 50-2000 μmol?mol. The parameters directly measured by LI-6400P included 302 net photosynthetic rate (P), intercellular COconcentration (C), transpiration rate (T) and n2 ir303 photosynthetic active radiation (PAR) for the research goals. 304 Data analysis 305 Light Curve Equation 306 The relationship between P and PAR of leaves in different seasons was fitted by the equation n 307 below(Walker 1989). 308 2309 (1) ,,，,,,，,,,,,,QAQAQkA4，，maxmaxmaxAR,,day310 2k In which, A denotes net photosynthetic rate (P311 )，Q denotes photosynthetic active radiation n 312 (PAR)，A denotes maximal net photosynthetic rate, Ф denotes apparent quantum efficiency， K max denotes curvature factor of the nonrectangular hyperbola，R313 denotes mitochondrial respiration in day 314 the light. And A and Ф are obtained by fitting P and PAR to the equation. maxn 315 If the result shows that the equation is fitted well，light compensation point (LCP) could be 316 calculated with the equation below: A,lnCmax0LCP,317 (2) , It is assumed that the value of PAR is defined as light saturation (LSP) when P318 reaches 99 n319 percent of A (Bassman and Zwier 1991). max 15 Seasonal Variation & Correlation with Environment of Photosynthesis and WUE ，，A,ln100Cmax0LSP,320 (3) , 321 322 In the two equations 2 and 3 above, Adenotes maximal net photosynthetic rate， φ denotes max 323 apparent quantum efficiency, Cdenotes an index that reflects net photosynthetic rate approaching 0 324 zero at a very weak irradiance. 325 A-C equation i 326 The relation between P and C could be fitted by the new photosynthetic biochemical equation ni 327 brought out by Ethier and Livingston (2004). 328 *CCV,,，，iicmaxAR,,cd329 (4) ()CKCO，imi2 330 In the equation above, A denotes RuBP-saturated CO assimilation rate, R denotes c2d *331 mitochondrial respiration in the light, V denotes maximal carboxylation rate, C denotes cmaxi332 intercellular CO photocompensation point, C denotes intercellular CO concentration, K(CO) 2i2m2i333 denotes apparent Michaelis-Menten constant for CO evaluated at C. 2i 334 Maximal carboxylation rate (V), CO saturation point (CSP) and CO compensation point cmax22 (CCP) could be obtained from the fitting between the equation and A-C335 curve measured. i 336 Water use efficiency (WUE) 337 WUE at the level of single leaf is defined as net productivity produced by per-unit water 338 transpired, and is equal to the ratio between photosynthesis and transpiration. The equation is 339 listed below (Dewar 1997). 340 PnWUE,341 (5) Tr 16 Seasonal Variation & Correlation with Environment of Photosynthesis and WUE 342 We used ANOVA test at p=0.05 and stepwise multiple linear regression analyses to quantify the relative effects of seasonally varying micro-environmental conditions on leaf-level photosynthesis. 343 344 Multiple regression is a flexible method of data analysis that may be appropriate whenever a 345 quantitative dependent variable is to be examined in relationship to any other independent factors. 346 Relationships may be nonlinear, independent variables may be quantitative or qualitative, and one 347 can examine the effects of a single variable or multiple variables with or without the effects of 348 other variables taken into account (Cohen et al. 2003). This was done across the seasons to 349 quantify the impact on photosynthesis of seasonal variation in environmental conditions. Further, we used stepwise regression analyses to quantify the effects of light levels, air temperatures, CO350 2 351 concentrations, atmospheric relative humidities and soil moistures (Bassow and Bazzaz 1998). 352 353 Acknowledgements 354 The studies was financially supported by the National Key Basic Research Plan (973 project) (No. 355 2002CB111504), the project of Turning cropland to Forest of State Forestry Administration, and 356 the Distinguished Young Scientist Fund of NSFC. We thank Mr. J. H. Ren, E. D. He, H. B. Yu, X. 357 Y. Zhao, X. J. Liu, J. Zhou, G. J. Liao, J. G. He, X. M. Gong of Beichuan Forestry Bureau of 358 Sichuan Province for providing aids in field work. 359 17 Seasonal Variation & Correlation with Environment of Photosynthesis and WUE 360 361 References 362 Bassman JB, Zwier JC (1991). Gas exchange characteristics of Populus trichocarpa, Populus 363 deltoides and Populus trichocarpa × P. deltoids clone. Tree Physiol 8, 145-149. 364 Bassow SL (1995). Canopy photosynthesis in a deciduous forest: Implications of shifts in species 365 composition and rising concentrations of CO. Dissertation. Harvard University, Cambridge, 2 366 Massachusetts. USA. 367 Bassow SL, Bazzaz FA (1998). How environmental conditions affect canopy leaf-level 368 photosynthesis in four deciduous tree species. Ecology 79, 2660-2675. 369 Bazzaz FA, Bassow SL, Berntson GM, Thomas SC (1996). Elevated CO and terrestrial 2 370 vegetation: implications for and beyond the global carbon budget. In: Walker B, Steffen W, eds. 371 Global change and terrestrial ecosystems. Cambridge University Press, Cambridge, UK. pp. 372 43-76. 373 Boyer JS (1982). Plant productivity and environment. Science 218, 443-448. 374 Brooks JR, Schulte PJ, Bond BJ, Coulombe R, Domec JC, Hinckley TM et al. (2003). Does 375 foliage on the same branch compete fore the same water? Experiments on Douglas-fir trees. 376 Trees 17, 101-108. 377 von Caemmerer S, Evans JR, Hudson GS, Andrews TJ (1994). The kinetics of 378 ribulose-1,5-bisphosphate carboxylase/oxygenase in vivo inferred from measurements of 379 photosynthesis in leaves of transgenic tobacco. Planta 195, 88-97. 380 Carmela MD, Antonio S, Marco B (2006). Thinning affects water use efficiency of hardwood 18 Seasonal Variation & Correlation with Environment of Photosynthesis and WUE 381 saplings naturally recruited in a Pinus radiate D. Don plantation. Forest Ecol Manag 222, 382 116-122. 383 Chen SP, Bai YF, Han XG (2003). Variations in composition and water use efficiency of plant 384 functional groups based on their water ecological groups in the Xilin river basin. Acta Bot Sin 385 45, 1251-1260. 386 Cohen J, Cohen P, West SG, Aiken LS (2003). Applied multiple regression/correlation analysis for the behavioral sciences, 3rd Ed. Mahwah, NJ: Lawrence Erlbaum Associates. 387 388 Cure JD, Acock B (1986). Crop responses to carbon dioxide doubling: a literature survey. Agr Forest Meteorol 38389 , 127-145. 13390 Damesin C, Rambal S, Joffre R (1998). Seasonal and annual changes in leaf δ in two co-occurring Mediterranean oaks: relations to leaf growth and drought progression. Funct Ecol 391 392 12, 778-785. 393 Dewar RC (1997). A simple model of light and water use evaluated for Pinus radiata. Tree Physiol 394 265. 17, 259– 395 Dickmann DI, Liu Z, Nguyen PV, Pregitzer KS et al. (1992). Photosynthesis ,water relations 396 and growth of two hybrid populus genotypes during a severe drought. Can J Forest Res 22, 397 1094-1106. 398 Dougherty PM, Teskey RO, Phelps JE, Hinkley TM (1979). Net photosynthesis and early 399 growth trends of a dominant white oak (Quercus alba L.). Plant Physiol 64, 930-935. 400 Ethier GJ, Livingston NJ (2004). On the need to incorporate sensitivity to CO transfer 2 401 conductance into the Farquhar-von Caemmerer-Berry leaf photosynthesis model. Plant, Cell 402 and Environment 27, 137-153. 19 Seasonal Variation & Correlation with Environment of Photosynthesis and WUE 403 Eamus D (1991). The interaction of rising CO and temperatures with water use efficiency. Plant 2 404 Cell Environ 14, 843-852. 405 Eschenbach C, Glauner R, Kleine M, Kappen L (1998). Photosynthesis rates of selected tree 406 species in lowland dipterocarp rainforest of Sabah, Malaysia [J]. Trees 12, 356-365. 407 Farquhar GD, von Caemmerer S, Berry JA (1980). A biochemical model of photosynthetic 408 CO assimilation in leaves of C species. Planta 149, 78-90. 22 409 Farquhar GD, von Caemmerer S, Berry JA (2001). Models of photosynthesis. Plant Physiol 410 125, 42-45. 411 Goulden ML, Munger JW, Fan SM, Daube BC, Wofsy SC (1996). Exchange of carbon dioxide 412 by a deciduous forest: response to interannual climate variability. Science 271, 1576-1578. 413 Gunderson CA, Norby RJ, Wullschleger SD (1993). Foliar gas exchange responses of two 414 deciduous hardwoods during three years of growth in elevated CO:No loss of photosynthetic 2 415 enhancement. Plant Cell Environ 16, 797-807. 416 Guo JP, Gao SH (2004). Growth chamber study on the impact of CO concentration and radiant 2 417 intensity on Salix psammophila photosynthetic rate. Acta Ecol Sin 24, 181-185. 418 Heath J, Ayres E, Possell M, Bardgett RD, Black HIJ, Grant H et al. (2005). Rising 419 Atmospheric CO Reduces Sequestration of Root-Derived Soil Carbon. Science 309, 2 420 1711-1713. 421 Hu XS, Wang SJ (1998). A review of studies on water stress and drought tolerance in tree species. 422 Sci Silvae Sinicae 34, 77-89. 423 Imhoff ML, Bounoua L, Ricketts T, Loucks C, Harriss R, Lawrence WT (2004). Global 424 patterns in human consumption of net primary production. Nature 429, 870-873. 20 Seasonal Variation & Correlation with Environment of Photosynthesis and WUE 425 Jiang GM, Han XG, Lin GH (1997). Response of plant growth to elevated [CO]: A review on 2 426 the chief methods and basic conclusions based on experiments in the external countries in past 427 decade. Acta Phytoecol Sin 21, 489-502. 428 Jiang GM, He WM (1999). Species- and habitat- variability of photosynthesis, transpiration and water use efficiency of different plant species in Maowusu sand area. Acta Bot Sin 41, 429 430 1114-1124. 431 Jiang GM, Qu CM (2000). Photosynthetic response of six woody species to elevated CO in 2 432 Quercus Liaotungensis forest in the Beijing mountainous areas. Acta Phytoecol Sin 24, 204-208. 433 434 Jiang GM, Zhu GJ (2001). Effects of natural high temperature and irradiation on photosynthesis 435 and related parameters in three arid sandy shrub species. Acta Phytoecol Sin 25, 525-531. 436 Jurik TW (1986). Seasonal patterns of leaf photosynthetic capacity in successional northern 437 hardwood tree species. Am J Bot 73, 131-138. 438 Korol RL, Kirschbaum MUF, Farquhar GD, Jeffreys M (1999). Effects of water status and soil 439 fertility on the C-isotope signature in Pinus radiate. Tree Physiol 19, 551-562. 440 Le Roux X, Grand S, Dreyer E, Daudet F (1999). Parameterization and testing of a 441 biochemically based photosynthesis model for walnut (Juglans regia)trees and seedlings. Tree 442 Physiol 19, 481-492. 443 Levitt J (1972). Responses of Plants to Environmental Stress. Academic Press. 444 Loik ME, Holl KD (1999). Photosynthetic responses to light for rainforest seedlings planted in 445 abandoned pasture, Costa Rica. Restoration Ecology 7, 382-391. 446 Mellilo JM, Mcguire AD, Kicklighter DW, Moore B, Vorosmarty CJ, Schloss AL (1993). 21 Seasonal Variation & Correlation with Environment of Photosynthesis and WUE 447 Global climate change and terrestrial net primary production. Nature 363, 234-240. 448 Nemry B, Francois L, Warnant P, Robinet F, Gerard J (1996). The seasonality of the CO 2 449 exchange between the atmosphere and the land biosphere: A study with a global mechanistic 450 vegetation model. J Geophys Res 101, 7111-7125. 451 Norby RJ, Luo Y (2004). Evaluating ecosystem responses to rising atmospheric CO and global 2 452 warming in a multi-factor world. New Phytol 162, 281-293. 453 Polley HW, Johnson HB, Marino BD, Mayeux HS (1993). Increase in C plant water-use 3 454 efficiency and biomass over glacial to present CO concentrations. Nature 361, 61-64. 2 455 Raich JW, Rastetter EB, Melillo JM, Kicklighter DW, Steudler PA, Peterson BJ et al. (1991). 456 Potential net primary productivity in South America: Application of a global model. Ecol Appl 457 1, 399-429. 458 Seiler JR, Johnson JD (1988). Physiological morphological responses of three half-sib families 459 of loblolly pine to water-stress conditioning. Forest Sci 34, 487-495. 460 Smit J, van den Driessche R (1992). Root growth and water use efficiency of Douglar-fir 461 (Pseudotsuga menziesi (Mirb.) Franco) and Lodgepole pine ( Pinus contorta Dougl.) seedlings. 462 Tree Physiol 11, 401-410. 463 Swindale LD, Bidinger FR (1981). Introduction: the human consequences of drought and crop 464 research priorities for their alleviation. In: The Physiology and Biochemistry of Drought 465 Resistance in Plants. pp. 1-13. 466 Tezara W, Mitchell VJ, Driscoll SD, Lawlor DW (1999). Water stress inhibits plant 467 photosynthesis by decreasing coupling factor and ATP. Nature 401, 914-917. 468 Tissue DT, Griffin KL, Thomas RB, Strain BR (1995). Effects of low and elevated CO on C 23 22 Seasonal Variation & Correlation with Environment of Photosynthesis and WUE 469 and C annuals.II Photosynthesis and leaf biochemistry. Oecologia 101, 21-28. 4 470 Walker DA (1989). Automated measurement of leaf photosynthetic O evolution as a function of 2 471 photon flux density. Phil Trans R Soc Lond B 323, 313-326. 472 Wang JR, Hawkins CDB, Letchford T (1998). Photosynthesis, water and nitrogen use efficiencies of four paper birch (Betula papyrifera) populations grown under different soil 473 474 moisture and nutrient regimes. Forest Ecol Manag 112, 233-244. 475 Warnant P, Francois L, Strivay D, Gerard JC (1994). CARAIB: a global model of terrestrial 476 biological productivity. Global Biogeochem Cy 8, 265-270. 477 Wu ZY, Raven PH (1998). Flora of China. Beijing: Science Press: St. Louis: Missouri Botanical 478 Garden Press. 479 Xue JH, Wu YB, Fang SZ (2003). A study on the vegetation restoration and ecological 480 reconstruction in degraded sites of forest rehabilitation areas. J Nanjing Forestry Univ 481 (Natural Sciences Edition) 27, 84-88. 482 Yuan BZ, Wang J, Zhao SL, Sun J (1998). An approach to the mechanism of plant 483 compensation. Chin J Ecol 17, 45-49. 484 Yan CR, Han XG, Chen LZ (2001). Water use efficiency of six woody species in relation to 485 micro-environmental factors of different habitats. Acta Ecol Sin 21, 1952-1956. 486 Zhang XQ, Xu DY (2000). Seasonal changes and daily courses of photosynthetic characteristics 487 of 18-yeal-old Chinese fir shoots in relation to shoot ages and positions within tree crown. Sci 488 Silvae Sinicae 36, 19-26. 13489 Zheng SX, Shangguan ZP (2005). Variation in the δC value of typical plants of loess plateau 490 over the last 70 years. Acta Phytoecol Sin 29, 289-295. 491 23 Seasonal Variation & Correlation with Environment of Photosynthesis and WUE 492 Table 1. Stepwise regression of maximal net photosynthetic rate (A), apparent quantum max 493 efficiency (φ), light saturation point (LSP) and light compensation point (LCP) to the 494 environmental factors 2Species Parameters Regression Equation Adjusted R J. regia A A = 1.849+0.087RH+0.158SM 0.950*** maxmax φ φ = -0.012+0.001RH+0.001SM 0.681*** LSP LSP = 676.563+10.878RH 0.703*** LCP LCP = 3.865-1.243SM+0.298T+0.261RH 0.950*** a Z. jujuba A A = 0.307+0.158 T +0.277SM 0.331** maxmaxa φ φ = 0.001+0.001 T 0.402*** a LSP LSP = -455.312+34.409 T +0.433PAR 0.631*** a LCP LCP = 414.216-2.979 T -0.791C 0.424** aa 495 RH, air relative humidity; SM, soil moisture; T, air temperature; PAR, photosynthetic active a 496 radiation; C, air CO concentration. The effect of regression was evaluated by ANOVA, a2 497 significance levels of P > 0.05, P <0.05, P < 0.01, and P < 0.001 are indicated by symbols ns, *, 498 **, and ***. 499 24 Seasonal Variation & Correlation with Environment of Photosynthesis and WUE 500 Table 2. Stepwise regression of maximal carboxylation rate (V), CO saturation point (CSP) cmax2 501 and COcompensation point (CCP) to the environmental factors 2 2Species Parameters Regression Equation Adjusted R J. regia V V= 43.499-0.391RH 0.932*** cmaxcmax CSP CSP = 426.888+60.206SM 0.965*** CCP CCP = 12.633+1.306T 0.916*** a Z. jujuba V V= 49.148-0.625RH+0.018PAR-1.671SM 0.745*** cmaxcmax CSP CSP = 1474.346-24.680T+29.861SM 0.513*** a CCP CCP = 57.965-0.291T 0.357** a 502 RH, air relative humidity; SM, soil moisture; T, air temperature; PAR, photosynthetic active a 503 radiation. The effect of regression was evaluated by ANOVA, significance levels of P > 0.05, P 504 < 0.05, P < 0.01, and P < 0.001 are indicated by symbols ns, *, **, and ***. 505 25 Seasonal Variation & Correlation with Environment of Photosynthesis and WUE 506 Table 3. Stepwise regression of water use efficiency (WUE) and transpiration rate(T) to the r507 environmental factors 2Species Parameters Regression Equation Adjusted R J. regia WUE WUE = 9.337-0.096T 0.814*** a T T= 0.559+0.043SM+0.010T 0.870*** rr a Z. jujuba WUE WUE = -0.246+0.094RH 0.479*** T T= 11.002-0.034RH-0.020 C 0.412** rr a 508 RH, air relative humidity; SM, soil moisture; T, air temperature; C, air CO concentration. The aa2 effect of regression was evaluated by ANOVA, significance levels of P > 0.05, P < 0.05, P < 0.01, 509 510 and P < 0.001 are indicated by symbols ns, *, **, and ***. 511 512 26 Seasonal Variation & Correlation with Environment of Photosynthesis and WUE 513 Table 4. Seasonal changes of environmental variables in Beichuan, Sichuan Province, China 514 Seasons Sites of PAR concentration Air CORelative Humidity Soil Moisture Air temperature 2 -2-1-1Species (μmol?m?s) (μmol?mol) (%) (%) (?) † Early Season J. regia 1036?32375.4?0.7 52.68?0.41 6.3?0.8 24.57?0.31 (in April) Z. jujuba 1011?43 371.3?0.9 47.11?0.39 5.5?1.3 22.72?0.09 Mid-Season J. regia 1023?46 369.3?0.6 49.29?0.80 9.8?1.1 37.13?0.10 (in July) Z. jujuba 1332?46 369.3?0.6 49.29?0.80 8.4?1.5 34.13?0.10 Late-Season J. regia 1019?11 380.0?0.1 63.01?0.05 13.7?0.9 20.44?0.02 (in October) Z. jujuba 1040?27 371.7?0.3 59.77?0.23 8.7?2.1 27.99?0.04 515 †Values shown are mean ? SE (n = 9). 27 Seasonal Variation & Correlation with Environment of Photosynthesis and WUE 516 517 Figure 1. Seasonal variations of maximal net photosynthetic rate (A), apparent quantum max 518 efficiency (φ), light saturation point (LSP) and light compensation point (LCP), for J. regia and Z. 519 jujuba. Error bars represent ?1 SE of the mean estimate. The values of histograms under different 520 lowercase letters are significantly different at P < 0.05. 521 Figure 2. Seasonal variations of maximal carboxylation rate (V), CO saturation point (CSP) cmax2 and CO compensation point (CCP) for J. regia and Z. jujuba. E522 rror bars represent ?1 SE of the 2 523 mean estimate. The values of histograms under different lowercase letters are significantly different at P < 0.05. 524 525 Figure 3. Seasonal variations of water use efficiency (WUE) and transpiration rate (T) for J. regia r and Z. jujuba. Error bars represent ?1 SE of the mean estimate. The values of histograms under 526 527 different lowercase letters are significantly different at P < 0.05. 528 529 Figure 1 28 Seasonal Variation & Correlation with Environment of Photosynthesis and WUE 530 531 Figure 2 29 Seasonal Variation & Correlation with Environment of Photosynthesis and WUE 532 533 Figure 3 30