Response of ecosystem carbon balance to asymmetric daytime vs nighttime warming in Artemisia ordosica shrublands

doi:10.17521/cjpe.2021.0485

Abstract

Abstract:

Aims We aimed to explore the response of net ecosystem productivity (NEP) and carbon use efficiency (CUE) to asymmetric daytime vs. nighttime warming in Artemisia ordosica shrublands, and to examine the sensitivity of carbon balance components to daytime vs. nighttime warming.

Methods The BIOME-BGC model was parameterized and validated against eddy covariance measurements of ecosystem carbon fluxes, and used for simulating the impacts of different warming scenarios on NEP and CUE and their components, including gross primary productivity (GPP), net primary productivity (NPP), ecosystem respiration (Re), autotrophic respiration (AR), heterotrophic respiration (HR), maintenance respiration (MR), and growth respiration (GR). Two warming scenarios were simulated: (1) asymmetric warming according to the historical trends from 1954 to 2020 (i.e. daytime warming 1.2 °C, nighttime warming 1.8 °C); (2) daytime or nighttime warming separately with different temperature increase treatments (2, 4, 6 °C).

Important findings (1) Modeled GPP on the daily and annual scales, Re on the daily timescale and NEP on the annual scale showed good agreement with the observed values (coefficient of determination (R²): 0.72-0.88; Nash-Sutcliffe efficiency coefficient (NS): 0.72-0.79). Modeled Re on the annual timescale and NEP on the daily timescale showed weak agreement with observed values (R²: 0.57 and 0.26; NS 0.46 and 0.12, respectively). (2) All warming scenarios promoted GPP, NPP, Re and all respiration components. GPP, Re, AR, and MR were more sensitive to daytime than to nighttime warming, while NPP, HR, GR were more sensitive to nighttime than daytime warming. (3) Greater increases in Re (about 13%) and AR (about 16%) than that in GPP (about 10%) under all warming scenarios, leading to the decreases in NEP and CUE. In addition, both NEP and CUE were more sensitive to daytime than nighttime warming. (4) NEP and CUE decreased by about 68% and 5% under the historical trend of asymmetric daytime vs. nighttime warming treatment. Greater response of NEP and CUE to the daytime warming than nighttime warming. Our results highlight the negative impacts of climatic warming on carbon sink of the semiarid shrublands, and justify the efforts to mitigate climate change are vital for dryland ecosystems.

Key words: net ecosystem productivity, carbon balance, BIOME-BGC, asymmetric warming, daytime warming, nighttime warming, Artemisia ordosica

HAN Cong, LIU Peng, MU Yan-Mei, YUAN Yuan, HAO Shao-Rong, TIAN Yun, ZHA Tian-Shan, JIA Xin. Response of ecosystem carbon balance to asymmetric daytime vs nighttime warming in Artemisia ordosica shrublands[J]. Chin J Plant Ecol, 2022, 46(12): 1473-1485.

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URL: https://www.plant-ecology.com/EN/10.17521/cjpe.2021.0485

https://www.plant-ecology.com/EN/Y2022/V46/I12/1473

Figures/Tables 7

Table 1 Different warming scenarios that responded by ecosystem carbon balance in Artemisia ordosica shrublands

序号 No.	增温情景 Warming scenario	日最高气温 Daily maximum air temperature (℃)	日最低气温 Daily minimum air temperature (℃)	说明 Description
0	D0N0	-	-	采用研究站2012-2020年观测气象数据驱动模型, 得到碳平衡对照值 The model outputs driven by meteorological data measured at the study site during 2012-2020 were considered benchmark carbon balance predictions
1	D1.2N1.8	+1.2	+1.8	依据1954-2020年气象数据, 确定历史非对称增温趋势, 模拟碳平衡对历史昼夜非对称增温的响应 Historical daytime and nighttime warming trends were analyzed using meteorology data for 1954-2020. Such trends were used to simulate the responses of ecosystem carbon balance to the diurnally asymmetric warming pattern
2	D2N0	+2.0	-	依据未来可能的增温幅度, 分别对日最高或最低气温增温2、4、6 ℃, 模拟碳平衡组分对日间或夜间增温的相对敏感性 Based on projections of possible future warming magnitudes, we simulated the sensitivity of carbon balance components to either daytime or nighttime warming of 2, 4, or 6 °C, respectively
3	D0N2	-	+2.0
4	D4N0	+4.0	-
5	D0N4	-	+4.0
6	D6N0	+6.0	-
7	D0N6	-	+6.0

Fig. 1 Trends in annual mean daily maximum (A) and minimum (B) air temperature in Yanchi County during 1954-2020.

Table 2 Model outputs and their acronyms

英文缩写 Abbreviation	中英文名称 Chinese and English name
HR	异养呼吸 Heterotrophic respiration
AR	自养呼吸 Autotrophic respiration
MR	维持呼吸 Maintenance respiration
GR	生长呼吸 Growth respiration
Re	生态系统呼吸 Ecosystem respiration
GPP	总初级生产力 Gross primary productivity
NPP	净初级生产力 Net primary productivity
NEP	净生态系统生产力 Net ecosystem productivity
CUE	碳利用效率 Carbon use efficiency

Fig. 2 Comparison between simulated (sim) and measured (obs) values for gross primary productivity (GPP), ecosystem respiration (Re) and net ecosystem productivity (NEP) on the daily (A-C) and annual (D-F) timescale in the Artemisia ordosica shrub ecosystem during 2016-2020. Solid lines represent linear regressions, while dashed lines represent y = x.

Fig. 3 Interannual variations (A-D) and relative changes (E-H) in heterotrophic respiration (HR), autotrophic respiration (AR), maintenance respiration (MR) and growth respiration (GR) for the Artemisia ordosica shrub ecosystem under different warming scenarios. D, daytime warming extent; N, nighttime warming extent. Grey area represents the standard error of the simulation in D1.2N1.8 warming scenario.

Fig. 4 Interannual variations (A-C) and relative changes (D-F) in gross primary productivity (GPP), ecosystem respiration (Re) and net ecosystem productivity (NEP) for the Artemisia ordosica shrub ecosystem under different warming scenarios. D, daytime warming extent; N, nighttime warming extent. Grey area represents the standard error of the simulation in D1.2N1.8 warming scenario.

Fig. 5 Interannual variations (A, B) and relative changes (C, D) in net primary productivity (NPP) and carbon use efficiency (CUE) for the Artemisia ordosica shrub ecosystem under different warming scenarios. D, daytime warming extent; N, nighttime warming extent. Grey area represents the standard error of the simulation in D1.2N1.8 warming scenario.

References 60

[1]	Aguilos M, Takagi K, Liang NS, Ueyama M, Fukuzawa K, Nomura M, Kishida O, Fukazawa T, Takahashi H, Kotsuka C, Sakai R, Ito K, Watanabe Y, Fujinuma Y, Takahashi Y, et al. (2014). Dynamics of ecosystem carbon balance recovering from a clear-cutting in a cool-temperate forest. Agricultural and Forest Meteorology, 197, 26-39. DOI URL
[2]	Ahlström A, Raupach MR, Schurgers G, Smith B, Arneth A, Jung M, Reichstein M, Canadell JG, Friedlingstein P, Jain AK, Kato E, Poulter B, Sitch S, Stocker BD, Viovy N, et al. (2015). Carbon cycle. The dominant role of semi-arid ecosystems in the trend and variability of the land CO₂ sink. Science, 348, 895-899. DOI PMID
[3]	Anderegg WRL, Ballantyne AP, Smith WK, Majkut J, Rabin S, Beaulieu C, Birdsey R, Dunne JP, Houghton RA, Myneni RB, Pan YD, Sarmiento JL, Serota N, Shevliakova E, Tans P, Pacala SW (2015). Tropical nighttime warming as a dominant driver of variability in the terrestrial carbon sink. Proceedings of the National Academy of Sciences of the United States of America, 112, 15591-15596. DOI PMID
[4]	Bond-Lamberty B, Peckham SD, Ahl DE, Gower ST (2007). Fire as the dominant driver of central Canadian boreal forest carbon balance. Nature, 450, 89-92. DOI
[5]	Buermann W, Bikash PR, Jung M, Burn DH, Reichstein M (2013). Earlier springs decrease peak summer productivity in North American boreal forests. Environmental Research Letters, 8, 024027. DOI: 10.1088/1748-9326/8/2/024027. DOI URL
[6]	Buermann W, Forkel M, O’Sullivan M, Sitch S, Friedlingstein P, Haverd V, Jain AK, Kato E, Kautz M, Lienert S, Lombardozzi D, Nabel JEMS, Tian HQ, Wiltshire AJ, Zhu D, et al. (2018). Widespread seasonal compensation effects of spring warming on northern plant productivity. Nature, 562, 110-114. DOI
[7]	Canarini A, Kiær LP, Dijkstra FA (2017). Soil carbon loss regulated by drought intensity and available substrate: a meta-analysis. Soil Biology & Biochemistry, 112, 90-99. DOI URL
[8]	Chen N, Zhang YJ, Zhu JT, Cong N, Zhao G, Zu JX, Wang ZP, Huang K, Wang L, Liu YJ, Zheng ZT, Tang Z, Zhu YX, Zhang T, Xu MJ, et al. (2021). Multiple-scale negative impacts of warming on ecosystem carbon use efficiency across the Tibetan Plateau grasslands. Global Ecology and Biogeography, 30, 398-413. DOI URL
[9]	Day TA, Ruhland CT, Xiong FS (2008). Warming increases aboveground plant biomass and C stocks in vascular- plant-dominated Antarctic tundra. Global Change Biology, 14, 1827-1843. DOI URL
[10]	Ding ZY, Pu J, Meng L, Lu R, Wang Y, Li Y, Dong Y, Wang S (2021). Asymmetric trends of extreme temperature over the Loess Plateau during 1998-2018. International Journal of Climatology, 41, E1663-E1685.
[11]	Easterling DR, Horton B, Jones PD, Peterson TC, Karl TR, Parker DE, Salinger MJ, Razuvayev V, Plummer N, Jamason P, Folland CK (1997). Maximum and minimum temperature trends for the globe. Science, 277, 364-367. DOI URL
[12]	Fang JY, Ke JH, Tang ZY, Chen AP (2001). Implications and estimations of four terrestrial productivity parameters. Acta Phytoecologica Sinica, 25, 414-419.
	[ 方精云, 柯金虎, 唐志尧, 陈安平 (2001). 生物生产力的“4P”概念、估算及其相互关系. 植物生态学报, 25, 414-419.]
[13]	Fitzhugh RD, Driscoll CT, Groffman PM, Tierney GL, Fahey TJ, Hardy JP (2001). Effects of soil freezing disturbance on soil solution nitrogen, phosphorus, and carbon chemistry in a northern hardwood ecosystem. Biogeochemistry, 56, 215-238. DOI URL
[14]	Gao GY, Wang D, Zha TS, Wang LX, Fu BJ (2022). A global synthesis of transpiration rate and evapotranspiration partitioning in the shrub ecosystems. Journal of Hydrology, 606, 127417. DOI: 10.1016/j.jhydrol.2021.127417. DOI URL
[15]	Huang J, Guan X, Ji F (2012). Enhanced cold-season warming in semi-arid regions. Atmospheric Chemistry and Physics, 12, 5391-5398.
[16]	Huang JP, Yu HP, Dai AG, Wei Y, Kang LT (2017). Drylands face potential threat under 2 °C global warming target. Nature Climate Change, 7, 417-422. DOI URL
[17]	IPCC Intergovernmental Panel on Climate Change(2021). Climate Change 2021: the Physical Science Basis. Cambridge University Press, Cambridge, UK.
[18]	Jia X, Zha TS, Gong JN, Zhang YQ, Wu B, Qin SG, Peltola H (2018). Multi-scale dynamics and environmental controls on net ecosystem CO₂ exchange over a temperate semiarid shrubland. Agricultural and Forest Meteorology, 259, 250-259. DOI URL
[19]	Jia X, Zha TS, Wu B, Zhang YQ, Gong JN, Qin SG, Chen GP, Qian D, Kellomäki S, Peltola H (2014). Biophysical controls on net ecosystem CO₂ exchange over a semiarid shrubland in northwest China. Biogeosciences, 11, 4679-4693. DOI URL
[20]	Jiang SJ, Wang KC, Mao YN (2020). Rapid local urbanization around most meteorological stations explain the observed daily asymmetric warming rates across China from 1985 to 2017. Journal of Climate, 33, 9045-9061. DOI URL
[21]	Kang MC, Zhu LP, Xu H, Zha TG, Zhang ZQ (2019). Modelling the responses of carbon and water fluxes with climate change for a poplar plantation in northern China based on the Biome-BGC model. Acta Ecologica Sinica, 39, 2378-2390.
	[ 康满春, 朱丽平, 许行, 查同刚, 张志强 (2019). 基于Biome-BGC模型的北方杨树人工林碳水通量对气候变化的响应研究. 生态学报, 39, 2378-2390.]
[22]	Li J, He NP, Xu L, Chai H, Liu Y, Wang DL, Wang L, Wei XH, Xue JY, Wen XF, Sun XM (2017). Asymmetric responses of soil heterotrophic respiration to rising and decreasing temperatures. Soil Biology & Biochemistry, 106, 18-27. DOI URL
[23]	Li XH, Sun OJX (2018). Testing parameter sensitivities and uncertainty analysis of Biome-BGC model in simulating carbon and water fluxes in broadleaved-Korean pine forests. Chinese Journal of Plant Ecology, 42, 1131-1144. DOI URL
	[ 李旭华, 孙建新 (2018). Biome-BGC模型模拟阔叶红松林碳水通量的参数敏感性检验和不确定性分析. 植物生态学报, 42, 1131-1144.] DOI
[24]	Liberati D, Guidolotti G, de Dato G, de Angelis P (2021). Enhancement of ecosystem carbon uptake in a dry shrubland under moderate warming: the role of nitrogen- driven changes in plant morphology. Global Change Biology, 27, 5629-5642. DOI PMID
[25]	Lindvall J, Svensson G (2015). The diurnal temperature range in the CMIP5 models. Climate Dynamics, 44, 405-421. DOI URL
[26]	Liu P, Zha TS, Jia X, Wang B, Guo XN, Zhang YQ, Wu B, Yang Q, Peltola H (2016). Diurnal freeze-thaw cycles modify winter soil respiration in a desert shrub-land ecosystem. Forests, 7, 161. DOI: 10.3390/f7080161. DOI URL
[27]	Luo Z, Sun OJ, Wang E, Ren H, Xu H (2010). Modeling productivity in mangrove forests as impacted by effective soil water availability and its sensitivity to climate change using biome-BGC. Ecosystems, 13, 949-965. DOI URL
[28]	Ma LQ, Qin F, Wang H, Qin YC, Xia HM (2019). Asymmetric seasonal daytime and nighttime warming and its effects on vegetation in the Loess Plateau. PLOS ONE, 14, e0218480. DOI: 10.1371/journal.pone.0218480. DOI URL
[29]	Niu SL, Wu MY, Han Y, Xia JY, Li LH, Wan SQ (2008). Water-mediated responses of ecosystem carbon fluxes to climatic change in a temperate steppe. New Phytologist, 177, 209-219. DOI PMID
[30]	Peng SB, Huang JL, Sheehy JE, Laza RC, Visperas RM, Zhong XH, Centeno GS, Khush GS, Cassman KG (2004). Rice yields decline with higher night temperature from global warming. Proceedings of the National Academy of Sciences of the United States of America, 101, 9971-9975. DOI PMID
[31]	Peng SS, Piao SL, Ciais P, Myneni RB, Chen AP, Chevallier F, Dolman AJ, Janssens IA, Peñuelas J, Zhang GX, Vicca S, Wan SQ, Wang SP, Zeng H (2013). Asymmetric effects of daytime and night-time warming on Northern Hemisphere vegetation. Nature, 501, 88-92. DOI
[32]	Pietsch SA, Hasenauer H, Thornton PE (2005). BGC-model parameters for tree species growing in central European forests. Forest Ecology and Management, 211, 264-295. DOI URL
[33]	Running SW, Nemani RR, Hungerford RD (1987). Extrapolation of synoptic meteorological data in mountainous terrain and its use for simulating forest evapotranspiration and photosynthesis. Canadian Journal of Forest Research, 17, 472-483. DOI URL
[34]	Su HX, Li GQ (2012). Simulating the response of the Quercus mongolica forest ecosystem carbon budget to asymmetric warming. Chinese Science Bulletin, 57, 1544-1552.
	[ 苏宏新, 李广起 (2012). 模拟蒙古栎林生态系统碳收支对非对称性升温的响应. 科学通报, 57, 1544-1552.]
[35]	Sun M, Zhang XL, Feng SY, Huo ZL (2014). Parameter optimization and validation for RZWQM2 model using PEST method. Transactions of the Chinese Society for Agricultural Machinery, 45(11), 146-153.
	[ 孙美, 张晓琳, 冯绍元, 霍再林 (2014). 基于PEST的RZWQM2模型参数优化与验证. 农业机械学报, 45(11), 146-153.]
[36]	Thornton PE, Law BE, Gholz HL, Clark KL, Falge E, Ellsworth DS, Goldstein AH, Monson RK, Hollinger D, Falk M, Chen J, Sparks JP (2002). Modeling and measuring the effects of disturbance history and climate on carbon and water budgets in evergreen needleleaf forests. Agricultural and Forest Meteorology, 113, 185-222. DOI URL
[37]	Thornton PE, Rosenbloom NA (2005). Ecosystem model spin-up: estimating steady state conditions in a coupled terrestrial carbon and nitrogen cycle model. Ecological Modelling, 189, 25-48. DOI URL
[38]	Turnbull MH, Murthy R, Griffin KL (2002). The relative impacts of daytime and night-time warming on photosynthetic capacity in Populus deltoides. Plant, Cell & Environment, 25, 1729-1737.
[39]	Turnbull MH, Tissue DT, Murthy R, Wang XZ, Sparrow AD, Griffin KL (2004). Nocturnal warming increases photosynthesis at elevated CO₂ partial pressure in Populus deltoides. New Phytologist, 161, 819-826. DOI PMID
[40]	Ueyama M, Harazono Y, Kim Y, Tanaka N (2009). Response of the carbon cycle in sub-arctic black spruce forests to climate change: reduction of a carbon sink related to the sensitivity of heterotrophic respiration. Agricultural and Forest Meteorology, 149, 582-602. DOI URL
[41]	Wan SQ, Xia JY, Liu WX, Niu SL (2009). Photosynthetic overcompensation under nocturnal warming enhances grassland carbon sequestration. Ecology, 90, 2700-2710. PMID
[42]	Wang J, Zhang Q, Song J, Ru JY, Zhou ZX, Xia JY, Dukes JS, Wan SQ (2020). Nighttime warming enhances ecosystem carbon-use efficiency in a temperate steppe. Functional Ecology, 34, 1721-1730. DOI URL
[43]	Wang Q, Watanabe M, Ouyang Z (2005). Simulation of water and carbon fluxes using BIOME-BGC model over crops in China. Agricultural and Forest Meteorology, 131, 209-224. DOI URL
[44]	White MA, Thornton PE, Running SW, Nemani RR (2000). Parameterization and sensitivity analysis of the BIOME- BGC terrestrial ecosystem model: net primary production controls. Earth Interactions, 4, 1-85. DOI URL
[45]	Wolkovich EM, Cook BI, Allen JM, Crimmins TM, Betancourt JL, Travers SE, Pau S, Regetz J, Davies TJ, Kraft NJB, Ault TR, Bolmgren K, Mazer SJ, McCabe GJ, McGill BJ, et al.(2012). Warming experiments underpredict plant phenological responses to climate change. Nature, 485, 494-497. DOI
[46]	Xia J, Han Y, Zhang Z, Zhang Z, Wan S (2009). Effects of diurnal warming on soil respiration are not equal to the summed effects of day and night warming in a temperate steppe. Biogeosciences, 6, 1361-1370. DOI URL
[47]	Xia JY, Chen JQ, Piao SL, Ciais P, Luo YQ, Wan SQ (2014). Terrestrial carbon cycle affected by non-uniform climate warming. Nature Geoscience, 7, 173-180. DOI
[48]	Xia JY, Lu RL, Zhu C, Cui EQ, Du Y, Huang K, Sun BY (2020). Response and adaptation of terrestrial ecosystem processes to climate warming. Chinese Journal of Plant Ecology, 44, 494-514. DOI URL
	[ 夏建阳, 鲁芮伶, 朱辰, 崔二乾, 杜莹, 黄昆, 孙宝玉 (2020). 陆地生态系统过程对气候变暖的响应与适应. 植物生态学报, 44, 494-514.] DOI
[49]	Xu L, Myneni RB, Chapin III FS, Callaghan TV, Pinzon JE, Tucker CJ, Zhu Z, Bi J, Ciais P, Tømmervik H, Euskirchen ES, Forbes BC, Piao SL, Anderson BT, Ganguly S, et al. (2013). Temperature and vegetation seasonality diminishment over northern lands. Nature Climate Change, 3, 581-586. DOI
[50]	Yang YH, Shi Y, Sun WJ, Chang JF, Zhu JX, Chen LY, Wang X, Guo YP, Zhang HT, Yu LF, Zhao SQ, Xu K, Zhu JL, Shen HH, Wang YY, et al. (2022). Terrestrial carbon sinks in China and around the world and their contribution to carbon neutrality. Scientia Sinica (Vitae), 52, 534-574.
	[ 杨元合, 石岳, 孙文娟, 常锦峰, 朱剑霄, 陈蕾伊, 王欣, 郭焱培, 张宏图, 于凌飞, 赵淑清, 徐亢, 朱江玲, 沈海花, 王媛媛, 等 (2022). 中国及全球陆地生态系统碳源汇特征及其对碳中和的贡献. 中国科学: 生命科学, 52, 534-574.]
[51]	You GY, Zhang ZY, Zhang RD (2018). Temperature sensitivity of photosynthesis and respiration in terrestrial ecosystems globally. Acta Ecologica Sinica, 38, 8392-8399.
	[ 游桂莹, 张志渊, 张仁铎 (2018). 全球陆地生态系统光合作用与呼吸作用的温度敏感性. 生态学报, 38, 8392-8399.]
[52]	You YF, Wang SY, Ma YX, Wang XY, Liu WH (2019). Improved modeling of gross primary productivity of alpine grasslands on the Tibetan Plateau using the biome-BGC model. Remote Sensing, 11, 1287-1308. DOI URL
[53]	Zha TS, Kellomäki S, Wang KY (2003). Seasonal variation in respiration of 1-year-old shoots of scots pine exposed to elevated carbon dioxide and temperature for 4 years. Annals of Botany, 92, 89-96. PMID
[54]	Zhai YJ, Li YH, Xu Y (2016). Aridity change characteristics over northern region of China under RCPs scenario. Plateau Meteorology, 35, 94-106. DOI
	[ 翟颖佳, 李耀辉, 徐影 (2016). RCPs情景下中国北方地区干旱气候变化特征. 高原气象, 35, 94-106.] DOI
[55]	Zhang DF, Gao XJ (2020). Climate change of the 21st century over China from the ensemble of RegCM4 simulations. Chinese Science Bulletin, 65, 2516-2526.
	[ 张冬峰, 高学杰 (2020). 中国21世纪气候变化的RegCM4多模拟集合预估. 科学通报, 65, 2516-2526.]
[56]	Zhang DF, Han ZY, Shi Y (2017). Comparison of climate projection between the driving CSIRO-Mk3.6.0 and the downscaling simulation of RegCM4.4 over China. Climate Change Research, 13, 557-568.
	[ 张冬峰, 韩振宇, 石英 (2017). CSIRO-Mk3.6.0模式及其驱动下RegCM4.4模式对中国气候变化的预估. 气候变化研究进展, 13, 557-568.]
[57]	Zhang YJ, Yu GR, Yang J, Wimberly MC, Zhang XZ, Tao J, Jiang YB, Zhu JT (2014). Climate-driven global changes in carbon use efficiency. Global Ecology and Biogeography, 23, 144-155. DOI URL
[58]	Zhu B, Chen Y (2020). Techniques and methods for field warming manipulation experiments in terrestrial ecosystems. Chinese Journal of Plant Ecology, 44, 330-339. DOI
	[ 朱彪, 陈迎 (2020). 陆地生态系统野外增温控制实验的技术与方法. 植物生态学报, 44, 330-339.] DOI
[59]	Zhu FY (2015). The Influence of Climate Change over the Temporal and Spatial Variation of Desertification of Four Main Dune Fields over the Past 30 Years in Northern China. Master degree dissertation, Nanjing University, Nanjing.
	[ 朱芳莹 (2015). 中国北方四大沙地近30年来的沙漠化时空变化及气候影响. 硕士学位论文, 南京大学, 南京.]
[60]	Zhu WZ (2013). Advances in the carbon use efficiency of forest. Chinese Journal of Plant Ecology, 37, 1043-1058. DOI URL
	[ 朱万泽 (2013). 森林碳利用效率研究进展. 植物生态学报, 37, 1043-1058.] DOI