Light Response Characteristics of Canopy Photosynthesis of a Tropical Evergreen Broadleaf Forest
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摘要:
目的 研究云南省典型热带常绿阔叶林的冠层光合作用光响应特征。 方法 基于云南省景洪市普文热带常绿阔叶林通量塔从2023年3月至2024年2月的观测数据,利用Michaelis-Menten方程,以1个月为时间窗口,拟合生态系统光合作用的3个光响应特征参数(以下简称“光合参数”),并与相应时间尺度的环境因子平均值进行相关分析。 结果 热带常绿阔叶林的表观初始光能利用效率(Apparent initial light use efficiency, ɑ)和生态系统暗呼吸速率(Dark ecosystem respiration, Rd)有相似的季节变异特征,均在10月份达到最大值,2月份为最小值;最大光合速率(Maximum photosynthesis rate, Pmax)在3至5月较低,其余月份变化不明显。与ɑ、Rd和Pmax的季节变异相关性最大的环境因子分别是风速、空气相对湿度和饱和水汽压差,冠层导度也对Rd和Pmax的季节变异有一定影响。 结论 热带常绿阔叶林的3个冠层光合参数有明显的季节变异特征,风速和水分条件相比辐射和温度对光合参数的季节变异有更大影响。 Abstract:Objective To study the light response characteristics of canopy photosynthesis in a tropical evergreen broadleaf forest in Yunnan Province. Methods Based on the flux tower data observed from March 2023 to February 2024 in a tropical evergreen broadleaf forest in Puwen Town, Yunnan Province, the Michaelis-Menten equation was used to fit the three photosynthetic parameters of the ecosystems with the time windows of 1 month. And the correlation analysis between ecosystem photosynthetic parameters and environmental factors in corresponding time scale was carried out. Results The apparent initial light use efficiency (ɑ) and dark ecosystem respiration (Rd) showed similar seasonal variation characteristics, with the maximum value in October and the minimum value in February. The maximum photosynthesis rate (Pmax) from March to May was low and had no obvious change in other months. The environmental factors that are most correlated with the seasonal variations of ɑ, Rd and Pmax were wind speed, relative air humidity and vapor pressure deficit, respectively. Canopy conductance also had an apparent effect on the seasonal variation of Rd and Pmax. Conclusion Three photosynthetic parameters of the tropical evergreen broadleaf forest showed obvious seasonal variations. Rather than radiation and temperature, wind speed and water conditions had greater effects on the seasonal variations of photosynthetic parameters. -
图 1 普文热带常绿阔叶林冠层上方大气湍流功率谱和谐谱特征
注:上排为功率谱,下排为谐谱;sw、sCO2、sH2O、sT分别代表垂直风速、CO2浓度、水汽、气温的功率谱;w’CO2’ 、w’u’、w’H2O’ 、w’T’分别代表CO2浓度、垂直风速与水平风速、水汽、气温的谐谱。The upper row is the power spectrum, and the below row is the co-spectrum; sw, sCO2, sH2O, sT represent the power spectrum of vertical wind velocity, carbon dioxide concentration, water vapor and air temperature, respectively; w’CO2’, w’u’, w’H2O’, w’T’ represent the co-spectrum of carbon dioxide concentration, vertical and horizontal wind velocity, water vapor and air temperature, respectively.
Figure 1. Power spectrum and co-spectrum for atmospheric turbulence above forest canopy in a tropical evergreen broadleaf forest in Puwen Town
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[1] 费学海, 2018. 云南典型森林生态系统碳交换及其对气候变化响应研究[D]. 北京: 中国科学院大学. [2] 景跃波, 2015. 云南热区西南桦人工林丛枝菌根研究[D]. 昆明: 云南大学. [3] 林雍, 陈智, 杨萌, 等, 2022. 中国干旱半干旱区生态系统光合参数的时空变异及其影响因素[J]. 植物生态学报, 46(12): 1461-1472. doi: 10.17521/cjpe.2021.0426 [4] 朴世龙, 何悦, 王旭辉, 等, 2022. 中国陆地生态系统碳汇估算: 方法、进展、展望[J]. 中国科学(地球科学), 52(6): 1010-1020. doi: 10.1360/SSTe-2021-0197 [5] 起德花, 费学海, 宋清海, 等, 2021. 2009–2013年哀牢山亚热带常绿阔叶林碳水通量观测数据集[J/OL]. 中国科学数据, 6(1): 87-97. http://www.dx. doi.org/10.11922/sciencedb.00186. DOI: 10.11922/csdata.2020.0089.zh. [6] 宋清海, 张一平, 谭正洪, 等, 2010. 热带季节雨林生态系统净光合作用特征及其影响因子[J]. 应用生态学报, 21(12): 3007-3014. doi: 10.13287/j.1001-9332.2010.0463 [7] 肖文发, 朱建华, 曾立雄, 等, 2023. 森林碳汇助力碳中和的几点认识[J]. 林业科学, 59(3): 1-11. doi: 10.11707/j.1001-7488.LYKX20220681 [8] 许丽霞, 江洪, 张敏霞, 等, 2017. 安吉毛竹林生态系统光合作用特征及其环境影响因子研究[J]. 江西农业大学学报, 39(5): 928-937. doi: 10.13836/j.jjau.2017120 [9] 万家鸣, 律江, 石云, 等, 2023. 散射辐射对杨树人工林生态系统总初级生产力的影响[J]. 林业科学, 59(5): 1-10. doi: 10.11707/j.1001-7488.LYKX20230249 [10] 周立国, 宋清海, 张一平, 等, 2017. 4种森林生态系统光合作用光响应参数特征的比较[J]. 生态学杂志, 36(7): 1815-1824. doi: 10.13292/j.1000-4890.201707.038 [11] 朱苑, 刘帆, 王传宽, 等, 2020. 帽儿山温带落叶阔叶林净生态系统碳交换的日变化及光响应特征[J]. 应用生态学报, 31(1): 72-82. doi: 10.13287/j.1001-9332.202001.040 [12] Bao X Y, Li Z G, Xie F T, 2019. Environmental influences on light response parameters of net carbon exchange in two rotation croplands on the North China Plain[J]. Scientific Reports, 9(1): 18702. doi: 10.1038/s41598-019-55340-2 [13] Chen S N, Wei W, Huang Y, 2024. Biophysical controls on canopy transpiration of Pinus tabulaeformis under different soil moisture conditions in the Loess Plateau of China[J]. Journal of Hydrology, 631: 130799. doi: 10.1016/j.jhydrol.2024.130799 [14] Chen W N, Wang S, Wang J S, et al, 2023. Evidence for widespread thermal optimality of ecosystem respiration[J]. Nature Ecology & Evolution, 7(9): 1379-1387. doi: 10.1038/s41559-023-02121-w [15] Falge E, Baldocchi D, Olson R, et al, 2001. Gap filling strategies for defensible annual sums of net ecosystem exchange[J]. Agricultural and Forest Meteorology, 107(1): 43-69. doi: 10.1016/S0168-1923(00)00225-2 [16] Foken T, Göockede M, Mauder M, et al, 2006. Post-field data quality control[M]//Handbook of Micrometeorology. Dordrecht: Kluwer Academic Publishers, 181-208. DOI: 10.1007/1-4020-2265-4_9 [17] Gilmanov T G, Aires L, Barcza Z, et al, 2020. Productivity, respiration, and light response parameters of world grassland and agroecosystems derived from flux tower measurements[J]. Rangeland Ecology & Management, 63(1): 16-39. doi: 10.2111/REM-D-09-00072.1 [18] Kaimal J C, Wyngaard J C, Izumi Y, et al, 1972. Spectral characteristics of surface layer turbulence[J]. Quarterly Journal of the Royal Meteorological Society, 98: 563-589. doi: 10.1002/qj.49709841707 [19] Lin Y, Chen Z, Yu G R, et al, 2024. Spatial patterns of light response parameters and their regulation on gross primary productivity in China[J]. Agricultural and Forest Meteorology, 345: 109833. doi: 10.1016/j.agrformet.2023.109833 [20] Niu X D, Chen Z C, Pang Y, 2023. Soil moisture shapes the environmental control mechanism on canopy conductance in a natural oak forest[J]. Science of the Total Environment, 857(1): 159363. doi: 10.1016/j.scitotenv.2022.159363 [21] Reichstein M, Falge E, Baldocchi D, et al, 2005. On the separation of net ecosystem exchange into assimilation and ecosystem respiration: review and improved algorithm[J]. Global Change Biology, 11(9): 1424-1439. doi: 10.1111/j.1365-2486.2005.001002.x [22] Song Q H, Fei X H, Zhang Y P, et al, 2017. Snow damage strongly reduces the strength of the carbon sink in a primary subtropical evergreen broadleaved forest[J]. Environmental Research Letters, 12(10): 104014. doi: 10.1088/1748-9326/aa82c4 [23] Song X Z, Chen X F, Zhou G M, 2017. Observed high and persistent carbon uptake by Moso bamboo forests and its response to environmental drivers[J]. Agricultural and Forest Meteorology, 247: 467-475. doi: 10.1016/j.agrformet.2017.09.001 [24] Tanner C B, Thurtell G W, 1969. Anemoclinometer measurements of Reynolds stress and heat transport in the atmospheric surface layer[D]. Madison: University of Wisconsin. [25] Tan Z H, Zhang Y P, Schaefer D, et al, 2011. An old-growth subtropical Asian evergreen forest as a large carbon sink[J]. Atmospheric Environment, 45(8): 1548-1554. doi: 10.1016/j.atmosenv.2010.12.041 [26] Wang B, Wang Z H, Wang C Z, 2021. Field evidence reveals conservative water use of poplar saplings under high aerosol conditions[J]. The Journal of Ecology, 109(5): 2190-2202. doi: 10.1111/1365-2745.13633 [27] Wang J, Feng L, Palmer P, et al, 2020. Large Chinese land carbon sink estimated from atmospheric carbon dioxide data[J]. Nature, 586(7831): 720-723. doi: 10.1038/s41586-020-2849-9 [28] Wang Z H, Wang C Z, Wang X, et al, 2022. Aerosol pollution alters the diurnal dynamics of sun and shade leaf photosynthesis through different mechanisms[J]. Plant, Cell & Environment, 45(10): 2943-2953. DOI: 10.1111/pce.14411 [29] Xiao X M, Zhang Q Y, Braswell B, et al, 2004. Modeling gross primary production of temperate deciduous broadleaf forest using satellite images and climate data[J]. Remote Sensing of Environment, 91(2): 256-270. doi: 10.1016/j.rse.2004.03.010 [30] Xu X J, Du H Q, Zhou G M, et al, 2016. Eddy covariance analysis of the implications of drought on the carbon fluxes of Moso bamboo forest in southeastern China[J]. Trees, 30(5): 1807-1820. doi: 10.1007/s00468-016-1414-5 [31] Xu M J, Wang Q Y, Yang F T, et al, 2022. The responses of photosynthetic light response parameters to temperature among different seasons in a coniferous plantation of subtropical China[J]. Ecological Indicators, 145: 109595. doi: 10.1016/j.ecolind.2022.109595 [32] You C H, Wang Y B, Tan X R, et al, 2022. Seasonal and interannual variations of ecosystem photosynthetic characteristics in a semi-arid grassland of Northern China[J]. Journal of Plant Ecology, 15(5): 961-976. doi: 10.1093/jpe/rtac065 [33] Zhang L M, Yu G R, Sun X M, et al, 2006. Seasonal variations of ecosystem apparent quantum yield (α) and maximum photosynthesis rate (Pmax) of different forest ecosystems in China[J]. Agricultural and Forest Meteorology, 137(3/4): 176-187. doi: 10.1016/j.agrformet.2006.02.006 [34] Zhang P, Chen S P, Zhang W L, et al, 2012. Biophysical regulations of NEE light response in a steppe and a cropland in Inner Mongolia[J]. Journal of Plant Ecology, 5(2): 238-248. doi: 10.1093/jpe/rtr017 [35] Zhang Y P, Tan Z H, Song Q H, et al, 2010. Respiration controls the unexpected seasonal pattern of carbon flux in an Asian tropical rain forest[J]. Atmospheric Environment, 44(32): 3886-3893. doi: 10.1016/j.atmosenv.2010.07.027 -