中国北方树木秋季物候的过程模拟及其区域分异归因

doi:10.17521/cjpe.2021.0254

摘要/Abstract

摘要：

揭示温带落叶树木秋季物候的发生机理对提高生态系统固碳量和植被生产力的预估精度具有重要意义。该研究利用低温和光周期乘积模型模拟了1981-2014年中国北方温带90余个站点6个树种的叶始变色期和落叶末期, 并对逐站点-物种的最优模型进行了模拟精度评价, 分析了最优模型模拟精度的时空差异及其随水分梯度的空间变化。主要结果如下: (1)在诱导叶片衰老方面, 光周期缩短的影响通常大于温度降低的影响。据此建立的叶始变色期和落叶末期最优模型模拟的平均均方根误差分别为6.9 d和6.0 d, 模拟与观测时间序列呈显著正相关关系的比例分别为71.4%和83.6%; (2)最优模型对区域平均和多年平均叶始变色期和落叶末期模拟的绝对误差小于2.4 d, 但模拟日期的时空变幅通常小于观测日期, 这与秋季物候发生日期的高度时间变异性密切相关; (3)水分条件在一定程度上影响叶片衰老诱导途径的选择, 表现为光周期缩短诱导叶片衰老的叶始变色期最优模型所占比例在干旱和半干旱区大于湿润和半湿润区, 而最优模型的模拟精度在湿润和半湿润区高于干旱和半干旱区。该研究验证了低温和光周期乘积模型在中国温带地区的适用性, 并揭示了水分条件对秋季物候发生机理和模拟精度的影响。

关键词: 秋季物候, 模拟, 低温和光周期乘积模型(TPM), 模拟效果的时空特征, 水分依赖性, 温带落叶树木

Abstract:

Aims Revealing occurrence mechanisms of autumn phenology in temperate deciduous trees is of vital importance for improving estimation accuracies of ecosystem carbon sequestration and vegetation productivity. This study aimed to uncover the mechanisms of leaf senescence in response to environmental changes and simulation accuracies of autumn phenology through process-based models, and further investigate impacts of water conditions on leaf senescence mechanisms and simulation accuracy of autumn phenology.

Methods We used the low temperature and photoperiod multiplicative model (TPM) to fit the first leaf coloration dates and leaf fall end dates of six tree species at more than 90 stations across the temperate zone of northern China from 1981 to 2014. The TPM contains two sub-models, i.e., photoperiod-initiated leaf senescence model (TPM_p) and temperature-initiated leaf senescence model. We evaluated simulation accuracies and their spatiotemporal variations of station-species specific optimum models, and analyzed regional differentiation of proportions of two sub-models among optimum models and its spatial dependence on arid-humid gradient.

Important findings (1) Photoperiod shortening plays more important roles in initiating leaf senescence than temperature decrease. The simulated average root mean square errors of optimum models for first leaf coloration and leaf fall end dates are 6.9 d and 6.0 d, respectively. Proportions of significantly positive correlations between simulated and observed time series are 71.4% (first leaf coloration) and 83.6% (leaf fall end), respectively. (2) Simulated regional mean absolute errors and multi-year mean absolute errors of optimum models for first leaf coloration date and leaf fall end date are less than 2.4 d. However, amplitudes of temporal and spatial variations in simulated phenological dates are usually smaller than those in observed phenological dates, which are closely related to the high temporal variability of autumn phenological occurrence date. (3) Water conditions affect the selection of leaf senescence initiation pathways to a certain extent. This is mainly manifested in that proportions of TPM_p among optimum models for first leaf coloration dates in arid and semi-arid regions are higher than those in humid and semi-humid regions, while simulation accuracies of optimum models in humid and semi-humid regions are higher than those in arid and semi-arid regions.

Key words: autumn phenology, simulation, low temperature and photoperiod multiplicative model (TPM), spatiotemporal characteristics of simulation effect, moisture dependence, temperate deciduous trees

陈奕竹, 郎伟光, 陈效逑. 中国北方树木秋季物候的过程模拟及其区域分异归因. 植物生态学报, 2022, 46(7): 753-765. DOI: 10.17521/cjpe.2021.0254

CHEN Yi-Zhu, LANG Wei-Guang, CHEN Xiao-Qiu. Process-based simulation of autumn phenology of trees and the regional differentiation attribution in northern China. Chinese Journal of Plant Ecology, 2022, 46(7): 753-765. DOI: 10.17521/cjpe.2021.0254

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链接本文: https://www.plant-ecology.com/CN/10.17521/cjpe.2021.0254

https://www.plant-ecology.com/CN/Y2022/V46/I7/753

图/表 9

图1 中国北方不同干湿区树木秋季物候观测站点的地理分布。

Fig. 1 Geographical distribution of autumn phenological stations in different arid-humid regions of northern China.

表1 中国北方不同树种秋季物候最优模型的序列比例和评价指标的比较

Table 1 Comparison of proportions and evaluation indicators of autumn phenology optimum models among different tree species in northern China

物候期 Phenophase	物种 species	TPM_p (%)	TPM_t (%)	RMSE平均值 Average RMSE	RMSE < 8 d比例 Proportion of RMSE < 8 d (%)	r > 0.6 (p < 0.05)比例 Proportion of r > 0.6 (p < 0.05) (%)
叶始变色期 First leaf coloration	刺槐 Robinia pseudoacacia	50.0	50.0	7.6	60.7	14.3
	旱柳 Salix matsudana	66.0	34.0	6.6	74.5	51.9
	杏 Armeniaca vulgaris	75.0	25.0	6.9	83.3	41.7
	榆树 Ulmus pumila	68.6	31.4	7.0	71.4	31.4
	毛白杨 Populus tomentosa	54.5	45.5	7.6	63.6	31.8
	小叶杨 Populus simonii	69.0	31.0	6.4	75.9	44.8
落叶末期 Leaf fall end	刺槐 Robinia pseudoacacia	56.7	43.3	6.4	70.0	30.0
	旱柳 Salix matsudana	59.6	40.4	6.0	80.9	44.7
	杏 Armeniaca vulgaris	58.3	41.7	5.3	95.8	50.0
	榆树 Ulmus pumila	62.9	37.1	5.5	88.6	51.4
	毛白杨 Populus tomentosa	60.9	39.1	6.1	73.9	43.5
	小叶杨 Populus simonii	56.7	43.3	6.6	73.3	33.3

图2 中国北方6种树木所有站点逐年最优模拟日期与观测日期之间的相关回归分析和均方根误差(RMSE)。A, 叶始变色期。B, 落叶末期。1, 刺槐。2, 旱柳。3, 杏。4, 榆树。5, 毛白杨。6, 小叶杨。

Fig. 2 Correlation and regression analyses and root-mean-square errors (RMSE) between optimum model simulated dates and observed dates for six tree species in each year at each station in northern China. A, First leaf coloration. B, Leaf fall end. 1, Robinia pseudoacacia. 2,Salix matsudana. 3, Armeniaca vulgaris. 4, Ulmus pumila. 5, Populus tomentosa. 6, Populus simonii.

图3 中国北方逐年6个树种模拟与观测日期空间变幅的比较。A, 叶始变色期。B, 落叶末期。1, 刺槐。2, 旱柳。3, 杏。4, 榆树。5, 毛白杨。6, 小叶杨。

Fig. 3 Comparison between spatial ranges of simulated and observed dates for six tree species in each year in northern China. A, First leaf coloration. B, Leaf fall end. 1, Robinia pseudoacacia. 2, Salix matsudana. 3, Armeniaca vulgaris. 4, Ulmus pumila. 5, Populus tomentosa. 6, Populus simonii.

图4 中国北方6个树种各站点模拟与观测日期年际变幅的比较。A, 叶始变色期。B, 落叶末期。1, 刺槐。2, 旱柳。3, 杏。4, 榆树。5, 毛白杨。6, 小叶杨。

Fig. 4 Comparison between interannual ranges of simulated and observed dates for six tree species at each station in northern China. A, First leaf coloration. B, Leaf fall end. 1, Robinia pseudoacacia. 2, Salix matsudana. 3, Armeniaca vulgaris. 4, Ulmus pumila. 5, Populus tomentosa. 6, Populus simonii.

表2 中国北方不同干湿地区叶始变色期和落叶末期最优模型的比例

Table 2 Proportions of optimum models for first leaf coloration date and leaf fall end date across different arid-humid regions in northern China

干湿区 Arid-humid region	叶始变色期 First leaf coloration		落叶末期 Leaf fall end
干湿区 Arid-humid region	TPM_p比例 Proportion of TPM_p (%)	TPM_t比例 Proportion of TPM_t (%)	TPM_p比例 Proportion of TPM_p (%)	TPM_t比例 Proportion of TPM_t (%)
干旱区 Arid region	74.3	25.7	56.8	43.2
半干旱区 Semi-arid region	68.2	31.8	63.6	36.4
半湿润区 Semi-humid region	60.0	40.0	63.4	36.6
湿润区 Humid region	57.7	42.3	42.3	57.7

表3 中国北方不同干湿地区叶始变色期和落叶末期最优模型模拟效果的比较

Table 3 Comparison of simulation effects of optimum models for first leaf coloration date and leaf fall end date in different arid-humid regions in northern China

干湿区 Arid-humid region	叶始变色期 First leaf coloration		落叶末期 Leaf fall end
干湿区 Arid-humid region	RMSE < 8 d的比例 Proportion of RMSE < 8 d (%)	显著正相关比例 Proportion of significantly positive correlation (%)	RMSE < 8 d所占比例 Proportion of RMSE < 8 d (%)	显著正相关比例 Proportion of significantly positive correlation (%)
干旱区 Arid region	62.9	62.9	83.8	89.2
半干旱区 Semi-arid region	77.3	72.7	79.5	75.0
半湿润区 Semi-humid region	66.3	71.3	80.5	81.7
湿润区 Humid region	92.3	80.8	76.9	96.2

表4 青藏高原和中国北方温带地区树木叶始变色期最优模型及模拟结果的比较

Table 4 Comparison of optimum models and simulation results of trees' first leaf coloration dates between Qingzang Plateau and temperate northern China

指标 Indicator	Lang等(2019)结果¹⁾Results from Lang et al. (2019)¹⁾	本研究结果 Results of this study
TPM_p拟合的时间序列比例 Proportion of time series fitted by TPM_p (%)	61.9	64.3
TPM_t拟合的时间序列比例 Proportion of time series fitted by TPM_t (%)	38.1	35.7
RMSE的数值范围 Range of RMSE (d)	4.1-18.9	2.6-16.9
RMSE平均值 Average RMSE (d)	8.2	6.9
RMSE ≤ 10 d的时间序列比例 Proportions of time series for RMSE ≤ 10 d (%)	85.7	88.1
模拟与观测日期显著正相关的时间序列比例 Proportion of time series with significantly positive correlation between simulated and observed first leaf coloration dates (%)	76.2	71.4

图5 中国北方6个树种叶始变色与落叶末观测日期标准差与模拟日期均方根误差(RMSE)之间的相关回归分析。A, 叶始变色期。B, 落叶末期。

Fig. 5 Correlation and regression analyses between observed standard deviation and simulated root mean squared error (RMSE) for first leaf coloration and leaf fall end dates of six tree species in northern China. A, First leaf coloration. B, Leaf fall end.

参考文献 41

[1]	Aikio S, Taulavuori K, Hurskainen S, Taulavuori E, Tuomi J (2019). Contributions of day length, temperature and individual variability on the rate and timing of leaf senescence in the common lilac Syringa vulgaris. Tree Physiology, 39, 961-970.
[2]	Archetti M, Richardson AD, O'Keefe J, Delpierre N (2013). Predicting climate change impacts on the amount and duration of autumn colors in a New England forest. PLOS ONE, 8, e57373. DOI: 10.1371/journal.pone.0057373. DOI URL
[3]	Chen XQ (2013). East Asia//Schwartz MD. Phenology: An Integrative Environmental Science. 2nd ed. Springer, Dordrecht, the Netherlands. 9-22.
[4]	Chen XQ (2017). Spatiotemporal Processes of Plant Phenology: Simulation and Prediction. Springer, Berlin.
[5]	Editorial Board of Climate Atlas of the Peopleʼs Republic of China (1979). Climate Atlas of the People's Republic of China. China Meterological Press, Beijing.
	[《中华人民共和国气候图集》编委会 (1979). 中华人民共和国气候图集. 气象出版社, 北京.]
[6]	China Meterological Administration (1993). Observation Criterion of Agicultural Meteorology. China Meterological Press, Beijing.
	[国家气象局 (1993). 农业气象观测规范. 气象出版社, 北京.]
[7]	Chuine I (2000). A unified model for budburst of trees. Journal of Theoretical Biology, 207, 337-347. PMID
[8]	Chuine I, Cour P, Rousseau DD (1998). Fitting models predicting dates of flowering of temperate-zone trees using simulated annealing. Plant, Cell & Environment, 21, 455-466.
[9]	Cleland EE, Chiariello NR, Loarie SR, Mooney HA, Field CB (2006). Diverse responses of phenology to global changes in a grassland ecosystem. Proceedings of the National Academy of Sciences of the United States of America, 103, 13740-13744. PMID
[10]	Delpierre N, Dufrêne E, Soudani K, Ulrich E, Cecchini S, Boé J, François C (2009). Modelling interannual and spatial variability of leaf senescence for three deciduous tree species in France. Agricultural and Forest Meteorology, 149, 938-948. DOI URL
[11]	Dufrêne E, Davi H, François C, Maire GI, Dantec VL, Granier A (2005). Modelling carbon and water cycles in a beech forest. Ecological Modelling, 185, 407-436. DOI URL
[12]	Estiarte M, Peñuelas J (2015). Alteration of the phenology of leaf senescence and fall in winter deciduous species by climate change: effects on nutrient proficiency. Global Change Biology, 21, 1005-1017. DOI PMID
[13]	Forsythe WC, Rykiel Jr EJ, Stahl RS, Wu H, Schoolfield RM (1995). A model comparison for daylength as a function of latitude and day of year. Ecological Modelling, 80, 87-95. DOI URL
[14]	Fracheboud Y, Luquez V, Björkén L, Sjödin A, Tuominen H, Jansson S (2009). The control of autumn senescence in European aspen. Plant Physiology, 149, 1982-1991. DOI PMID
[15]	Fu YSH, Campioli M, Vitasse Y, de Boeck HJ, van den Berge J, AbdElgawad H, Asard H, Piao S, Deckmyn G, Janssens IA (2014). Variation in leaf flushing date influences autumnal senescence and next yearʼs flushing date in two temperate tree species. Proceedings of the National Academy of Sciences of the United States of America, 111, 7355-7360.
[16]	Garonna I, de Jong R, de Wit AJW, Mücher CA, Schmid B, Schaepman ME (2014). Strong contribution of autumn phenology to changes in satellite-derived growing season length estimates across Europe (1982-2011). Global Change Biology, 20, 3457-3470. DOI PMID
[17]	Hogg EH, Price DT, Black TA (2000). Postulated feedbacks of deciduous forest phenology on seasonal climate patterns in the western Canadian interior. Journal of Climate, 13, 4229-4243. DOI URL
[18]	Keenan TF, Richardson AD (2015). The timing of autumn senescence is affected by the timing of spring phenology: implications for predictive models. Global Change Biology, 21, 2634-2641. DOI URL
[19]	Lang WG, Chen XQ, Qian SW, Liu GH, Piao SL (2019). A new process-based model for predicting autumn phenology: How is leaf senescence controlled by photoperiod and temperature coupling? Agricultural and Forest Meteorology, 268, 124-135. DOI URL
[20]	Liu GH, Chen XQ, Zhang QH, Lang WG, Delpierre N (2018). Antagonistic effects of growing season and autumn temperatures on the timing of leaf coloration in winter deciduous trees. Global Change Biology, 24, 3537-3545. DOI URL
[21]	Liu GH, Chen XQ, Fu YS, Delpierre N (2019). Modelling leaf coloration dates over temperate China by considering effects of leafy season climate. Ecological Modelling, 394, 34-43. DOI URL
[22]	Matos FS, de Oliveria LR, de Freitas RG, Evaristo AB, Missio RF, Oliva Cano MA, Dias LADS (2012). Physiological characterization of leaf senescence of Jatropha curcas L. populations. Biomass & Bioenergy, 45, 57-64.
[23]	Menzel A, Fabian P (1999). Growing season extended in Europe. Nature, 397, 659. DOI URL
[24]	Morin X, Roy J, Sonié L, Chuine I (2010). Changes in leaf phenology of three European oak species in response to experimental climate change. New Phytologist, 186, 900-910. DOI URL
[25]	Nash JE, Sutcliffe JV (1970). River flow forecasting through conceptual models part I-A discussion of principles. Journal of Hydrology, 10, 282-290. DOI URL
[26]	Norby RJ, Hartz-Rubin JS, Verbrugge MJ (2003). Phenological responses in maple to experimental atmospheric warming and CO₂ enrichment. Global Change Biology, 9, 1792-1801. DOI URL
[27]	Parmesan C (2006). Ecological and evolutionary responses to recent climate change. Annual Review of Ecology, Evolution, and Systematics, 37, 637-669. DOI URL
[28]	Piao S, Wang X, Wang K, Li X, Bastos A, Canadell JG, Ciais P, Friedlingstein P, Sitch S (2020). Interannual variation of terrestrial carbon cycle: issues and perspectives. Global Change Biology, 26, 300-318. DOI URL
[29]	Pilegaard K, Ibrom A (2020). Net carbon ecosystem exchange during 24 years in the Sorø Beech Forest-Relations to phenology and climate. Tellus B: Chemical and Physical Meteorology, 72, 1-17.
[30]	Pudas E, Leppälä M, Tolvanen A, Poikolainen J, Venäläinen A, Kubin E (2008). Trends in phenology of Betula pubescens across the boreal zone in Finland. International Journal of Biometeorology, 52, 251-259. DOI PMID
[31]	Richardson AD, Andy Black T, Ciais P, Delbart N, Friedl MA, Gobron N, Hollinger DY, Kutsch WL, Longdoz B, Luyssaert S, Migliavacca M, Montagnani L, William Munger J, Moors E, Piao S, et al. (2010). Inﬂuence of spring and autumn phenological transitions on forest ecosystem productivity. Philosophical Transactions of the Royal Society B, 365, 3227-3246. DOI URL
[32]	Richardson AD, Keenan TF, Migliavacca M, Ryu Y, Sonnentag O, Toomey M (2013). Climate change, phenology, and phenological control of vegetation feedbacks to the climate system. Agricultural and Forest Meteorology, 169, 156-173. DOI URL
[33]	Ryu Y, Baldocchi DD, Ma S, Hehn T (2008). Interannual variability of evapotranspiration and energy exchange over an annual grassland in California. Journal of Geophysical Research, 113, D9104. DOI: 10.1029/2007JD009263. DOI URL
[34]	Soolanayakanahally RY, Guy RD, Silim SN, Song MH (2013). Timing of photoperiodic competency causes phenological mismatch in balsam poplar (Populus balsamifera L.). Plant, Cell & Environment, 36, 116-127.
[35]	Tao Z, Wang H, Dai J, Alatalo J, Ge Q (2018). Modeling spatiotemporal variations in leaf coloring date of three tree species across China. Agricultural and Forest Meteorology, 249, 310-318. DOI URL
[36]	Worrall J (1998). Autumn leaf colouration. The Forestry Chronicle, 74, 668-669.
[37]	Wu CY, Chen JM, Black TA, Price DT, Kurz WA, Desai AR, Gonsamo A, Jassal RS, Gough CM, Bohrer G, Dragoni D, Herbst M, Gielen B, Berninger F, Vesala T, et al. (2013). Interannual variability of net ecosystem productivity in forests is explained by carbon flux phenology in autumn. Global Ecology and Biogeography, 22, 994-1006. DOI URL
[38]	Xie Y, Wang X, Silander Jr JA (2015). Deciduous forest responses to temperature, precipitation, and drought imply complex climate change impacts. Proceedings of the National Academy of Sciences of the United States of America, 112, 13585-13590.
[39]	Zani D, Crowther TW, Mo L, Renner SS, Zohner CM (2020). Increased growing-season productivity drives earlier autumn leaf senescence in temperate trees. Science, 370, 1066-1071. DOI URL
[40]	Zhang Y, Chen HYH, Reich PB (2012). Forest productivity increases with evenness, species richness and trait variation: a global meta-analysis. Journal of Ecology, 100, 742-749. DOI URL
[41]	Zhu WQ, Tian HQ, Xu XF, Pan YZ, Chen GS, Lin WP (2012). Extension of the growing season due to delayed autumn over mid and high latitudes in north America during 1982-2006. Global Ecology and Biogeography, 21, 260-271. DOI URL