植物生态学报 ›› 2018, Vol. 42 ›› Issue (5): 526-538.DOI: 10.17521/cjpe.2017.0305
所属专题: 遥感生态学
收稿日期:
2017-11-21
修回日期:
2018-02-11
出版日期:
2018-05-20
发布日期:
2018-07-20
通讯作者:
唐艳鸿
基金资助:
ZHOU Tong1,CAO Ru-Yin2,WANG Shao-Peng1,CHEN Jin3,TANG Yan-Hong1,*()
Received:
2017-11-21
Revised:
2018-02-11
Online:
2018-05-20
Published:
2018-07-20
Contact:
Yan-Hong TANG
Supported by:
摘要:
长期以来, 气候与植物物候关系的研究大多基于线性模型, 但植被物候对气候变化的响应可能是非线性的。该文利用非线性模型——生存分析模型来分析时间序列中过去事件(气候因子)对目的变量(物候)的作用: 用生存分析模型分析了春季气温和降水量对内蒙古草地、青藏高原草甸和欧洲地区木本植物返青期的影响。其中, 内蒙古与青藏高原的物候信息来自遥感数据, 欧洲地区物候信息为实测数据。蒙特卡洛方法用于拟合模型参数。结果表明: 生存分析模型适合对上述不同研究对象的物候影响因素进行分析, 并能模拟非线性效应; 在内蒙古草地, 春季气温和降水对春季返青有很大的影响, 而青藏高原草甸和欧洲地区木本植物返青期对春季降水响应较小, 对春季气温变化的响应随Holdridge干燥度指数上升而下降; 在预测返青期时发现: 春季平均气温提高1 ℃会导致上述地区返青期提前1-6天; 而春季气温与降水的增加会导致返青期发生明显的非线性变化, 这种非线性效应无法基于线性模型模拟出来。结果说明生存分析模型既能用于分析不同尺度下植物物候与气候的关系, 也能用于模型预测, 尤其适合探讨大幅度气候变化对物候的非线性影响。
周彤, 曹入尹, 王少鹏, 陈晋, 唐艳鸿. 中国草地和欧洲木本植物返青期对气温和降水变化的响应: 基于生存分析的研究. 植物生态学报, 2018, 42(5): 526-538. DOI: 10.17521/cjpe.2017.0305
ZHOU Tong, CAO Ru-Yin, WANG Shao-Peng, CHEN Jin, TANG Yan-Hong. Responses of green-up dates of grasslands in China and woody plants in Europe to air temperature and precipitation: Empirical evidences based on survival analysis. Chinese Journal of Plant Ecology, 2018, 42(5): 526-538. DOI: 10.17521/cjpe.2017.0305
属 Genus | 物种 Species | 植株数 Plant No. |
---|---|---|
七叶树属 Aesculus | 欧洲七叶树 Aesculus hippocastanum | 31 |
桤木属 Alnus | Alnus glutinosa | 4 |
桦木属 Betula | 垂枝桦 Betula pendula | 19 |
山毛榉属 Fagus | Fagus sylvatica | 16 |
栎属 Quercus | 夏栎 Quercus robur | 16 |
梣属 Fraxinus | 欧梣 Fraxinus excelsior | 9 |
茶藨子属 Ribes | Ribes grossularia | 12 |
表1 欧洲研究区木本植物物种
Table 1 List of woody plant species in European database
属 Genus | 物种 Species | 植株数 Plant No. |
---|---|---|
七叶树属 Aesculus | 欧洲七叶树 Aesculus hippocastanum | 31 |
桤木属 Alnus | Alnus glutinosa | 4 |
桦木属 Betula | 垂枝桦 Betula pendula | 19 |
山毛榉属 Fagus | Fagus sylvatica | 16 |
栎属 Quercus | 夏栎 Quercus robur | 16 |
梣属 Fraxinus | 欧梣 Fraxinus excelsior | 9 |
茶藨子属 Ribes | Ribes grossularia | 12 |
图1 中国与欧洲的研究点分布。 A图中三角形表示位于青藏高原的气象站点, 圆点表示位于内蒙古的气象站点。B图中圆点表示欧洲地区木本植物物候观测点。
Fig. 1 Spatial distribution of study sites in China (A) and Europe (B). The triangles/dots in (A) are the distributions of meteorological stations in Qinghai-Xizang Plateau and in Nei Mongol, respectively. The points in (B) indicate the locations of phenological observation sites for woody plants in Europe.
图2 春季降水与气温对内蒙古草原、青藏高原草甸和欧洲地区木本植物返青期的影响。每一方框的上下横线分别表示样本统计量的四分位值即75%和25%的位点, 框内中央横线是中位数, 上下端短横线分别是统计量的90%和10%的位点, 圆点表示统计分布的特异最大或最小值。
Fig. 2 The changes in model coefficients of green-up date with spring precipitation and air temperature for the grasslands in Nei Mongol, meadows in Qinghai-Xizang Plateau, and woody plants in Europe. In these boxplots, the top and bottom values of the bars indicate the 25th and 75th percentiles, respectively; the black line within the box indicates the median; whiskers below and above the box indicate the 10th and 90th percentiles; and points indicate outliers. Letters on top of the whiskers are the results of an analysis of variance: different letters indicate statistically significant difference between the mean values (p < 0.01, t-test).
图3 内蒙古草原、青藏高原草甸和欧洲地区木本植物的Holdridge干燥度指数(HAI)的分布统计特征。统计变量的说明参见图2。
Fig. 3 Boxplots of the Holdridge aridity index (HAI) for the grasslands of Nei Mongol, meadows in Qinghai-Xizang Plateau, and woody plants in Europe. See Fig. 2 for explanations of the symbols.
图4 内蒙古草原、青藏高原草甸和欧洲地区木本植物降水与气温对返青期的影响强度与Holdridge干燥度指数(HAI)的关系。
Fig. 4 The changes in the model coefficient of precipitation and temperature with the Holdridge aridity index (HAI) for the grasslands of Nei Mongol,meadows in Qinghai-Xizang Plateau, and woody plants in Europe.
图5 在春季气温上升的3种情境下(+1、+2、+3 ℃)所研究生态系统返青期的变化。负值为返青期延后, 正值为返青期提前。A, 升温1、2和3 ℃情况下所有研究点返青期变化量的频数。B、C、D分别为升温1、2 和3 ℃时各个生态系统返青期变化量的频数。
Fig. 5 Predicted changes of green-up date under the three scenarios of elevated spring-temperature at 1, 2 and 3 °C for the target areas. Negative and positive dates indicate the delayed and advanced days of the green-up date, respectively. A, Predicted changes under all the three temperature scenarios. B, C, D, Predicted changes for each target area with the temperature increase of 1, 2 and 3 °C, respectively.
图6 在3种春季降水增加的情景下(+10、+20、+30 mm)所研究生态系统返青期的变化情况。负值表示返青期延后, 正值表示返青期提前。A, 增水10、20和30 mm情况下所有研究点返青期变化量的频数。B、C、D分别为增水10、20和30 mm时各个生态系统返青期变化量的频数。
Fig. 6 Predicted changes of green-up date under the three scenarios of elevated spring-precipitation at 10, 20 and 30 mm among each target area. Negative and positive dates indicate the delayed and advanced days of the green-up date, respectively. A, Predicted changes for all the three precipitation scenarios. B, C, D, Predicted changes for each target area with the precipitation increase at 10, 20 and 30 mm, respectively.
图7 利用线性回归模型计算的3个地区返青期气温敏感性。统计变量的说明参见图2。
Fig. 7 Boxplots of temperature sensitivity of green-up date from linear regression models for the three cases of the study. See Fig. 2 for explanations of the symbols.
升温情景 Warming scenarios (℃) | 内蒙古 Nei Mongol | 青藏高原 Qinghai-Xizang Plateau | 欧洲 Europe | ||||||
---|---|---|---|---|---|---|---|---|---|
生存分析法 Survival analysis | 线性回归法 Linear analysis | p | 生存分析法 Survival analysis | 线性回归法 Linear analysis | p | 生存分析法 Survival analysis | 线性回归法 Linear analysis | p | |
+ 1 | 0.92 | 0.13 | 0.04 | 3.56 | 1.87 | < 0.001 | 5.66 | 4.47 | < 0.001 |
+ 2 | 2.33 | 0.26 | 0.01 | 7.29 | 3.74 | < 0.001 | 10.5 | 8.94 | < 0.001 |
+ 3 | 4.17 | 0.39 | < 0.01 | 10.88 | 5.61 | < 0.001 | 14.06 | 13.41 | < 0.001 |
表2 升温1 、2 和3 ℃下内蒙古、青藏高原和欧洲地区返青期提前天数的平均值(从3月1日起每天比2009年升温1、2和3 ℃)
Table 2 The mean advanced days of green-up date for the two grasslands in Nei Mongol and the Qinghai-Xizang Plateau, and woody plants in Europe under three warming scenarios: elevated spring temperature at +1, +2 and +3 °C for each day as compared with the days from March 1st, 2009.
升温情景 Warming scenarios (℃) | 内蒙古 Nei Mongol | 青藏高原 Qinghai-Xizang Plateau | 欧洲 Europe | ||||||
---|---|---|---|---|---|---|---|---|---|
生存分析法 Survival analysis | 线性回归法 Linear analysis | p | 生存分析法 Survival analysis | 线性回归法 Linear analysis | p | 生存分析法 Survival analysis | 线性回归法 Linear analysis | p | |
+ 1 | 0.92 | 0.13 | 0.04 | 3.56 | 1.87 | < 0.001 | 5.66 | 4.47 | < 0.001 |
+ 2 | 2.33 | 0.26 | 0.01 | 7.29 | 3.74 | < 0.001 | 10.5 | 8.94 | < 0.001 |
+ 3 | 4.17 | 0.39 | < 0.01 | 10.88 | 5.61 | < 0.001 | 14.06 | 13.41 | < 0.001 |
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