Chin J Plan Ecolo ›› 2018, Vol. 42 ›› Issue (5): 526-538.doi: 10.17521/cjpe.2017.0305

• Research Articles • Previous Articles     Next Articles

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

ZHOU Tong1,CAO Ru-Yin2,WANG Shao-Peng1,CHEN Jin3,TANG Yan-Hong1,*()   

  1. 1 College of Urban and Environmental Sciences, Peking University, Beijing 100871, China
    2 School of Resources and Environment, University of Electronic Science and Technology of China, Chengdu 611731, China
    3 State Key Laboratory of Earth Surface Processes and Resource Ecology, Institute of Remote Sensing Science and Engineering, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China
  • Received:2017-11-21 Revised:2018-02-11 Online:2018-07-20 Published:2018-05-20
  • Contact: Yan-Hong TANG E-mail:tangyh@pku.edu.cn
  • Supported by:
    Supported by the National Natural Science Foundation of China.(41601381)

Abstract:

Aims Linear models have been widely used to examine the impacts of climatic factors on plant phenology, although the relationship between phenology and climate could be nonlinear. Based on survival analysis, robust nonlinear models were empirically developed to examine the phenological changes in relation to air temperature and precipitation for the grasslands in China and individual woody plants in Europe.

Methods Three datasets were used in our survival analysis: two datasets of the remotely-sensed vegetation phenology for grasslands in Nei Mongol grasslands and meadows in Qinghai-Xizang Plateau, and a dataset of the phenological observations of individual woody plants in Europe. Monte Carlo simulations were performed to estimate model parameters in our survival analysis.

Important findings The survival analysis appeared to be a powerful tool in modeling the nonlinear changes in green-up date (GUD) to the climatic factors. The analyses showed that both spring temperature and precipitation are significantly correlated with the GUD in the semi-arid grasslands in Nei Mongol. For Qinghai-Xizang Plateau and Europe, the spring temperature seemed highly correlated with GUD, while the correlation was weak with the higher Holdridge aridity index. The survival model predicted that the GUD in the three regions would be advanced by 1-6 days with an increase in temperature of 1 °C. A combined increase in spring temperature and precipitation would lead to nonlinear responses, suggesting the need for developing nonlinear models. Our empirical exercise in this study demonstrated that the survival analysis could offer an alternative tool for predicting plant phenology under the changing climate.

Key words: climate change, grassland, phenology, survival analysis, Qinghai-Xizang Plateau

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

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."

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)."

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."

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."

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."

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."

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."

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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