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为了揭示植物物候空间格局形成的生态机制和植物物候对气候变化响应的空间敏感性,我们利用中国温带46个站点1986~2005年的榆树展叶始期和落叶末期观测数据,通过建立基于气温的空间物候模型,模拟多年平均和逐年榆树生长季节开始和结束日期的空间格局,并对模型进行了广泛的空间外推检验.研究结果表明:多年平均及逐年2~4月和9~11月平均气温的空间格局控制着多年平均及逐年榆树生长季节开始和结束日期的空间格局.各地多年平均榆树生长季节开始日期与2~4月气温的空间序列呈显著负相关,多年平均气温-物候空间模型对榆树生长季节开始日期的方差解释量为90%(p<0.001),模拟的均方根误差为4.7 d;而生长季节结束日期与9~11月气温的空间序列呈显著正相关,模型对榆树生长季节结束日期的方差解释量为79%(p<0.001),均方根误差为6 d.同样,各地逐年榆树生长季节开始日期与2~4月平均气温的空间序列均呈显著负相关,逐年气温-物候空间模型对榆树生长季节开始日期的方差解释量介于72%~87%之间(p<0.001);而生长季节结束日期与9~11月平均气温的空间序列均呈显著正相关,模型对榆树生长季节结束日期的方差解释量介于48%~76%之间(p<0.001).总体来看,逐年模型对全部建模站点生长季节开始和结束日期模拟的均方根误差分别为7.3和9 d.基于多年平均和逐年模型的生长季节开始和结束日期的空间预测精度与模型的空间模拟精度相近,说明这些模型具有较强的空间外推能力.进一步分析显示,榆树生长季节开始日期对气温的空间响应速率在区域平均2~4月气温较高的暖年大于区域平均2~4月气温较低的冷年.这一发现表明,冬、春季的气候变暖可以提高榆树生长季节开始日期对气温空间响应的敏感性.
In order to reveal the ecological mechanism of the spatial pattern of plant phenology and the spatial sensitivity of plant phenology to the response to climate change, we used the observation data of the beginning and late deciduous stages of Ulmus pumila between 1986 and 2005 in 46 sites in the temperate zone in China. Phenology model, simulated multi-year average and year-by-year elm growth season start and end dates of the spatial pattern, and the model of a wide range of spatial extrapolation test results show that: the average and annual 2 ~ April and September ~ November average temperature The spatial pattern of multi-year average and year-by-year Elm growth season control the spatial pattern of the beginning and end dates of Elm.The long-term average elm tree growth seasons began to show a significant negative correlation with the spatial variability of temperature from February to April. The mean annual mean temperature The explained variance of the elm growing season start date was 90% (p <0.001), and the simulated root mean square error was 4.7 days. However, the ending date of the growing season was positively correlated with the spatial sequence of temperature from September to November. The variance explained at the end of the growing season was 79% (p <0.001), with a root mean square error of 6 days. Similarly, year-by-year elm growth seasons There was a significant negative correlation between the date and the spatial sequence of mean air temperature from February to April. The annual variance of temperature-phenological spatial model was between 72% -87% (p <0.001), while the growth There was a significant positive correlation between the end of the season and the spatial sequence of mean temperature from September to November, and the variance explained by the model for the end of the growing season was between 48% and 76% (p <0.001) .In general, The root mean square error (RMSE) of the model for simulating the start and end dates of the growing season was 7.3 and 9 days respectively.The spatial prediction accuracy of the start and end dates of the growing season based on the multi-year average and year-by-year models was similar to that of the model, Indicating that these models have strong spatial extrapolation ability.Further analysis showed that the spatial response rate of temperature on the start date of Elm growing season is higher than that of the regional average from February to April This finding suggests that warming in winter and spring can increase the sensitivity of the elm tree growth season to the spatial temperature response.