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作 者:耿浩林[1,2] 王玉辉[1] 王风玉[1] 贾丙瑞[1]
机构地区:[1]中国科学院植物研究所植被与环境变化国家重点实验室,北京100093 [2]中国科学院研究生院,北京100039
出 处:《生态学报》2008年第10期4629-4634,共6页Acta Ecologica Sinica
基 金:国家重点基础研究发展计划资助项目(2007CB106806);国家自然科学基金资助项目(No.40671183; 40625015)~~
摘 要:利用内蒙古羊草草原围栏样地连续两年的地上、地下生物量数据和当地同时期的降水、气温资料,分析了退化羊草草原,在恢复过程中植被根冠比动态及与水热因子之间的关系。研究结果表明:(1)羊草草原植被地上、地下生物量季节变化均呈单峰型曲线,峰值出现在8月。(2)羊草草原植被根冠比具有明显的季节变化,生长季初和生长季末根冠比值较大,最低值出现在地上生物量最高的8月中下旬。(3)羊草草原植被月根冠比与上上月月降水量相关关系极显著,与上月平均气温相关关系显著;以根冠比为因变量,上上月月降水量、上月平均气温为自变量可分别建立线性回归方程。该方程可以较好地模拟羊草草原植被生长季内月根冠比的动态变化,这样在草地恢复过程中,可由上月的水热因子来指导下月的草地管理,并为更准确地估算草原生态系统生产力及碳储存动态提供重要参数。We conducted a field study to examine the relationships between the root-shoot ratio dynamics and precipitation and temperature for the typical Leymus chinensis steppe recovering from grazing in Inner Mongolia. A former pasture land that had been fenced off for two years was selected for the study; and aboveground and belowground biomass were collected from the study site along with local precipitation and temperature observations during the study period to be used in the analysis. Our results indicated that : ( 1 ) The seasonal change of both aboveground and belowground biomass in Leymus ehinensis demonstrated a pattern with a single peak occurring in August. (2) the root-shoot ratio showed seasonal variation, with relatively high values at the beginning of and the end of the growing season. The minimum ratio occurred in late August when the aboveground biomass reached its maximum. (3) the monthly root-shoot ratio was significantly correlated (p 〈 0. 05 ) with the monthly precipitation two months previous, and with the monthly mean temperature of the previous month.A regression model was built with the root-shoot ratio as the independent variables. This regression could be used to model steppe during the growing season. Therefore, precipitation and dependent variable and precipitation and temperature as the monthly root-shoot ratio dynamics of Leymus chinensis temperature information from current and previous months can be used to make predictions for the next month. These predictions could be used to plan vegetation management and provide more accurate estimates for steppe ecosystem productivity and carbon stock dynamics
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