作 者 ：侯琳*,雷瑞德,张硕新,刘建军
期 刊 ：生态学报 2010年 30卷 19期 页码：5225~5236
Keywords:soil respiration, temporal and spatial variation, temperature, soil volumetric moisture, Pinus tabulaeformis forest,
摘 要 ：土壤呼吸是陆地生态系统碳循环的关键生态过程，土壤呼吸的时空变异及其影响因子已成为生态学研究的主要内容之一。采用红外线开路气室法和便携式微气象站，连续测定了秦岭火地塘林区天然次生油松林地不同部位土壤呼吸速率和不同土层深度土壤温度和土壤体积含水率，结果表明：（1）植物生长季，试验地上部与中部、中部与下部，土壤呼吸日均值间存在显著差异。植物休眠季，全坡面土壤呼吸日均值差异不显著。同一观测部位植物生长季与休眠季，土壤呼吸日均值差异显著。观测期内全样地土壤呼吸日均值为（38.64±6.43）g m-2d-1；（2）同一地形部位不同观测月中和不同地形部位同一观测时间，土壤呼吸月均值大多存在显著差异，植物生长季和休眠季，全样地土壤呼吸均值分别为（46.98±2.21）g m-2d-1和（35.94±101）g m-2d-1，全样地土壤呼吸月均值为（1.18±0.20）kgm-2月-1，休眠季土壤日均呼吸约为整个观测季的43.34%；（3）当土壤温度>9.0℃时，土壤温度与土壤呼吸速率间均存在显著的指数关系。回归模型的决定系数均大于0.87，均方差根不超过0.21，模型有效性系数不小于0.85，残差系数的绝对值不超过0.007。（4）植物生长季0-5 cm和5-10 cm土层及植物休眠季0-5 cm土层，土壤呼吸日累积值均值与相应土层深度土壤体积含水率均值间存在三次函数关系，回归模型的决定系数分别为0456，0.513和0.143；植物休眠季5-10 cm 土层，土壤呼吸日累积值均值与土壤体积含水率均值间存在幂函数关系，回归模型的决定系数为0.650。
Abstract:Soil respiration plays a key role in the carbon cycle of terrestrial ecosystems. Temporal and spatial variations in soil respiration and its sensitivity to environmental factors have attracted ecologists on a global scale. To accurately estimate the carbon balance of an ecosystem, understanding the temporal and spatial variations of soil respiration is crucial. China′s Qingling Mountains are a key ecological area the area′s forests have multiple functions. The Pinus tabulaeformis forest is a predominant forest type in the central Qingling Mountains. Little research on the temporal and spatial variations of soil respiration has been conducted there. Our goal was to observe soil respiration in forest environments over time to determine how CO2 efflux is correlated with location, soil temperature, and soil moisture. Soil respiration was measured at 12 plots by the open path chamber method in a natural secondary Pinus tabulaeformis forest on a southwest slope from early October, 2006 to the end of September, 2007 except from January to April in 2007 in the natural secondary Pinus tabulaeformis forest of the Huoditang forest zone.The results showed that: (1) during the growing period, significant differences in soil diurnal mean accumulated respiration values between the upper to the middle and the middle to the lower on the slope. No significant difference in soil diurnal mean accumulated respiration values occurred among positions during the dormant period. Soil diurnal mean accumulated respiration values differ significantly between the growing and dormant periods. From 09:00 to 19:00 in the growing period and from 10:00 to 20:00 in the dormant period, the soil respiration rate was more than those at other times. The diurnal mean soil accumulated respiration is (38.64±6.43) g m-2d-1 in the whole plot. (2) The significant differences of most soil monthly mean accumulated respiration values occurred at the same position on the slope in different months and at different places on the slope in the same month. The soil CO2 efflux was (46.98±221) gm-2d-1 in the growing period and (35.94±1.01) g m-2d-1 in the dormant period. The monthly mean soil respiration is (1.18±0.20) kgm-2month-1 in the whole plot. The mean CO2 efflux during the dormant period was 43.34% of the total throughout the entire observation period. (3) A remarkable relationship was shown between soil diurnal mean accumulated respiration and its temperature when the temperature was beyond 9.0℃. The modeling coefficient of determination was more than 0.87,root mean square error was not more than 0.21, modeling efficiency was no less than 085 and absolute residual mass was not more than 0.007. (4) At depths of 0-5 cm and 5-10 cm during the growing period, the cubic functions could be obtained between soil diurnal mean accumulated respiration and soil volumetric moisture, but the determination coefficient of modeling was 0.456 at the depth of 0-5 cm and 0.513 at the depth of 5-10cm.At the depths of 0-5 cm and 5-10 cm during the dormant period, the cubic and power functions could be obtained between soil diurnal mean accumulated respiration and soil volumetric moisture, respectively. The determination coefficient of modeling was 0.143 and 0.65, respectively.
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