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不同取样尺度和数量下针阔混交林土壤呼吸的空间异质性
摘要点击 174  全文点击 61  投稿时间:2018-06-05  修订日期:2018-07-18
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中文关键词  取样尺度  取样数量  土壤呼吸  空间异质性  针阔叶混交林
英文关键词  sampling scale  sampling number  soil respiration  spatial heterogeneity  mixed broadleaf-conifer forest
作者单位E-mail
严俊霞 山西大学黄土高原研究所, 太原 030006 yjx422@sxu.edu.cn 
孙琦 山西大学黄土高原研究所, 太原 030006  
李君剑 山西大学黄土高原研究所, 太原 030006  
李洪建 山西大学黄土高原研究所, 太原 030006  
中文摘要
      区域土壤呼吸通量估算大多用样地尺度的测定结果进行外推,因此对样地尺度土壤呼吸(Rs)及其影响因子的空间关系和取样尺度、取样数量对测定结果的准确性进行评价非常重要.以山西省庞泉沟自然保护区针阔混交林作为研究样地,运用传统统计分析与地统计分析相结合的方法,分析了4、2、1 m取样尺度下土壤水分(Ws)、土壤温度(T10)、凋落物量(Lw)、凋落物含水量(Lm)、土壤全碳(C)、全氮(N)和全碳/全氮(C/N)对土壤呼吸速率(Rs)空间变异的影响.结果表明3个取样间隔下,Rs的均值没有显著差异,但其变异程度随着取样尺度的增大而增加,变异系数在16%~22%之间.在4 m取样间隔下,RsWsLw、C、C/N呈极显著正相关(P<0.01),与N呈显著正相关(P<0.05);在2 m取样间隔下,RsT10呈极显著负相关(P<0.01),与其他因子相关不显著;在1 m取样间隔下,Rs与其他影响因子的相关性都不显著.随着取样尺度的减小,Rs的空间自相关性逐渐减弱,由高度自相关变为弱相关,表明随着采样距离的减小,结构因素对Rs的作用减弱,随机因素的作用逐渐增大.同一置信水平下相同取样数量随着取样尺度的减小,估计误差降低.95%置信水平下,2 m和1 m取样尺度时9个样本产生的Rs误差在±12%左右,而4 m尺度的误差为±16%;90%置信水平下,2 m和1 m取样尺度时9个样本产生的误差在±10%以内,4 m尺度则为±13%.研究结果可以为样地尺度进行Rs季节测定样点的科学布设提供依据.
英文摘要
      By upscaling the observed results at the plot scale, the carbon efflux from soils in a region can be estimated. Therefore, it is very important to investigate the spatial relations of soil respiration (Rs) and its environment and to evaluate the effect of the sampling scale and number on the accuracy of Rs measurement at the spatial scale. Based on field observation data for a mixed broadleaf-conifer forest in the Pangquangou Nature Reserve of the Shanxi Province, two analysis methods, that is, traditional statistics and geostatistics, were used to analyze the influence of the soil water content (Ws), soil temperature (T10), litter mass (Lw), litter moisture content (Lm), soil total carbon (C), total nitrogen (N), and ratio of C/N and sulfur (S) on the Rs heterogeneity at 4, 2, and 1 m sampling scales. The results show no significant differences between the average Rs values for the three sampling scales, but the degree of variation of Rs, which was evaluated based on the coefficient of determination, increases with increasing sampling scales, ranging from 16% to 22%. At the 4 m sampling interval, the correlations between Rs and Ws, Lw, C, and C/N are highly significant (P<0.01) and significant for N (P <0.05). At the 2 m sampling interval, Rs shows a highly negative significant correlation with T10 (P<0.01) and insignificant correlations with the other factors. At the 1 m sampling interval, significant relations between Rs and all other factors were not observed. With the decrease of the sampling interval scale, the spatial autocorrelation of Rs decreases gradually, ranging from high to weak autocorrelations.This indicates that the role the structural factors play decreases with the decrease of the sampling scale, but that of the random factors increases gradually. At the same confidence level for a certain sampling number, the estimated error in Rs decreases with decreasing sampling scale. The analysis of the effect of the sampling number at different sampling scales on the accuracy of Rs shows that the error of Rs at both the 2 and 1 m sampling scales is approximately±12% at the 95% confidence interval and±16% at the 4 m sampling scale. At the 90% confidence interval, the error of Rs at both the 2 and 1 m sampling scales is less than ±10%; at the 4 m scale, it is ±13%. Our results provide insights into how to arrange the sampling sites at the plot scale to measure the seasonal Rs.

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