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 scalesampling numbersoil respirationspatial heterogeneitymixed 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（R_s）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（W_s）、土壤温度（T₁₀）、凋落物量（L_w）、凋落物含水量（L_m）、土壤全碳（C）、全氮（N）和全碳/全氮（C/N）对土壤呼吸速率（R_s）空间变异的影响.结果表明3个取样间隔下，R_s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，R_s与W_s、L_w、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，R_s与T₁₀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，R_s与其他影响因子的相关性都不显著.随着取样尺度的减小，R_s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R_s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R_s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R_s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 (R_s) and its environment and to evaluate the effect of the sampling scale and number on the accuracy of R_s 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 (W_s), soil temperature (T₁₀), litter mass (L_w), litter moisture content (L_m), soil total carbon (C), total nitrogen (N), and ratio of C/N and sulfur (S) on the R_s heterogeneity at 4, 2, and 1 m sampling scales. The results show no significant differences between the average R_s values for the three sampling scales, but the degree of variation of R_s, 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 R_s and W_s, L_w, C, and C/N are highly significant (P<0.01) and significant for N (P <0.05). At the 2 m sampling interval, R_s 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 R_s and all other factors were not observed. With the decrease of the sampling interval scale, the spatial autocorrelation of R_s 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 R_s decreases with decreasing sampling scale. The analysis of the effect of the sampling number at different sampling scales on the accuracy of R_s shows that the error of R_s 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 R_s 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 R_s.

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