Stand-scale spatial patterns of soil microbial biomass in natural cold-temperate beech forests along an elevation gradient
Stand-scale spatial patterns of soil microbial biomass in natural cold-temperate beech forests along an elevation gradient
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DOI：
10.1016/j.soilbio.2009.03.028
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发表时间：
2009-07
影响因子：
9.7
通讯作者：
Xin Zhao;Quan Wang;Y. Kakubari
中科院分区：
文献类型：
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作者：
Xin Zhao;Quan Wang;Y. Kakubari
本研究的重点是在土壤微生物量（SMB）的典型顶极山毛榉（毛毛榉）在日本冷温带山区森林生态系统的林分尺度的空间异质性。三个山毛榉为主的网站选择沿着海拔梯度和网格采样，在每个站点收集土壤样品。在海拔1500米处观测到最高的SMB平均密度。（44.9gCm−2），最低记录在海拔700米处。（18.9gCm−2）; 550米站点的平均SMB密度（36.5gCm−2）接近所有三个站点的总体中位数。地质统计学，这是专门设计考虑到空间自相关，然后被用来分析收集的数据。除了在550 m站点的<3.5 m处观察到的小尺度空间依赖性之外，所有站点在10- 18 m的滞后距离处通常都表现出林分尺度空间自相关性。强调空间依赖性的相关性分析表明，SMB是显着相关的体积密度在550和1500米的网站，溶解有机碳（DOC）在700和1500米的网站，和氮（N）在550和700米的网站。然而，没有土壤参数表现出显着的相关性与SMB在每个网站，和一些变量也不同（负或正）相关SMB在不同的网站。这表明，控制SMB的空间分布的因素是非常复杂的，并响应于当地的原位条件。SMB回归模型由普通最小二乘（OLS）和广义最小二乘（GLS）模型生成。GLS的性能只有上级OLS时，交叉变异函数准确拟合。然而，地质统计学是更可取的，因为这些技术考虑到样本的空间非平稳性。此外，为每个研究中心提供了给定最小可检出差异（MDD）的采样数量，以供未来SMB监测。
This study focuses on spatial heterogeneity in the soil microbial biomass (SMB) of typical climax beech (Fagus crenata) at the stand scale in forest ecosystems of the cold-temperate mountain zones of Japan. Three beech-dominated sites were selected along an altitudinal gradient and grid sampling was used to collect soil samples at each site. The highest average SMB density was observed at the site 1500m a.s.l. (44.9gCm−2), the lowest was recorded at the site 700m a.s.l. (18.9gCm−2); the average SMB density at the 550m site (36.5gCm−2) was close to the overall median of all three sites. Geostatistics, which is specifically designed to take spatial autocorrelation into account, was then used to analyze the data collected. All sites generally exhibited stand-scale spatial autocorrelation at a lag distance of 10–18m in addition to the small-scale spatial dependence noted at <3.5m at the 550m site. Correlation analysis with an emphasis on spatial dependency showed SMB to be significantly correlated with bulk density at the 550 and 1500m sites, dissolved organic carbon (DOC) at the 700 and 1500m sites, and nitrogen (N) at the 550 and 700m sites. However, no soil parameter showed a significant correlation with SMB at every site, and some variables were also differently correlated (negative or positive) with SMB at different sites. This suggests that the factors controlling the spatial distribution of SMB are very complex and responsive to local in situ conditions. SMB regression models were generated from both the ordinary least-squares (OLS) and generalized least-squares (GLS) models. GLS performance was only superior to OLS when cross-variograms were accurately fitted. Geostatistics is preferable, however, since these techniques take the spatial non-stationarity of samples into account. In addition, the sampling numbers for given minimum detectable differences (MDDs) are provided for each site for future SMB monitoring.