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首页> 外文期刊>Soil Biology & Biochemistry >Stand-scale spatial patterns of soil microbial biomass in natural cold-temperate beech forests along an elevation gradient
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Stand-scale spatial patterns of soil microbial biomass in natural cold-temperate beech forests along an elevation gradient

机译:天然高寒山毛榉林土壤微生物生物量的海拔尺度空间分布

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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 1500 m a.s.l. (44.9 gC m-2), the lowest was recorded at the site 700 m a.s.l. (18.9 gC m-2); the average SMB density at the 550 m site (36.5 gC m-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 10-18 m in addition to the small-scale spatial dependence noted at <3.5 m at the 550 m site. Correlation analysis with an emphasis on spatial dependency showed SMB to be significantly correlated with bulk density at the 550 and 1500 m sites, dissolved organic carbon (DOC) at the 700 and 1500 m sites, and nitrogen (N) at the 550 and 700 m 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.
机译:这项研究的重点是日本冷温带森林生态系统中典型高潮山毛榉(Fagus crenata)在土壤尺度上土壤微生物生物量(SMB)的空间异质性。沿高度梯度选择了三个山毛榉为主的地点,并使用网格采样在每个地点收集土壤样本。在1500 m a.s.l处观测到最高的平均SMB密度。 (44.9 gC m-2),最低记录发生在700 m a.s.l.处。 (18.9 gC m-2); 550 m站点(36.5 gC m-2)的平均SMB密度接近所有三个站点的总体中位数。专门用于考虑空间自相关的地统计学,然后用于分析收集的数据。除了在550 m站点上<3.5 m处注意到的小规模空间依赖性外,所有站点通常都在10-18 m的滞后距离上显示标准规模的空间自相关。着重于空间依赖性的相关分析表明,SMB与550和1500 m处的容重,700和1500 m处的溶解有机碳(DOC)以及550和700 m处的氮(N)显着相关。网站。但是,在每个站点上,没有土壤参数显示出与SMB显着相关,并且在不同站点上,一些变量与SMB也有不同的相关性(负或正)。这表明控制SMB空间分布的因素非常复杂,并且对局部原位条件有响应。 SMB回归模型是从普通最小二乘法(OLS)和广义最小二乘(GLS)模型生成的。只有正确拟合了交叉变异函数后,GLS的性能才优于OLS。但是,最好使用地统计学,因为这些技术考虑了样本的空间非平稳性。此外,还为每个站点提供了给定的最小可检测差异(MDD)的采样号,以用于将来的SMB监视。

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