midDRIFTS-based partial least square regression analysis allows predicting microbial biomass, enzyme activities and 16S rRNA gene abundance in soils of temperate grasslands
midDRIFTS-based partial least square regression analysis allows predicting microbial biomass, enzyme activities and 16S rRNA gene abundance in soils of temperate grasslands
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基于 midDRIFTS 的偏最小二乘回归分析可以预测温带草原土壤中的微生物生物量、酶活性和 16S rRNA 基因丰度
DOI：
10.1016/j.soilbio.2012.09.030
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发表时间：
2013
影响因子：
9.7
通讯作者：
Cadisch
中科院分区：
文献类型：
--
作者：
Rasche;Marhan;Berner;Kandeler;Cadisch
本研究的目的是支持漫反射傅里叶变换中红外光谱（midDRIFTS）的偏最小二乘回归（PLSR）分析，以准确预测土壤微生物学特性在6个温带草原生态系统不同的土地利用强度;三个网站的低土地利用强度（低LUI）和三个施肥割草地（高LUI）的能力。此外，潜力midDRIFTS-PLSR分析对比草原生态系统的土壤之间的空间研究进行了评估。对304个样品进行了基于midDRIFTS-PLSR的土壤微生物生物量、β-d-葡萄糖苷酶、木糖苷酶和脲酶活性以及基于16 S rRNA基因定量的细菌丰度预测。基于残差预测偏差（RPD），两种LUI中基于midDRIFTS-PLSR的预测准确度对于所有土壤微生物学特性都是“可接受的”，特别是土壤微生物生物量（决定系数（R2）= 0.92; RPD = 3.55）、脲酶（0.91; 3.42）和β-d-葡萄糖苷酶（0.89; 3.01）。开发了16 S rRNA基因拷贝（0.88; 2.93）和木糖苷酶（0.84; 2.55）的“中等成功”准确度预测。空间midDRIFTS-PLSR为基础的预测草地生态系统之间的土壤微生物生物量是“可以接受的”，而其他研究的土壤微生物学特性显示只有“适度成功”的预测。midDRIFTS-PLSR的潜力，以预测一系列的土壤微生物学特性，包括分子数据与“可接受的”准确度在两个调查的草地生态系统与对比的土地利用强度得到证实。然而，普遍的“适度成功”的预测精度之间的草地生态系统可能是由于依赖于土地利用的具体数据集的校准和验证。对于成本和时间有效的midDRIFTS-PLSR为基础的方法的应用前景，土壤微生物学特性的预测考虑特别是，但还没有“可接受的”分子数据将大大推进在对比生态系统的空间和时间尺度上的土壤微生物群落的丰度和功能动态的理解。
The objective of this study was to underpin the capability of diffuse reflectance Fourier transform mid-infrared spectroscopy (midDRIFTS)-based partial least squares regression (PLSR) analyses to accurately predict soil microbiological properties across six temperate grassland ecosystems differing in their land-use intensity; three sites of low land-use intensity (low LUI) and three fertilized mown meadows (high LUI). In addition, the potential of midDRIFTS-PLSR analyses for spatial studies between soils of contrasting grassland ecosystems was evaluated. 304 samples were subjected to midDRIFTS-PLSR-based predictions of soil microbial biomass, activities of beta-d-glucosidase, xylosidase and urease, as well as bacterial abundance based on 16S rRNA gene quantification. Accuracies of midDRIFTS-PLSR-based predictions across both LUI were, on basis of the residual prediction deviation (RPD), ‘acceptable’ for all soil microbiological properties, in particular soil microbial biomass (coefficient of determination (R2) = 0.92; RPD = 3.55), urease (0.91; 3.42) and beta-d-glucosidase (0.89; 3.01). Predictions of ‘moderately successful’ accuracy were developed for 16S rRNA gene copies (0.88; 2.93) and xylosidase (0.84; 2.55). Spatial midDRIFTS-PLSR-based predictions between grassland ecosystems were only ‘acceptable’ for soil microbial biomass, while the other studied soil microbiological properties revealed only ‘moderately successful’ predictions. The potential of midDRIFTS-PLSR to predict a range of soil microbiological properties including molecular data with ‘acceptable’ accuracies across the two investigated grassland ecosystems with contrasting land-use intensities was substantiated. However, the prevailing ‘moderately successful’ prediction accuracies between grassland ecosystems were probably due to the dependence on land use-specific data sets for calibration and validation. For prospective application of cost- and time-efficient midDRIFTS-PLSR-based approaches, predictions of soil microbiological properties considering particularly, but not yet ‘acceptable’ molecular data will greatly advance the understanding on the abundance and functional dynamics of soil microbial communities across spatial and temporal scales of contrasting ecosystems.
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DOI：
--
发表时间：
2011
期刊：
影响因子：
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作者：
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影响因子：
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