Soil ecosystems consist of complex interactions between biological communities and physico-chemical variables, all of which contribute to the overall quality of soils. Despite this, changes in bacterial communities are ignored by most soil monitoring programs, which are crucial to ensure the sustainability of land management practices. We applied 16S rRNA gene sequencing to determine the bacterial community composition of over 3000 soil samples from 606 sites in New Zealand. Sites were classified as indigenous forests, exotic forest plantations, horticulture, or pastoral grasslands; soil physico-chemical variables related to soil quality were also collected. The composition of soil bacterial communities was then used to predict the land use and soil physico-chemical variables of each site. Soil bacterial community composition was strongly linked to land use, to the extent where it could correctly determine the type of land use with 85% accuracy. Despite the inherent variation introduced by sampling across ~ 1300 km distance gradient, the bacterial communities could also be used to differentiate sites grouped by key physico-chemical properties with up to 83% accuracy. Further, individual soil variables such as soil pH, nutrient concentrations and bulk density could be predicted; the correlations between predicted and true values ranged from weak (R2 value = 0.35) to strong (R2 value = 0.79). These predictions were accurate enough to allow bacterial communities to assign the correct soil quality scores with 50–95% accuracy. The inclusion of biological information when monitoring soil quality is crucial if we wish to gain a better, more accurate understanding of how land management impacts the soil ecosystem. We have shown that soil bacterial communities can provide biologically relevant insights on the impacts of land use on soil ecosystems. Furthermore, their ability to indicate changes in individual soil parameters shows that analysing bacterial DNA data can be used to screen soil quality.

中文翻译：

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利用土壤细菌群落预测物理化学变量和土壤质量。


土壤生态系统由生物群落和物理化学变量之间复杂的相互作用组成，所有这些都有助于土壤的整体质量。尽管如此，大多数土壤监测项目都忽视了细菌群落的变化，这对于确保土地管理实践的可持续性至关重要。我们应用 16S rRNA 基因测序来确定新西兰 606 个地点的 3000 多个土壤样本的细菌群落组成。地点被分为本土森林、外来森林种植园、园艺或田园草原；还收集了与土壤质量相关的土壤理化变量。然后利用土壤细菌群落的组成来预测每个地点的土地利用和土壤理化变量。土壤细菌群落组成与土地利用密切相关，能够以 85% 的准确度正确确定土地利用类型。尽管在约 1300 公里距离梯度上采样会带来固有的变化，但细菌群落也可用于区分按关键物理化学特性分组的位点，准确度高达 83%。此外，还可以预测土壤 pH 值、养分浓度和堆积密度等各个土壤变量；预测值和真实值之间的相关性范围从弱（R2 值 = 0.35）到强（R2 值 = 0.79）。这些预测足够准确，可以让细菌群落以 50-95% 的准确度分配正确的土壤质量评分。如果我们希望更好、更准确地了解土地管理如何影响土壤生态系统，那么在监测土壤质量时纳入生物信息至关重要。 我们已经证明，土壤细菌群落可以提供有关土地利用对土壤生态系统影响的生物学相关见解。此外，它们指示单个土壤参数变化的能力表明，分析细菌 DNA 数据可用于筛选土壤质量。