Soil microbes play a key role in shaping terrestrial ecosystems. It is therefore essential to understand what drives their distribution. While multivariate analyses have been used to characterise microbial communities and drivers of their spatial patterns, few studies have focused on predicting the distribution of amplicon sequence variants (ASVs). Here, we evaluate the potential of species distribution models (SDMs) to predict the presence–absence and relative abundance distribution of bacteria, archaea, fungi, and protist ASVs in the western Swiss Alps. Advanced automated selection of abiotic covariates was used to circumvent the lack of knowledge on the ecology of each ASV. Presence–absence SDMs could be fitted for most ASVs, yielding better predictions than null models. Relative abundance SDMs performed less well, with low fit and predictive power overall, but displayed a good capacity to differentiate between sites with high and low relative abundance of the modelled ASV. SDMs for bacteria and archaea displayed better predictive power than for fungi and protists, suggesting a closer link of the former with the abiotic covariates used. Microorganism distributions were mostly related to edaphic covariates. In particular, pH was the most selected covariate across models. The study shows the potential of using SDM frameworks to predict the distribution of ASVs obtained from topsoil DNA. It also highlights the need for further development of precise edaphic mapping and scenario modelling to enhances prediction of microorganism distributions in the future.

中文翻译：

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我们能否准确预测土壤微生物存在的分布和相对丰度？


土壤微生物在塑造陆地生态系统中发挥着关键作用。因此，了解驱动其分布的因素非常重要。虽然多变量分析已被用来描述微生物群落及其空间模式的驱动因素，但很少有研究关注预测扩增子序列变体（ASV）的分布。在这里，我们评估了物种分布模型 (SDM) 预测瑞士阿尔卑斯山西部细菌、古细菌、真菌和原生生物 ASV 的存在与不存在及其相对丰度分布的潜力。使用先进的非生物协变量自动选择来避免对每种 ASV 生态学知识的缺乏。存在-不存在 SDM 可以适用于大多数 ASV，从而产生比零模型更好的预测。相对丰度 SDM 表现较差，总体拟合度和预测能力较低，但显示出区分建模 ASV 相对丰度高和低的位点的良好能力。细菌和古细菌的 SDM 比真菌和原生生物的 SDM 显示出更好的预测能力，这表明前者与所使用的非生物协变量之间的联系更紧密。微生物分布主要与土壤协变量有关。特别是，pH 值是模型中选择最多的协变量。该研究显示了使用 SDM 框架来预测从表土 DNA 中获得的 ASV 分布的潜力。它还强调需要进一步开发精确的土壤测绘和场景建模，以增强对未来微生物分布的预测。