Department of Plant and Environmental Science, University of Copenhagen, Frederiksberg, Denmark.
Biome Makers Inc., 95605, West Sacramento, CA, USA.
Commun Biol. 2022 Mar 18;5(1):241. doi: 10.1038/s42003-022-03202-5.
The microbial biodiversity found in different vitivinicultural regions is an important determinant of wine terroir. It should be studied and preserved, although it may, in the future, be subjected to manipulation by precision agriculture and oenology. Here, we conducted a global survey of vineyards' soil microbial communities. We analysed soil samples from 200 vineyards on four continents to establish the basis for the development of a vineyard soil microbiome's map, representing microbial biogeographical patterns on a global scale. This study describes vineyard microbial communities worldwide and establishes links between vineyard locations and microbial biodiversity on different scales: between continents, countries, and between different regions within the same country. Climate data correlates with fungal alpha diversity but not with prokaryotes alpha diversity, while spatial distance, on a global and national scale, is the main variable explaining beta-diversity in fungal and prokaryotes communities. Proteobacteria, Actinobacteria and Acidobacteria phyla, and Archaea genus Nitrososphaera dominate prokaryotic communities in soil samples while the overall fungal community is dominated by the genera Solicoccozyma, Mortierella and Alternaria. Finally, we used microbiome data to develop a predictive model based on random forest analyses to discriminate between microbial patterns and to predict the geographical source of the samples with reasonable precision.
不同葡萄种植区的微生物多样性是葡萄酒风土的重要决定因素。尽管未来精准农业和酿酒学可能会对其进行干预,但仍应对其进行研究和保护。在这里,我们对葡萄园土壤微生物群落进行了全球调查。我们分析了来自四大洲 200 个葡萄园的土壤样本,为开发葡萄园土壤微生物组图谱奠定了基础,该图谱代表了全球范围内的微生物生物地理格局。本研究描述了世界各地的葡萄园微生物群落,并在不同尺度上建立了葡萄园位置与微生物多样性之间的联系:在各大洲、各国以及同一国家的不同地区之间。气候数据与真菌α多样性相关,但与原核生物α多样性无关,而在全球和国家尺度上,空间距离是解释真菌和原核生物群落β多样性的主要变量。土壤样本中,变形菌门、放线菌门和酸杆菌门以及古菌属硝化螺旋菌主导着原核生物群落,而整个真菌群落则主要由 Sollicoccozyma、Mortierella 和 Alternaria 属主导。最后,我们使用微生物组数据来开发基于随机森林分析的预测模型,以区分微生物模式并预测样本的地理来源,具有合理的精度。