The specific microbial biodiversity linked to a certain vineyard location has been reported to be a crucial aspect in the concept of wine-terroir together with edaphic, climatic and human factors. These biogeographical patterns referred as the microbial-terroir. In this work, we applied an HTS amplicon library approach to conduct a global survey of the soil microbial communities of vineyards. We analyzed 200 soil vineyards worldwide, in four continents trying to settle the basis for the development of a vineyard-soil-microbiome map to represent the microbial-wine-terroir on a global scale. We highlighted that this link exists at different scales; among continents, states, and between different wine-regions within the same country. Geography has a strong effect on microbial communities composition at a global scale, which is also maintained at a country scale. Also, for the very first time, we describe the microbial community of a new and northern winemaker regions such as Denmark, that

The specific microbial biodiversity linked to a certain vineyard location has been reported to be a crucial aspect in the concept of wine-terroir together with edaphic, climatic and human factors. These biogeographical patterns referred as the microbial-terroir.In this work, we applied an HTS amplicon library approach to conduct a global survey of the soil microbial communities of vineyards. We analyzed 200 soil vineyards worldwide, in four continents trying to settle the basis for the development of a vineyard-soil-microbiome map to represent the microbial-wine-terroir on a global scale.We highlighted that this link exists at different scales; among continents, states, and between different wine-regions within the same country. Geography has a strong effect on microbial communities composition at a global scale, which is also maintained at a country scale. Also, for the very first time, we describe the microbial community of a new and northern winemaker regions such as Denmark, that can be of great interest for viticulture adaptation in a context of climate change. Finally, we developed a predictive model, based on random forest analyses, to discriminate between microbial patterns to identify the geographical source of the samples with reasonable precision.