Unravelling microbial biogeography: Elevational patterns and causes of diversity in unicellular organisms from all three domains of life (Archaea, Bacteria and Eukarya) - Fernández - Parra; Leonardo (2023)
RESULTS OBTAINED: Goal 1: Characterize and describe the microbial diversity of Chile at broad spatial scales Goal achieved: To describe a significant (and so far, unknown) fraction of Chilean microbial diversity we collected soil samples in major terrestrial climates of Chile covering extensive latitudinal and altitudinal gradients. Microbes present in soil samples were identified using approaches such as morphology, DNA barcoding and metabarcoding. Results: From metabarcoding we recovered 18,738 high-quality reads with 151 bp sequence length on average, 36,998 unique representative sequences and 7,258 microbial operational taxonomic units (OTUs). Rarefaction curves confirmed that we succeeded in recovering much of the microbial diversity present at each major climate (Fig. 1A). Arid and temperate climates exhibited the highest Shannon diversity indices (Fig. 1B). Temperate climates showed a higher percentage of unique OTUs than arid and polar climates (Fig. 1C). Hundreds of sequences could not be assigned to any known taxon and might represent new taxa to science (Fig. 1D). Phylogenetic analyses revealed that Chilean microbes are an evolutionary diverse group (Fig. 1E). Goal 2: Investigate microbial diversity patterns at broad spatial scales Goal achieved: First, we used species distribution modelling (SDM) based on a generalised linear model (GLM) to predict the spatial distribution of microbial diversity in Chile. We used a GLM because our tests revealed that this model outperformed the predictions made by other models (e.g., Random Forest). The prediction was conducted linking our georeferenced genetic diversity data to environmental data from WorldClim, SoilGrids, etc. The fit and relative likelihood of the selected model and its parameters (set of environmental variables) were determined using the Akaike information criterion (AIC). Second, we investigated patterns (areas) of microbial endemism in Chile using an endemicity analysis and our georeferenced diversity data. The recovered areas of endemism were ...