Browsing by Author "Ramond, Jean-Baptiste"

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    Contribution of soil bacteria to the atmosphere across biomes
    (2023) Archer, Stephen D. J.; Lee, Kevin C.; Caruso, Tancredi; Alcami, Antonio; Araya, Jonathan G.; Cary, S. Craig; Cowan, Don A.; Etchebehere, Claudia; Gantsetseg, Batdelger; Gomez-Silva, Benito; Hartery, Sean; Hogg, Ian D.; Kansour, Mayada K.; Lawrence, Timothy; Lee, Charles K.; Lee, Patrick K. H.; Leopold, Matthias; Leung, Marcus H. Y.; Maki, Teruya; Mckay, Christopher P.; Al Mailem, Dina M.; Ramond, Jean-Baptiste; Rastrojo, Alberto; Santl-Temkiv, Tina; Sun, Henry J.; Tong, Xinzhao; Vandenbrink, Bryan; Warren-Rhodes, Kimberley A.; Pointing, Stephen B.
    The dispersion of microorganisms through the atmosphere is a continual and essential process that underpins biogeography and ecosystem development and function. Despite the ubiquity of atmospheric microorganisms globally, specific knowledge of the determinants of atmospheric microbial diversity at any given location remains unresolved. Here we describe bacterial diversity in the atmospheric boundary layer and underlying soil at twelve globally distributed locations encompassing all major biomes, and characterise the contribution of local and distant soils to the observed atmospheric community. Across biomes the diversity of bacteria in the atmosphere was negatively correlated with mean annual precipitation but positively correlated to mean annual temperature. We identified distinct non-randomly assembled atmosphere and soil communities from each location, and some broad trends persisted across biomes including the enrichment of desiccation and UV tolerant taxa in the atmospheric community. Source tracking revealed that local soils were more influential than distant soil sources in determining observed diversity in the atmosphere, with more emissive semi-arid and arid biomes contributing most to signatures from distant soil. Our findings highlight complexities in the atmospheric microbiota that are relevant to understanding regional and global ecosystem connectivity.
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    Modelling soil prokaryotic traits across environments with the trait sequence database ampliconTraits and the R package MicEnvMod
    (2024) Donhauser, Jonathan; Domenech-Pascual, Anna; Han, Xingguo; Jordaan, Karen; Ramond, Jean-Baptiste; Frossard, Aline; Romani, Anna M.; Prieme, Anders
    We present a comprehensive, customizable workflow for inferring prokaryotic phenotypic traits from marker gene sequences and modelling the relationships between these traits and environmental factors, thus overcoming the limited ecological interpretability of marker gene sequencing data. We created the trait sequence database ampliconTraits, constructed by cross-mapping species from a phenotypic trait database to the SILVA sequence database and formatted to enable seamless classification of environmental sequences using the SINAPS algorithm. The R package MicEnvMod enables modelling of trait - environment relationships, combining the strengths of different model types and integrating an approach to evaluate the models' predictive performance in a single framework. Traits could be accurately predicted even for sequences with low sequence identity (80 %) with the reference sequences, indicating that our approach is suitable to classify a wide range of environmental sequences. Validating our approach in a large trans-continental soil dataset, we showed that trait distributions were robust to classification settings such as the bootstrap cutoff for classification and the number of discrete intervals for continuous traits. Using functions from MicEnvMod, we revealed precipitation seasonality and land cover as the most important predictors of genome size. We found Pearson correlation coefficients between observed and predicted values up to 0.70 using repeated split sampling cross validation, corroborating the predictive ability of our models beyond the training data. Predicting genome size across the Iberian Peninsula, we found the largest genomes in the northern part. Potential limitations of our trait inference approach include dependence on the phylogenetic conservation of traits and limited database coverage of environmental prokaryotes. Overall, our approach enables robust inference of ecologically interpretable traits combined with environmental modelling allowing to harness traits as bioindicators of soil ecosystem functioning.