Browsing by Author "Donhauser, Jonathan"

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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.
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    Soil organic matter properties drive microbial enzyme activities and greenhouse gas fluxes along an elevational gradient
    (2024) Han, Xingguo; Domenech-Pascual, Anna; Casas-Ruiz, Joan Pere; Donhauser, Jonathan; Jordaan, Karen; Ramond, Jean-Baptiste; Prieme, Anders; Romani, Anna M.; Frossard, Aline
    Mountain ecosystems, contributing substantially to the global carbon (C) and nitrogen (N) biogeochemical cycles, are heavily impacted by global changes. Although soil respiration and microbial activities have been extensively studied at different elevation, little is known on the relationships between environmental drivers, microbial functions, and greenhouse gas fluxes (GHGs; carbon dioxide [CO2], methane [CH4] and nitrous oxide [N2O]) in soils of different elevation. Here, we measured how in situ GHG fluxes were linked to soil properties, soil organic matter (SOM) quantity and composition (the proportion of humic-like vs. protein-like OM), microbial biomass, enzyme activities and functional gene abundances in natural soils spanning an elevational gradient of similar to 2400 m in Switzerland. Soil CO2 fluxes did not significantly vary from low (lowland zone) to higher (montane and subalpine zones) elevation forests, but decreased significantly (P<0.001) from the treeline to the mountain summit. Multivariate analyses revealed that CO2 fluxes were controlled by C-acquiring enzymatic activities which were mainly controlled by air mean annual temperature (MAT) and SOM quantity and composition. CH4 fluxes were characterized by uptake of atmospheric CH4, but no trend was observed along the elevation. N2O fluxes were also dominated by uptake of atmospheric N2O. The flux rates remained stable with increasing elevation below the treeline, but decreased significantly (P<0.001) from the treeline to the summit. N2O fluxes were driven by specific nitrifying and denitrifying microbial genes (ammonia-oxidizing amoA and N2O-producing norB), which were again controlled by SOM quantity and composition. Our study indicates the treeline as a demarcation point changing the patterns of CO2 and N2O fluxes along the elevation, highlighting the importance of SOM quantity and composition in controlling microbial enzyme activities and GHG fluxes.