Soil protist diversity in the Swiss western Alps is better predicted by topo‐climatic than by edaphic variables

• DOI: 10.1111/jbi.13755
• Publication Date: 2019-12-02T19:33:48Z
• Authors: Christophe V. W. ...; Olivier Broennimann; Aline Buri; Erika Yashiro; Eric Pinto‐Figueroa; David Singer; Quentin Blandenier; Edward A. D. Mitc...; Hélène Niculita‐H...; Antoine Guisan; Enrique Lara
• Keywords: Edaphic; Diversity index

Abstract Aim Trends in spatial patterns of diversity macroscopic organisms can be well predicted from correlative models, using topo‐climatic variables for plants and animals allowing inference over large scales. By contrast, soil microorganisms is generally considered as mostly driven by edaphic and, therefore, difficult to extrapolate on a scale based predictive models. Here, we compared the power versus predicting various protist groups at regional scale. Location Swiss western Alps. Taxa Full community nine clades belonging respectively three functional groups: parasites (Apicomplexa, Peronosporomycetes Phytomyxea), phagotrophs (Sarcomonadea, Tubulinea Spirotrichea) phototrophs (Chlorophyta, Trebouxiophyceae Diatomeae). Methods We extracted DNA 178 sites along wide range elevations with random‐stratified sampling design. defined Operational Taxonomic Units assemblages metabarcoding V4 region rRNA small subunit gene. assessed modelled (Shannon index) all above‐mentioned taxonomic (topography, slope southness, steepness average summer temperature) (soil temperature, relative humidity, pH, electroconductivity, phosphorus percentage, carbon/nitrogen, loss ignition shale percentage) Generalized Additive Models (GAM). Results The respective significance varied among and—to certain extent—functional while many explained significantly this was less case parasites. Topo‐climatic had better than variables, yet groups. Main conclusions (particularly temperature if consider their GAMs) were, average, predictors landscape variables. However, these differed considerably groups; such relationships may due direct and/or indirect (e.g. biotic) influences (like parasitic taxa, where low most likely absence information hosts’ distribution). Future prospects include models predict hotspots disease outbreaks.