Soil protist diversity in the Swiss western Alps is better predicted by topo-climatic than by edaphic variables
AuthorSeppey, Christophe Victor W.; Broennimann, Olivier; Buri, Aline; Yashiro, Erika; Pinto-Figueroa, Eric; Singer, David; Blandenier, Quentin; Mitchell, Edward A.D.; Niculita-Hirzel, Helene; Guisan, Antoine; Lara, Enrique
Location - Swiss western Alps.
Taxa - Full protist community and nine clades belonging respectively to three functional groups: parasites (Apicomplexa, Peronosporomycetes and Phytomyxea), phagotrophs (Sarcomonadea, Tubulinea and Spirotrichea) and phototrophs (Chlorophyta, Trebouxiophyceae and Diatomeae).
Methods - We extracted soil DNA from 178 sites along a wide range of elevations with a random‐stratified sampling design. We defined protist Operational Taxonomic Units assemblages by metabarcoding of the V4 region of the rRNA small subunit gene. We assessed and modelled the diversity (Shannon index) patterns of all above‐mentioned taxonomic groups based on topo‐climatic (topography, slope southness, slope steepness and average summer temperature) and edaphic (soil temperature, relative humidity, pH, electroconductivity, phosphorus percentage, carbon/nitrogen, loss on ignition and shale percentage) variables in Generalized Additive Models (GAM).
Results - The respective significance of topo‐climatic and edaphic variables varied among taxonomic and—to a certain extent—functional groups: while many variables explained significantly the diversity of the three phototrophs this was less the case for the three parasites. Topo‐climatic variables had a better predictive power than edaphic variables, yet predictive power varied among taxonomic groups.
Main conclusions - Topo‐climatic variables (particularly slope steepness and summer temperature if we consider their significance in the GAMs) were, on average, better predictors of protist diversity at the landscape scale than edaphic variables. However, the predictive power of these variables on diversity differed considerably among taxonomic groups; such relationships may be due to direct and/or indirect (e.g. biotic) influences (like with parasitic taxa, where low predictive power is most likely explained by the absence of information on the hosts’ distribution). Future prospects include using such spatial models to predict hotspots of diversity and disease outbreaks.