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

Aim: General trends in spatial patterns of macroscopic organisms diversity can be reasonably well predicted from correlative models, using for instance topo-climatic variables for plants and animals. On the other hand, soil microorganisms diversity is generally considered as mostly explained though edaphic variables, difficult to extrapolate on a large spatial scale for predictive models. Here, we compared the power of topo-climatic vs. edaphic variables for predicting thediversity of various soil protist groups at the regional scale.Location: Western Swiss Alps.Taxa: Full protist community and nine clades belonging to three functional groups: parasites (Apicomplexa, Oomycota, Phytomyxea), phagotrophs (Sarcomonadea, Tubulinea, Spirotrichea) and phototrophs (Chlorophyta, Trebouxiophyceae, Bacillariophyta).Methods: We extracted soil environmental DNA from 178 sites along a wide range of elevations with a random-stratified sampling design. We defined protist Operational Taxonomic Units (OTUs) assemblages by metabarcoding of the V4 region of the ribosomal RNA small sub-unit gene. We assessed and modelled the diversity (Shannon index) patterns of all selected groups as a function of topo-climatic and edaphic variables using Generalized Additive Models.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 phototrophs this was less the case for parasites. Generally, topo-climatic variables had a better predictive power than edaphic variables, yet predictive power varied largely amongtaxonomic and functional groups.Main conclusions: Topo-climatic variables are, on average, better predictors of protist diversity at landscape scale than edaphic variables, which opens the way to wide-scale sampling designs avoiding costly and time-consuming laboratory protocols. However, drivers of diversity differ considerably between groups according to the different lifestyles. Future prospects include the use of such spatial models to predict hotspots of diversity or pathogens outbreaks.