Seppey, C. V. W. | bioRxiv Science

bioRxiv2022

Microbial landscape ecology: landscape structure better predicts soil protist diversity than local edaphic variables in alpine meadows

ContextHuman-induced changes in landscape structure are among the main causes of biodiversity loss. Despite their important contribution to biodiversity and ecosystem functioning, microbes - and particularly protists - remain spatially understudied. Soil microbiota are most often driven by local soil properties, but the influence of the surrounding landscape is rarely assessed. ObjectivesWe assessed the effect of landscape structure on soil protist alpha and beta diversity in meadows in the western Swiss Alps. MethodsWe sampled 178 plots along an elevation gradient representing a broad range of environmental conditions and land-use. We measured landscape structure around each plot at 5 successive spatial scales (i.e. neighbourhood windows of increasing radius, ranging from 100 to 2000 m around a plot). We investigated the changes of protist alpha and beta diversity as a function of landscape structure, local environmental conditions and geographic distance. ResultsLandscape structures played a key role for protist alpha and beta diversity. The percentage of meadows, forests, or open habitats had the highest influence among all landscape metrics. The importance of landscape structure was comparable to that of environmental conditions and spatial variables, and increased with the size of the neighbourhood window considered. ConclusionsOur results suggest that dispersal from neighbouring habitats is a key driver of protist alpha and beta diversity which highlight the importance of landscape-scale assembly mechanisms for microbial diversity. Landscape structure emerges as a key driver of microbial communities which has profound implications for our understanding of the consequences of land-use change on soil microbial communities and their associated functions.

ecology↗

bioRxiv2019

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

AimGeneral 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 allowing inference over large scales. By contrast, soil microorganisms diversity is generally considered as mostly driven by edaphic variables and, therefore, difficult to extrapolate on a large spatial scale based on predictive models. Here, we compared the power of topo-climatic vs. edaphic variables for predicting the diversity of various soil protist groups at the regional scale.\n\nLocationSwiss western Alps.\n\nTaxaFull protist community and nine clades belonging to three functional groups: parasites (Apicomplexa, Oomycota, Phytomyxea), phagotrophs (Sarcomonadea, Tubulinea, Spirotrichea) and phototrophs (Chlorophyta, Trebouxiophyceae, Bacillariophyta).\n\nMethodsWe 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 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.\n\nResultsThe 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 among taxonomic and functional groups.\n\nMain conclusionsTopo-climatic variables are, on average, better predictors of protist diversity at the landscape scale than edaphic variables, which opens the way to wide-scale sampling designs avoiding costly and time-consuming laboratory protocols. However, predictors of diversity differ considerably among taxonomic and functional groups; such relationships may be due to direct and/or indirect, e.g. biotic influences. Future prospects include using such spatial models to predict hotspots of diversity or pathogens outbreaks.

ecology↗