Aim: Trends in spatial patterns of macroscopic organisms diversity can be well predicted from correlative models, using 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. Location: Swiss western Alps. Taxa: Full protist community and nine clades belonging respectively to three functional groups: parasites (Apicomplexa, Peronosporomycetes, Phytomyxea), phagotrophs (Sarcomonadea, Tubulinea, Spirotrichea) phototrophs (Chlorophyta, Trebouxiophyceae, 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 sub-unit 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.

中文翻译：

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与土壤变量相比，地形气候更好地预测了瑞士西部阿尔卑斯山的土壤原生生物多样性

目标：宏观生物多样性空间模式的趋势可以从相关模型中很好地预测，使用植物和动物的地形气候变量允许大规模推断。相比之下，土壤微生物多样性通常被认为主要由土壤变量驱动，因此难以基于预测模型在大空间尺度上进行推断。在这里，我们比较了地形气候变量与土壤变量在区域尺度上预测各种土壤原生生物群落多样性的能力。地点：瑞士西部阿尔卑斯山。分类群：完整的原生生物群落和九个分别属于三个功能组的进化枝：寄生虫（顶复门、霜霉门、植物门）、吞噬菌（肉孢属、管状、螺旋藻）光养菌（绿藻门、三门藻门、硅藻门）。方法：我们采用随机分层抽样设计从大范围海拔的 178 个地点提取土壤 DNA。我们通过 rRNA 小亚单位基因的 V4 区域的元条形码定义了原生生物操作分类单位组合。我们根据地形气候（地形、斜坡南度、斜坡陡度和夏季平均温度）和土壤（土壤温度、相对湿度、pH、电导率、磷）对所有上述分类群的多样性（香农指数）模式进行了评估和建模。百分比、碳/氮、烧失量和页岩百分比）变量在广义加性模型 (GAM) 中。结果：地形气候和土壤变量的各自重要性在分类学和 - 在一定程度上 - 功能组之间有所不同：虽然许多变量可以显着解释三种光养生物的多样性，但这三种寄生虫的情况却并非如此。地形气候变量比土壤变量具有更好的预测能力，但预测能力因分类群而异。主要结论：平均而言，地形气候变量（特别是坡度和夏季温度，如果我们考虑它们在 GAM 中的重要性）是景观尺度上原生生物多样性的更好预测因子，而不是土壤变量。然而，这些变量对多样性的预测能力在分类群之间有很大差异。这种关系可能是由于直接和/或间接（例如生物）影响（如寄生类群，其中预测能力低的原因很可能是由于缺乏有关宿主分布的信息）。