A major fraction of biodiversity on Earth resides in soils. This diversity consists of prokaryotes (bacteria and archaea) and eukaryotes (fungi, protists and metazoa). Especially the prokaryotic diversity is almost entirely studies using molecular tools, with a recent transfer also to eukaryotes.. Eukaryotes differ from prokaryotes in many additional characteristics such as genomic organization and mode of evolution, which together determine the methods to study their diversity. As such, approaches that have been designed for prokaryotes like environmental sequencing cannot simply be transferred to eukaryotes. Indeed, methodological and analytical differences between domains range from how the basic diversity units are measured to interpretation of community taxonomic composition and quantitative data. Jurburg et al. (“All together now: Limitations and recommendations for the simultaneous analysis of all eukaryotic soil sequences” Molecular Ecology Methods, 2021 https://doi.org/10.1111/1755-0998.13401) have highlighted some important eukaryote-specific technical issues that need consideration into account in DNA-based studies. We here highlight additional domain-specific considerations that should be taken into account in eukaryote-focused studies. Only that will allow to reliably and comprehensively unravel the entire eukaryotic biodiversity in soils.

Soil microbes play a key role in shaping terrestrial ecosystems. It is therefore essential to understand what drives their distributions. While multivariate analyses have been used to characterise microbial communities and drivers of their spatial patterns, few studies focused on modelling the distribution of Operational Taxonomic Units (OTUs). Here, we evaluate the potential of species distribution models (SDMs), to predict the presence-absence and relative abundance distribution of bacteria, archaea, fungi and protist OTUs from the Swiss Alps. Advanced automated selection of abiotic covariates was used to circumvent the lack of knowledge on the ecology of each OTU. ‘Presence-absence’ SDMs were successfully applied to most OTUs, yielding better predictions than null models. ‘Relative-abundance’ SDMs were less successful, yet, they were able to correctly rank sites according to their relative abundance values. Archaea and bacteria SDMs displayed better predictive power than fungi and protist ones, indicating a closer link of the latter with the abiotic covariates used. Microorganism distributions were mostly related to edaphic covariates. In particular, pH was the most selected covariate across models. The study shows the potential of using SDM frameworks to predict the distribution of OTUs obtained from environmental DNA (eDNA) data. It underscores the importance of edaphic covariates and the need for further development of precise edaphic mapping and scenario modelling to enhance prediction of microorganism distributions in the future.

Soil protists are increasingly studied due to a release from previous methodological constraints and the acknowledgement of their immense diversity and functional importance in ecosystems. However, these studies often lack a sufficient depth in knowledge, which is visible in the form of falsely used terms and false- or over-interpreted data with conclusions that cannot be drawn from the data obtained. As we welcome that also non-experts include protists in their still mostly bacterial and/or fungal focused studies, our aim here is to help avoid some common errors. We provide an overview of current terms to be used when working on soil protists, like protist instead of protozoa, predator instead of grazer, microorganisms rather than microflora and terms to be used to describe the prey spectrum of protists. We then highlight some do’s and don’ts in soil protist ecology including challenges related to interpreting 18S rRNA gene amplicon sequencing data. We caution against the use of standard bioinformatic settings optimized for bacteria and the uncritical reliance on incomplete and partly erroneous reference databases. We also show why causal inferences cannot be drawn from sequence-based correlation analyses or any sampling/monitoring, study in the field without thorough experimental confirmation and sound understanding of the biology of taxa. Together, we envision this work to help non-experts to more easily include protists in their soil ecology analyses, and obtain more reliable interpretations from their protist data and other biodiversity data that, in the end, will help to better understand soil ecology.