Soil spatial complexity occcurs on micro- and macroscales

Investigating microbial community composition in soils presents unique challenges. Compared to well-mixed ecosystems, microbial life (i.e. growth, activity, dormancy, and turnover) in soil is strongly limited by the complex network of pores, as well as gas transport and diffusion in the aqueous phase \cite{Bickel2020a,Young_2004,Vos_2013}⁠. Soil microarchitecture is a key factor that influences the potential for microorganisms to interact with each other and should be considered when planning experiments \cite{Wilpiszeski_2019}. In practice, however, the analysis of soil microbial communities through amplicon sequencing does not account for soil microarchitecture. Researchers commonly use bulk homogenization approaches to extract nucleic acids from 250 - 500 mg of fresh soil which naturally obscures the physical structure and spatial arrangements of microbial cells in this soil sample. From the microbial perspective, nucleic acid extraction represents a macroscopic measurement of the “whole” microbial community. This practice does not negatively affect soil microbiome analyses unless interactions among microbial taxa are inferred (e.g. via network analysis, see section XYZ). In such a case, we strongly recommend future studies to discuss the potential effects of soil heterogeneity on a microscale to carefully evaluate the potential for microbial interactions within soil.
The spatial heterogeneity of soil and the microbial communities therein does not only persist on the microscale, but certainly also on a centimeter, meter, field, or ecosystem scale \cite{Becker_2006,Wolfe_2006,Franklin_2003}. Sampling “the same soil” a few meters apart or at different depths in the soil profile might result in individual samples with varying biogeochemical properties such as pH, water saturation, soil texture, and also plant root distribution \cite{Zhang_2021}. Choosing a sufficient number of replicates to assess sample or plot variability while balancing the cost-to-gain ratio is certainly an important measure to address soil heterogeneity. Moreover, it represents valuable information on the extent of variance given in the communities of individual treatments (see also section 6). Thus, it is critical to carefully evaluate the representativeness of technical and biological replicates. A common assumption is that replicates are more similar to each other as compared to treatments which can introduce bias into soil microbiome interpretation. For example, a recent study showed distinct and consistend differences in bacterial and fungal communities between replicate soil samples throughout a season even though 10-15 cores were randomly sampled in individual subplots and pooled \cite{Carini_2020}⁠. Another study showed that chemical soil properties as well as microbial biomass and communities exhibited high levels of spatial variation across 49 samples in a 6 \(\times\) 6 m forest plot \cite{_tursov__2016}⁠. Pooling of samples, individual extractions of DNA/RNA and/or amplification reactions made from a single DNA template can certainly dampen confounding effects of community heterogeneity. Nevertheless, existing intraplot variability and representativeness of samples, as well as appropriateness of sampling strategies to correctly address it must be critically assessed in any study on soil microbiomes. Otherwise, drawing of generalized macroecological conclusions from soil samples taken and pooled across large distances may yield speculative information at best \cite{Zhang_2020}\cite{Dini_Andreote_2020}

Temporal scales to consider when analyzing microbial dynamics

When designing an experiment, one must not only consider the spatial scales at which microorganisms live and interact, but as well the temporal scale at which sampling should occur to capture dynamics of interest. Amplicon sequencing represents a snapshot of microbial prevalence at a given moment which makes the rate of community change a critical parameter when temporal dynamics are to be investigated. Given that microbial turnover among different soils is expected to range from weeks to years as well (e.g. \cite{Spohn_2016}), it is difficult to assess the best temporal sampling strategy a priori. If for example effects of root exudation on soil microbial community dynamics are of interest, it is important to consider the different temporal scales of the processes to be correlated. Root exudation varies with plant development stage and shows diurnal patterns, whereas community changes on a DNA level may not be detectable on such a short temporal scale. Any patterns of a single sampling time point would rather represent a legacy community that established around plant roots than the current state of a community that can be linked to root exudation (composition, rate) measured at the same time point.
Another soil parameter that might mask the detection of community shifts is intrinsically linked with microbial turnover: relic or environmental/exogenous DNA . Relic DNA is extracellular DNA from nonviable cells that has leaked into the environment and that is thought to persist in soils for months to years \cite{Levy_Booth_2007,Carini_2016}. Relic DNA has been estimated to comprise between 30% and 97% of the amplifiable soil DNA pool and has been successfully removed from soil samples via the application of DNases or propidium monoazide \cite{Lennon_2018,Carini_2020}⁠. The latter study found greater differences in soil communities across several timepoints where relic DNA was removed as compared to samples where relic DNA was still present. Consequently, the presence of relic DNA may complicate the interpretation of sequencing data by over- or under-estimating microbial diversity which may be of particular concern when temporal dynamics are key to the scientific question.
One possibility to address short temporal dynamics while eliminating bias of relic DNA is ribosomal RNA (rRNA) amplicon sequencing via complementary DNA (cDNA) synthesis. The lifetime of rRNA in soils is relatively short and has been estimated to range from days to a few weeks depending on biogeochemical parameters such as temperature, pH, and water saturation \cite{Schostag_2020,Blazewicz_2013}. Thus, rRNA-targeted amplicon sequencing may increase the chances of capturing dynamics within soil microbial communities over time and may be used to carefully asses the "active" fraction therof \cite{Vieira_2019}. Caution should still be directed to the persistence of relic DNA and rRNA in soil. If community dynamics are to be investigated in even shorter time periods (e.g. minutes, hours) we suggest combining amplicon sequencing with methods for targeting the metabolically active cell fraction (as discussed in section 7).