Amplicon sequencing has helped to observe and understand changes of microbial communities in various soil ecosystems over time, ranging from days and weeks to months and years (REFs). However,
The authors concluded that “forest topsoil should be considered a spatially heterogeneous environment in which the mean estimates of ecosystem-level processes and microbial community composition may confound the existence of highly specific microenvironments ”.
These challenges can be addressed by properly normalizing data and applying appropriate statistical methods during analysis, as well as combining amplicon sequencing with more quantiative experimental approaches. Additionally, improved ecological insights can be achieved from amplicon sequencing through the use of more targeted sequencing experiments (ie stable-isotope probing, deuterium labeling, BONCAT labeling of active cells; Table 1 (showing amplicon seq approach and the questions addressed with this method)).
Scientists studying microbial communities in soil face unique challenges at each step of a sequencing experiment. These challenges include the immense biological diversity of soil organisms that is thought to be higher than any other habitat on earth (citation), technical challenges related to soil sample processing, temporal variation (e.g. seasonality, root exudation, local disturbances, drying/rewetting events etc, REFs), spatial heterogeneity of the soil matrix (citation), and the interpretation of large and complex datasets in the context of ecosystem dynamics (REFs).
After nearly two decades of applying amplicon sequencing in soils, we take a look back on the growing understanding of microbial communities and offer a path forward at this turning point in the field.
In this perspective, we aim at highlighting soil-specific challenges that persist in the analysis and interpretation of amplicon sequence data due to unique considerations faced in this highly heterogeneous environment.
We also observe an increase in studies that use a "sequence and you shall see" mentality that often leads to correlation-based speculations at best.
Recent studies and reviews have referred to the limitations of this approach, including compositionality of amplicon data and the problematic nature of inferring interactions between microorganisms in communities using sequencing alone (e.g. \cite{Blanchet_2020}, 3).