These challenges also bring to light the biases associated with applying diversity metrics in sequencing studies. Address challenges with calculating and interpreting alpha-diversity metrics in amplicon datasets... We might address this by stating that using alpha diversity metrics with 16S data is reasonable due to the availability of reference data for this particular marker gene. However, when we consider ITS or other marker genes with relatively more incomplete reference genome sets, these metrics may become inaccurate and misleading. Discussion of the use of alpha and other diversity metrics in sequencing studies have been addressed in X, Y, and Z...
In tracing the natural isotopic abundance, the activity of microorganisms or even specific pathways can sometimes be deduced. This approach has been particularly useful in identifying if CH4 is of biogenic origin and through which metabolic pathway was it formed \cite{Conrad_2005}. Recently, methodological advances have been made to use isotopic fractionation values to model biotransformation of N-species in soil environments \cite{Denk_2017}. In contrast,
Early research looking at congruence of gene phylogenies in symbiotic diazotrophs between symbiotic (located on a symbiotic island within the genome) and housekeeping genes indicated various processes playing a role in evolution of the microbes, with the vertical transmission of genetic information being a prominent contributor of functional traits in prokaryotes (Menna and Hungria 2011). This finding also demonstrates an important issue with inferring functional information from phylogenetic barcoding such as 16S. 
Nucleic acid extraction from soil represent a macroscopic measurement of the “whole” microbial community that as such may not reveal the potential for interaction of its individual members. Therefore, and for reasons of compositionality (section XYZ), we advise researchers to be very careful before inferring information on microbial interactions from sequencing data for example through co-occurrence network analysis (section XYZ).
PLFAs is also one of the rare measurement methods that allows to assess shifts in the relative proportions of fungi and bacteria (but not archaea due to fundamental differences in composition of their biomembranes from the rest of the domain of life), something which cannot be achieved with qPCR or amplicon sequencing. Datasets for fungi and bacteria are always separated when derived from PCR-based methods due to the need to use different primer sets. The joint interpretation of bacteria and fungal community compositions based on amplicon sequencing data – even if combined with qPCR data, thus always lacks the information on potential shift across these kingdoms. This could be overcome, if important for the research question, by investing in the additional effort of conducting complementary PLFA measurements (or metagenomics – maybe we should mention here as well?)