and even deduce growth rates using a method called qSIP \cite{Hungate2015}. More modern apporaches to SIP also use a combination of Raman microspectroscopy or NanoSIMS with FISH to detect and identify isotopically labelled organisms \cite{Musat_2016,Wang_2016}.
More recent advances in linking microorganisms to functions includen so-called 'next-generation physiology' approaches. Similar to SIP, these methods require the introduction of a labeled or noncanonical indicator molecule into the sample for the detection of metabolically active organisms.
Following separation of the labeled community fraction, these so-called 'next-generation physiology' approaches can be used in combination with amplicon sequencing.
However, powerful as it may be, SIP is unfortunately limited to tracing assimilatory processes only, since dissimlatory processes leave no trace in the biomass. Moreover, SIP is limited to using elements that have a stable isotope and are found in the major biomolecules of the cell. In practical terms this is limited to C, N, H or O (but not P) \cite{Angel_2019}. Using SIP, the extent of multiple microbial guilds has been extended, including methanotrophs \cite{Knief2003}, methanogens \cite{Lu_2005}, ammonia oxidisers \cite{Pratscher_2011}, diazotrophs \cite{Buckley_2007}, cellulose-degrading \cite{Pepe-Ranney2016} and many others. SIP has also been instrumental in deciphering microbial interatcions \cite{Ho2016,Murase_2007}, and soil food webs \cite{Gorka_2019,DeRito_2005}.
Due to exchange of genetic information across microorganisms, classification of microbial taxa to functional roles or guilds based on marker gene sequence identities remains problematic. As an alternative, metagenomic and metatranscriptomic sequencing are increasingly being used to describe the functional gene diversity and expression in various environmental samples \citep{leaves}. These approaches remain promising for improving the link between organisms and their ecological roles, although both sequencing and bioinformatic efforts needed for gaining functionally relevant insights into ecosystem processes by these approaches are usually magnitudes higher than those needed for analyzing amplicon sequencing data.
- Shallow metatranscriptome sequencing to obtain insight into expression