One relatively accessible quantitative method  is qPCR. The method is affordable and well-established and have been applied in numerous studies in soil and other sample types in order to reduce the effects of compositional sequencing data on interpretation of microbial community studies (e.g. \citealp{Tkacz_2018,Zemb2020,Vandeputte2017,Kleyer_2017}), and others). In this way, qPCR in combination with sequencing can be used for detection, quantification and identification of bacteria, archaea and eukaryotes in complex communities. Zhang and colleagues applied 16S amplicon sequencing, as well as qPCR and other cell-counting approaches, and found that qPCR as a cell enumeration method correlates with other forms of cell quantification \cite{Zhang2017}⁠. This and other studies support the use of multiple approaches to improve the inferences that can be made from microbial community studies, particularly when these studies are conducted in heterogeneous environments or compare across different sampling sites or time periods. However, the ability of qPCR to provide a quantitative measure for microbial abundance in the soil remains limited for the following reasons: 1. Absolute quantification requires an external standard that needs to be synthesized for every qPCR assay. Moreover, any error or bias in the quantification of the standard will be propagated in the results of the assay. 2. The sensitivity of a qPCR assay to differences in gene counts between samples depends on the total amplification efficiency of the assay (E), as well of the difference in amplification efficiency between the sample (a mixed community) and the standard (typically stemming from a single species). Reproducibility is also of concern in qPCR as identical assays can produce significantly different results when performed by different opperators or using different kits \cite{Ebentier2013}. In practical terms, under optimal conditions, qPCR can reliably detect around twofold difference in copy numbers between samples and down to 100 copies \cite{Taylor_2019}. However, such sensitivity can only be achieved when working with medical samples or pure cultures, where nucleic acids purity is very high, while for environmental samples the thresholds are often much higher. 3. Impurities such as humic substances are commonly present in nucleic acid extractions from soils and are known to inhibit enzymatic reactions such as PCR. This leads to an almost unaviodable underestimation of gene or transcript copy numbers in soil samples. A relatively novel alternative to traditional qPCR is the digital PCR (dPCR), which is available as droplet-based (e.g. BioRad's QX200 Droplet Digital PCR)  or chip-based (e.g. Thermo's QuantStudio™ 3D Digital System) platforms, can overcome many of these aforementioned limitations. dPCR methods require no external standard for quantification, offer a much higher sensitivity and are relatively unaffected by the presence of PCR inhibitors \cite{Dong_2015}.
Some of these approaches remain limited at present due to the sheer biological diversity of soil ecosystems. However, with an expanding view of the diversity of microorganisms and growing number of published reference genomes, more accurate quantitative approaches are within reach. In combining sequencing with quantitative measurements, one can obtain absolute abundances of organisms in a given sample, making investigations of complex microbial communities more robust.

Persistent challenges in linking sequences to ecology

Inferring function from taxonomic affiliation/phylogeny

As amplicon sequencing is the detection of a section of a single gene, the taxonomic resolution and ecological insights that can be extracted remain limited. It is critical to consider that taxonomic classifications can be influenced by the reference database selected, many of which remain incomplete due to bias in the types of organisms for which we have reference sequences (51)⁠. Often it occurs that ASVs within a given study are similar to a given taxon at the phylum level taxonomic rank but cannot be described at the higher taxonomic levels. From this perspective, it is critical to point out that function is not conserved at the phylum level (or even genus level), and therefore processes cannot always be predicted and assigned to taxa using amplicon sequencing in a meaningful way for ecological investigations (52, 53)⁠. For example, assignment of taxa into r-strategists via their taxonomic affiliation with a phylum that is generally assumed to represent fast-growing organisms among soil microbiologists (e.g. Proteobacteria), and using these assumptions to explain processes in soil samples, should be avoided (Jeewani et al. 2020). In such cases, we recommend to follow up by either FISH-counting of the identified species,  to include functional gene-targeted sequencing, or SIP experiments to learn more about the species or community that is hypothesized to perform an ecosystem process. 
Mention PICRUST2, FunGuild, Tax4Fun here, can be used to create hypotheses but not to interpret the data

Inferences from co-occurrence analysis

All the issues described so far also permeate studies that use amplicon sequencing data for co-occurrence analysis. This analysis consists in checking which species occur together and which ones suppress each other in a high number of environmental samples. Its outcome produces networks with biological species as nodes and edges representing associations between them. For microbiome's datasets the associations are most often assigned trough detection of significant correlations between relative abundances, where spurious links can be detected if the data is not appropriately handled. Here as well, log ratios can be applied to deal with compositional nature of the data set, as done by several popular network construction tools, e.g. SparCC (log ratios) and SPIECEASI (clr). Another option available is to convert relative abundances into absolute values by using the total gene copy numbers obtained from qPCR. After its construction, the network can simply be used as a form of data visualization or for further theoretical analysis. This network analysis uses tools from the field of complex systems aiming to link structure of the microbial community to its function, identify important/keystone species and even make predictions about system's stability to environmental perturbations. Despite being very promising, the successful interpretation of the network analysis relies on our ability to establish a connection between co-occurrence patterns and meaningful ecological relationships. However, detecting causal relations among species from co-occurrence patterns is still a major challenge in ecology. Also for soil, it is important to keep in mind that the data contained in each environmental sample is only a snapshot of a complex temporal dynamics. In fact, what we have is a noisy signal which reflects several biological processes including: birth, death, dispersal, as well as intra- and inter-specific interactions; all subjected to environmental filtering. Moreover, while interactions occur at the level of individual microorganisms the detectable abundance patterns can only be measured on relatively large and possible highly heterogeneous soil samples, as mentioned in section on spatial structure of soil. This represents an additional kind of confounding effect that can introduce many spurious associations, posing additional challenges unique to the study of soil ecosystem. A careful comparison with null models and complementing the analysis with environmental information can help to interpret the results and eliminate some indirect associations between species.  In summary, the field of network inference is a rapidly evolving one and we constantly see new alternatives proposed to solve currently standing issues. Nevertheless we still lack a definite framework which allows to generate co-occurrence networks with a straight forward and easy interpretation. The current approach can still be a very useful step to formulate hypothesis on potential microbial interactions and the organization of communities in soil.

Suggestions for more robust statistical analyses in sequencing studies

Data generated from amplicon sequencing is inherently compositional and provides relative abundances, which are generally independent of the total microbial load of the original sample. It has been previously shown that analyzing compositional datasets with standard statistical techniques (including Pearson correlations or t tests on proportions) can lead to very high (up to 100%) false positive discovery rates (56, 57)⁠. The potential high false positive rates will undoubtedly lead any data set to present some correlations with microbiome data, which is, for the soil science, at an unprecedented rate given that microbiome data presents thousands of different individual variables. The possibility to obtain significant results, therefore, may also lead to an “abuse” of the statistical significance (also referred to “p hacking”). While exploratory analysis is useful, researchers should always remember that an effect or association does not exist just because it was statistically significant, and even more important is that inference should be scientific and not merely statistical. In recent years, the discussion around the abuse of p-values and their importance has risen (58–60)⁠, and some alternative options have been proposed (60)⁠, including the use of more stringent p-values for claims of new discoveries (61, 62)⁠. Clearly the issue is much more complicated than a simple critique to the p-value, but involves scientific research at all levels, including the publish or perish culture insinuated in academic fields, and therefore we address the reader to further explore this topic through the above-mentioned citations.
Nevertheless, the issue of generating false conclusions based on spurious correlation exists, which include the variability inherent in amplicon sequencing data. When adopting a “let’s sequence and see” approach, many correlations (including false positive) will be generated. Given that exploratory research often leads to follow-up research, increasing our confidence will reduce the chances of research born on unsubstantiated findings. Adopting a more stringent p-value threshold will reduce the false positive rate, at the cost of type II errors. In order to avoid this, if we wanted to adopt a more stringent p-value while maintaining statistical power, it was shown that a 70% increase in sample size has to be achieved. We understand that this is often unrealistic, but we also recognize that this could save future efforts born on unsubstantiated research. Instead, currect research often focus more often on expanding the depth of analyses on the same few samples at the expense of replication.
Considerations of soil intraplot variability or number of replicates used to analyze similarities/dissimilarities of microbial communities directly affects the ability to detect differences. To explore how increasing sample size can increase statistical power in soil microbiome analyses, we calculated the dependency of permutational multivariate analysis of variance (PERMANOVA) statistical power to effect size with different number of replicates. Although the data set chosen  \cite{Zheng_2019}  captures a wide range of possible microbial communities, this may not be representative over all possible soil environments. Therefore, we encourage the reader to interpret the data shown only as an example. We used the R package micropower \cite{Kelly_2015} which allows to simulate distance matrices from a set of parameters to generate available PERMANOVA power or necessary sample size for a planned microbiome analysis. We used data from both the 16S rRNA gene and the ITS1 region filtered to include only bacteria and archaea (16S) and fungi (ITS). We calculated the Jaccard similarity index (Supplementary Fig. 1a,b) and used the average and standard deviation across all samples as parameters in the micropower package to simulate OTU/ASV tables with similar parameters. We also calculate the average statistical power (  ω2 ) for a range of effect sizes for the 16S data (Fig. 3b), defined as 'Low' (0.001-0.04), 'Medium' (0.04-0.08) and 'High' (0.08-0.12). Our analysis shows that, while for strong differences in microbial community the number of replicates does not affect the statistical power, by increasing the replicate number from 4 to 5 we were able to almost double the statistical power for small effect size ('Low') and achieve a power above 0.8 for medium effect sizes. These effects were even stronger when we doubled the number of replicates (4 to 8). Similar effects were obtained for the fungal data set (Supplementary Fig. 1c).