The biological component of soil is extremely important for the maintenance of soil health (Doran and Zeiss, 2000). The microbial component of the soil is responsible for a myriad of functions (80–90% of processes in soil are reactions mediated by microbes), including soil humus formation, cycling of nutrients, degradation of xenobiotics, improvement of soil structure, and effects on plant health (Nannipieri et al., 2003). The structural organization of soil particles and soil pores provides a spatially heterogeneous habitat for microorganisms, characterized by different substrate, nutrient and oxygen concentrations, water contents, as well as variable pH values (Sessitsch et al., 2001).

In the available literature, we can find many definitions of soil quality that take into account various parameters (physical, chemical, microbiological, and biochemical). The most common is the definition that “soil quality indexes could be defined as the minimum set of parameters that, when interrelated, provide numerical data on the capacity of a soil to carry out one or more functions” (Acton and Padbury, 1994). So far, no universal formula has been developed to allow precise measurements of soil quality, especially for agricultural soils. The reason for this may be the lack of standardization of many methods, and heterogeneity of soil and natural conditions (vegetation as well as climate). Different statistical methods are used to establish soil quality in agricultural soils. These methods are focused on biotic and abiotic indicators. In the literature, we can find indexes constructed exclusively by enzyme activities such as: enzyme activity number (EAN), biological index of fertility (BIF), and soil alteration index. The EAN index contains the sum of four enzymes: dehydrogenase, phosphatase, protease, and amylase (Beck, 1984). The Soil Alteration index contains the sum of seven enzymes: arylsulphatase, β-glucosidase, phosphatase, urease, invertase, dehydrogenase, and phenoloxidase (Puglisi et al., 2006). For most indexes, dehydrogenases are the dominant group of enzymes in the assessment of soil quality. In addition, many parameters are used to evaluate soil quality, including pH, microbial biomass, and organic matter content. Recently, molecular and genetic methods have also been included (Bastida et al., 2008). The results from DNA analysis could be useful, not only for determining the structure of microorganism communities, but also for determining what functions they perform. These analyses should also include a taxonomic examination of the fungi in agricultural soils. The continually evolving genetic studies and metagenomics will probably soon lead to the development of such indicators in soil quality assessment.

In recent years metagenomics revolutionized the knowledge of microorganisms inhabiting different environments, including soil. It allowed identification of non-culturable microbes and their metabolic capacities. These data contributed to the expansion of the existing “tree of life,” shedding more light on the evolution of living organisms (Hug et al., 2016; Tedersoo et al., 2017). Most of the metagenomic studies focus on bacterial diversity (Sengupta and Dick, 2015; Eo and Park, 2016; Fernandez et al., 2016), although fungal-bacteria ratio is often high in soil ecosystems in which fungi contribute more than 50% to the total biomass (Ananyeva et al., 2006).

Czaban et al. (2010), when studying the soils of their experimental object (founded in 1881), found relationships between CFU (colony-forming unit) numbers of various microbial populations and the volumes of soil pores. Especially interesting was the strong relationship between indexes of the soil fungal communities, presenting the relationships among hydrophilic, xerotolerant and xerophilic fungi, and the mean size of the soil pore. According to another study, at pH 4.5, a significant increase in the fungi-bacteria ratio and a general microbial activity decline was observed. This effect may be caused by, among other things, the releasing of free aluminum in the soil, which occurs at about pH 5.0. The second observation was strong inhibition of plant growth, which decreases the availability of organic root-derived carbon for the bacteria (Aciego Pietri and Brookes, 2008).

The aim of this study was to investigate differences in the fungal microbiome composition in eight different types of soil that are the most common soil types in Poland. The study was conducted on microplots (Figure 1), that were founded in 1881. The advantage of using microplots is the equal cultivation of each soil and exposure to the same weather conditions. The microplots include good quality soils (as Gleyic Chernozem, Fluvic Cambisol, Cambic Leptosol, Cambisol (Eutric), Haplic Luvisol, as well as acidic, poor quality soils: Brunic Arenosol I, Brunic Arenosol II, and Haplic Cambisol.

Until now, microplots have been used for studies to determine the influence of soil type on crop yields, measurement of the total number of microorganisms (culture-dependent techniques), and also to determine physicochemical parameters of soils, such size of soil particles and soil porosity (Table 1). This study is the first approach to determine the composition of fungi by a culture-independent method; will allow us to monitor changes in microbial composition over the years, starting from 2016.

The removal of variables, such as location, different effects of wind, and other factors, allowed us to determine the impact of soil type, its structure, and characteristics on microorganisms’ ability to colonize and survive. Our research hypothesis assumes that, depending on the type of soil, the fungal communities will differ across soil types, and secondly, the buffering capacity of soil will promote the maintenance of a stable microbial composition over the years.

The microplot experiment was established in 1881, making it one of the oldest in Europe. The combination of different soils in a small area allows precise measurements, which are independent of weather conditions or agrotechnical treatments. One of the concerns about this type of research is the likelihood of soil degradation due to the lack of access to primary parent rocks and water sources that occurred in the places where they originated. In both, historical and current studies it was repeatedly examined and confirmed, that these microplots are valuable experimental objects, enabling the understanding of the effects of soil type on plant yields (Martyniuk, 1997; Sułek, 2010), providing information on the relationship between fungi occurrence, soil pore sizes, and other physical parameters (Czaban et al., 2010). Some studies demonstrated that the soil studied in this experiment has retained values such as pH, organic carbon content, bioavailable potassium, phosphorus or magnesium forms, at nearly a constant level for more than 20 years, indicating a high buffer capacity of the soil (Siebielec et al., 2015).

One of the most recent studies on this experimental object was the analysis of a bacterial microbiome using NGS sequencing (16S rRNA region) and functional analyses using the Biolog^® system on ECO plates (Grządziel and Gałązka, 2017). The authors confirmed that the composition of bacteria is closely correlated with the type of soil, but primarily with the soil pH. Eutrophic soils are characterized by the greater biodiversity of bacteria (Shannon index), as well as the higher activity of dehydrogenases and general metabolism (Biolog^®, ECO). Conversely to the bacteria, the current study on fungal composition has shown that the metabolic activity of fungi was negatively correlated with soil quality and pH (Figure 7). The dystrophic soils were characterized by a lower rate of fungal biodiversity than the eutrophic soils of good quality, but at the same time, the utilization of substrates (Biolog^®, FF plates) was more intensive in the dystrophic soils. This shows that not only alpha-diversity and the number of individual species are important for soil health, but above all, the functional biodiversity that these microorganisms provide makes the ecosystem function efficiently and sustainably, as it was stated by (Frac et al., 2018).

The most abundant phyla in each soil were Ascomycota (70–88%) and Basidiomycota (4–20%) which is consistent with other researches on different soils, for example forest soils (Buée et al., 2009), with a predominance of Basidiomycota or tundra soils (Schadt et al., 2003), where Ascomycota dominated. The third most abundant phylum was Mortierellomycota (4–15%) represented only by the genus Mortierella sp. which was also found in a relatively high number in forest soils (Buée et al., 2009). Mortierella sp. is known as saprobic and ubiquitous, as is transported by a wind and rain. An increasing number of studies concern its ability solubilize phosphorus, as well as its usefulness in increasing crop yields and in establishing symbiosis with agricultural plants (Fröhlich-Nowoisky et al., 2015). In our study, Mortierella sp. did not show any significant preference for soil quality or pH, accounting for 6–11% in acidic soils, 13–15% in eutrophic soils, except for Cambic Leptosol, where it was 23%. Interestingly, some of the fungi commonly described as dominant in many soils have shown low prevalence in our studies. One example is Trichoderma sp., which is considered to be one of the most common fungi in nature due to the high-stress tolerance and rapid growth rate (Hagn et al., 2003; Pagano et al., 2017), in our research was present in only 0.06–1%. Similarly, the Fusarium sp., considered as dominant in most of the soils (Wakelin et al., 2008), here represented between 1 and 4.5% in seven of the eight soils, exceptionally in the Haplic Cambisol 8%. On the other hand Solicoccozyma sp. is commonly found in soils with a high salt content (Mokhtarnejad et al., 2016; Zajc et al., 2017) in our microplots accounted from 2 to 9%.

It has been confirmed repeatedly that one of the most important factors affecting the composition of fungal communities in the soil is the pH, e.g. (Zhang et al., 2016), but it is also noted that most fungi are endemic, their biodiversity comparisons should be more local than global (Frac et al., 2018), that is why global comparisons between fungi abundances data sometimes do not correlate with each other.

Modern experimental methods as NGS or functional analysis (e.g., Biolog^®) allow to generate a lot of useful data in a relatively short time, so to learn the composition of hundreds of different species of bacteria or fungi and also insight into the metabolic profile of entire communities. The authors are aware of the limitations of modern methods (incomplete reference databases, the problem with reproducibility of results caused by dynamic changes in soil). Methods that are now considered outdated or even historic also had their advantages and disadvantages. The research conducted by (Czaban et al., 2010) on the correlation of the mean soil pore size and soil fungi diversity was based mainly on culture methods. However, in very general terms, these results correlate very well with those obtained today. For example, his results suggested that the mean size of soil pore is positively correlated with a fungi abundance, and the fungi CFU (colony forming units) are negatively correlated with bacterial CFU number. He also stated that acidic soils promote the growth of the fungi, due to the higher tolerance to the hydrogen ions, conversely to the most of bacteria. In this study, we proved that those relations are still valid (Figure 7), more specifically, mean soil pore size and a low soil pH is positively correlated to total fungi metabolism and negatively correlated to the bacteria abundance and its activity.

Referring to another research conducted on this experimental site and based on classical methods (Siebielec et al., 2015) investigated the occurrence of bacteria and fungi and concluded that the most numerous bacteria were found in the Gleyic Chernozem and Fluvic Cambisol, while in dystrophic and acidic soils, the total number of bacteria was several dozen times smaller. The highest number of fungi was found in acidic soils (Brunic Arenosol I and II). When we compare these results with the modern methods used in this work, the overall conclusions are similar. What is interesting and contradicts Siebielec’s work is that the number of bacteria Azotobacter sp., referring to his results, was relatively high in soils with pH above 5.6, while the results based on sequencing (Grządziel and Gałązka, 2017) did not indicate their presence in any of the soils. The authors are not sure whether these differences result from the fact that depending only on culture methods, a classification error was made (based only on bacteria’s ability to atmospheric nitrogen fixation), or whether the relative amount of Azotobacter sp. in soils was insufficient to be detected by the NGS or a third possibility, short (250 bp) fragments of the 16S rRNA gene were classified into a different genus due to a possible high genetic similarity.

These studies have shown that microbial communities are closely linked to the soil type, which has been repeatedly proven in studies in various regions of the world. There was a strong division of soils where the metabolic potential of fungi is significant (dystrophic soils; Cambisol Chapel, Brunic Arenosols) while bacteria metabolism is relatively low and those in which, despite greater biodiversity (e.g., Shannon’s H Index), the total metabolism was relatively decreased (eutrophic soils: Gleyic Chernozem, Fluvic Cambisol, Cambic Leptosol, Haplic Luvisol, Cambisol). The results obtained by modern methods (NGS, Biologists ECO/FF plates) were also correlated with historical research, conducted, among others, on the basis of individual colony counting, cultivation of bacteria, morphological identification.

AG contributed to design of the experiments, statistical analysis, and manuscript writing and revision. JG contributed to experimentation, manuscript writing, and bioinformatics analysis. JG and AG contributed to data interpretation and manuscript preparation.

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.