Current State-of-the-Art of NGS in Soil Microbial Ecology Interpreted Through the Hierarchical Environmental Filtering (HEF) Framework

1. Introduction

For more than a century, soil classification has relied primarily on morphological, physical, and chemical characteristics that reflect the long-term processes of pedogenesis. These properties remain the foundation of modern soil taxonomy because they integrate the cumulative effects of parent material, climate, organisms, topography, and time during soil development. However, soils are also among the most biologically diverse ecosystems on Earth, harboring extraordinarily complex microbial communities that regulate nutrient cycling, organic matter decomposition, soil aggregation, greenhouse gas fluxes, and numerous other ecosystem functions. Until recently, this biological dimension remained only partially understood because most soil microorganisms could not be cultivated using conventional microbiological techniques.

The advent of next-generation sequencing (NGS) fundamentally transformed soil microbial ecology by enabling comprehensive characterization of entire microbial communities directly from environmental DNA. Rather than focusing on individual cultivable microorganisms, NGS has revealed remarkable taxonomic and functional diversity while simultaneously demonstrating that microbial communities are neither randomly assembled nor uniformly distributed across landscapes. Instead, growing evidence indicates that soil microbiomes are shaped by reproducible ecological processes operating across multiple spatial and temporal scales, providing unprecedented opportunities to investigate the mechanisms governing microbial community assembly.

Although NGS has revolutionized the description of soil microbial diversity, sequencing technologies alone cannot explain why particular microbial communities consistently occur under specific environmental conditions. Understanding the ecological principles responsible for microbial community organization therefore requires integrating molecular approaches with concepts from soil science, microbial ecology, and ecosystem theory. In this review, we propose the concept of Hierarchical Environmental Filtering (HEF) as a conceptual framework that integrates numerous independent observations describing soil microbial community assembly. Rather than focusing solely on sequencing technologies, we discuss how modern NGS approaches have transformed our understanding of microbial community assembly, evaluate their methodological limitations, and examine how HEF provides an ecological context for interpreting NGS-derived observations. Finally, we outline future perspectives for integrating high-throughput sequencing with functional ecology, environmental monitoring, and sustainable soil management.

In this review, we propose Hierarchical Environmental Filtering as a conceptual framework that integrates numerous independent observations describing soil microbial community assembly. The objectives of this review are therefore fourfold: (i) to evaluate how next-generation sequencing has transformed the characterization of soil microbial communities; (ii) to examine the ecological mechanisms responsible for the assembly, persistence, and variability of these communities; (iii) to assess the limitations imposed by environmental variability, anthropogenic disturbance, sampling design, and methodological uncertainty; and (iv) to explore how integration of NGS with ecological theory, functional approaches, and computational analysis can improve our understanding and monitoring of soil microbial systems.

2. Development of Next-Generation Sequencing Approaches for Soil Microbiome Characterization

The first major breakthrough was the application of high-throughput amplicon sequencing targeting the bacterial and archaeal 16S rRNA gene together with the fungal Internal Transcribed Spacer (ITS) region. These approaches substantially improved taxonomic resolution and revealed that soil microbial communities contain an enormous diversity of previously undetected microorganisms. Importantly, amplicon sequencing demonstrated that low-abundance microorganisms, collectively referred to as the rare biosphere, constitute an integral component of microbial community structure rather than random background diversity. As discussed by Lynch and Neufeld [1], these rare taxa frequently represent phylogenetically distinct lineages that provide a reservoir of ecological functions and contribute to community resilience despite their low abundance. Consequently, microbial community identity could no longer be inferred solely from dominant taxa but instead emerged as a property of the entire microbial assemblage, including both abundant and rare community members.

Yet this concept immediately raises an apparent paradox. Microbial communities are highly dynamic, responding rapidly to fluctuations in moisture, temperature, vegetation, nutrient availability, and anthropogenic disturbance. If microbial populations change continuously, how can they simultaneously characterize certain biotopes, ecosystems or soil types? Resolving this apparent contradiction requires moving beyond sequencing technologies themselves toward the ecological mechanisms governing microbial community assembly.

Despite this remarkable increase in taxonomic resolution, amplicon sequencing alone cannot explain why similar microbial communities repeatedly occur within particular soil types. Taxonomic composition provides information about community membership but offers only limited insight into ecological functioning. Communities that differ substantially in species composition may perform similar ecosystem functions, whereas taxonomically similar communities may differ considerably in their metabolic capabilities. Recognizing these limitations, Knight et al. [2] emphasized that understanding microbiomes requires moving beyond inventories of community composition toward analyses of functional potential.

Shotgun metagenomic sequencing represented the next conceptual step in microbiome research, enabling comprehensive characterization of microbial diversity and functional potential across diverse ecosystems [3,4]. Unlike targeted amplicon sequencing, which primarily provides taxonomic information, shotgun metagenomics simultaneously characterizes the taxonomic composition, functional gene repertoire, and strain-level diversity of entire microbial communities [5]. This approach enables the identification of genes involved in nitrogen fixation, carbon cycling, sulfur oxidation, phosphorus cycling, stress tolerance, and numerous other ecological processes that collectively determine soil functioning [5]. Bahram et al. [6] demonstrated that functional gene profiles may discriminate ecosystem types more consistently than taxonomic composition alone, indicating that soil microbial assemblages reflect not only the taxa present but also the ecological functions assembled under particular environmental conditions. Consequently, integration of taxonomic composition with functional potential has substantially strengthened the ecological interpretation of soil microbiomes and provided a more comprehensive basis for linking microbial community structure with soil properties and ecosystem functioning.

Recent advances in third-generation sequencing technologies have substantially improved the taxonomic resolution of microbiome analyses by enabling full-length 16S rRNA gene sequencing and more accurate species-level identification [7,8,9]. Long-read sequencing platforms developed by Pacific Biosciences (PacBio) and Oxford Nanopore Technologies (ONT) overcome many limitations associated with conventional short-read amplicon sequencing by generating full-length 16S rRNA sequences, thereby improving phylogenetic resolution and reducing taxonomic ambiguity. These advances have substantially increased the accuracy of bacterial community profiling and enhanced the identification of microbial biomarkers across diverse environments. Consequently, increasing sequencing resolution improves not only the completeness of taxonomic inventories but also the reliability of ecological interpretations based on microbial community composition.

As sequencing technologies became increasingly powerful, it also became evident that methodological variation itself could obscure genuine biological patterns. Sequencing results are highly sensitive to experimental design, laboratory contamination, sequencing strategies, and downstream analytical procedures [10,11,12]. DNA extraction protocols differ substantially in their efficiency of lysing microbial cells and recovering microbial DNA, leading to systematic differences in the representation of Gram-positive and Gram-negative microorganisms [13,14]. Similarly, primer selection and amplification of different hypervariable regions of the 16S rRNA gene substantially influence taxonomic recovery and community composition, making direct comparisons among studies difficult [15]. Downstream bioinformatic processing introduces additional variability. The transition from Operational Taxonomic Units (OTUs) to Amplicon Sequence Variants (ASVs) considerably improved sequence resolution [16], yet differences among reference databases, including SILVA [17], Greengenes [18], and the continuously updated UNITE database for fungi [19], continue to influence taxonomic assignment and downstream ecological interpretation. Variability in sequencing depth further complicates interpretation because insufficient coverage may fail to detect low-abundance members of the rare biosphere, making it difficult to distinguish genuine ecological absence from methodological under-sampling [5,11,20].

The increasing complexity of NGS datasets has also transformed the role of computational analysis in microbial ecology. High-dimensional sequencing datasets often comprise thousands of taxa whose ecological relationships cannot be adequately resolved using conventional statistical approaches alone. Consequently, machine learning approaches have become increasingly important for identifying reproducible patterns, predicting microbial community composition, and linking microbiome structure with environmental variables [21,22]. Among supervised learning methods, Random Forest has proven particularly effective for modelling complex, non-linear relationships between soil bacterial communities and environmental characteristics, enabling accurate prediction of land use, soil physicochemical properties, and soil quality indicators [23].

Ultimately, the analytical power of NGS extends beyond describing microbial diversity. An important objective is to identify persistent or core microbial assemblages that recur under comparable environmental conditions despite geographic variation and moderate environmental disturbance. Toju et al. [24] emphasized the ecological significance of persistent core microbiomes, whereas Neu et al. [25] highlighted the need for standardized approaches to defining and quantifying core microbial communities before they can be reliably compared across studies and environments. Characterization of such persistent communities may improve soil health assessment, environmental monitoring, and the interpretation of ecosystem responses to disturbance. Realizing this potential, however, requires standardized methodologies, validation across contrasting environments, and careful consideration of the ecological meaning of microbiome-derived indicators [26,27].

Importantly, sequencing technologies do not generate microbial communities; they merely reveal them. The reproducible patterns detected in NGS datasets should therefore not be interpreted as intrinsic properties of microbial taxa but as measurable outcomes of ecological processes governing community assembly. In other words, sequencing technologies provide the analytical window through which the consequences of long-term environmental selection become observable. Understanding why these analyses emerge therefore requires shifting attention from sequencing technologies themselves toward the ecological mechanisms that assemble microbial communities.

In this review, we propose that reproducible patterns in soil microbiome composition emerge because microbial community assembly is constrained by a hierarchy of environmental filters operating during soil development and ecosystem functioning. We refer to this conceptual framework as Hierarchical Environmental Filtering (HEF). Within this framework, pedogenesis establishes the fundamental physicochemical template of the soil [28], whereas climate and large-scale biogeographic processes constrain microbial distributions [29,30], vegetation acts as a major ecological filter shaping microbial community composition [31], and anthropogenic land use further modifies microbial community organization [32]. Consequently, soil microbiomes should be interpreted not as static taxonomic inventories but as emergent properties of long-term ecological assembly shaped by hierarchical environmental filtering.

3. Secondary Ecological Filtering: Refining Microbial Community Assembly

Although pedogenesis establishes the physicochemical template upon which soil microbial communities assemble, it does not fully determine community composition. Even within soils sharing a common pedogenetic origin, microbial communities may differ substantially across soil horizons because ecological selection continues under local physicochemical conditions after soil formation [33]. Once the structural environment has been established, microbial communities are subsequently shaped by a second level of ecological filtering imposed by climate, vegetation, rhizosphere processes, and biotic interactions. These secondary filters do not replace pedogenetic constraints; rather, they refine the ecological space created during soil formation, thereby shaping the dynamic component of community assembly.

Among the secondary ecological filters proposed in the HEF framework, climate operates at the broadest spatial scale, influencing microbial communities through regional temperature and moisture regimes [34]. Temperature and moisture regulate microbial metabolism, decomposition rates, nutrient turnover, seasonal ecosystem processes, and microbial responses to climatic extremes [35,36]. Seasonal climatic variability can substantially influence microbial community dynamics, with bacterial communities often responding more strongly than fungal communities to seasonal environmental fluctuations [36,37]. Unlike pedogenesis, which operates over centuries to millennia, climatic conditions fluctuate continuously and therefore primarily influence microbial activity rather than the long-term physicochemical template of the soil. Jansson and Hofmockel [38] emphasized that climatic variability influences both the composition and functioning of soil microbiomes through changes in temperature, moisture, resource availability, and plant–soil interactions, although functional responses often occur more rapidly than detectable shifts in community composition. Recent experimental evidence further demonstrates that soil microbiomes exhibit consistent and predictable responses to climatic extremes across diverse ecosystems, with the magnitude of these responses largely determined by local climatic history and soil properties [35]. Within the HEF framework, climate should therefore be regarded as a modulator of ecological filtering rather than an independent driver of microbial assembly.

Within the HEF framework, climate acts as a broad-scale ecological filter that modifies microbial communities through regional temperature and moisture regimes while interacting with local soil conditions. Temperature and moisture regulate microbial metabolism, decomposition rates, nutrient turnover, seasonal ecosystem processes, and microbial responses to climatic extremes [35,36,39]. Seasonal climatic variability can substantially influence microbial community dynamics, with bacterial communities often responding more strongly than fungal communities to seasonal environmental fluctuations [36,37]. Unlike pedogenesis, which operates over centuries to millennia, climatic conditions fluctuate continuously and therefore primarily influence microbial activity rather than the long-term physicochemical template of the soil. Jansson and Hofmockel [38] emphasized that climatic variability influences both the composition and functioning of soil microbiomes through changes in temperature, moisture, resource availability, and plant–soil interactions, although functional responses often occur more rapidly than detectable shifts in community composition . Consequently, within the HEF framework, climate should be regarded as a modulator of ecological filtering rather than an independent driver of microbial assembly.

Vegetation introduces an additional layer of biological selection by continuously modifying the chemical environment surrounding plant roots [40,41]. Through root exudation, litter deposition, nutrient uptake, and symbiotic interactions, plants selectively enrich microorganisms capable of exploiting specific carbon substrates or establishing mutualistic associations [41]. This rhizosphere-driven selection creates localized microbial communities that may differ substantially from those inhabiting adjacent bulk soil despite sharing the same pedogenetic origin [40,42]. The rhizosphere therefore represents one of the strongest biological filters operating within terrestrial ecosystems, generating highly specialized microbial assemblages associated with particular host species and exhibiting greater network complexity than adjacent bulk soils [41,42].

Importantly, plant-mediated filtering does not occur independently of soil properties. The influence of vegetation is itself constrained by the physicochemical environment established during pedogenesis. Root architecture, exudation patterns, nutrient acquisition strategies, and microbial recruitment differ among soils because mineralogy, water availability, aeration, and nutrient status regulate both plant physiology and microbial responses. Thus, vegetation should not be viewed as an alternative explanation for microbial community assembly but rather as a biological filter acting within the environmental boundaries defined by soil formation.

Biological interactions among microorganisms provide an additional level of ecological filtering. Competition for limited resources, metabolic cooperation, syntrophic relationships, predation by protists, viral infections, and antagonistic interactions collectively determine which microorganisms successfully establish stable populations after initial colonization. Community assembly therefore reflects not only environmental selection but also self-organization emerging from microbial interaction networks. Increasing evidence suggests that microbial communities possess emergent properties arising from these interactions, including functional redundancy, ecological resilience, and network stability, none of which can be explained by the ecological characteristics of individual taxa alone [43,44].

The role of biological filtering is particularly evident in fungal communities. Compared with bacteria, fungi often exhibit stronger host specificity, greater dependence on plant-derived carbon, and more pronounced responses to changes in litter chemistry and root exudation. Tedersoo et al. [45] demonstrated that vegetation represents one of the principal ecological drivers of global soil fungal diversity, acting together with climate, soil properties, and geographic factors to structure fungal communities. These findings indicate that fungal communities are often more responsive to vegetation-driven ecological filtering than bacterial communities because their assembly reflects the combined influence of pedogenetic constraints and ongoing plant–microbe interactions.

Despite the strong influence of climate and vegetation, secondary ecological filters rarely eliminate the underlying influence of soil formation. Instead, they modify microbial assemblages within the physicochemical constraints established during pedogenesis. This nested organization helps explain why similar vegetation may support different microbial communities when growing on contrasting soils, whereas microbial assemblages occurring within comparable pedogenetic environments may retain recognizable compositional patterns despite differences in vegetation. Community assembly therefore reflects the sequential action of multiple ecological filters operating across spatial and temporal scales rather than the dominance of any single environmental variable [34,38].

This hierarchical organization has important implications for the interpretation of microbial assemblies. Stable microbial communities emerge not because they remain unchanged through time but because successive ecological filters repeatedly constrain assembly toward similar community states. Consequently, they should be viewed as probabilistic ecological outcomes rather than fixed taxonomic identities. Their apparent stability depends on the persistence of the environmental filters that generate them, whereas their variability reflects the strength of secondary ecological selection acting on the pedogenetic template [38].

The action of these secondary filters also explains why microbial communities exhibit considerable ecological plasticity. Under natural conditions, climatic fluctuations and vegetation dynamics continuously reshape microbial community composition while preserving the broader community structure imposed by pedogenesis. However, sufficiently intense environmental disturbances may override the underlying pedogenetic signal. Understanding how anthropogenic disturbances modify or override pedogenetic influences is therefore essential for interpreting NGS-derived microbial patterns and distinguishing natural ecological variation from disturbance-driven community reassembly.

4. Anthropogenic Overrides and Environmental Plasticity: Restructuring Soil Microbial Community Assembly

The reproducibility of microbial communities depends on the long-term stability of the ecological filters governing community assembly. As discussed in the preceding sections, pedogenesis establishes the structural template of the soil, while climatic conditions, vegetation, and biological interactions progressively refine microbial communities within this environmental framework. Under relatively undisturbed conditions, these nested filters repeatedly generate microbial communities with recognizable ecological signatures associated with particular soil types. However, this hierarchical organization is not immutable. Environmental disturbances capable of modifying the strength or sequence of ecological filters may partially disrupt community assembly and thereby reduce the distinctiveness of microbial communities.

Among all anthropogenic disturbances, agricultural management represents the most pervasive driver of microbial community reassembly. Intensive tillage, mineral fertilization, irrigation, liming, pesticide application, and simplified crop rotations profoundly alter the physical, chemical, and biological properties of agricultural soils, reshaping microbial community assembly on timescales that are orders of magnitude shorter than those of pedogenesis [46,47]. Rather than creating entirely new microbial communities, these practices impose a common set of selective pressures that increasingly outweigh the natural environmental constraints responsible for soil-specific community assembly. Hartmann et al. [48] demonstrated that intensive agricultural management rapidly restructures microbial communities, shifting them away from the long-term equilibrium established under natural ecosystem development. Consequently, microbial assemblages become increasingly structured by management practices rather than by intrinsic pedogenetic characteristics [49,50,51].

Nitrogen fertilization provides one of the clearest examples of anthropogenic ecological override within the HEF framework. Long-term nitrogen enrichment consistently favours fast-growing copiotrophic microorganisms capable of exploiting nutrient-rich conditions while suppressing oligotrophic taxa adapted to nutrient-limited environments [52]. Across diverse ecosystems, nitrogen enrichment promotes bacterial groups associated with rapid resource acquisition at the expense of taxa characteristic of undisturbed soils. Within the HEF framework, these changes should not be interpreted merely as shifts in microbial diversity. Rather, they weaken the natural resource gradients established during soil development, redirecting community assembly toward alternative ecological states imposed by anthropogenic nutrient enrichment.

Table 1. Mechanisms of anthropogenic microbial community reassembly within the Hierarchical Environmental Filtering (HEF) framework.

Anthropogenic driver	Ecological filter primarily affected	Mechanism of microbial community reassembly	Expected microbial response	Implications for ecological interpretation of the soil microbiome	Evidence
Reduced influence of natural resource gradients	Resource availability	Increased nutrient availability alters natural resource gradients	Enrichment of copiotrophic taxa; decline of oligotrophic taxa	Reduced specificity of soil microbial signatures	[52]
Increasing influence of management over pedogenetic controls	Multiple environmental filters	Tillage, fertilization and crop management modify soil physicochemical conditions	Restructuring of microbial communities	Reduced pedogenetic specificity	[48,49,50]
Altered correspondence between microbial composition and underlying soil properties	Resource and biological filters	Modification of vegetation and soil resource conditions	Changes in microbial diversity and community structure	Reduced stability of soil-specific signatures	[53,54]

Table 1. Mechanisms of anthropogenic microbial community reassembly within the Hierarchical Environmental Filtering (HEF) framework.

Anthropogenic driver	Ecological filter primarily affected	Mechanism of microbial community reassembly	Expected microbial response	Implications for ecological interpretation of the soil microbiome	Evidence
Reduced influence of natural resource gradients	Resource availability	Increased nutrient availability alters natural resource gradients	Enrichment of copiotrophic taxa; decline of oligotrophic taxa	Reduced specificity of soil microbial signatures	[52]
Increasing influence of management over pedogenetic controls	Multiple environmental filters	Tillage, fertilization and crop management modify soil physicochemical conditions	Restructuring of microbial communities	Reduced pedogenetic specificity	[48,49,50]
Altered correspondence between microbial composition and underlying soil properties	Resource and biological filters	Modification of vegetation and soil resource conditions	Changes in microbial diversity and community structure	Reduced stability of soil-specific signatures	[53,54]

Collectively, recent studies suggest that intensive land use may increase similarity among microbial communities occurring in contrasting soils [53,54]. In natural ecosystems, different pedogenetic pathways generate distinct physicochemical environments that contribute to the assembly of different microbial communities. Under prolonged cultivation, however, these differences may become increasingly obscured because tillage, fertilization, irrigation, and crop management impose similar selective pressures across otherwise contrasting soils. Consequently, microbial community composition increasingly reflects contemporary management conditions in addition to the longer-term influence of pedogenesis. Within the HEF framework, this represents an anthropogenic reorganization of environmental filtering rather than simply a reduction in microbial diversity.

Unlike soil mineralogy or texture, which remain relatively stable over long temporal scales, microbial activity responds rapidly to fluctuations in temperature, moisture availability, and substrate inputs. Sorensen et al. [55] demonstrated that seasonal environmental transitions rapidly alter the metabolically active fraction of soil microbial communities, highlighting the dynamic nature of microbial functioning over short temporal scales. Importantly, these short-term responses primarily affect microbial activity rather than the overall composition of the community. Consequently, transient changes detected by sequencing- or function-based approaches should not necessarily be interpreted as evidence of altered community assembly but may instead reflect temporary shifts in microbial activity superimposed upon a relatively stable ecological framework [38].

The magnitude of environmental plasticity also differs among major microbial groups. Fungal communities generally exhibit stronger responses than bacterial communities to changes in plant physiology, litter inputs, and root exudation because many fungal taxa maintain close ecological associations with living vegetation. Recent studies have shown that fungal community composition is more strongly influenced by changes in litter quality and plant root community attributes than bacterial communities, highlighting the greater sensitivity of fungi to vegetation-driven ecological filtering [56,57]. Consequently, fungi may serve as sensitive indicators of ongoing ecosystem processes, although this increased responsiveness may reduce their reliability as long-term indicators of soil type.

A further challenge arises from the vertical organization of soil itself. Soil horizons represent ecologically distinct units that differ fundamentally in organic matter content, nutrient availability, aeration, moisture, and root density. Consequently, microbial communities may differ substantially between adjacent soil horizons, often exceeding differences observed among soils sampled from the same horizon [33,58]. Failure to standardize sampling depth therefore introduces substantial variation that can easily be misinterpreted as differences among soil types. Within the HEF framework, soil horizons should be regarded as independent ecological units because each horizon represents a unique combination of structural and physicochemical conditions established during pedogenesis.

Table 2. Ecological and methodological constraints affecting interpretation of soil microbiomes within the Hierarchical Environmental Filtering (HEF) framework.

Ecological constraint	Mechanism	Effect on microbial data	Implication for NGS-based interpretation	Evidence
Standardization or explicit reporting of sampling period	Rapid changes in microbial activity	Temporal variation in active microbiome	Standardization of sampling period	[38,55]
Taxon-specific interpretation of temporal variation	Strong sensitivity to vegetation-derived inputs	Greater responsiveness to litter and root-associated factors	Taxon-specific interpretation	[56,57]
Standardization of sampling depth and horizon	Vertical physicochemical stratification	Strong depth-dependent variation	Standardization of sampling depth	[33,58]
Distinguishing historical molecular signals from living communities	Persistence of extracellular DNA	Inflation of diversity estimates	Distinguishing molecular archives from living communities	[59]

Table 2. Ecological and methodological constraints affecting interpretation of soil microbiomes within the Hierarchical Environmental Filtering (HEF) framework.

Ecological constraint	Mechanism	Effect on microbial data	Implication for NGS-based interpretation	Evidence
Standardization or explicit reporting of sampling period	Rapid changes in microbial activity	Temporal variation in active microbiome	Standardization of sampling period	[38,55]
Taxon-specific interpretation of temporal variation	Strong sensitivity to vegetation-derived inputs	Greater responsiveness to litter and root-associated factors	Taxon-specific interpretation	[56,57]
Standardization of sampling depth and horizon	Vertical physicochemical stratification	Strong depth-dependent variation	Standardization of sampling depth	[33,58]
Distinguishing historical molecular signals from living communities	Persistence of extracellular DNA	Inflation of diversity estimates	Distinguishing molecular archives from living communities	[59]

Finally, methodological advances have demonstrated that not all DNA recovered from soil originates from living microbial communities. Carini et al. [59] showed that extracellular relic DNA preserved on mineral surfaces may constitute a substantial proportion of the DNA extracted from soils. Because relic DNA reflects historical rather than contemporary microbial communities, its inclusion in sequencing datasets may artificially inflate estimates of microbial diversity and obscure current ecological relationships. This finding highlights the important distinction between detecting microbial DNA and characterizing the ecological processes underlying community assembly. Within the HEF framework, accurate interpretation of microbial assemblages therefore requires distinguishing persistent molecular archives from living microbial communities.

Collectively, these observations demonstrate that soil microbial communities are neither immutable taxonomic entities nor temporally invariant ecological systems. Their composition reflects the combined influence of hierarchical environmental filters operating across different spatial and temporal scales. When pedogenetic constraints remain dominant, microbial communities may repeatedly converge toward comparable ecological states. Conversely, sufficiently strong anthropogenic disturbances can redirect community assembly toward alternative configurations increasingly governed by contemporary land use and resource conditions. Interpretation of NGS-derived microbial patterns must therefore consider the relative strength of pedogenetic, climatic, biological, and anthropogenic filters rather than attributing observed community composition to any single environmental factor.

5. Future Perspectives: From Microbial Inventories to Ecological Interpretation

The rapid development of sequencing technologies and computational ecology has fundamentally expanded our ability to characterize soil microbial communities. Nevertheless, the ultimate value of microbial sequencing extends beyond increasingly detailed descriptions of microbial diversity. The central challenge is no longer to determine which microorganisms inhabit particular soils, but to understand how ecological assembly generates reproducible biological signatures that can be interpreted, predicted, and ultimately applied in soil science. Future research should therefore shift from descriptive microbiome inventories toward mechanistic models capable of linking community assembly with soil genesis and ecosystem functioning.

Building on recent frameworks for soil bioindicator assessment and best-practice recommendations for microbiome research [2,60], we propose six minimum criteria for defensible microbial community–based soil diagnostics:

• Analytical repeatability. Microbial profiles should remain consistent across technical, DNA-extraction, sequencing, and bioinformatic replicates.
• Ecological reproducibility. Major biological patterns should persist across the temporal and spatial variability relevant to the ecological question.
• Quantitative robustness. Relationships between microbial variables and environmental conditions should be evaluated using appropriate statistical or predictive metrics rather than descriptive differences alone.
• External validation. Observed relationships should be evaluated across independent sites, sampling periods, analytical batches, or laboratories whenever possible.
• Incremental ecological information. Microbiome-derived information should be interpreted alongside conventional soil descriptors, including pH, texture, organic carbon, moisture, soil horizon, vegetation, and land use.
• Interpretability and uncertainty. The ecological basis of observed microbial patterns, associated uncertainty, and conditions under which interpretations may fail should be explicitly reported.

One of the most promising directions is the transition from taxonomic profiling to functional ecology. Although amplicon sequencing has revolutionized our understanding of microbial diversity, taxonomic composition alone provides only indirect information about ecosystem processes. Functionally similar communities may differ substantially in taxonomic composition, whereas closely related microorganisms may occupy contrasting ecological niches. Integrating shotgun metagenomics with metatranscriptomics, metaproteomics, and metabolomics could therefore provide a multidimensional perspective on microbial community assembly by simultaneously characterizing community composition, functional potential, gene expression, protein synthesis, and metabolic activity. Such multi-omics approaches could transform soil microbiome analysis from taxonomic description into comprehensive ecological signatures of soil functioning [27].

Equally important is the growing integration of artificial intelligence into microbial ecology. Contemporary sequencing datasets contain thousands of interacting taxa and generate high-dimensional ecological data that can challenge conventional statistical approaches. Machine learning algorithms are increasingly being used to identify reproducible community patterns and predict microbial community composition or soil properties from complex environmental datasets [21,22]. Rather than replacing ecological theory, these computational approaches provide powerful tools for detecting complex and potentially nonlinear relationships underlying community assembly. Future predictive models could integrate microbial composition, functional genes, environmental variables, and pedological characteristics into unified frameworks capable of predicting soil properties and ecosystem states from microbiome data [23,61,62].

Another major challenge concerns the identification of stable core microbiomes associated with particular soil types. The concept of a core microbiome generally focuses on microorganisms that consistently occur across samples or environments under defined ecological conditions [24]. However, from the perspective of hierarchical environmental filtering, ecological persistence may be more informative than taxonomic persistence alone. Future studies should therefore identify microbial assemblages that are repeatedly assembled under similar pedogenetic conditions. Such ecological core communities would represent biological manifestations of persistent environmental filtering rather than fixed taxonomic inventories. This distinction may prove fundamental for developing reliable microbiome-based indicators of soil identity.

The integration of microbial information with conventional soil measurements represents another promising research direction. Physical and chemical soil properties provide essential information about pedogenetic history and present environmental conditions, whereas microbial communities provide a dynamic biological dimension reflecting ecosystem functioning and contemporary ecological responses. Combining these complementary information layers may improve monitoring of soil degradation, restoration trajectories, land-use effects, and changes in ecosystem functioning. Evidence that microbial community composition responds systematically to soil properties, land use, environmental gradients, and ecological restoration supports the value of such integrative approaches [63,64,65].

However, successful integration of NGS-based microbiome analysis into soil ecological assessment will require rigorous methodological standardization. Differences in sampling strategy, sequencing platforms, DNA extraction protocols, bioinformatic pipelines, and reference databases continue to limit direct comparisons among studies [2,60]. Perhaps even more importantly, ecological interpretation requires standardized consideration of sampling depth, seasonality, land-use history, and disturbance intensity, all of which influence microbial community assembly [33,38,58]. Without harmonized analytical protocols, methodological variability may exceed biological variation, substantially reducing the reproducibility of microbial sequencing across laboratories and geographic regions.

Within the framework proposed in this review, the future of soil microbial ecology lies not in discovering increasingly large numbers of microbial taxa but in understanding the ecological principles that repeatedly organize these taxa into reproducible community structures. Hierarchical Environmental Filtering provides one possible conceptual framework for this transition because it explicitly links pedogenesis, environmental selection, biological interactions, and anthropogenic disturbance within a single ecological model of community assembly. Importantly, this framework does not seek to replace existing ecological theories but rather to integrate numerous independent observations into a coherent explanation for the emergence of reproducible microbial communities.

Ultimately, microbial sequencing should be regarded not merely as a means of generating increasingly detailed taxonomic inventories but as an analytical tool for understanding ecological organization. Its greatest scientific value lies in revealing how environmental selection, biological interactions, and disturbance collectively shape microbial community assembly. As sequencing technologies continue to improve and ecological models become increasingly predictive, integration of microbiome data with soil physicochemical measurements, functional analyses, and environmental observations should provide increasingly powerful approaches for investigating soil ecosystem processes. Achieving this goal will depend not only on technological innovation but also on our ability to understand the ecological mechanisms governing microbial community assembly.

6. Conclusions: Interpreting Soil Microbial Community Assembly Through Hierarchical Environmental Filtering

The rapid expansion of sequencing technologies has generated an unprecedented amount of information describing soil microbial communities. Yet the central challenge of contemporary soil microbial ecology is no longer exclusively technological. A fundamental ecological question remains: why do reproducible patterns of microbial community organization emerge within soils despite immense biological complexity and environmental variability?

Throughout this review, we propose that these patterns can be interpreted through the hierarchical organization of environmental filtering. Soil microbial communities should not be considered passive collections of microorganisms responding independently to individual environmental variables. Rather, they represent ecological systems assembled through successive constraints operating during soil formation and ecosystem development. Pedogenesis establishes the fundamental physicochemical architecture of the habitat; resource availability defines metabolic opportunities; vegetation and biological interactions further refine community composition; climate modulates microbial activity and ecosystem functioning; and anthropogenic disturbance may modify or override these natural environmental filters.

This perspective provides an ecological context for interpreting NGS-derived observations. Soil microbial assemblages are dynamic outcomes of community assembly rather than static taxonomic entities. Their apparent reproducibility reflects the persistence of environmental constraints, whereas their temporal and spatial variability reflects ecological plasticity and responsiveness to changing environmental conditions. Intensive anthropogenic disturbance can alter this organization by modifying soil physicochemical properties and resource gradients, thereby redirecting microbial assembly toward alternative community states.

The Hierarchical Environmental Filtering framework proposed here does not seek to replace established theories of microbial community assembly. Instead, it provides an integrative framework for connecting observations from pedology, microbial ecology, ecosystem science, and environmental genomics. Within this perspective, sequencing technologies are powerful observational tools: they reveal the biological consequences of environmental filtering but do not themselves explain the ecological processes responsible for microbial community organization.

Future progress will therefore depend on combining continued advances in sequencing with standardized sampling, functional multi-omics, robust computational analyses, and ecological theory. We propose that soil microbial communities can be understood as biological manifestations of environmental filtering operating across multiple spatial and temporal scales. Developing this ecological perspective should improve interpretation of NGS data and deepen our understanding of the relationships among pedogenesis, microbial biodiversity, environmental change, and soil ecosystem functioning.