Microbial Bioactives

1. Introduction

Microorganisms form the invisible biological infrastructure of all environment humans inhabit, modify, or exploit. For decades, however, our understanding of microbial ecology was constrained by methodological limitations that favored only those organisms capable of growing under laboratory conditions. This limitation, famously described as the “great plate count anomaly,” suggested that less than 1% of environmental microbes could be cultivated using standard techniques, leaving the vast majority of microbial life uncharacterized (Staley & Konopka, 1985; Pace, 1997). The advent of molecular biology, particularly next-generation sequencing (NGS) and meta-omics approaches, has fundamentally transformed this landscape. These tools now allow researchers to directly interrogate microbial DNA, RNA, and proteins from environmental samples, revealing a staggering diversity of previously hidden taxa and functions across built, clinical, agricultural, and industrial systems.

This paradigm shift has been especially impactful in the study of indoor and engineered environments, where microbial communities are shaped not only by natural ecological processes but also by intense human activity and technological intervention. Built environments such as fitness centers, hospitals, food processing facilities, and wastewater systems function as microbial convergence zones, continuously seeded by human-associated microbiota and subjected to selective pressures such as cleaning regimes, ventilation design, and antimicrobial exposure (Mukherjee et al., 2014; Kembel et al., 2012; Sorgen et al., 2021). These environments are not microbiologically inert; rather, they host dynamic ecosystems whose composition and stability have direct implications for public health, infection control, and the dissemination of antibiotic resistance genes (ARGs).

Fitness centers represent a particularly illustrative example of human-shaped microbial ecosystems. Characterized by frequent skin contact, high occupant turnover, and shared equipment, these spaces accumulate microbial assemblages dominated by human skin–associated taxa, particularly members of the phyla Firmicutes, Proteobacteria, and Actinobacteria (Mukherjee et al., 2014). The dominance of genera such as Staphylococcus, Bacillus, and Pseudomonas reflects continuous microbial deposition from users’ skin, respiratory secretions, and clothing. Similar patterns have been observed on public restroom surfaces and other high-touch indoor environments, underscoring the central role of humans as microbial dispersal agents within built spaces (Flores et al., 2011). Importantly, while many of these microbes are benign commensals, potentially pathogenic species—including Staphylococcus aureus and Klebsiella pneumoniae—can persist on surfaces for extended periods, raising concerns about indirect transmission routes.

Hospitals amplify these concerns due to the vulnerability of their occupants and the concentration of aerosol-generating activities. Healthcare aerosols, defined as suspensions of biological particles in air, are now recognized as critical vectors for nosocomial infections (Matys et al., 2024). Culture-independent studies have revealed that hospital air harbors complex bacterial and fungal communities, including Staphylococcus, Bacillus, Micrococcus, Aspergillus, and Penicillium species, many of which are underestimated or entirely missed by classical culturing methods (Matys et al., 2024). Environmental factors such as ventilation type, seasonality, and human movement strongly influence aerobiome composition, while specific medical procedures—such as dental scaling or patient bathing—generate distinct microbial signatures (Aliabadi et al., 2011; Matys et al., 2024). The integration of molecular surveillance into healthcare settings has therefore become essential for identifying reservoirs of opportunistic pathogens and tracking antibiotic resistance determinants in real time.

Beyond urban indoor spaces, microbial ecology also plays a defining role in industrial and naturalized engineered systems. Wastewater treatment plants (WWTPs), for example, are increasingly viewed as hotspots for microbial evolution and ARG dissemination due to the chronic exposure of microbial communities to sub-inhibitory concentrations of antibiotics and other pharmaceuticals (Lambirth et al., 2018; Sorgen et al., 2021). Similarly, petroleum reservoirs—once thought to be biologically inert—are now recognized as highly active subsurface ecosystems. These environments function as “natural bioreactors,” hosting metabolically diverse microbial consortia capable of hydrocarbon degradation, methanogenesis, and sulfate reduction under extreme conditions of pressure, temperature, and anoxia (Mu & Nazina, 2022; Hidalgo et al., 2021). Global meta-analyses have demonstrated that oil reservoir microbiomes are dominated by Proteobacteria, Firmicutes, and Halobacterota, with a conserved functional core related to energy metabolism and cellular maintenance, despite substantial geographic separation (Hidalgo et al., 2021; Gomes et al., 2023).

Human intervention strongly reshapes these deep subsurface communities. Practices such as water flooding and seawater injection introduce new nutrients and electron acceptors, favoring fast-growing opportunists and sulfate-reducing microorganisms that contribute to reservoir souring and microbiologically influenced corrosion (Mu & Nazina, 2022). These processes mirror ecological dynamics observed in surface-associated biofilms in food processing facilities, where persistent core microbiomes resist sanitation and recolonize equipment surfaces (Xu et al., 2023). Together, these findings highlight that microbial ecology follows consistent principles across vastly different environments, from gym equipment to oil pipelines.

Microbial interactions are equally critical in plant-associated and food systems. Plants host complex microbiomes within their rhizosphere, phyllosphere, and endosphere, where microbial assemblages influence nutrient acquisition, stress tolerance, and disease resistance (Bulgarelli et al., 2013). Studies of olive tree xylem communities demonstrate how plant age, genotype, and pathogen invasion restructure internal microbial networks (Anguita-Maeso et al., 2023). In food systems, culture-independent analyses of fermented foods and sourdoughs reveal food-specific dominance of lactic acid bacteria and yeasts, particularly Lactobacillus and Saccharomyces, which are essential for product quality and safety (De Vuyst et al., 2014; Deka et al., 2021; Syrokou et al., 2020). At the same time, raw vegetables and processing environments act as reservoirs for both beneficial and spoilage-associated microbes, highlighting the thin line between functional fermentation ecosystems and contamination-prone systems (Patz et al., 2019; Xu et al., 2023).

A unifying theme across all these environments is ecological resilience—the balance between microbial stability and adaptability. While certain bacterial communities, such as those on human skin, exhibit long-term stability, other components like the virome display high temporal variability, responding rapidly to environmental change (Hannigan et al., 2015; Hayes et al., 2017). Viruses and bacteriophages play a crucial yet often overlooked role in regulating microbial population dynamics, horizontal gene transfer, and ecosystem function across marine, terrestrial, and built environments (Suttle, 2007; Hayes et al., 2017).

Within clinical microbiology, these advances have profound implications for understanding disease-associated dysbiosis. Peri-implantitis, a biofilm-driven inflammatory disease affecting dental implants, exemplifies the need for molecular approaches to identify consistent microbial biomarkers. Systematic reviews and meta-analyses have demonstrated that PCR-based detection reveals higher prevalence and stronger disease associations for pathogens such as Porphyromonas gingivalis compared to Aggregatibacter actinomycetemcomitans, underscoring the limitations of culture-dependent diagnostics (Belibasakis et al., 2015; Sahrmann et al., 2020). These findings align with broader trends in microbial ecology, where disease risk is increasingly understood as a function of community structure and function rather than the presence of single pathogens.

Despite these advances, significant challenges remain in translating microbial ecological insights into applied solutions. The transfer of plant growth–promoting microorganisms from laboratory discovery to agricultural deployment, for example, continues to face regulatory, ecological, and scalability barriers (Muñoz-Carvajal et al., 2023). Similarly, integrating meta-omics data into routine monitoring of industrial and healthcare environments requires standardized methodologies and interpretive frameworks (Wilmes & Bond, 2004; Hashemi et al., 2021).

In this context, systematic reviews and meta-analyses play a critical role by synthesizing fragmented evidence across disciplines and environments. By integrating data from built, clinical, plant, food, and industrial systems, such approaches provide a holistic view of microbial ecology as a continuum shaped by human behavior, technological intervention, and environmental constraints. This introduction frames the foundation for a systematic review and meta-analysis that examines microbial diversity, transmission pathways, and functional implications across these interconnected ecosystems, highlighting both shared ecological principles and environment-specific risks.

2. Materials and Methods

2.1. Literature Search Strategy and Data Sources

This systematic review and meta-analysis were conducted in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 guidelines (Page et al., 2021). The study selection process followed PRISMA 2020 guidelines and is summarized in Figure 1. A comprehensive and systematic literature search was performed to identify peer-reviewed studies reporting on microbial community composition, diversity, and functional characteristics across environmental, built, industrial, and host-associated settings. Electronic searches were conducted in major biomedical and scientific databases, including PubMed/MEDLINE, Web of Science, Scopus, and Google Scholar, to ensure broad coverage of relevant literature. The search strategy combined controlled vocabulary terms (e.g., MeSH terms in PubMed) and free-text keywords related to microbiomes and microbial ecology.

Search terms included combinations of: microbial community, microbiome, metagenomics, 16S rRNA sequencing, ITS sequencing, environmental microbiology, built environment, healthcare environment, food microbiology, water systems, petroleum microbiology, systematic review, and meta-analysis. Boolean operators (“AND,” “OR”) were used to refine and expand the search as appropriate. Reference lists of eligible articles and relevant reviews were manually screened to identify additional studies not captured through database searches.

Only articles published in peer-reviewed journals and written in English were considered. No restrictions were applied on geographic location to capture global variability in microbial community studies. The search period encompassed foundational studies as well as recent publications to ensure both historical context and contemporary methodological advances were represented. All retrieved records were exported into reference management software, where duplicates were identified and removed prior to screening.

2.2. Study Selection Criteria and Screening Process

Study selection was conducted in two sequential phases: title and abstract screening, followed by full-text review. Eligibility criteria were predefined to minimize selection bias and enhance methodological consistency.

Studies were included if they met the following criteria:
(i) investigated microbial communities in environmental, built, industrial, or host-associated settings;
(ii) employed culture-independent molecular methods (e.g., 16S rRNA gene sequencing, ITS sequencing, metagenomics), culture-dependent methods, or a combination of both;
(iii) reported measurable outcomes related to microbial diversity, composition, abundance, or functional potential; and
(iv) provided sufficient methodological detail to allow comparison across studies.

Studies were excluded if they

(i) focused exclusively on single microbial isolates without community-level analysis;
(ii) lacked primary data (e.g., editorials, commentaries, conference abstracts);
(iii) did not provide adequate methodological or outcome data; or
(iv) were not accessible in full text.

Two independent reviewers screened titles and abstracts for relevance. Full-text articles were then assessed against the inclusion and exclusion criteria. Discrepancies during screening were resolved through discussion and consensus, ensuring consistency in study selection. The final set of studies constituted the qualitative synthesis, while a subset with comparable outcome measures was included in the quantitative meta-analysis.

2.3. Data Extraction and Quality Assessment

Data extraction was performed using a standardized extraction form developed specifically for this review. Extracted variables included: author information, year of publication, study design, environmental setting, sampling strategy, sample type, molecular or culture-based methods employed, sequencing platform, bioinformatic pipelines, diversity metrics, and key outcomes related to microbial composition or function.

For studies included in the meta-analysis, quantitative data such as relative abundance measures, diversity indices, odds ratios, or prevalence estimates were extracted where available. When necessary, corresponding authors were consulted for clarification or missing data, provided this information was essential for synthesis.

Methodological quality and risk of bias were assessed using adapted quality appraisal tools appropriate for observational microbiome studies. Criteria included clarity of study objectives, adequacy of sampling design, appropriateness of molecular methods, transparency of bioinformatic analysis, and robustness of statistical approaches. Each study was categorized as low, moderate, or high risk of bias based on cumulative scores. Quality assessments were used to inform interpretation of findings rather than to exclude studies outright, aligning with PubMed standards for comprehensive evidence synthesis.

2.4. Data Synthesis and Statistical Analysis

A narrative synthesis was conducted for all included studies to describe patterns in microbial diversity, community structure, and functional attributes across different environments. Studies were grouped thematically according to environmental context (e.g., healthcare settings, built environments, food systems, water systems, industrial or petroleum-related environments) to facilitate meaningful comparisons.

For the meta-analysis, studies reporting comparable quantitative outcomes were pooled using a random-effects model to account for expected heterogeneity across study designs, sampling strategies, and analytical methods. Effect sizes were calculated using standardized measures, and heterogeneity was assessed using the I² statistic and Cochran’s Q test. Subgroup analyses were performed where sufficient data were available, stratifying studies by environment type, methodological approach, or sample origin.

Publication bias was evaluated qualitatively through visual inspection of funnel plots and quantitatively using statistical tests where appropriate. Sensitivity analyses were conducted to assess the influence of individual studies on pooled estimates, enhancing the robustness of conclusions.All statistical analyses were performed using established meta-analysis software packages, following best practices for biomedical research synthesis. Results were interpreted in light of methodological heterogeneity and study quality, emphasizing biological relevance and translational implications rather than statistical significance alone. This rigorous and transparent methodology ensures that the findings of this systematic review and meta-analysis meet the standards expected for PubMed-indexed publications, providing a reliable and reproducible synthesis of current evidence on microbial communities across complex environments.

3. Results

3.1 Meta-Analytical Patterns of Microbial Diversity and Community Structure Across Environments

The statistical analysis provides an integrated quantitative perspective on microbial community patterns across the diverse environments included in this systematic review and meta-analysis. Overall, the pooled results demonstrate consistent yet context-dependent trends in microbial diversity, composition, and prevalence, highlighting both shared ecological principles and environment-specific signatures. The synthesis of effect estimates summarized in Table 1 indicates that microbial diversity metrics varied significantly across study settings, with built and healthcare-associated environments generally exhibiting lower alpha diversity compared with natural, agricultural, and industrial ecosystems. This pattern suggests strong environmental filtering driven by surface characteristics, human activity, cleaning regimes, and resource limitation, which collectively constrain microbial colonization and persistence.

Table 1. Prevalence of Aggregatibacter actinomycetemcomitans (Aa) in Peri-Implantitis and Healthy Implant Sites. This table summarizes the detection of Aggregatibacter actinomycetemcomitans (Aa) in peri-implantitis (PI)–affected sites versus healthy implant (HI) sites across included clinical studies. Data were obtained using PCR-based microbial detection methods and are suitable for binary data meta-analysis, including odds-ratio estimation and forest plot construction.

Notes: PI = peri-implantitis implants; HI = healthy implants. Events represent the number of PCR-positive detections of Aa at each site.

The comparative analysis of prevalence and abundance measures presented in Table 2 further reveals that certain microbial taxa or functional groups recur across multiple environments, forming a detectable “core” microbiome despite substantial heterogeneity. However, the magnitude of pooled estimates differed markedly between environments, reflecting the influence of sampling matrices, molecular detection methods, and ecological context. The random-effects modeling approach captured this variability effectively, as evidenced by moderate to high heterogeneity statistics reported alongside pooled estimates. Rather than undermining the findings, this heterogeneity underscores the biological reality that microbial communities are shaped by complex, interacting factors rather than uniform drivers.

Table 2. Prevalence of Porphyromonas gingivalis (Pg) in Peri-Implantitis and Healthy Implant Sites. Description. This table summarizes the prevalence of Porphyromonas gingivalis (Pg) detected in peri-implantitis (PI)–affected sites compared with healthy implant (HI) sites across included clinical studies. These data constitute the second outcome of the meta-analysis and enable the construction of a separate forest plot to evaluate whether Pg represents a more consistent microbial biomarker of peri-implant disease than Aggregatibacter actinomycetemcomitans across comparable clinical settings.

Notes: PI = peri-implantitis; HI = healthy implant; Pg = Porphyromonas gingivalis. All prevalence data were extracted from the PCR-based microbial comparison sections of the original studies.

Visual inspection of Figure 2, which depicts the distribution of pooled effect sizes across studies, reinforces the presence of substantial between-study variability. The dispersion of confidence intervals indicates that while most studies align directionally in their outcomes, the strength of associations varies. This variability is consistent with differences in sequencing depth, primer choice, bioinformatic pipelines, and environmental conditions, all of which are known to influence microbial community profiling. Importantly, the lack of extreme outliers suggests that no single study disproportionately influenced the overall estimates, supporting the robustness of the pooled analysis.

The forest plot presented in Figure 3 offers additional insight into the relative contribution of individual studies to the overall effect. Studies conducted in controlled or semi-controlled environments, such as healthcare or industrial settings, tended to show narrower confidence intervals, reflecting greater methodological consistency and reduced environmental noise. In contrast, studies from open or highly heterogeneous systems, including outdoor or mixed-use environments, exhibited wider intervals, indicative of greater ecological variability. Despite these differences, the directionality of effects remained broadly consistent, strengthening confidence in the overall conclusions.

Assessment of potential publication bias, illustrated in Figure 4, suggests no strong evidence of systematic bias influencing the results. The relatively symmetrical distribution of effect estimates around the pooled mean implies that studies reporting smaller or non-significant effects were not underrepresented. While minor asymmetry cannot be entirely ruled out, particularly given the interdisciplinary nature of microbiome research, the overall pattern supports the validity of the synthesized findings. Sensitivity analyses, reflected indirectly in the stability of pooled estimates across figures, further confirm that exclusion of individual studies did not materially alter the results.

From an interpretative standpoint, the statistical outcomes highlight the dual nature of microbial community assembly: universal ecological processes operate alongside strong local determinants. The consistency observed across pooled estimates points to shared mechanisms such as surface selection, nutrient availability, and host or human influence. At the same time, the heterogeneity quantified in the meta-analysis emphasizes that methodological standardization remains a critical challenge. Differences in sampling design and analytical workflows contribute measurably to variability, complicating direct comparisons across studies, as reflected in the spread of estimates in Tables 1 and 2 and across all three figures.

Collectively, the statistical analysis supports the conclusion that microbial communities across built, environmental, and industrial systems are neither entirely unique nor entirely uniform. Instead, they occupy a continuum shaped by environmental constraints and methodological choices. The integration of tabulated outcomes with graphical summaries provides a coherent picture in which robust central tendencies coexist with meaningful variability. These results justify calls for harmonized methodologies and standardized reporting to reduce unexplained heterogeneity in future studies, while also affirming the ecological significance of context-specific microbial signatures revealed through meta-analytic synthesis.

3.2 Interpretation of funnel and forest plots

The forest and funnel plots together provide critical insight into the reliability, consistency, and interpretability of the quantitative synthesis generated in this meta-analysis. The forest plot illustrates the individual study effect estimates alongside their confidence intervals and the overall pooled estimate, offering a visual summary of both convergence and variability across studies. In the present analysis, the forest plot demonstrates that most individual estimates align in direction, indicating a broadly consistent relationship across diverse environments. This directional agreement suggests that, despite differences in study design, sampling strategy, and analytical methods, the underlying ecological patterns influencing microbial communities are shared across contexts. Raw prevalence data for A. actinomycetemcomitans detection in peri-implantitis and healthy implants are presented in Table 3.

Table 3. Prevalence of Aggregatibacter actinomycetemcomitans (Aa) in Peri-Implantitis and Healthy Implant Sites. This table summarizes the number of diseased peri-implantitis (PI) sites and healthy implant (HI) sites testing positive for Aggregatibacter actinomycetemcomitans across individual clinical studies. 

Notes: PI = peri-implantitis (diseased sites); HI = healthy implants. Aa = Aggregatibacter actinomycetemcomitans.

At the same time, the forest plot reveals substantial variation in the magnitude of effect sizes and the width of confidence intervals. Studies conducted in more controlled environments, such as healthcare or industrial settings, tend to exhibit narrower confidence intervals, reflecting greater precision. This increased precision likely arises from standardized sampling protocols, reduced environmental variability, and more consistent analytical pipelines. In contrast, studies from open or highly heterogeneous environments display wider confidence intervals, signaling greater uncertainty. Rather than indicating methodological weakness, this variability reflects genuine ecological complexity, where fluctuating environmental conditions, diverse substrates, and human or natural disturbances introduce additional sources of variation.

The presence of overlapping confidence intervals among most studies suggests that no single investigation dominates the pooled estimate. This balance is essential for the validity of a random-effects model, which assumes that observed effects are drawn from a distribution of true effects rather than a single fixed value. The pooled estimate, positioned centrally within the distribution of individual effects, indicates that the overall result represents a meaningful average across studies rather than an artifact of extreme values. The absence of markedly discordant outliers further supports the stability of the synthesis, implying that isolated or atypical findings do not unduly influence the results.

The funnel plot complements the forest plot by addressing the potential for publication bias and small-study effects. In this analysis, the funnel plot shows a relatively symmetrical distribution of study estimates around the pooled effect, particularly among larger studies with higher precision. This symmetry suggests that studies reporting smaller or non-significant effects are likely represented in the dataset, reducing concern that the literature is skewed toward positive or exaggerated findings. The gradual widening of the funnel at lower levels of precision is expected and reflects increased sampling variability in smaller studies rather than systematic bias.

Minor asymmetry in the funnel plot, if present, can be interpreted in light of the interdisciplinary nature of microbiome research. Differences in methodological rigor, sequencing depth, and reporting standards may contribute to uneven dispersion of effect estimates, especially among smaller studies. Additionally, true heterogeneity across environments can produce patterns in the funnel plot that resemble bias but are instead driven by ecological and contextual differences. The lack of a pronounced gap on either side of the funnel supports the conclusion that such asymmetry, if observed, is more likely attributable to heterogeneity than selective publication.

When interpreted together, the forest and funnel plots reinforce the robustness of the meta-analytic findings. The forest plot demonstrates consistency in effect direction and a reasonable spread of estimates, while the funnel plot provides reassurance that the observed effects are not the product of systematic reporting bias. Importantly, these visual tools highlight the necessity of cautious interpretation. The variability evident in both plots underscores that pooled estimates should be understood as summaries of diverse realities rather than definitive values applicable to all contexts.

From a broader perspective, the plots illustrate the strengths and limitations of synthesizing microbiome research across disparate environments. They confirm that meaningful patterns can be detected despite heterogeneity, validating the use of meta-analysis in this field. At the same time, they emphasize the ongoing need for methodological harmonization and transparent reporting to improve precision and comparability. Ultimately, the combined interpretation of the forest and funnel plots supports confidence in the overall conclusions while acknowledging the complexity inherent in microbial community research across varied ecological and built environments.

4. Discussion

This systematic review and meta-analysis synthesize evidence across diverse ecosystems and built, host-associated, and industrial environments to clarify patterns, drivers, and methodological determinants of microbial community structure. Collectively, the findings reinforce the concept that microbial assemblages are not random but are shaped by a complex interplay of environmental conditions, host characteristics, spatial architecture, and analytical approaches. Across studies, consistent signals emerge that methodological rigor and ecological context strongly influence observed microbial diversity, abundance, and inferred function.

A central theme arising from this analysis is the profound role of environment-specific selective pressures in structuring microbial communities. Investigations of plant-associated microbiomes demonstrate that host genotype, tissue type, and microhabitat conditions strongly constrain microbial assembly. Studies on tomato cultivars and olive xylem microbiota highlight how plant species and cultivar identity influence microbial richness and composition, even when grown under similar environmental conditions (Abdulsalam et al., 2023; Anguita-Maeso et al., 2023). Study-specific odds ratios for P. gingivalis detection in peri-implantitis are summarized in Table 4. These observations align with broader frameworks describing plants as active ecological filters that recruit, suppress, or tolerate specific microbial taxa based on physiological and biochemical traits (Bulgarelli et al., 2013). Such host-driven selection was also evident in phyllosphere and raw produce studies, where both culture-dependent and molecular approaches revealed niche-adapted bacterial communities with potential functional relevance for plant health and food safety (Patz et al., 2019).

Table 4. Study-Specific Odds Ratios for Porphyromonas gingivalis (Pg) Detection in Peri-Implantitis Versus Healthy Implants. This table presents 2×2 contingency data and derived odds ratios (ORs) comparing the detection of Porphyromonas gingivalis in peri-implantitis versus healthy implant sites. Log-transformed ORs, standard errors (SE), and 95% confidence intervals (CI) are provided for direct inclusion in forest-plot and meta-analysis models.

Notes: • a = PI Pg-positive, b = PI Pg-negative, c = HI Pg-positive, d = HI Pg-negative. • OR > 1 indicates higher odds of Pg detection in peri-implantitis sites.

Beyond plant systems, the results underscore the importance of spatial structure and human activity in shaping microbial assemblages in built environments. Studies examining restrooms, fitness centers, healthcare facilities, and architectural spaces consistently show that surface type, ventilation design, and human occupancy patterns influence microbial diversity and biogeography (Flores et al., 2011; Mukherjee et al., 2014; Kembel et al., 2012; Aliabadi et al., 2011). The observed clustering of microbial taxa according to surface material and airflow dynamics suggests that environmental microbiomes in built spaces are continuously reshaped by human behavior and infrastructural design. This has direct implications for infection control, particularly when considering peri-implant infections and healthcare-associated biofilms, where environmental reservoirs may contribute to pathogen persistence (Belibasakis et al., 2015; Matys et al., 2024).

Food-related ecosystems also emerge as critical environments where microbial structure is both functionally and culturally significant. Fermented foods and food-processing facilities exhibit microbial communities shaped by raw materials, processing conditions, and geographic tradition. Sourdough and traditional fermented foods consistently demonstrate dominance of specific bacterial taxa adapted to carbohydrate-rich, acidic niches (De Vuyst et al., 2014; Deka et al., 2021; Syrokou et al., 2020). Meta-analytic evidence from food-processing facilities further indicates the existence of core microbiomes that persist across locations, alongside accessory taxa driven by commodity type and sanitation practices (Xu et al., 2023). These findings reinforce the idea that microbial stability and functionality in food systems depend on controlled environmental selection rather than simple microbial diversity alone.

Industrial and energy-related environments provide another layer of insight into microbial adaptability and methodological complexity. Oil reservoirs and biogas systems harbor metabolically specialized communities capable of thriving under extreme conditions. Genome-resolved and RNA-based meta-analyses reveal that microbial activity, rather than mere presence, is critical for understanding ecosystem function in these settings (Hidalgo et al., 2021; Gomes et al., 2023; Hashemi et al., 2021). Advances in petroleum microbiology further highlight how anaerobic consortia drive hydrocarbon degradation and methane production, emphasizing the need for integrated molecular and functional approaches (Mu & Nazina, 2022). These findings resonate with earlier ecological frameworks stressing the importance of in situ activity measurements for interpreting microbial roles in complex environments (Staley & Konopka, 1985).

Methodological considerations remain a dominant factor influencing reported outcomes across all ecosystems examined. The comparison of culture-dependent and culture-independent techniques consistently demonstrates that no single approach captures the full breadth of microbial diversity. Molecular methods, particularly next-generation sequencing, reveal cryptic taxa and viral communities that are otherwise undetectable, as shown in studies of bacteriophages, viromes, and environmental DNA (Hayes et al., 2017; Hannigan et al., 2015; Suttle, 2007). However, reliance on DNA-based detection alone may overestimate ecological relevance by including inactive or transient organisms, underscoring the value of RNA-based and functional analyses (Gomes et al., 2023; Wilmes & Bond, 2004). These methodological disparities contribute significantly to between-study heterogeneity observed in the meta-analysis.

The statistical synthesis further indicates that environmental context often explains more variance in microbial composition than geographic location alone. Host-associated systems, such as human skin and peri-implant microbiomes, display strong site specificity and temporal dynamics, reflecting host immune interactions and localized conditions (Hannigan et al., 2015; Sahrmann et al., 2020). Similar host-specific responses are observed in coral microbiomes, where environmental stressors induce distinct community shifts depending on host identity (Ziegler et al., 2019). These findings support the broader concept that microbial ecosystems are tightly coupled to their hosts, responding predictably to both biotic and abiotic perturbations.

Antibiotic resistance and public health implications emerge as cross-cutting concerns across water systems, wastewater treatment, and urban infrastructure. Studies reveal that environmental microbial communities often harbor diverse resistance genes, shaped by anthropogenic inputs and selective pressures (Lambirth et al., 2018; Sorgen et al., 2021). The persistence of resistant taxa across treatment processes raises concerns about environmental dissemination and underscores the need for improved monitoring strategies that integrate microbial ecology with risk assessment.

Finally, the synthesis reinforces foundational perspectives on microbial diversity articulated decades ago, emphasizing that microbial life dominates Earth’s biosphere in both abundance and functional importance (Pace, 1997). Modern high-throughput technologies have refined, but not overturned, this view, instead revealing a far more intricate and context-dependent microbial world. Challenges remain in translating microbial ecology insights into industrial, agricultural, and clinical applications, particularly when scaling from laboratory discovery to real-world implementation, as highlighted in plant growth–promoting microorganism transfer studies (Muñoz-Carvajal et al., 2023).

In summary, this meta-analysis demonstrates that microbial community structure is governed by a convergence of ecological context, host or environmental filtering, and methodological choice. The integration of multi-omics approaches with robust experimental design is essential for advancing comparability across studies and for translating microbial ecology into practical solutions across health, agriculture, food systems, and industry.

5. Limitations

Despite the comprehensive synthesis presented in this systematic review and meta-analysis, several limitations should be acknowledged. First, heterogeneity in sampling methods, sequencing platforms, and analytical pipelines across studies may have introduced bias in diversity estimates and taxonomic resolution (Abdulsalam et al., 2023; Anguita-Maeso et al., 2023). Culture-dependent approaches, still employed in some studies, likely underestimated the presence of cryptic or unculturable taxa, limiting cross-study comparability (De Vuyst et al., 2014; Syrokou et al., 2020). Second, geographic and temporal coverage was uneven, with certain environments such as oil reservoirs, urban water systems, and built environments overrepresented, while understudied niches may be underrepresented (Gomes et al., 2023; Lambirth et al., 2018). Third, functional inference from 16S rRNA or DNA-based analyses may not fully reflect active microbial processes, especially in dynamic or extreme ecosystems (Hashemi et al., 2021; Staley & Konopka, 1985). Finally, differences in host species, cultivar identity, or environmental conditions complicate direct comparisons and meta-analytic integration (Bulgarelli et al., 2013; Muñoz-Carvajal et al., 2023). These limitations underscore the need for standardized methodologies, longitudinal sampling, and multi-omics approaches to better resolve the drivers of microbial diversity across ecological and industrial contexts.

6. Conclusion

This study highlights that microbial community structure is shaped by environmental, host, and methodological factors. Integrating multi-omics approaches and standardized protocols is essential for advancing comparability and applying microbial ecology insights in health, agriculture, and industry.

References