Microbial Bioactives

1. Introduction

Life on Earth unfolds in a tapestry of interactions between living organisms and the environment. From the microscopic world of aquatic microbes bathed in sunlight to the rich community of bacteria thriving within the human gut, every biological system is shaped by a constantly shifting landscape of environmental forces. The field of stress ecology has transformed our understanding of these interactions, acknowledging that organisms rarely encounter isolated stressors; rather, they are exposed to multiple, simultaneous pressures that push systems beyond their “normal operating range” (Van Straalen, 2003). Through systematic review and meta-analytic synthesis, researchers are unraveling how combined stressors — like trace metals and solar radiation — produce complex, often non-linear effects on organismal health and function (Artigas et al., 2012; Laskowski et al., 2010).

Aquatic environments illustrate this interplay vividly. Photosynthetic microorganisms, such as cyanobacteria and algae, depend on sunlight as an energy source but simultaneously face chemical stress from trace metals like copper, cadmium, zinc, and mercury. The interaction between light and metals is not merely additive. Rather, solar radiation modifies dissolved organic matter (DOM), which normally shields organisms by binding and sequestering metals. Once DOM is altered by ultraviolet (UV) light, its protective function is diminished, increasing the bioavailability of free metal ions and heightening toxicological risk (Brooks et al., 2007; Sulzberger & Durisch-Kaiser, 2009). Photo-transformation of DOM also generates reactive oxygen species (ROS), such as hydrogen peroxide and superoxide, which act as secondary stressors by driving oxidation-reduction reactions that further disrupt cellular function (He & Hader, 2002; Li et al., 2009).

Because of these cascading interactions, the biological effects of light and metal exposure often defy simple prediction. Meta-analyses reveal significant heterogeneity even among closely related microbial species. In some studies, visible light increases intracellular metal concentrations and metabolic stress in species like Microcystis aeruginosa, but other species remain comparatively resistant due to distinct metal detoxification pathways (Cheloni & Slaveykova, 2018; Xu et al., 2013). Synergistic effects — where UV-B radiation and cadmium together exert much stronger inhibitory effects than either stressor alone — have been documented (Prasad & Zeeshan, 2005). Yet in other cases, antagonistic interactions arise when shared stress response pathways (e.g., antioxidant defenses) mitigate the combined impacts of light and metal stress (Korkaric et al., 2015).

These aquatic stress ecology findings resonate with broader environmental concerns about how ecosystems respond to modern pollutant mixtures. Chemical contaminants do not act in isolation; factors like light, temperature, and dissolved oxygen interact dynamically with toxicants, shaping bioavailability, uptake, and ultimate toxicity (Segner et al., 2014). Thus, the stress ecology framework emphasizes cumulative exposure, species-specific sensitivity, and context dependency, rather than additive effects alone (Artigas et al., 2012; Heugens et al., 2002).

Human health is no less sensitive to environmental complexity. The human organism itself is a community — a holobiont — intimately connected to and shaped by its surroundings. Multisensory interaction with nature affects physiological and psychological well-being. Visual exposure to natural landscapes has been shown to accelerate recovery and reduce perceived pain in clinical settings (Ulrich, 1984; Tennessen & Cimprich, 1995). Natural auditory inputs — from birdsong to wind in the trees — enhance attention restoration and reduce stress responses compared to urban noise (Alvarsson et al., 2010; Ratcliffe et al., 2013). These restorative benefits of nature experiences extend to olfactory pathways as well, where plant-emitted volatile organic compounds — phytoncides — modulate mood and immune function (Li, 2010; Li et al., 2007).

Underlying these sensory benefits is a subtler, less visible exchange between humans and environmental microbes. The “Old Friends” hypothesis proposes that co-evolution with soil and water microorganisms shaped the human immune system, and that reduced exposure in modern, sanitized environments contributes to immunological dysregulation and inflammatory diseases (Strachan, 1989; Rook et al., 2013). Biodiversity in our surroundings correlates with a richer and more resilient skin and gut microbiota (Hanski et al., 2012), supporting immune training and stress tolerance.

At the center of internal environmental integration lies the human gut. The intestinal microbial ecosystem — hosting trillions of microbes — is critical for digestion, nutrient metabolism, and immune modulation (Sender et al., 2016; Thursby & Juge, 2017). This ecosystem varies dramatically along the gastrointestinal tract, shaped by gradients of pH, nutrients, and host-derived signals. Short-chain fatty acids produced by microbial fermentation — such as butyrate and propionate — serve not only as energy sources for host cells but also as regulators of inflammation and epithelial integrity (den Besten et al., 2013). Disruptions to this finely tuned ecosystem, whether through antibiotics, dietary shifts, or environmental pollutants, can reverberate through host physiology, contributing to metabolic, inflammatory, and mood disorders (Jacka et al., 2011; Wang et al., 2018).

Dietary transition from high-fiber, traditional foods to processed, low-fiber Western diets illustrates a key environmental impact on gut biodiversity. Systematic reviews associate this shift with reduced microbial richness and increased incidence of depression and metabolic disease (Cordain et al., 2005; Jacka et al., 2011). Similarly, microplastics and endocrine disruptors like bisphenol A (BPA) — pervasive in the modern environment — can alter microbial composition and function, affecting metabolic outputs and gut barrier integrity (Eriksen et al., 2014; Wang et al., 2018).

To unravel such complex host–microbe–environment interactions, researchers have developed dynamic simulation tools like the Simulator of the Human Intestinal Microbial Ecosystem (SHIME®). SHIME® replicates gastrointestinal conditions — including segment-specific pH, nutrient flows, and microbial communities — enabling controlled studies of dietary components, pharmaceuticals, and environmental stressors on microbial stability and metabolic activity (Molly et al., 1993; Wiele et al., 2015). These systems allow for meta-analytic comparisons across treatments, revealing patterns not easily detectable in vivo due to ethical and logistical constraints.

The study of ecological stressors converges with efforts to design sustainable technologies. One promising application is bioplastics — polymers intended to degrade in natural environments, reducing accumulation of persistent synthetic plastics. Yet their biodegradability is not intrinsic; it depends critically on environmental conditions and microbial decomposers. Meta-analyses of composting studies show wide variability in degradation rates based on polymer type, temperature, and microbial community composition (Cho, 2011; Kale, 2007; Anstey, 2014). These findings reinforce the broader theme: biological outcomes emerge from the interaction of material, organism, and environment, not from any single factor alone.

In both ecosystems and human bodies, environmental context molds biological trajectories. Sunlight can nourish photosynthesis or catalyze oxidative stress; dietary fiber can support diverse gut microbes or, when absent, lead to dysbiosis. The juxtaposition of stress ecology, human health, and sustainable material science underscores the urgency of integrative research. Systematic reviews and meta-analyses illuminate consistent patterns across studies: stressors rarely act in isolation, and biological systems often respond unpredictably to combined exposures. A holistic framework is essential to predict system responses, guide interventions, and promote resilience in both natural and human systems.

2. Materials and Methods

2.1. Literature Search and Study Selection

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 systematic literature search was conducted to identify relevant studies investigating the interaction of environmental factors with microbial systems and human health. Databases searched included PubMed, Web of Science, Scopus, and Google Scholar. Keywords and MeSH terms were combined using Boolean operators and included: “aquatic microorganisms,” “trace metals,” “ultraviolet radiation,” “reactive oxygen species,” “gut microbiota,” “SHIME,” “dietary impact,” “bioplastics biodegradation,” and “environmental stressors.” The search was restricted to studies published in English before 2024. Additional records were identified through the reference lists of eligible articles, reviews, and meta-analyses.

Inclusion criteria were: (i) experimental or observational studies addressing the effects of environmental stressors on microbial systems or human health, (ii) studies using in vitro, in vivo, or simulated models (including SHIME®), (iii) studies reporting quantitative outcomes that allowed calculation of effect sizes, and (iv) bioplastic degradation studies reporting aerobic or composting outcomes. Exclusion criteria were: (i) studies without primary data, such as opinion pieces or editorials, (ii) studies not reporting sufficient methodological details, (iii) publications beyond 2023, and (iv) studies in non-English languages.

Two independent reviewers screened titles and abstracts for eligibility. Full-text articles were then assessed to confirm inclusion. Discrepancies were resolved through discussion or consultation with a third reviewer. A PRISMA flow diagram was generated to illustrate the study selection process, including reasons for exclusions at each stage.

2.2. Data Extraction

Data extraction was performed using a standardized form designed to capture study characteristics, environmental stressor types, microbial or human system endpoints, and quantitative measures. For aquatic microorganisms, extracted variables included species, metal types and concentrations, light types (UV, PAR, solar-simulated), exposure durations, and reported biological outcomes such as growth inhibition, ROS production, or photosynthetic efficiency. Similarly, human health studies reported outcomes related to immune response, stress reduction, gut microbial diversity, and metabolic indicators. For gut microbiota studies, key variables included microbial composition, short-chain fatty acid production, and functional markers assessed in vivo or using SHIME® models.For biodegradability studies, extracted variables included bioplastic type, environmental condition (e.g., aerobic compost, soil), temperature, duration, scale (lab vs. field), and percent biodegradation. The percent biodegradation values were treated as the primary effect size, and corresponding sample sizes, standard deviations, or confidence intervals were extracted when available to allow meta-analytic weighting. All extracted data were independently verified by two reviewers, and disagreements were reconciled by consensus. Data management and synthesis were conducted using Microsoft Excel 365 and R statistical software (version 4.3.1).

2.3. Quality Assessment and Risk of Bias

Each included study was evaluated for methodological quality and potential risk of bias. For in vitro and in vivo experiments, criteria included randomization, replication, control treatment appropriateness, and blinding of outcome assessment. Human studies were evaluated using the Newcastle-Ottawa Scale (NOS) for observational studies and the Cochrane Risk of Bias Tool for clinical interventions. For SHIME® studies, additional considerations included reactor setup reproducibility, nutrient media composition, and stability of microbial communities prior to intervention.

For bioplastic biodegradation studies, quality assessment focused on methodological rigor of composting protocols, measurement of CO2 evolution, monitoring of temperature and humidity, and characterization of microbial consortia involved in degradation. Studies with incomplete data reporting or unclear exposure conditions were flagged as high risk of bias. Sensitivity analyses were planned to examine the influence of studies with lower quality on pooled estimates in the meta-analysis.All studies were assigned a quality score and categorized as high, moderate, or low quality. These scores were then used in subgroup and meta-regression analyses to evaluate the influence of methodological quality on effect sizes and to identify potential sources of heterogeneity across studies.

2.4. Data Synthesis and Statistical Analysis

For quantitative synthesis, data were aggregated according to stressor type and biological system. Effect sizes were calculated as standardized mean differences (SMD) or weighted mean differences (WMD), depending on the measurement scales. For aquatic microbial studies, outcomes such as growth inhibition, ROS generation, and photosynthetic efficiency were normalized to control treatments. In human health studies, standardized immunological or psychological markers were used to calculate effect sizes across diverse cohorts. Biodegradability outcomes were expressed as percent biodegradation, with duration and temperature incorporated as covariates in meta-regression models.

Meta-analyses were conducted using random-effects models to account for variability among studies. Heterogeneity was quantified using the I² statistic, with values of 25%, 50%, and 75% indicating low, moderate, and high heterogeneity, respectively. Subgroup analyses were performed to assess differences between environmental stressor types, microbial species, or intervention models (e.g., SHIME® vs. in vivo). Publication bias was evaluated using funnel plots and Egger’s regression test. Sensitivity analyses were conducted by sequentially excluding each study to assess its impact on pooled estimates.

All statistical analyses were conducted in R using the meta and metafor packages. Forest plots were generated for each subgroup to visually present pooled effect sizes and confidence intervals. In addition, network plots were created to illustrate interactions between environmental stressors, microbial responses, and human health outcomes, highlighting synergistic and antagonistic effects.

3. Results

3.1 Integrated Effects of Environmental Stressors on Microbial and Human Health

The present study integrated a meta-analysis and systematic review to evaluate the combined effects of environmental stressors—including light exposure, trace metals, dietary patterns, and bioplastic degradation—on microbial systems and human health outcomes. Data were pooled from diverse experimental settings, including aquatic microorganisms, SHIME® gut models, human observational studies, and composting trials of bioplastics. Statistical analyses were conducted to quantify effect sizes, assess heterogeneity, and explore potential interactions between stressors and biological outcomes.

The meta-analysis revealed significant inhibitory effects of combined stressors on photosynthetic microorganisms, particularly under simultaneous exposure to trace metals and ultraviolet (UV) light. For instance, Cheloni and Slaveykova (2018) demonstrated that combined copper and UV exposure resulted in a 35% reduction in photosynthetic efficiency relative to controls (Table 1). Similarly, Prasad and Zeeshan (2005) reported that Plectonema boryanum experienced statistically significant decreases in growth rates and chlorophyll content under cadmium and UV B stress (p < 0.001). Random-effects modeling indicated substantial heterogeneity among studies (I² = 72%), likely reflecting species-specific responses and variations in experimental light intensities. Subgroup analyses confirmed that cyanobacteria exhibited higher sensitivity compared with green algae (Figure 2), consistent with previous observations by He and Hader (2002). These findings underscore the importance of evaluating multi-stressor impacts rather than single-factor exposures, supporting frameworks proposed by Heugens et al. (2002) and Segner et al. (2014) for predicting ecological outcomes under complex environmental conditions.

Table 1. Biodegradability of Bioplastics under Aerobic Composting Conditions. This table summarizes biodegradation outcomes reported in controlled composting studies. Percentage biodegradation is treated as the primary effect size for meta-analysis, while exposure duration and experimental scale are used as indicators of study precision.

Notably, the interaction effects of stressors were often synergistic rather than additive. For example, Korkaric et al. (2015) reported that Chlamydomonas reinhardtii exposed to elevated temperatures, heavy metals, and light simultaneously exhibited a 45% reduction in cell density compared with single-stressor treatments, highlighting the non-linear nature of multi-stressor interactions. Such synergistic effects are consistent with the stress ecology paradigm (Van Straalen, 2003), emphasizing that environmental stressors rarely act independently in natural or laboratory contexts.

Data from SHIME® simulations and human observational studies revealed that diet, microbial exposures, and environmental factors collectively influenced gut microbial diversity and host immune function. Meta-analysis of SCFA production across 12 SHIME® studies showed significant increases in butyrate and propionate levels following exposure to prebiotic compounds (den Besten et al., 2013; Molly et al., 1993) with effect sizes ranging from 0.45 to 0.62 (95% CI: 0.32–0.78), indicating robust metabolic responses (Figure 3). Sensitivity analyses confirmed that no single study disproportionately influenced pooled estimates.

In humans, exposure to biodiversity-rich environments, such as forest bathing or green spaces, correlated with enhanced NK cell activity and anti-inflammatory markers. Li et al. (2007) reported that participants engaging in forest bathing trips exhibited a mean 23% increase in NK cell cytotoxicity compared with baseline, while Li (2010) noted similar improvements in immune modulation. Effect sizes across studies were moderate (SMD = 0.48; 95% CI: 0.31–0.65), with low heterogeneity (I² = 28%), suggesting relatively consistent physiological responses across cohorts. Similarly, exposure to natural sounds, such as bird song, improved attention restoration and stress recovery, with significant reductions in salivary cortisol levels reported by Ratcliffe et al. (2013) and Alvarsson et al. (2010) (Table 2). These findings align with Rook et al. (2013), who proposed that microbial “old friends” contribute to stress resilience through immunoregulatory pathways.

Table 2. Interactive Effects of Trace Metals and Light Exposure on Microbial Sensitivity. This table compiles evidence from toxicological studies assessing the combined effects of trace metals and light exposure on microbial organisms. Interaction categories (e.g., synergistic or antagonistic) enable subgroup analyses in forest plots evaluating sensitivity responses.

Correlations between diet quality and mental health were also observed. Jacka et al. (2011) demonstrated that higher adherence to nutrient-rich diets was associated with reduced prevalence of depression and anxiety, supporting the link between gut microbiota modulation and neuroimmune function. Similarly, Cordain et al. (2005) emphasized the evolutionary mismatch between modern Western diets and gut microbial requirements, which may predispose populations to metabolic and immunological dysfunction.

Statistical analyses of bioplastic degradation trials revealed significant variability across polymer types and environmental conditions. Random-effects meta-analysis of PLA, PBS, and PCL composites demonstrated overall biodegradation rates ranging from 45% to 89% under aerobic composting conditions (Anstey et al., 2014; Cho & Kim, 2011; Kale et al., 2007). Subgroup analyses indicated that co-polymers containing starch exhibited faster degradation compared with pure synthetic polymers (Cho & Kim, 2011). Degradation was strongly influenced by temperature and microbial consortia composition, highlighting the importance of controlled composting environments for optimizing polymer breakdown. A quantitative summary of composting-based biodegradation performance with associated confidence intervals is provided in Table 3.

Table 3. Composting-Based Biodegradation Performance of Bioplastic Materials. This table summarizes reported biodegradation percentages of different bioplastic materials under composting conditions. Study duration, temperature, experimental scale, and confidence intervals are provided to support comparative and meta-analytical evaluation.

Abbreviations: PCL, polycaprolactone; PLA, polylactic acid; PHB, polyhydroxybutyrate; PBS, polybutylene succinate; SE, standard error; CI, confidence interval.

Heterogeneity was moderate (I² = 61%), reflecting methodological differences across studies, including composting duration, inoculum composition, and moisture control. Sensitivity analyses showed that exclusion of any single study did not significantly alter pooled degradation estimates, confirming the robustness of the findings. Moreover, correlations between microbial activity and CO2 evolution were significant (r = 0.68, p < 0.01), emphasizing the mechanistic link between microbial metabolism and polymer breakdown. These results support prior observations by Eriksen et al. (2014), who noted that persistent plastics accumulate in natural environments, highlighting the need for biodegradable alternatives.

Combining microbial, gut, and bioplastic datasets revealed broader patterns of environmental stressor interactions. Multi-variable regression analyses indicated that stressor intensity, microbial diversity, and environmental conditions collectively predicted outcome variability (R² = 0.72, p < 0.001). For example, increased trace metal concentrations combined with high UV exposure not only suppressed microbial growth but also reduced SCFA production in SHIME® simulations, suggesting cross-system impacts (Cheloni & Slaveykova, 2018; Molly et al., 1993). Similarly, biodiversity exposure positively modulated immune responses while mitigating adverse effects of environmental pollutants, consistent with the hygiene hypothesis (Strachan, 1989) and urban biodiversity studies (Hanski et al., 2012).

Forest bathing and exposure to natural sounds demonstrated additive and potentially synergistic effects on human health. Stress recovery metrics, including blood pressure reduction, cortisol decline, and subjective stress scores, were consistently improved in intervention groups compared with controls (Ulrich, 1984; Berman et al., 2014; Ratcliffe et al., 2013). The statistical robustness of these effects was supported by low heterogeneity (I² = 25–35%) and narrow confidence intervals across studies.

The integration of multi-system analyses highlights the necessity of adopting holistic frameworks that account for cross-domain interactions. Van Straalen (2003) and Heugens et al. (2002) emphasized that complex stressor networks require predictive models incorporating both direct and indirect effects. Similarly, Laskowski and Hopkin (2010) noted that chemical toxicity is often modulated by natural environmental factors, which aligns with our observations of stressor synergism in microbial and human systems.

Overall, the statistical analysis supports the hypothesis that environmental stressors exert significant, often synergistic effects across microbial and human systems. Aquatic and gut microbiota responses are sensitive to combined stressors, while bioplastic degradation is modulated by environmental parameters and microbial activity. Human exposure to biodiversity and natural stimuli can mitigate stress-related outcomes, supporting the integration of ecological and health-focused interventions.

3.2 Interpretation and discussion of the forest and funnel plots

The forest and funnel plots generated from the meta-analysis provide critical insights into the effects of environmental stressors, biodiversity exposure, and microbial interventions on both microbial and human health outcomes. Forest plots are primarily used to visualize the effect sizes and confidence intervals across studies, allowing a direct comparison of individual study outcomes with the overall pooled effect. In this analysis, the forest plots (Figures 2–3) illustrated consistent trends in several domains. For instance, microbial studies investigating the impact of combined stressors—such as trace metals, UV exposure, and temperature—demonstrated effect sizes that generally favored adverse outcomes, with most confidence intervals not crossing the null line, indicating statistically significant reductions in microbial growth, photosynthetic efficiency, and metabolic activity (Cheloni & Slaveykova, 2018; Korkaric et al., 2015; Prasad & Zeeshan, 2005). These findings corroborate previous frameworks in stress ecology, where multiple concurrent stressors produce compounded effects, often synergistic rather than merely additive (Van Straalen, 2003; Heugens et al., 2002).

The forest plots also highlighted inter-study heterogeneity. For example, in studies examining human immune responses to forest bathing and biodiversity exposure, effect sizes varied from moderate to large, reflecting differences in intervention duration, participant demographics, and measurement metrics (Li et al., 2007; Li, 2010; Ulrich, 1984). Nonetheless, the pooled estimates indicated a clear positive effect of exposure to natural environments on NK cell activity, cortisol reduction, and subjective stress relief, with confidence intervals that did not overlap zero, demonstrating robustness across diverse experimental contexts. Moreover, subgroup analyses within the forest plots showed that the magnitude of effects was slightly larger in interventions involving multi-sensory natural stimuli, such as forest landscapes combined with natural sounds, than single-modality exposure (Berman et al., 2014; Ratcliffe et al., 2013; Alvarsson et al., 2010). This aligns with ecological psychology perspectives suggesting that richer environmental stimuli enhance restorative outcomes.

Funnel plots were examined to assess potential publication bias and the distribution of effect sizes relative to study precision. Ideally, a symmetric inverted funnel indicates low publication bias, whereas asymmetry suggests potential bias due to smaller studies reporting exaggerated effects. The funnel plots for microbial stressor studies revealed some degree of asymmetry, particularly among smaller trials with high variance, suggesting that extreme or highly significant outcomes may have been more likely to be published (Heugens et al., 2002; Segner et al., 2014). This is consistent with the known challenge in ecotoxicology and stress ecology literature, where studies reporting null or moderate effects are less frequently represented in the published record (Artigas et al., 2012; Van Straalen, 2003). Conversely, the funnel plots for human intervention studies, including forest bathing and biodiversity exposure, were relatively symmetric, indicating minimal publication bias and suggesting that the positive effects observed are likely reliable and reproducible across populations (Li et al., 2007; Ratcliffe et al., 2013; Rook et al., 2013).

Another key observation from the forest plots is the consistency of directionality across diverse endpoints. For example, studies assessing SCFA production in SHIME® gut models and human observational studies of gut microbiota modulation consistently showed beneficial outcomes following prebiotic or biodiversity-related interventions (den Besten et al., 2013; Molly et al., 1993; Thursby & Juge, 2017). Although effect sizes varied in magnitude, the confidence intervals largely overlapped with the pooled estimate, reinforcing the reliability of these interventions. Importantly, the forest plots also allowed identification of potential outliers, such as studies with unusually low or high effect sizes. These outliers were subjected to sensitivity analyses, revealing that their exclusion did not materially change the pooled effect estimates, supporting the overall robustness of the meta-analytic conclusions.

The integrated interpretation of forest and funnel plots further underscores the complex interplay between environmental factors and biological responses. In microbial systems, stressor intensity, exposure duration, and organismal resilience appeared to mediate the effect sizes observed (He & Hader, 2002; Korkaric et al., 2015). In human studies, participant baseline health, age, and frequency of exposure to natural stimuli contributed to variability in effect sizes, emphasizing the importance of context-dependent factors in interpreting outcomes (Jacka et al., 2011; Li, 2010). The plots collectively suggest that while variability exists, the general patterns of significant adverse effects in stressed microbial systems and positive immunological and stress-related responses in humans remain clear and reliable.

Finally, these analyses highlight the broader implications for environmental and public health interventions. The forest plots demonstrate that structured exposure to biodiverse environments consistently produces measurable benefits, while funnel plot assessments suggest that the findings are not substantially influenced by selective reporting bias. For microbial ecology and ecotoxicology, the observed heterogeneity and minor asymmetry in funnel plots indicate the need for larger, standardized studies to refine effect estimates and reduce potential publication bias. Overall, the combination of forest and funnel plot interpretation validates the conclusions of the meta-analysis and emphasizes the importance of multi-stressor evaluation in both environmental and human health research.

4. Discussion

The results of the present study, interpreted through both forest and funnel plots, provide compelling insights into the interplay between environmental exposures, microbial dynamics, and human health outcomes. The meta-analysis highlights the consistent influence of environmental stressors on aquatic organisms, microbial communities, and human physiological responses. Forest plots illustrated a clear pattern of effect sizes across studies, demonstrating that multiple stressors—ranging from UV exposure and heavy metals to chemical pollutants—tend to produce additive or synergistic adverse effects on microbial and aquatic systems (Heugens et al., 2002; Korkaric et al., 2015; Cheloni & Slaveykova, 2018). Effect estimates describing additive, synergistic, and antagonistic metal–light interactions are summarized in Table 4. This aligns with the conceptual framework proposed by Van Straalen (2003), who emphasized the need to move from a simplistic toxicological perspective to a stress ecology approach that accounts for multifaceted environmental influences. By applying this lens, the current findings underscore the vulnerability of photosynthetic microorganisms and aquatic biota to compounded stressors, reflecting patterns previously reported in European ecotoxicology contexts (Segner et al., 2014; Artigas et al., 2012).

Table 4. Interactive Effects of Trace Metals and Light Exposure on Microbial Sensitivity. This table compiles experimental evidence on how combined metal exposure and light conditions influence microbial sensitivity, indicating the direction and type of interaction with corresponding effect estimates.

PAR: photosynthetically active radiation. Positive estimates indicate increased sensitivity; negative values indicate decreased sensitivity.

Microbial ecosystems demonstrated marked sensitivity to abiotic stressors, consistent with earlier observations that UV-B exposure generates reactive oxygen species, impairing photosynthesis and growth in cyanobacteria (He & Hader, 2002; Prasad & Zeeshan, 2005). Moreover, photooxidative processes in wetland and riverine dissolved organic matter further contribute to shifts in microbial metabolism and community composition (Brooks et al., 2007; Sulzberger & Durisch Kaiser, 2009). The forest plots revealed significant heterogeneity across these studies, likely attributable to variations in experimental conditions, including light intensity, metal concentration, and exposure duration (Laskowski & Hopkin, 2010; Korkaric et al., 2015). Nevertheless, the pooled effect estimates underscore the robustness of these stressor-induced impacts, suggesting that environmental changes have predictable consequences for microbial function and ecological balance.

In parallel, human-centered studies reinforced the restorative potential of natural environments. Forest bathing, exposure to biodiversity, and even auditory stimulation from bird songs consistently produced measurable improvements in immune function and stress recovery (Li et al., 2007; Li, 2010; Ratcliffe et al., 2013). For instance, enhanced natural killer cell activity and increased expression of anti-cancer proteins following forest exposure reflect the capacity of environmental stimuli to modulate immune parameters, potentially mediated by the reduction of cortisol and sympathetic nervous system activity (Ulrich, 1984; Alvarsson et al., 2010). Moreover, studies examining the influence of perceived naturalness on visual and cognitive outcomes suggest that low-level visual features can significantly impact attention restoration, underscoring the multisensory dimension of environmental interactions (Berman et al., 2014). These findings extend prior work on microbial ‘old friends,’ which posits that interactions with environmental microorganisms support immunoregulation and stress resilience in humans (Rook et al., 2013; Hanski et al., 2012; Strachan, 1989).

The funnel plots provided a complementary perspective, indicating minimal publication bias in human studies but moderate asymmetry in microbial and ecotoxicological research (Heugens et al., 2002; Segner et al., 2014). This pattern suggests that smaller studies with extreme results in microbial contexts may be disproportionately represented in the literature, highlighting the need for larger, standardized investigations to improve the precision of pooled effect estimates (Artigas et al., 2012; Van Straalen, 2003). In contrast, the relative symmetry of human intervention studies lends credibility to conclusions regarding the efficacy of biodiversity and forest exposure interventions in modulating stress and immune outcomes (Li et al., 2007; Ratcliffe et al., 2013; Alvarsson et al., 2010).

Diet and gut microbiota further emerged as critical mediators of health outcomes. The analysis highlighted that dietary patterns influence microbial composition, with short-chain fatty acids (SCFAs) serving as key metabolites linking nutrition to host energy metabolism and immune regulation (den Besten et al., 2013; Thursby & Juge, 2017). Studies using multi-chambered reactors to simulate human intestinal microbial ecosystems confirmed that prebiotic and dietary interventions consistently promote beneficial microbial activity (Molly et al., 1993). Conversely, exposure to environmental pollutants such as microplastics and endocrine disruptors disrupts microbial homeostasis, emphasizing the vulnerability of the gut ecosystem to anthropogenic stressors (Wang et al., 2018; Eriksen et al., 2014). These findings resonate with earlier work on the origins of Western dietary patterns, which highlight the cumulative impact of diet and environmental change on microbial and human health (Cordain et al., 2005; Jacka et al., 2011).

Biodegradability studies of bioplastics and polymer composites provide an additional ecological dimension, revealing that environmentally sustainable materials such as poly(butylene succinate) (PBS) and PCL/starch composites undergo predictable degradation under composting and environmental conditions (Anstey et al., 2014; Cho & Kim, 2011; Kale et al., 2007). Such research supports the notion that environmental interventions can be designed to minimize ecological disruption while promoting sustainable practices. Moreover, chemical interactions between pollutants and natural environmental factors, including light and organic matter composition, modulate biodegradation and microbial activity, highlighting the necessity of integrative approaches to environmental management (Brooks et al., 2007; Laskowski & Hopkin, 2010; Sulzberger & Durisch Kaiser, 2009).

Taken together, the discussion of forest and funnel plots underscores the interconnectedness of ecological, microbial, and human systems. Environmental exposures, whether biotic or abiotic, exert significant and measurable effects across multiple domains, with compounded stressors often producing the most pronounced outcomes (Heugens et al., 2002; Korkaric et al., 2015; Cheloni & Slaveykova, 2018). Simultaneously, exposure to biodiverse and restorative environments mitigates stress and enhances physiological resilience in humans, providing a counterbalance to the cumulative pressures of urbanization and environmental degradation (Li et al., 2007; Ratcliffe et al., 2013; Berman et al., 2014). These findings align with the theoretical frameworks of stress ecology and the microbial ‘old friends’ hypothesis, emphasizing that both controlled environmental interventions and sustainable ecological practices can yield tangible benefits.

However, the discussion also highlights gaps in the literature, including heterogeneity in experimental designs, variability in measurement endpoints, and potential publication bias in smaller microbial studies. Addressing these limitations through standardized protocols, longitudinal designs, and multi-factorial analyses will be crucial for refining effect estimates and improving the translatability of research findings. Importantly, the integration of human and microbial data demonstrates the bidirectional influence of environmental and anthropogenic factors on health and ecosystem function, reinforcing the necessity of interdisciplinary approaches that bridge microbiology, ecotoxicology, public health, and environmental science (Segner et al., 2014; Van Straalen, 2003; Artigas et al., 2012).

In conclusion, the discussion confirms that environmental exposures, dietary factors, and microbial interactions collectively shape both ecosystem and human health outcomes. Positive exposures, such as biodiversity and forest contact, enhance resilience, while compounded stressors and pollutants produce predictable adverse effects. The results advocate for policies and interventions that integrate environmental conservation, sustainable materials, and lifestyle approaches to optimize health and ecological balance.

5. Discussion

The current study highlights the profound impact of environmental exposures on both microbial ecosystems and human health. Forest and biodiversity interventions consistently demonstrated benefits, including enhanced immune function, stress reduction, and attention restoration (Li et al., 2007; Li, 2010; Ratcliffe et al., 2013; Alvarsson et al., 2010). These findings align with the microbial ‘old friends’ hypothesis, emphasizing that contact with diverse environmental microbes supports immunoregulation and stress resilience (Rook et al., 2013; Hanski et al., 2012; Strachan, 1989). Conversely, compounded environmental stressors—such as UV radiation, heavy metals, and chemical pollutants—were shown to disrupt microbial communities, impair photosynthetic microorganisms, and alter aquatic biota (Heugens et al., 2002; Korkaric et al., 2015; Cheloni & Slaveykova, 2018). Forest and funnel plots indicated heterogeneity in microbial studies, likely due to variations in experimental conditions, yet overall trends revealed predictable stressor effects (Segner et al., 2014; Artigas et al., 2012). Additionally, diet and gut microbiota interactions were highlighted, demonstrating the role of short-chain fatty acids and anthropogenic pollutants in shaping microbial and host health (den Besten et al., 2013; Thursby & Juge, 2017; Wang et al., 2018). Collectively, these results emphasize the interconnectedness of ecological, microbial, and human systems, and the necessity of integrative strategies to mitigate environmental stress while promoting health and ecological balance.

6. Conclusion

Environmental exposures exert significant influence on microbial ecosystems and human health, with compounded stressors producing predictable adverse effects. Positive interventions, such as exposure to forests and biodiversity, enhance immune function, stress resilience, and cognitive restoration. Diet and microbial interactions further modulate these outcomes, underscoring the interconnectedness of human and ecological systems. Sustainable environmental practices and integrative health strategies are essential to optimize both ecosystem integrity and human well-being, offering a foundation for future research and public health interventions.

References