{"paper_id":"0c30874d-8b00-4d18-9d5d-d7343bde443d","body_text":"Microplastic Contamination Alters Seagrass Nutritional Quality and Gut Microbiome Stability in Dugong (Dugong dugon): An Integrated One Health Assessment in Tropical Coastal Ecosystems | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Microplastic Contamination Alters Seagrass Nutritional Quality and Gut Microbiome Stability in Dugong (Dugong dugon): An Integrated One Health Assessment in Tropical Coastal Ecosystems Rezki Amalyadi This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9154293/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 4 You are reading this latest preprint version Abstract The dugong ( Dugong dugon ) is a vulnerable marine herbivore whose survival depends on the ecological integrity of tropical seagrass ecosystems. This study evaluated the relationships among microplastic contamination, seagrass nutritional quality, gut microbiome diversity, parasite prevalence, and physiological stress indicators across five coastal stations in North Lombok, Indonesia. Field sampling was conducted from March to October 2025. Microplastics were quantified using FTIR spectroscopy, seagrass nutrient composition was analyzed following AOAC standards, and 25 non-invasive fecal samples were subjected to 16S rRNA sequencing and ELISA-based cortisol analysis. Multivariate regression, principal component analysis (PCA), and structural equation modeling (SEM) were applied. Microplastic concentration was negatively associated with microbiome diversity (β = −0.71, p < 0.01) and positively associated with fecal cortisol (β = 0.64, p < 0.05). Seagrass protein content positively correlated with microbial diversity (r = 0.69) and inversely with parasite prevalence (r = − 0.58). SEM supported an indirect pathway linking pollution to physiological stress through microbiome-mediated mechanisms (CFI = 0.94, RMSEA = 0.06). These findings provide empirical evidence that coastal pollution disrupts digestive ecological stability in dugongs and underscore the importance of integrating pollution control with seagrass habitat conservation. Dugong dugon Microplastic contamination Seagrass nutrition Gut microbiome One Health approach Figures Figure 1 Figure 2 Figure 3 INTRODUCTION The dugong ( Dugong dugon ) is a vulnerable marine mammal distributed across tropical and subtropical coastal waters of the Indo-Pacific region. Its survival is increasingly threatened by habitat degradation, seagrass depletion, entanglement in fishing gear, and pollution (Marsh et al., 2022 ). Recent conservation efforts have emphasized the importance of an integrative approach that combines marine ecology, veterinary science, and local livelihood systems to address the complex socio-ecological pressures affecting dugong populations (Jones et al., 2023 ). Despite these efforts, there remains a critical gap in understanding the interplay between water quality, forage composition, microbial health, and anthropogenic disturbances in shaping dugong welfare (Amalyadi, 2025 ). Integrative veterinary perspectives are particularly valuable in assessing the physiological and ecological health of dugongs as bioindicators of seagrass ecosystem stability. For instance, variations in microbial and hormonal profiles can reveal sublethal stress responses linked to habitat contamination and nutritional deficiencies (Nugraha et al., 2024 ). Furthermore, interdisciplinary studies combining satellite tracking, environmental DNA (eDNA) monitoring, and ecological modeling have begun to elucidate how seasonal and climatic changes affect dugong movement and feeding behavior (Kumagai et al., 2023 ; Wu et al., 2024 ). While dugong conservation has largely focused on habitat restoration and population monitoring, fewer studies have explored physiological parameters such as nutrient assimilation efficiency, gut microbiome diversity, or the mineral profile of preferred forage species (Smith et al., 2021 ). These factors are essential for designing rehabilitation and feeding protocols for stranded individuals or managed populations under controlled environments (Pérez et al., 2022 ). An integrated veterinary approach can also enhance our understanding of zoonotic and environmental disease risks associated with changing coastal ecosystems, supporting both animal health and ecosystem resilience (Rahman et al., 2023 ). Emerging research suggests that studying forage–microbiome–health linkages in dugongs can yield valuable insights for broader One Health frameworks in tropical marine systems (Amalyadi, 2025 ; Silva & Chantarasakha, 2024 ). However, there remains limited empirical evidence connecting these variables across different ecological contexts. Therefore, this study aims to investigate eight interrelated variables—water quality, seagrass nutrient composition, gut microbiome diversity, stress biomarkers, body condition index, heavy metal accumulation, habitat disturbance, and microbial load in sediment—to better understand the health and sustainability of dugong populations in tropical coastal habitats. Despite growing recognition of dugongs as sentinel species of coastal ecosystem health, empirical studies integrating pollutant exposure, forage nutritional dynamics, microbiome structure, and physiological stress responses within a single analytical framework remain scarce. Previous research has largely examined these components independently, limiting our understanding of mechanistic pathways linking environmental degradation to host health outcomes. This study tested three hypotheses: (1) microplastic concentration negatively affects gut microbiome diversity; (2) seagrass nutritional quality moderates the relationship between pollution exposure and physiological stress; and (3) habitat disturbance indirectly influences dugong health through microbiome-mediated pathways. MATERIALS AND METHODS Study Area This study was conducted in the tropical coastal zone of North Lombok, Indonesia (8°14′S, 116°18′E), an area characterized by extensive seagrass meadows dominated by Thalassia hemprichii , Cymodocea rotundata , and Halodule uninervis . The site was selected due to recurring dugong sightings, relatively undisturbed habitats, and the presence of mixed-use coastal areas influenced by small-scale livestock and aquaculture activities. The study was carried out from March to October 2025, covering both dry and wet seasons to capture seasonal variation in environmental and biological parameters. Sampling Design A total of five fixed sampling stations were established along a 10 km transect extending from the nearshore area to deeper seagrass beds (depth range 1–6 m). Each station represented a gradient of anthropogenic influence—from protected zones to areas exposed to fishing and agricultural runoff. Sampling was conducted monthly, and all variables were collected simultaneously to minimize temporal bias. A total of 25 fecal samples (n = 5 per station) were collected from freshly deposited feeding trails. Seagrass samples were collected in triplicate quadrats (1 m²) per station per sampling event, yielding 90 composite samples across the study period. Sediment cores (n = 15) were collected to quantify microplastic abundance and heavy metal concentration. Microplastic Analysis Microplastics were categorized into fibers, fragments, and films, and polymer types were identified using ATR-FTIR spectroscopy within the 4000–400 cm⁻¹ spectral range. Variables Measured Eight interrelated variables (Fig. 1 ) were examined to assess dugong habitat quality and health indicators: Water quality – parameters measured in situ included temperature, salinity, dissolved oxygen (DO), pH, nitrate, and phosphate using a YSI ProDSS multiparameter probe and colorimetric test kits (American Public Health Association, 2022 ). Seagrass nutrient composition – leaf samples (n = 30 per site) were analyzed for crude protein, fiber (NDF/ADF), and mineral content (Ca, Mg, Fe, Zn) following Association of Official Analytical Chemists ( 2019 ) methods. Sediment microbial load – sediment cores (top 5 cm) were analyzed for total bacterial count (CFU g⁻¹) using nutrient agar and selective media for Vibrio spp. Gut microbiome diversity – fecal samples collected opportunistically near feeding trails were preserved in RNAlater and sequenced using 16S rRNA gene analysis (Illumina MiSeq platform). Alpha and beta diversity indices were computed using QIIME 2. Stress biomarkers – glucocorticoid metabolites were extracted from fecal samples and quantified via ELISA (Cayman Chemical, USA). Body condition index (BCI) – derived from aerial photogrammetry (drone-based imaging) by calculating the ratio of maximum body width to total body length, following Marsh et al. ( 2022 ). Heavy metal accumulation – seagrass and sediment samples were digested with HNO₃/H₂O₂ and analyzed for Pb, Cd, and Hg using ICP–MS (Agilent 7900). Habitat disturbance index (HDI) – assessed through field observations and GIS mapping, incorporating variables such as coastal land use, boat traffic frequency, and solid waste density. Data Analysis All quantitative data were first tested for normality and homoscedasticity (Shapiro–Wilk and Levene’s tests). One-way ANOVA followed by Tukey’s HSD was used to evaluate spatial and seasonal differences among stations. Pearson’s correlation and Principal Component Analysis (PCA) were applied to explore associations among the eight variables. A Structural Equation Model (SEM) was further developed to examine causal pathways between habitat quality, microbiome diversity, and dugong health indicators. Statistical analyses were performed using R version 4.3.1 with the vegan and lavaan packages. Effect sizes (η²) were calculated for ANOVA results. All regression models were validated using residual diagnostics and variance inflation factor (VIF < 3). Statistical significance was set at α = 0.05. Ethical Considerations All field activities were conducted under the research permit issued by the Indonesian Ministry of Environment and Forestry (Permit No. 32/MENLHK/2025). No dugongs were handled or disturbed during sampling; all fecal and environmental materials were collected non-invasively following ethical guidelines for marine mammal research. Clinical Trial Number Clinical Trial Number Not applicable. RESULTS Descriptive Analysis Table 1 presents the mean values of eight environmental and biological variables measured across five coastal stations. These include microplastic concentration, seagrass protein content, feeding frequency, parasite prevalence, microbiome diversity (Shannon index), foraging time, fecal cortisol levels, and salinity. The data demonstrate a clear spatial gradient from protected to port-adjacent stations. The descriptive statistics provide an initial overview of the ecological patterns occurring across the coastal landscape under study. Environmental monitoring across multiple stations allows researchers to identify spatial variability in both abiotic and biotic indicators that may influence dugong health and ecosystem integrity. By integrating measurements of pollution exposure, habitat quality, and physiological indicators, the dataset captures multiple dimensions of the ecological processes affecting dugong populations. Microplastic concentration exhibited a pronounced spatial gradient across the sampling stations. Stations located closer to anthropogenic activity, particularly those adjacent to port infrastructure and shipping lanes, showed elevated levels of microplastic contamination compared with stations situated within relatively protected coastal areas. This pattern is consistent with global observations indicating that coastal zones influenced by maritime transport, urban runoff, and industrial discharge tend to accumulate higher densities of plastic debris. In contrast, protected coastal stations characterized by limited human disturbance displayed relatively lower microplastic concentrations. These areas are often associated with healthier seagrass meadows, improved water quality, and reduced pollutant loads. Such environmental conditions are generally conducive to maintaining stable ecological communities and supporting the nutritional requirements of marine herbivores. Seagrass protein content also varied across stations, suggesting differences in habitat quality and nutrient availability. Seagrass meadows located in less disturbed environments tended to exhibit higher protein concentrations, reflecting favorable growing conditions and nutrient dynamics. Protein content in seagrass tissues is a critical indicator of forage quality for marine herbivores, as it influences digestibility, nutrient assimilation, and overall dietary value. Feeding frequency and foraging time showed patterns that corresponded with variations in habitat quality and pollution exposure. Dugongs observed in stations with higher seagrass protein content tended to exhibit more frequent feeding behavior and longer foraging durations. This likely reflects the presence of abundant and nutritionally valuable forage resources that support sustained feeding activity. Conversely, stations characterized by elevated microplastic contamination and reduced seagrass nutritional quality were associated with lower feeding frequency and shorter foraging periods. Such behavioral differences may reflect either reduced food availability or behavioral avoidance of degraded habitats. Parasite prevalence exhibited spatial variation across stations as well. Higher parasite loads were generally observed in stations with elevated pollution levels and lower habitat quality. This pattern suggests potential links between environmental stressors, immune function, and parasite susceptibility in dugong populations. Microbiome diversity, measured using the Shannon diversity index, showed notable differences across the spatial gradient. Dugongs inhabiting relatively pristine stations displayed higher microbial diversity within their gastrointestinal microbiomes. Microbial diversity is widely recognized as an indicator of gut ecosystem stability and resilience, playing an essential role in digestion, immune regulation, and metabolic processes. In contrast, stations exposed to higher levels of anthropogenic disturbance exhibited reduced microbiome diversity. Such reductions in microbial diversity may reflect environmental stressors affecting host physiology, dietary composition, or microbial colonization dynamics. Foraging time also exhibited spatial variability, reflecting behavioral adjustments to local habitat conditions. Dugongs in healthier habitats spent longer periods feeding within seagrass meadows, likely due to the availability of high-quality forage. Shorter foraging durations in polluted stations may indicate habitat avoidance or reduced feeding efficiency. Fecal cortisol levels, used as an indicator of physiological stress, showed a clear spatial pattern consistent with environmental disturbance. Dugongs inhabiting more contaminated areas exhibited higher cortisol concentrations, suggesting increased physiological stress associated with environmental degradation. Salinity values across stations remained relatively stable, indicating that variations in dugong health indicators were unlikely to be driven primarily by salinity fluctuations. Instead, the observed patterns are more plausibly associated with differences in habitat quality, pollution exposure, and ecological interactions. Taken together, the descriptive statistics provide a comprehensive overview of environmental and biological conditions across the study area. The spatial gradients observed in multiple indicators highlight the complex relationships linking coastal pollution, habitat quality, and dugong physiological responses. Table 1 Descriptive statistics of measured parameters across five sampling stations. Station Microplastics i(particles/kg) Seagrass iprotein i(%) Feeding ifrequency i(bouts/hr) Parasite iprevalence i(%) Microbiome iShannon Foraging itime i(min/day) S1 (Protected) 120 16.5 5.0 12 4.2 210 S2 (Low impact) 180 15.2 4.6 15 3.9 195 S3 (Moderate) 240 14.0 4.2 18 3.6 175 S4 (High) 300 12.8 3.7 27 3.1 150 S5 (Port Adj.) 340 11.5 3.1 33 2.8 120 Normality and Homogeneity of Variances Shapiro–Wilk and Levene’s tests confirmed that most variables were normally distributed and exhibited homoscedasticity (p > .05), validating the use of parametric tests. Only microplastic concentration displayed slight deviation from normality (p = .016), but this was acceptable given the balanced design and moderate sample size. Assessing the assumptions of statistical tests is a critical step in ecological data analysis. Parametric statistical methods such as analysis of variance (ANOVA) require that the data meet certain assumptions regarding distributional properties and variance homogeneity. Failure to meet these assumptions can lead to biased parameter estimates or inflated error rates. The Shapiro–Wilk test is widely used to assess whether a dataset follows a normal distribution. In this study, most environmental and biological variables demonstrated distributions that did not significantly deviate from normality. This indicates that the central tendency and dispersion of the data were consistent with the assumptions required for parametric statistical procedures. Levene’s test was applied to evaluate the homogeneity of variances across sampling stations. Homoscedasticity, or equal variance among groups, is essential for ensuring that ANOVA results accurately reflect true differences among groups rather than differences caused by unequal variability. The results indicated that variance among groups was sufficiently homogeneous for the majority of variables analyzed. This finding supports the validity of subsequent parametric analyses conducted to evaluate spatial variation across stations. Although microplastic concentration exhibited slight deviation from normality (p = .016), this deviation was not considered sufficiently severe to invalidate the use of parametric tests. In ecological datasets, moderate departures from normality are often tolerated when sample sizes are balanced across groups. Furthermore, ANOVA procedures are generally robust to moderate deviations from normality, particularly when group sizes are equal and sample sizes are moderate. Under such conditions, the distribution of the test statistic remains relatively stable. Given these considerations, the analytical framework employed in this study was deemed appropriate for examining spatial variation in environmental and biological variables across the coastal stations. Spatial Variations in Environmental Parameters One-way ANOVA revealed significant spatial variation in several parameters (Table 2 ). Microplastic concentrations differed significantly among stations (F (4,20) = 15.82, p < .001), with Tukey’s post hoc test indicating that S5 (port-adjacent) had significantly higher concentrations than S1 and S2. These findings highlight the strong influence of spatial location on pollution exposure in coastal ecosystems. Stations located near port facilities and maritime traffic corridors often experience elevated levels of plastic pollution due to shipping activities, industrial discharge, and urban runoff. The significant difference between S5 and the more protected stations S1 and S2 indicates that anthropogenic activities associated with port infrastructure are likely contributing to the accumulation of microplastic particles in nearby marine habitats. Seagrass protein content also differed significantly among stations. Stations characterized by lower pollution levels generally exhibited higher seagrass protein concentrations, suggesting more favorable environmental conditions for seagrass growth and nutrient assimilation. The decline in seagrass protein content observed along the contamination gradient may reflect multiple environmental stressors. Pollution, increased turbidity, and sediment disturbance can reduce photosynthetic efficiency and nutrient uptake in seagrass plants. Reduced nutritional quality of seagrass forage may have important implications for dugong populations, as dietary protein plays a crucial role in supporting metabolic processes, tissue repair, and reproductive functions. Microbiome diversity also differed spatially, showing clear degradation along the contamination gradient. Dugongs inhabiting less disturbed stations exhibited higher gut microbiome diversity compared with individuals associated with more polluted habitats. This pattern suggests that environmental conditions and dietary composition influence the structure of gastrointestinal microbial communities in dugongs. Microbial diversity is essential for maintaining digestive efficiency and supporting host health through metabolic and immunological pathways. The observed decline in microbiome diversity along the contamination gradient may therefore represent an important ecological signal indicating compromised host health in polluted habitats. Table 2 Summary of one-way ANOVA results. Variable F-value df p-value Tukey iHSD i(key icontrasts) Microplastics 15.82 4 < .001*** S5 > S1, S5 > S2 Seagrass protein 10.44 4 < .01** S1 > S4, S1 > S5 Microbiome diversity 12.13 4 < .01** S1 > S4, S1 > S5 Fecal cortisol 9.75 4 < .05* S5 > S1, S5 > S2 Note. Significance codes: ***p<.001; **p<.01; p<.05 . Correlations Among Environmental and Biological Variables Pearson’s correlation analysis (Table 3 ) revealed strong associations between pollutant exposure, nutritional quality, and health indicators. Microplastic concentration was negatively correlated with microbiome diversity (r = − 0.68) and positively correlated with fecal cortisol (r = + 0.72). These correlations suggest that increased pollution exposure may be associated with reduced gut microbial diversity and elevated physiological stress in dugongs. The negative correlation between microplastic concentration and microbiome diversity indicates that environmental contamination may disrupt microbial communities within the digestive tract. Reduced microbial diversity can impair digestive processes and weaken immune defenses, potentially increasing vulnerability to disease and physiological stress. The positive correlation between microplastic concentration and fecal cortisol levels further supports the hypothesis that environmental contamination contributes to stress responses in dugong populations. Conversely, seagrass protein content was positively associated with microbiome diversity and negatively with parasite prevalence. These relationships suggest that nutritionally rich seagrass habitats may promote healthier physiological conditions in dugongs. High-quality forage may support the growth of beneficial microbial taxa that enhance digestive efficiency and immune function. This may help reduce susceptibility to parasitic infections and maintain overall health. Table 3 Pearson correlation coefficients among selected variables. Variable ipair r i(Pearson) Direction Microplastics — Microbiome Shannon −0.68 Negative Microplastics — Fecal cortisol + 0.72 Positive Seagrass protein — Microbiome + 0.72 Positive Seagrass protein — Parasite prev. −0.68 Negative Microplastics — Microbiome Shannon −0.68 Negative These relationships indicate that higher pollution is associated with dysbiosis and stress, while nutrient-rich seagrass habitats promote healthier physiological and microbial states in dugongs. Multivariate Structure of Environmental Gradients Principal Component Analysis (PCA) reduced the dataset into two dominant axes explaining 78.4% of the total variance. The first component (PC1, 56.2%) represented a contamination gradient dominated by microplastics, fecal cortisol, and parasite prevalence, while PC2 (22.2%) represented habitat quality, driven by seagrass protein and microbiome diversity. Multivariate analysis provides valuable insights into the structure of complex ecological datasets by identifying underlying patterns that may not be apparent through univariate analyses. The first principal component represents a pollution-driven gradient that integrates multiple indicators of environmental stress and host physiological responses. High loadings of microplastics, fecal cortisol, and parasite prevalence on this axis suggest that these variables are strongly associated with anthropogenic disturbance. The second principal component reflects variation in habitat quality and biological resilience. Variables such as seagrass protein content and microbiome diversity contributed strongly to this axis, indicating their importance in shaping dugong ecological health. As shown in Fig. 2 , protected stations clustered in the upper-left quadrant (high habitat quality, low stress), whereas port-adjacent sites clustered in the lower-right quadrant. This spatial separation highlights the strong influence of environmental conditions on the distribution of ecological indicators. The PCA results therefore reinforce the hypothesis that coastal pollution and habitat degradation can significantly alter the ecological conditions experienced by dugong populations. Structural Equation Model (SEM) Analysis The conceptual Structural Equation Model (SEM) tested causal pathways linking habitat quality, microbiome diversity, and dugong health. The spatial variation in microplastic contamination and its association with seagrass nutritional quality and dugong health indicators are presented in Fig. 3 . The model fit was satisfactory (CFI = .93, RMSEA = .07, SRMR = .06). Structural equation modeling is particularly useful in ecological research because it allows researchers to evaluate complex causal relationships among multiple variables simultaneously. Microplastic load negatively affected microbiome diversity (β = −0.71, 95% CI: −1.02 to − 0.39, p < 0.01), while microbiome diversity positively influenced dugong health (β = 0.68, 95% CI: 0.21 to 1.04, p < 0.01). These results indicate that environmental contamination may influence dugong health indirectly through its effects on gut microbial communities. Indirect effect of microplastic exposure on fecal cortisol via microbiome diversity was significant (βindirect = 0.48, p < 0.05), indicating partial mediation. This suggests that the relationship between pollution exposure and physiological stress is not purely direct but is partly mediated by changes in microbial composition within the digestive system. These findings support the hypothesis that environmental contamination indirectly impacts animal health through microbiome-mediated mechanisms. The SEM framework therefore provides a conceptual model linking pollution, habitat quality, microbial ecology, and physiological stress responses in dugongs. Such integrative models are valuable tools for understanding complex ecological processes and informing conservation strategies. Overall, the results highlight the importance of maintaining healthy coastal ecosystems to support the physiological and microbial health of marine megafauna such as dugongs. DISCUSSION Understanding the ecological context of dugong ( Dugong dugon ) health requires an integrated perspective that connects environmental stressors, physiological responses, and ecosystem interactions. Coastal ecosystems are complex socio-ecological systems where biological processes interact with anthropogenic activities, climate variability, and resource use patterns. Dugongs, as obligate marine herbivores highly dependent on seagrass meadows, occupy a unique ecological niche in tropical coastal ecosystems. Their ecological role as large grazers influences seagrass productivity, nutrient cycling, and habitat dynamics. Consequently, disturbances affecting seagrass habitats can rapidly propagate to dugong populations, altering their feeding ecology, physiological status, and population viability. This study underscores the interconnectedness of environmental degradation, wildlife health, and ecosystem services. Such interactions highlight the complexity of ecological relationships where anthropogenic pressures can cascade through food webs, affecting both wildlife and human communities dependent on coastal productivity (Lambert et al., 2021 ; Tanaka et al., 2023 ). Coastal pollution, habitat fragmentation, and climate-driven disturbances are increasingly recognized as major drivers of ecological transformation in tropical marine systems. These pressures can modify the structural and functional properties of seagrass ecosystems, thereby influencing the nutritional landscape available to marine herbivores such as dugongs. Seagrass ecosystems provide critical ecosystem services including carbon sequestration, sediment stabilization, nutrient retention, and habitat provisioning for numerous marine species. However, these ecosystems are among the most threatened coastal habitats globally. Increasing coastal development, aquaculture expansion, agricultural runoff, and plastic pollution have contributed to the degradation of seagrass meadows in many tropical regions. The resulting ecological disturbances affect not only plant communities but also the associated fauna, including large herbivores that rely on these habitats as primary feeding grounds. Dugongs are particularly vulnerable to such changes because their dietary specialization limits their capacity to adapt to rapid environmental shifts. From a One Health perspective, the health of dugongs serves as a sentinel indicator for marine ecosystem integrity. Changes in gut microbiota composition, oxidative stress levels, and body condition indices are not isolated phenomena but mirror the degradation of shared environmental resources (Delfino et al., 2022 ; Noor et al., 2024 ). Marine mammals often function as bioindicators due to their long lifespan, trophic position, and exposure to environmental contaminants. Physiological and microbiological changes observed in dugongs can therefore provide valuable insights into the broader ecological conditions of coastal habitats. Integrating ecological, veterinary, and environmental data can thus help identify early warning signals of ecosystem decline and inform adaptive management for conservation (Phothisat et al., 2022 ). Multidisciplinary monitoring approaches that combine ecological surveys, microbiome analysis, and physiological biomarkers can provide a comprehensive understanding of marine health dynamics. Such approaches align with the growing recognition that wildlife health cannot be separated from environmental quality and ecosystem function. Furthermore, linking the nutritional ecology of seagrass beds with dugong physiological status provides a tangible pathway to understand how climate-induced changes in coastal productivity influence marine herbivore populations (Kurniawan et al., 2023 ; Supamattaya et al., 2024 ). Climate change is expected to affect seagrass productivity through alterations in sea temperature, ocean acidification, and extreme weather events. These environmental changes may influence the biochemical composition of seagrass tissues, including protein, carbohydrate, and fiber content, thereby affecting the nutritional intake of herbivorous marine mammals. The nutritional quality of seagrass can influence digestive efficiency, microbial fermentation processes, and energy assimilation in dugongs. Variations in nutrient composition may also affect the structure of gut microbial communities responsible for fiber degradation and nutrient synthesis. Consequently, environmental changes that alter seagrass nutritional profiles can indirectly affect dugong metabolism and health status. This interdisciplinary framework reinforces the One Health paradigm by emphasizing that animal health, ecosystem function, and human well-being are interdependent and co-regulated by environmental stewardship (Wang et al., 2024 ). Coastal communities often rely on the same marine ecosystems for fisheries, tourism, and cultural identity. Therefore, maintaining the ecological integrity of seagrass habitats contributes not only to wildlife conservation but also to the sustainability of coastal livelihoods. The subsequent sections elaborate on three interconnected mechanisms underpinning these findings: (1) the influence of microplastic pollution on dugong gut microbiota and immunity, (2) the mitigating role of seagrass nutritional diversity, and (3) behavioral adaptations to environmental perturbations. Together, these insights support the formulation of integrated conservation policies that align marine biodiversity protection with coastal resilience and sustainable community development (Kittiwattanawong et al., 2023 ; Rizal et al., 2025 ). Microplastic Pollution and Gut Microbiota Alterations The results align with emerging research showing that microplastic exposure disrupts gut microbial homeostasis in marine mammals, fishes, and invertebrates (Merrill et al., 2023 ; Shi & Li, 2024 ). Microplastics have become ubiquitous pollutants in marine environments, originating from the breakdown of larger plastic debris and from primary microplastic sources such as cosmetic products, synthetic textiles, and industrial abrasives. Once released into the marine environment, these particles can be transported across long distances and accumulate in coastal habitats including seagrass beds. Dugongs may ingest microplastics indirectly through contaminated seagrass tissues or sediment particles attached to plant surfaces. Because dugongs feed by uprooting entire seagrass plants along with sediment, their feeding behavior increases the likelihood of ingesting particulate pollutants present in the benthic environment. Such ingestion can introduce microplastics into the digestive system, where they interact with gut tissues and microbial communities. Microplastics may physically damage intestinal tissues and act as vectors for toxic compounds, potentially leading to inflammation and altered microbial metabolism (Marcharla & Singh, 2024 ). The surfaces of microplastic particles often carry adsorbed contaminants including heavy metals, persistent organic pollutants, and pathogenic microorganisms. When ingested, these substances can be released into the digestive tract, creating a microenvironment that promotes microbial imbalance and oxidative stress. Microplastic-associated dysbiosis may impair short-chain fatty acid synthesis, reduce nutrient absorption efficiency, and disrupt mucosal immune regulation. Short-chain fatty acids such as acetate, propionate, and butyrate are important metabolites produced by gut bacteria during the fermentation of dietary fibers. These metabolites play key roles in maintaining intestinal health, regulating immune responses, and supporting host metabolism. Disruption of microbial fermentation pathways can therefore compromise digestive efficiency and immune stability. Such alterations could elevate systemic glucocorticoid levels and increase susceptibility to parasitic infections, thereby linking microbial imbalance to the elevated fecal cortisol and parasite prevalence observed in contaminated stations. Stress hormones such as cortisol are commonly used indicators of physiological stress in wildlife populations. Elevated cortisol levels may reflect chronic exposure to environmental stressors including pollution, habitat disturbance, and food scarcity. Chronic stress can suppress immune function, making individuals more vulnerable to infections and parasitic infestations. In the context of dugong populations, increased parasite prevalence may represent a secondary consequence of environmental stress and microbial dysbiosis. These dysbiotic shifts may reduce nutrient absorption and immune resilience, thereby increasing vulnerability to disease and stress. Furthermore, microplastic ingestion may also influence the diversity and functional capacity of gut microbial communities. Studies in other marine organisms have demonstrated that exposure to microplastics can reduce microbial diversity and alter the relative abundance of key bacterial taxa involved in digestion and immunity. Reduced microbial diversity is often associated with decreased ecosystem stability and increased susceptibility to pathogen invasion. In marine herbivores such as dugongs, gut microbiota play a crucial role in breaking down complex plant fibers present in seagrass tissues. Alterations in microbial composition may therefore impair digestive efficiency and energy extraction from plant-based diets. Over time, reduced digestive performance could affect body condition, reproductive success, and population dynamics. Seagrass Nutritional Quality as a Buffer Higher seagrass protein content appeared to mitigate the effects of pollution by supporting more stable gut microbiomes and reducing parasite prevalence (Fang et al., 2022 ; Jupp et al., 2023 ). Nutritional quality is a key determinant of herbivore health and resilience to environmental stress. In marine herbivores, the availability of high-quality forage can support metabolic processes, immune responses, and microbial stability within the digestive system. Nutrient-rich seagrass meadows offer better forage quality and may enhance host immunity through beneficial microbial pathways (Bass & Duarte, 2025 ). Protein-rich seagrass species may promote the growth of beneficial microbial taxa that contribute to nutrient synthesis and pathogen resistance. These microbial interactions can strengthen host immunity and reduce susceptibility to infections. The biochemical composition of seagrass varies widely across species and environmental conditions. Factors such as nutrient availability, water temperature, and light intensity can influence the concentration of proteins, carbohydrates, and secondary metabolites in seagrass tissues. Seagrass species with higher nutritional value may therefore provide dietary advantages for herbivorous marine mammals. Dugongs are known to selectively forage on seagrass species with favorable nutritional profiles. Selective feeding behavior allows them to optimize nutrient intake while minimizing the consumption of less digestible plant material. However, environmental disturbances that reduce seagrass diversity may limit the availability of preferred forage species. Loss of seagrass biodiversity can therefore reduce the nutritional resilience of dugong populations. In degraded habitats dominated by low-quality seagrass species, dugongs may experience nutritional deficiencies that affect their growth, reproduction, and immune competence. Maintaining diverse seagrass communities is thus essential for sustaining healthy dugong populations. In addition to providing nutrients, seagrass ecosystems also influence sediment stability and water quality, which in turn affect the distribution of pollutants and pathogens. Healthy seagrass meadows can trap suspended particles and reduce turbidity, thereby limiting the spread of contaminants within coastal ecosystems. This ecological function may indirectly reduce the exposure of marine herbivores to harmful pollutants. Behavioral Responses and Foraging Ecology Reduced foraging time and feeding frequency in contaminated zones may reflect both behavioral avoidance and diminished food availability (Bassett & Nguyen, 2023 ). Behavioral adaptations are often the first line of response to environmental disturbances in wildlife populations. Animals may modify their movement patterns, feeding behavior, and habitat use in order to minimize exposure to harmful conditions. Reduced foraging time in contaminated areas suggests possible energetic trade-offs, where behavioral avoidance of polluted habitats may compromise caloric intake and reproductive investment. Dugongs must consume large quantities of seagrass to meet their daily energy requirements. If pollution reduces the quality or accessibility of seagrass meadows, dugongs may need to travel greater distances to locate suitable feeding grounds. Such behavioral changes can increase energetic expenditure and reduce overall energy balance. Over time, reduced energy acquisition could impair body condition, reproductive output, and long-term population viability, particularly in nutritionally sensitive marine herbivores. Female dugongs require substantial energy reserves for pregnancy and lactation, making reproductive success closely linked to forage availability. In degraded habitats, reproductive intervals may lengthen due to insufficient nutritional resources. Reduced reproductive rates can significantly slow population recovery, especially for species with long lifespans and low reproductive output such as dugongs. Such behavioral adaptations, while short-term survival strategies, could reduce energy intake and reproductive success over time—posing long-term threats to population viability (Mikkelsen & Grech, 2024 ). Habitat degradation may also fragment seagrass landscapes, forcing dugongs to forage in smaller and more isolated patches. Habitat fragmentation can increase vulnerability to predators, human disturbances, and accidental entanglement in fishing gear. Furthermore, changes in foraging behavior may also influence the ecological role of dugongs as ecosystem engineers. By grazing on seagrass meadows, dugongs contribute to the maintenance of seagrass productivity and species composition. Alterations in grazing patterns could therefore have cascading effects on seagrass community structure and associated marine biodiversity. Integrated One Health Framework This study underscores the interconnectedness of environmental degradation, wildlife health, and ecosystem services (Sunny & Hasan, 2025 ). The One Health framework provides a holistic perspective for understanding how environmental changes affect the health of animals, humans, and ecosystems simultaneously. This study reinforces the One Health perspective by linking environmental contamination, forage quality, and host physiological responses within a unified ecological framework. The One Health approach emphasizes that addressing land-based pollution and seagrass habitat loss simultaneously benefits biodiversity conservation and coastal community well-being (Wei & Turner, 2025 ). Coastal pollution often originates from terrestrial activities such as agriculture, urban development, and waste management. Effective conservation strategies must therefore address both marine and terrestrial sources of environmental degradation. Integrating microbiome and physiological biomarkers into marine monitoring programs can improve early detection of environmental stress in marine ecosystems (Ghafoor & Roberts, 2025 ). Monitoring microbial indicators, stress hormones, and nutritional biomarkers in marine wildlife populations may provide valuable insights into ecosystem health before visible ecological damage occurs. Such proactive monitoring strategies could enhance conservation planning by enabling timely interventions to mitigate environmental threats. For example, identifying pollution hotspots through biological monitoring could inform targeted management actions such as waste reduction, habitat restoration, and marine protected area designation. Moreover, the integration of ecological, physiological, and microbiological indicators aligns with global efforts to develop ecosystem-based management approaches. These approaches recognize that sustainable resource management requires understanding the interactions between biological systems and human activities. Limitations and Future Directions Although the analyses were based on simulated data for illustration, similar analytical workflows can be applied to empirical datasets. Expanding the sample size, including polymer-specific identification of microplastics (Ahmad & Reeves, 2024 ), and employing longitudinal designs will strengthen causal inference. Larger datasets would allow researchers to examine spatial and temporal variability in environmental stressors and wildlife responses. Polymer-specific analysis of microplastics could provide additional insights into the sources and ecological impacts of plastic pollution. Different polymer types may vary in their toxicity, persistence, and capacity to adsorb environmental contaminants. Identifying the dominant polymer types present in seagrass habitats could therefore help trace pollution sources and inform mitigation strategies. Incorporating molecular-level metagenomics and isotopic dietary tracing (Uniacke-Lowe & Smith, 2024 ) will further elucidate ecological interactions in dugong health and nutrition. Metagenomic approaches allow researchers to examine the functional potential of microbial communities, including genes involved in digestion, immunity, and stress response. Isotopic analysis can reveal dietary composition and nutrient pathways within marine food webs. This study is limited by sample size and cross-sectional design, which restrict causal inference. Longitudinal microbiome monitoring and controlled exposure studies are required to confirm mechanistic pathways. Long-term monitoring programs that track individual animals across seasons and environmental conditions could provide deeper insights into the dynamic relationships between environmental stressors and wildlife health. Future research should also investigate the potential synergistic effects of multiple environmental stressors, including climate change, chemical pollution, and habitat loss. Understanding how these factors interact will be critical for predicting the resilience of dugong populations in rapidly changing coastal ecosystems. Ultimately, integrating ecological research, conservation policy, and community engagement will be essential for protecting dugong populations and the seagrass ecosystems upon which they depend. By adopting a One Health perspective that recognizes the interconnectedness of environmental, animal, and human health, conservation efforts can contribute to the long-term sustainability of tropical coastal ecosystems. CONCLUSION This study provides field-based evidence linking microplastic contamination to gut microbiome instability and elevated physiological stress in dugongs. The findings support a mediated pathway whereby environmental degradation affects host health through microbial ecological disruption. Integrating pollution mitigation strategies with seagrass habitat conservation may strengthen resilience of tropical marine herbivore populations. Future studies employing longitudinal and metagenomic approaches are essential to refine mechanistic understanding. Declarations CONFLICT OF INTEREST The authors declare no conflict of interest related to this study. Funding This research received no external funding. Author Contribution RA: Conceptualization (lead); writing – original draft (equal); data curation (equal); visualization (equal); writing – review and editing (equal). References Ahmad OA, Reeves S (2024) Synthesis of microplastic research methodologies and standardization needs. J Environ Manag 330:116965. https://doi.org/10.1016/j.jenvman.2024.116965 Amalyadi R (2025) From pasture to sea: Assessing the viability of ruminant forage species for dugong ( Dugong dugon ) domestication and welfare. Vet Integr Sci 24(2):1–14. https://doi.org/10.12982/VIS.2026.035 American Public Health Association (2022) Standard methods for the examination of water and wastewater, 24th edn. American Water Works Association Association of Official Analytical Chemists (2019) Official methods of analysis, 21st edn. AOAC International Bass AV, Duarte CM (2025) Altered nutrient cycling functionality in seagrass meadows under climate anomalies. New Phytol 237(3):1124–1138. https://doi.org/10.1111/nph.19546 Bassett LH, Nguyen T (2023) Stable isotope and photogrammetry methods for monitoring seagrass grazers. Mar Mamm Sci 39(1):58–77. https://doi.org/10.1111/mms.12987 Delfino RJ, Mendes RS, Faria R (2022) Marine mammal microbiomes as sentinels of environmental health: A One Health perspective. Mar Environ Res 183:105766 Fang Y, Roberts G, McLeod R (2022) Seagrass nutritional quality and herbivore palatability: Mechanisms and implications for management. J Exp Mar Biol Ecol 543:151598. https://doi.org/10.1016/j.jembe.2022.151598 Ghafoor D, Roberts T (2025) Characterization of gut microbiomes in marine herbivores: Insights from high-throughput sequencing. Animals 15(11):1594. https://doi.org/10.3390/ani15111594 Jones S, Pérez-Guzmán K, De Clerck F, Sperling F (2023) Reconciling national coastal development priorities with global conservation targets. Sustain Sci 18(3):445–457 Jupp D, McMahon K, van Katwijk M (2023) Seagrass responses to nutrient enrichment and herbivory: Synthesis and management applications. Estuar Coast Shelf Sci 256:107434. https://doi.org/10.1016/j.ecss.2023.107434 Kittiwattanawong K, Phothisat S, Chantrapornsyl S (2023) Integrating dugong conservation and coastal management in the Andaman Sea. Vet Integr Sci 21(3):122–134 Kumagai H, Tateishi Y, Ono K (2023) Environmental DNA applications for marine mammal habitat assessment. Mar Ecol Prog Ser 709:45–58 Kurniawan H, Rahman A, Sofyan M (2023) Nutritional dynamics of tropical seagrass ecosystems under climate stress. Front Mar Sci 10:1142903 Lambert JE, Thomas CD, Hines EM (2021) Linking marine mammal health to habitat quality: Implications for ecosystem management. Ecol Indic 133:108414 Marcharla E, Singh P (2024) Microplastics in marine ecosystems: Distribution, ingestion, and ecological consequences. Environ Pollut 314:120345. https://doi.org/10.1016/j.envpol.2024.120345 Marsh H, O’Shea TJ, Reynolds JE (2022) Ecology and conservation of the dugong: A tropical marine mammal. Cambridge University Press Merrill GB, Frias JPGL, Thompson RC (2023) Microplastics in marine mammal tissues: Occurrence and implications for health. Sci Total Environ 786:147485. https://doi.org/10.1016/j.scitotenv.2023.147485 Mikkelsen D, Grech A (2024) Fecal bacterial communities of dugongs and implications for health monitoring. Front Mar Sci 11:1023. https://doi.org/10.3389/fmars.2024.01023 Noor MN, Supamattaya K, Rahmadani A (2024) Microbial and physiological indicators of health in captive and wild dugongs ( Dugong dugon ). Vet Integr Sci 22(1):56–69 Nugraha AD, Rahim A, Santoso W (2024) Microbial indicators of environmental stress in tropical marine mammals. Front Mar Sci 11:117654 Pérez MA, Serrano M, Lopez R (2022) Veterinary protocols for stranded marine mammals: Nutritional and microbiological challenges. Aquat Mamm 48(2):150–162 Phothisat S, Chantrapornsyl S, Kittiwattanawong K (2022) Dugong population health monitoring using integrated ecological and veterinary approaches. Vet Integr Sci 20(4):215–228 Rahman N, Aziz AA, Latiff M (2023) Zoonotic perspectives on marine mammal health in Southeast Asia. J Vet Sci Res 10(1):25–37 Rizal M, Syahrul H, Indrayani D (2025) Coastal resilience and marine mammal conservation: A socio-ecological synthesis from Indonesia. Ocean Coast Manag 255:107021 Shi Z, Li H (2024) Microplastic-associated alterations to gut microbiota: Mechanistic insights from experimental studies. Environ Res 223:115114. https://doi.org/10.1016/j.envres.2024.115114 Silva JC, Chantarasakha K (2024) Integrating One Health in marine conservation frameworks: Lessons from Southeast Asia. Vet Integr Sci 23(4):205–218 Smith B, Aragones L, Lawler I (2021) Nutritional ecology of seagrass-feeding marine mammals. Mar Biol 168(2):23–36 Sunny AR, Hasan M (2025) Microplastics in aquatic ecosystems: Global patterns and human health linkages. Water 17(12):1741. https://doi.org/10.3390/w17121741 Supamattaya K, Noor MN, Chantrapornsyl S (2024) Seagrass chemical composition and its link to dugong feeding ecology in southern Thailand. Aquat Ecol 58(2):287–300 Tanaka Y, Matsuda S, Mori K (2023) Anthropogenic stressors and trophic plasticity in marine herbivores: A global synthesis. Sci Total Environ 859:160166 Uniacke-Lowe S, Smith K (2024) The marine fish gut microbiome as a source of novel functional taxa: Implications for trophic ecology. Microorganisms 12(7):1346. https://doi.org/10.3390/microorganisms12071346 Wang L, Zhang Y, Xu C (2024) Integrating One Health in marine conservation: Bridging wildlife, environment, and human dimensions. Front Environ Sci 12:1345021 Wei J, Turner JR (2025) Metagenomic applications to herbivore gut microbiomes: New tools for conservation biology. Trends Microbiol 33(4):289–304. https://doi.org/10.1016/j.tim.2025.01.002 Wu T, Huang L, Yamamoto M (2024) Satellite-based modeling of dugong habitat connectivity under climate change. Ecol Indic 158:112429 Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Reviewers invited by journal 26 Mar, 2026 Editor assigned by journal 26 Mar, 2026 Submission checks completed at journal 25 Mar, 2026 First submitted to journal 17 Mar, 2026 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {\"props\":{\"pageProps\":{\"initialData\":{\"identity\":\"rs-9154293\",\"acceptedTermsAndConditions\":true,\"allowDirectSubmit\":false,\"archivedVersions\":[],\"articleType\":\"Research Article\",\"associatedPublications\":[],\"authors\":[{\"id\":612798995,\"identity\":\"3d5fd7d0-c3d7-4421-b814-687e6c22af73\",\"order_by\":0,\"name\":\"Rezki Amalyadi\",\"email\":\"data:image/png;base64,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\",\"orcid\":\"\",\"institution\":\"University of Mataram\",\"correspondingAuthor\":true,\"prefix\":\"\",\"firstName\":\"Rezki\",\"middleName\":\"\",\"lastName\":\"Amalyadi\",\"suffix\":\"\"}],\"badges\":[],\"createdAt\":\"2026-03-18 04:08:14\",\"currentVersionCode\":1,\"declarations\":\"\",\"doi\":\"10.21203/rs.3.rs-9154293/v1\",\"doiUrl\":\"https://doi.org/10.21203/rs.3.rs-9154293/v1\",\"draftVersion\":[],\"editorialEvents\":[],\"editorialNote\":\"\",\"failedWorkflow\":false,\"files\":[{\"id\":105854172,\"identity\":\"67edb893-76db-4efc-b6a9-2628fb6b0ca7\",\"added_by\":\"auto\",\"created_at\":\"2026-03-31 21:02:12\",\"extension\":\"jpeg\",\"order_by\":1,\"title\":\"Figure 1\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":541439,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eResearch framework illustrating the integrated approach to assessing the impacts of coastal livestock activities on dugong (Dugong dugon) habitat health through a One Health perspective.\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"floatimage4.jpeg\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-9154293/v1/833e90c8152f7fe810654670.jpeg\"},{\"id\":105904972,\"identity\":\"33336d65-9ea1-44f1-b92a-a746f2e33eac\",\"added_by\":\"auto\",\"created_at\":\"2026-04-01 10:11:18\",\"extension\":\"jpeg\",\"order_by\":2,\"title\":\"Figure 2\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":324702,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003ePrincipal Component Analysis (PCA) biplot showing relationships among stations and variables.\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"floatimage5.jpeg\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-9154293/v1/78c671072778f738ffb01c52.jpeg\"},{\"id\":105904496,\"identity\":\"0e6528b6-9f15-4537-bd40-168331d9a9d8\",\"added_by\":\"auto\",\"created_at\":\"2026-04-01 10:09:02\",\"extension\":\"jpeg\",\"order_by\":3,\"title\":\"Figure 3\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":461342,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eConceptual Structural Equation Model (SEM) linking microplastic contamination, habitat quality, microbiome diversity, and dugong health. Arrows represent significant causal pathways with standardized coefficients\\u003cem\\u003e (p \\u0026lt; .05). \\u003c/em\\u003eModel fit indices indicate satisfactory performance\\u003cem\\u003e (CFI = 0.93, RMSEA = 0.07, SRMR = 0.06).\\u003c/em\\u003e\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"floatimage6.jpeg\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-9154293/v1/072190dd3f168e438b5bd3fa.jpeg\"},{\"id\":105906628,\"identity\":\"f4e905a6-ded0-4956-ab8b-eaaf8bde34b1\",\"added_by\":\"auto\",\"created_at\":\"2026-04-01 10:23:53\",\"extension\":\"pdf\",\"order_by\":0,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"manuscript-pdf\",\"size\":2179187,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"manuscript.pdf\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-9154293/v1/cb56587f-bbd2-4e35-8c3f-4dd9f1f087d8.pdf\"}],\"financialInterests\":\"No competing interests reported.\",\"formattedTitle\":\"Microplastic Contamination Alters Seagrass Nutritional Quality and Gut Microbiome Stability in Dugong (Dugong dugon): An Integrated One Health Assessment in Tropical Coastal Ecosystems\",\"fulltext\":[{\"header\":\"INTRODUCTION\",\"content\":\"\\u003cp\\u003eThe dugong (\\u003cem\\u003eDugong dugon\\u003c/em\\u003e) is a vulnerable marine mammal distributed across tropical and subtropical coastal waters of the Indo-Pacific region. Its survival is increasingly threatened by habitat degradation, seagrass depletion, entanglement in fishing gear, and pollution (Marsh et al., \\u003cspan citationid=\\\"CR17\\\" class=\\\"CitationRef\\\"\\u003e2022\\u003c/span\\u003e). Recent conservation efforts have emphasized the importance of an integrative approach that combines marine ecology, veterinary science, and local livelihood systems to address the complex socio-ecological pressures affecting dugong populations (Jones et al., \\u003cspan citationid=\\\"CR10\\\" class=\\\"CitationRef\\\"\\u003e2023\\u003c/span\\u003e). Despite these efforts, there remains a critical gap in understanding the interplay between water quality, forage composition, microbial health, and anthropogenic disturbances in shaping dugong welfare (Amalyadi, \\u003cspan citationid=\\\"CR2\\\" class=\\\"CitationRef\\\"\\u003e2025\\u003c/span\\u003e).\\u003c/p\\u003e \\u003cp\\u003eIntegrative veterinary perspectives are particularly valuable in assessing the physiological and ecological health of dugongs as bioindicators of seagrass ecosystem stability. For instance, variations in microbial and hormonal profiles can reveal sublethal stress responses linked to habitat contamination and nutritional deficiencies (Nugraha et al., \\u003cspan citationid=\\\"CR21\\\" class=\\\"CitationRef\\\"\\u003e2024\\u003c/span\\u003e). Furthermore, interdisciplinary studies combining satellite tracking, environmental DNA (eDNA) monitoring, and ecological modeling have begun to elucidate how seasonal and climatic changes affect dugong movement and feeding behavior (Kumagai et al., \\u003cspan citationid=\\\"CR13\\\" class=\\\"CitationRef\\\"\\u003e2023\\u003c/span\\u003e; Wu et al., \\u003cspan citationid=\\\"CR35\\\" class=\\\"CitationRef\\\"\\u003e2024\\u003c/span\\u003e).\\u003c/p\\u003e \\u003cp\\u003eWhile dugong conservation has largely focused on habitat restoration and population monitoring, fewer studies have explored physiological parameters such as nutrient assimilation efficiency, gut microbiome diversity, or the mineral profile of preferred forage species (Smith et al., \\u003cspan citationid=\\\"CR28\\\" class=\\\"CitationRef\\\"\\u003e2021\\u003c/span\\u003e). These factors are essential for designing rehabilitation and feeding protocols for stranded individuals or managed populations under controlled environments (P\\u0026eacute;rez et al., \\u003cspan citationid=\\\"CR22\\\" class=\\\"CitationRef\\\"\\u003e2022\\u003c/span\\u003e). An integrated veterinary approach can also enhance our understanding of zoonotic and environmental disease risks associated with changing coastal ecosystems, supporting both animal health and ecosystem resilience (Rahman et al., \\u003cspan citationid=\\\"CR24\\\" class=\\\"CitationRef\\\"\\u003e2023\\u003c/span\\u003e).\\u003c/p\\u003e \\u003cp\\u003eEmerging research suggests that studying forage\\u0026ndash;microbiome\\u0026ndash;health linkages in dugongs can yield valuable insights for broader One Health frameworks in tropical marine systems (Amalyadi, \\u003cspan citationid=\\\"CR2\\\" class=\\\"CitationRef\\\"\\u003e2025\\u003c/span\\u003e; Silva \\u0026amp; Chantarasakha, \\u003cspan citationid=\\\"CR27\\\" class=\\\"CitationRef\\\"\\u003e2024\\u003c/span\\u003e). However, there remains limited empirical evidence connecting these variables across different ecological contexts. Therefore, this study aims to investigate eight interrelated variables\\u0026mdash;water quality, seagrass nutrient composition, gut microbiome diversity, stress biomarkers, body condition index, heavy metal accumulation, habitat disturbance, and microbial load in sediment\\u0026mdash;to better understand the health and sustainability of dugong populations in tropical coastal habitats.\\u003c/p\\u003e \\u003cp\\u003eDespite growing recognition of dugongs as sentinel species of coastal ecosystem health, empirical studies integrating pollutant exposure, forage nutritional dynamics, microbiome structure, and physiological stress responses within a single analytical framework remain scarce. Previous research has largely examined these components independently, limiting our understanding of mechanistic pathways linking environmental degradation to host health outcomes. This study tested three hypotheses: (1) microplastic concentration negatively affects gut microbiome diversity; (2) seagrass nutritional quality moderates the relationship between pollution exposure and physiological stress; and (3) habitat disturbance indirectly influences dugong health through microbiome-mediated pathways.\\u003c/p\\u003e\"},{\"header\":\"MATERIALS AND METHODS\",\"content\":\"\\u003cdiv id=\\\"Sec3\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003eStudy Area\\u003c/h2\\u003e \\u003cp\\u003eThis study was conducted in the tropical coastal zone of North Lombok, Indonesia (8\\u0026deg;14\\u0026prime;S, 116\\u0026deg;18\\u0026prime;E), an area characterized by extensive seagrass meadows dominated by \\u003cem\\u003eThalassia hemprichii\\u003c/em\\u003e, \\u003cem\\u003eCymodocea rotundata\\u003c/em\\u003e, and \\u003cem\\u003eHalodule uninervis\\u003c/em\\u003e. The site was selected due to recurring dugong sightings, relatively undisturbed habitats, and the presence of mixed-use coastal areas influenced by small-scale livestock and aquaculture activities. The study was carried out from March to October 2025, covering both dry and wet seasons to capture seasonal variation in environmental and biological parameters.\\u003c/p\\u003e \\u003c/div\\u003e\\n\\u003ch3\\u003eSampling Design\\u003c/h3\\u003e\\n\\u003cp\\u003eA total of five fixed sampling stations were established along a 10 km transect extending from the nearshore area to deeper seagrass beds (depth range 1\\u0026ndash;6 m). Each station represented a gradient of anthropogenic influence\\u0026mdash;from protected zones to areas exposed to fishing and agricultural runoff. Sampling was conducted monthly, and all variables were collected simultaneously to minimize temporal bias. A total of 25 fecal samples (n\\u0026thinsp;=\\u0026thinsp;5 per station) were collected from freshly deposited feeding trails. Seagrass samples were collected in triplicate quadrats (1 m\\u0026sup2;) per station per sampling event, yielding 90 composite samples across the study period. Sediment cores (n\\u0026thinsp;=\\u0026thinsp;15) were collected to quantify microplastic abundance and heavy metal concentration.\\u003c/p\\u003e\\n\\u003ch3\\u003eMicroplastic Analysis\\u003c/h3\\u003e\\n\\u003cp\\u003eMicroplastics were categorized into fibers, fragments, and films, and polymer types were identified using ATR-FTIR spectroscopy within the 4000\\u0026ndash;400 cm⁻\\u0026sup1; spectral range.\\u003c/p\\u003e\\n\\u003ch3\\u003eVariables Measured\\u003c/h3\\u003e\\n\\u003cp\\u003eEight interrelated variables (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig1\\\" class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003e) were examined to assess dugong habitat quality and health indicators:\\u003c/p\\u003e \\u003cp\\u003e \\u003col\\u003e \\u003cspan\\u003e \\u003cli\\u003e \\u003cp\\u003eWater quality \\u0026ndash; parameters measured in situ included temperature, salinity, dissolved oxygen (DO), pH, nitrate, and phosphate using a YSI ProDSS multiparameter probe and colorimetric test kits (American Public Health Association, \\u003cspan citationid=\\\"CR3\\\" class=\\\"CitationRef\\\"\\u003e2022\\u003c/span\\u003e).\\u003c/p\\u003e \\u003c/li\\u003e \\u003c/span\\u003e \\u003cspan\\u003e \\u003cli\\u003e \\u003cp\\u003eSeagrass nutrient composition \\u0026ndash; leaf samples (n\\u0026thinsp;=\\u0026thinsp;30 per site) were analyzed for crude protein, fiber (NDF/ADF), and mineral content (Ca, Mg, Fe, Zn) following Association of Official Analytical Chemists (\\u003cspan citationid=\\\"CR4\\\" class=\\\"CitationRef\\\"\\u003e2019\\u003c/span\\u003e) methods.\\u003c/p\\u003e \\u003c/li\\u003e \\u003c/span\\u003e \\u003cspan\\u003e \\u003cli\\u003e \\u003cp\\u003eSediment microbial load \\u0026ndash; sediment cores (top 5 cm) were analyzed for total bacterial count (CFU g⁻\\u0026sup1;) using nutrient agar and selective media for \\u003cem\\u003eVibrio\\u003c/em\\u003e spp.\\u003c/p\\u003e \\u003c/li\\u003e \\u003c/span\\u003e \\u003cspan\\u003e \\u003cli\\u003e \\u003cp\\u003eGut microbiome diversity \\u0026ndash; fecal samples collected opportunistically near feeding trails were preserved in RNAlater and sequenced using 16S rRNA gene analysis (Illumina MiSeq platform). Alpha and beta diversity indices were computed using QIIME 2.\\u003c/p\\u003e \\u003c/li\\u003e \\u003c/span\\u003e \\u003cspan\\u003e \\u003cli\\u003e \\u003cp\\u003eStress biomarkers \\u0026ndash; glucocorticoid metabolites were extracted from fecal samples and quantified via ELISA (Cayman Chemical, USA).\\u003c/p\\u003e \\u003c/li\\u003e \\u003c/span\\u003e \\u003cspan\\u003e \\u003cli\\u003e \\u003cp\\u003eBody condition index (BCI) \\u0026ndash; derived from aerial photogrammetry (drone-based imaging) by calculating the ratio of maximum body width to total body length, following Marsh et al. (\\u003cspan citationid=\\\"CR17\\\" class=\\\"CitationRef\\\"\\u003e2022\\u003c/span\\u003e).\\u003c/p\\u003e \\u003c/li\\u003e \\u003c/span\\u003e \\u003cspan\\u003e \\u003cli\\u003e \\u003cp\\u003eHeavy metal accumulation \\u0026ndash; seagrass and sediment samples were digested with HNO₃/H₂O₂ and analyzed for Pb, Cd, and Hg using ICP\\u0026ndash;MS (Agilent 7900).\\u003c/p\\u003e \\u003c/li\\u003e \\u003c/span\\u003e \\u003cspan\\u003e \\u003cli\\u003e \\u003cp\\u003eHabitat disturbance index (HDI) \\u0026ndash; assessed through field observations and GIS mapping, incorporating variables such as coastal land use, boat traffic frequency, and solid waste density.\\u003c/p\\u003e \\u003c/li\\u003e \\u003c/span\\u003e \\u003c/ol\\u003e \\u003c/p\\u003e \\u003cp\\u003e \\u003c/p\\u003e \\u003cdiv id=\\\"Sec7\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003eData Analysis\\u003c/h2\\u003e \\u003cp\\u003eAll quantitative data were first tested for normality and homoscedasticity (Shapiro\\u0026ndash;Wilk and Levene\\u0026rsquo;s tests). One-way ANOVA followed by Tukey\\u0026rsquo;s HSD was used to evaluate spatial and seasonal differences among stations. Pearson\\u0026rsquo;s correlation and Principal Component Analysis (PCA) were applied to explore associations among the eight variables. A Structural Equation Model (SEM) was further developed to examine causal pathways between habitat quality, microbiome diversity, and dugong health indicators. Statistical analyses were performed using R version 4.3.1 with the \\u003cem\\u003evegan\\u003c/em\\u003e and \\u003cem\\u003elavaan\\u003c/em\\u003e packages. Effect sizes (η\\u0026sup2;) were calculated for ANOVA results. All regression models were validated using residual diagnostics and variance inflation factor (VIF\\u0026thinsp;\\u0026lt;\\u0026thinsp;3). Statistical significance was set at α\\u0026thinsp;=\\u0026thinsp;0.05.\\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec8\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003eEthical Considerations\\u003c/h2\\u003e \\u003cp\\u003eAll field activities were conducted under the research permit issued by the Indonesian Ministry of Environment and Forestry (Permit No. 32/MENLHK/2025). No dugongs were handled or disturbed during sampling; all fecal and environmental materials were collected non-invasively following ethical guidelines for marine mammal research.\\u003c/p\\u003e \\u003c/div\\u003e\\n\\u003ch3\\u003eClinical Trial Number\\u003c/h3\\u003e\\n\\u003cdiv class=\\\"Heading\\\"\\u003eClinical Trial Number\\u003c/div\\u003e \\u003cp\\u003eNot applicable.\\u003c/p\\u003e\"},{\"header\":\"RESULTS\",\"content\":\"\\u003cdiv id=\\\"Sec11\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003eDescriptive Analysis\\u003c/h2\\u003e \\u003cp\\u003eTable\\u0026nbsp;\\u003cspan refid=\\\"Tab1\\\" class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003e presents the mean values of eight environmental and biological variables measured across five coastal stations. These include microplastic concentration, seagrass protein content, feeding frequency, parasite prevalence, microbiome diversity (Shannon index), foraging time, fecal cortisol levels, and salinity. The data demonstrate a clear spatial gradient from protected to port-adjacent stations. The descriptive statistics provide an initial overview of the ecological patterns occurring across the coastal landscape under study. Environmental monitoring across multiple stations allows researchers to identify spatial variability in both abiotic and biotic indicators that may influence dugong health and ecosystem integrity. By integrating measurements of pollution exposure, habitat quality, and physiological indicators, the dataset captures multiple dimensions of the ecological processes affecting dugong populations.\\u003c/p\\u003e \\u003cp\\u003eMicroplastic concentration exhibited a pronounced spatial gradient across the sampling stations. Stations located closer to anthropogenic activity, particularly those adjacent to port infrastructure and shipping lanes, showed elevated levels of microplastic contamination compared with stations situated within relatively protected coastal areas. This pattern is consistent with global observations indicating that coastal zones influenced by maritime transport, urban runoff, and industrial discharge tend to accumulate higher densities of plastic debris. In contrast, protected coastal stations characterized by limited human disturbance displayed relatively lower microplastic concentrations. These areas are often associated with healthier seagrass meadows, improved water quality, and reduced pollutant loads. Such environmental conditions are generally conducive to maintaining stable ecological communities and supporting the nutritional requirements of marine herbivores.\\u003c/p\\u003e \\u003cp\\u003eSeagrass protein content also varied across stations, suggesting differences in habitat quality and nutrient availability. Seagrass meadows located in less disturbed environments tended to exhibit higher protein concentrations, reflecting favorable growing conditions and nutrient dynamics. Protein content in seagrass tissues is a critical indicator of forage quality for marine herbivores, as it influences digestibility, nutrient assimilation, and overall dietary value. Feeding frequency and foraging time showed patterns that corresponded with variations in habitat quality and pollution exposure. Dugongs observed in stations with higher seagrass protein content tended to exhibit more frequent feeding behavior and longer foraging durations. This likely reflects the presence of abundant and nutritionally valuable forage resources that support sustained feeding activity.\\u003c/p\\u003e \\u003cp\\u003eConversely, stations characterized by elevated microplastic contamination and reduced seagrass nutritional quality were associated with lower feeding frequency and shorter foraging periods. Such behavioral differences may reflect either reduced food availability or behavioral avoidance of degraded habitats. Parasite prevalence exhibited spatial variation across stations as well. Higher parasite loads were generally observed in stations with elevated pollution levels and lower habitat quality. This pattern suggests potential links between environmental stressors, immune function, and parasite susceptibility in dugong populations. Microbiome diversity, measured using the Shannon diversity index, showed notable differences across the spatial gradient. Dugongs inhabiting relatively pristine stations displayed higher microbial diversity within their gastrointestinal microbiomes. Microbial diversity is widely recognized as an indicator of gut ecosystem stability and resilience, playing an essential role in digestion, immune regulation, and metabolic processes.\\u003c/p\\u003e \\u003cp\\u003eIn contrast, stations exposed to higher levels of anthropogenic disturbance exhibited reduced microbiome diversity. Such reductions in microbial diversity may reflect environmental stressors affecting host physiology, dietary composition, or microbial colonization dynamics. Foraging time also exhibited spatial variability, reflecting behavioral adjustments to local habitat conditions. Dugongs in healthier habitats spent longer periods feeding within seagrass meadows, likely due to the availability of high-quality forage. Shorter foraging durations in polluted stations may indicate habitat avoidance or reduced feeding efficiency. Fecal cortisol levels, used as an indicator of physiological stress, showed a clear spatial pattern consistent with environmental disturbance. Dugongs inhabiting more contaminated areas exhibited higher cortisol concentrations, suggesting increased physiological stress associated with environmental degradation.\\u003c/p\\u003e \\u003cp\\u003eSalinity values across stations remained relatively stable, indicating that variations in dugong health indicators were unlikely to be driven primarily by salinity fluctuations. Instead, the observed patterns are more plausibly associated with differences in habitat quality, pollution exposure, and ecological interactions. Taken together, the descriptive statistics provide a comprehensive overview of environmental and biological conditions across the study area. The spatial gradients observed in multiple indicators highlight the complex relationships linking coastal pollution, habitat quality, and dugong physiological responses.\\u003c/p\\u003e \\u003cp\\u003e \\u003cdiv class=\\\"gridtable\\\"\\u003e\\u003ctable float=\\\"Yes\\\" id=\\\"Tab1\\\" border=\\\"1\\\"\\u003e \\u003ccaption language=\\\"En\\\"\\u003e \\u003cdiv class=\\\"CaptionNumber\\\"\\u003eTable 1\\u003c/div\\u003e \\u003cdiv class=\\\"CaptionContent\\\"\\u003e \\u003cp\\u003eDescriptive statistics of measured parameters across five sampling stations.\\u003c/p\\u003e \\u003c/div\\u003e \\u003c/caption\\u003e \\u003ccolgroup cols=\\\"7\\\"\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c1\\\" colnum=\\\"1\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"char\\\" char=\\\".\\\" class=\\\"colspec\\\" colname=\\\"c2\\\" colnum=\\\"2\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"char\\\" char=\\\".\\\" class=\\\"colspec\\\" colname=\\\"c3\\\" colnum=\\\"3\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"char\\\" char=\\\".\\\" class=\\\"colspec\\\" colname=\\\"c4\\\" colnum=\\\"4\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"char\\\" char=\\\".\\\" class=\\\"colspec\\\" colname=\\\"c5\\\" colnum=\\\"5\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"char\\\" char=\\\".\\\" class=\\\"colspec\\\" colname=\\\"c6\\\" colnum=\\\"6\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"char\\\" char=\\\".\\\" class=\\\"colspec\\\" colname=\\\"c7\\\" colnum=\\\"7\\\"\\u003e\\u003c/div\\u003e \\u003cthead\\u003e \\u003ctr\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eStation\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eMicroplastics i(particles/kg)\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eSeagrass iprotein i(%)\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003eFeeding ifrequency i(bouts/hr)\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003eParasite iprevalence i(%)\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003eMicrobiome iShannon\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003eForaging itime i(min/day)\\u003c/p\\u003e \\u003c/th\\u003e \\u003c/tr\\u003e \\u003c/thead\\u003e \\u003ctbody\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eS1 (Protected)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e120\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e16.5\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e5.0\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e12\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e4.2\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e210\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eS2 (Low impact)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e180\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e15.2\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e4.6\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e15\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e3.9\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e195\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eS3 (Moderate)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e240\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e14.0\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e4.2\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e18\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e3.6\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e175\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eS4 (High)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e300\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e12.8\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e3.7\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e27\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e3.1\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e150\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eS5 (Port Adj.)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e340\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e11.5\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e3.1\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e33\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e2.8\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e120\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003c/tbody\\u003e \\u003c/colgroup\\u003e \\u003c/table\\u003e\\u003c/div\\u003e \\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec12\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003eNormality and Homogeneity of Variances\\u003c/h2\\u003e \\u003cp\\u003eShapiro\\u0026ndash;Wilk and Levene\\u0026rsquo;s tests confirmed that most variables were normally distributed and exhibited homoscedasticity (p \\u0026gt; .05), validating the use of parametric tests. Only microplastic concentration displayed slight deviation from normality (p = .016), but this was acceptable given the balanced design and moderate sample size. Assessing the assumptions of statistical tests is a critical step in ecological data analysis. Parametric statistical methods such as analysis of variance (ANOVA) require that the data meet certain assumptions regarding distributional properties and variance homogeneity. Failure to meet these assumptions can lead to biased parameter estimates or inflated error rates.\\u003c/p\\u003e \\u003cp\\u003eThe Shapiro\\u0026ndash;Wilk test is widely used to assess whether a dataset follows a normal distribution. In this study, most environmental and biological variables demonstrated distributions that did not significantly deviate from normality. This indicates that the central tendency and dispersion of the data were consistent with the assumptions required for parametric statistical procedures. Levene\\u0026rsquo;s test was applied to evaluate the homogeneity of variances across sampling stations. Homoscedasticity, or equal variance among groups, is essential for ensuring that ANOVA results accurately reflect true differences among groups rather than differences caused by unequal variability. The results indicated that variance among groups was sufficiently homogeneous for the majority of variables analyzed. This finding supports the validity of subsequent parametric analyses conducted to evaluate spatial variation across stations.\\u003c/p\\u003e \\u003cp\\u003eAlthough microplastic concentration exhibited slight deviation from normality (p = .016), this deviation was not considered sufficiently severe to invalidate the use of parametric tests. In ecological datasets, moderate departures from normality are often tolerated when sample sizes are balanced across groups. Furthermore, ANOVA procedures are generally robust to moderate deviations from normality, particularly when group sizes are equal and sample sizes are moderate. Under such conditions, the distribution of the test statistic remains relatively stable. Given these considerations, the analytical framework employed in this study was deemed appropriate for examining spatial variation in environmental and biological variables across the coastal stations.\\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec13\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003eSpatial Variations in Environmental Parameters\\u003c/h2\\u003e \\u003cp\\u003eOne-way ANOVA revealed significant spatial variation in several parameters (Table\\u0026nbsp;\\u003cspan refid=\\\"Tab2\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003e). Microplastic concentrations differed significantly among stations (F (4,20)\\u0026thinsp;=\\u0026thinsp;15.82, p \\u0026lt; .001), with Tukey\\u0026rsquo;s post hoc test indicating that S5 (port-adjacent) had significantly higher concentrations than S1 and S2. These findings highlight the strong influence of spatial location on pollution exposure in coastal ecosystems. Stations located near port facilities and maritime traffic corridors often experience elevated levels of plastic pollution due to shipping activities, industrial discharge, and urban runoff. The significant difference between S5 and the more protected stations S1 and S2 indicates that anthropogenic activities associated with port infrastructure are likely contributing to the accumulation of microplastic particles in nearby marine habitats. Seagrass protein content also differed significantly among stations. Stations characterized by lower pollution levels generally exhibited higher seagrass protein concentrations, suggesting more favorable environmental conditions for seagrass growth and nutrient assimilation. The decline in seagrass protein content observed along the contamination gradient may reflect multiple environmental stressors. Pollution, increased turbidity, and sediment disturbance can reduce photosynthetic efficiency and nutrient uptake in seagrass plants.\\u003c/p\\u003e \\u003cp\\u003eReduced nutritional quality of seagrass forage may have important implications for dugong populations, as dietary protein plays a crucial role in supporting metabolic processes, tissue repair, and reproductive functions. Microbiome diversity also differed spatially, showing clear degradation along the contamination gradient. Dugongs inhabiting less disturbed stations exhibited higher gut microbiome diversity compared with individuals associated with more polluted habitats. This pattern suggests that environmental conditions and dietary composition influence the structure of gastrointestinal microbial communities in dugongs. Microbial diversity is essential for maintaining digestive efficiency and supporting host health through metabolic and immunological pathways. The observed decline in microbiome diversity along the contamination gradient may therefore represent an important ecological signal indicating compromised host health in polluted habitats.\\u003c/p\\u003e \\u003cp\\u003e \\u003cdiv class=\\\"gridtable\\\"\\u003e\\u003ctable float=\\\"Yes\\\" id=\\\"Tab2\\\" border=\\\"1\\\"\\u003e \\u003ccaption language=\\\"En\\\"\\u003e \\u003cdiv class=\\\"CaptionNumber\\\"\\u003eTable 2\\u003c/div\\u003e \\u003cdiv class=\\\"CaptionContent\\\"\\u003e \\u003cp\\u003eSummary of one-way ANOVA results.\\u003c/p\\u003e \\u003c/div\\u003e \\u003c/caption\\u003e \\u003ccolgroup cols=\\\"5\\\"\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c1\\\" colnum=\\\"1\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"char\\\" char=\\\".\\\" class=\\\"colspec\\\" colname=\\\"c2\\\" colnum=\\\"2\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"char\\\" char=\\\".\\\" class=\\\"colspec\\\" colname=\\\"c3\\\" colnum=\\\"3\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c4\\\" colnum=\\\"4\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c5\\\" colnum=\\\"5\\\"\\u003e\\u003c/div\\u003e \\u003cthead\\u003e \\u003ctr\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eVariable\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eF-value\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003edf\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003ep-value\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003eTukey iHSD i(key icontrasts)\\u003c/p\\u003e \\u003c/th\\u003e \\u003c/tr\\u003e \\u003c/thead\\u003e \\u003ctbody\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eMicroplastics\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e15.82\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e4\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e\\u0026lt;\\u0026thinsp;.001***\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003eS5\\u0026thinsp;\\u0026gt;\\u0026thinsp;S1, S5\\u0026thinsp;\\u0026gt;\\u0026thinsp;S2\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eSeagrass protein\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e10.44\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e4\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e\\u0026lt;\\u0026thinsp;.01**\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003eS1\\u0026thinsp;\\u0026gt;\\u0026thinsp;S4, S1\\u0026thinsp;\\u0026gt;\\u0026thinsp;S5\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eMicrobiome diversity\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e12.13\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e4\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e\\u0026lt;\\u0026thinsp;.01**\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003eS1\\u0026thinsp;\\u0026gt;\\u0026thinsp;S4, S1\\u0026thinsp;\\u0026gt;\\u0026thinsp;S5\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eFecal cortisol\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e9.75\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e4\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e\\u0026lt;\\u0026thinsp;.05*\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003eS5\\u0026thinsp;\\u0026gt;\\u0026thinsp;S1, S5\\u0026thinsp;\\u0026gt;\\u0026thinsp;S2\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003c/tbody\\u003e \\u003c/colgroup\\u003e \\u003ctfoot\\u003e \\u003ctr\\u003e\\u003ctd colspan=\\\"5\\\"\\u003eNote. Significance codes: ***p\\u0026lt;.001; **p\\u0026lt;.01; \\u003cem\\u003ep\\u0026lt;.05\\u003c/em\\u003e.\\u003c/td\\u003e\\u003c/tr\\u003e \\u003c/tfoot\\u003e \\u003c/table\\u003e\\u003c/div\\u003e \\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec14\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003eCorrelations Among Environmental and Biological Variables\\u003c/h2\\u003e \\u003cp\\u003ePearson\\u0026rsquo;s correlation analysis (Table\\u0026nbsp;\\u003cspan refid=\\\"Tab3\\\" class=\\\"InternalRef\\\"\\u003e3\\u003c/span\\u003e) revealed strong associations between pollutant exposure, nutritional quality, and health indicators. Microplastic concentration was negatively correlated with microbiome diversity (r\\u0026thinsp;=\\u0026thinsp;\\u0026minus;\\u0026thinsp;0.68) and positively correlated with fecal cortisol (r\\u0026thinsp;=\\u0026thinsp;+\\u0026thinsp;0.72). These correlations suggest that increased pollution exposure may be associated with reduced gut microbial diversity and elevated physiological stress in dugongs. The negative correlation between microplastic concentration and microbiome diversity indicates that environmental contamination may disrupt microbial communities within the digestive tract. Reduced microbial diversity can impair digestive processes and weaken immune defenses, potentially increasing vulnerability to disease and physiological stress.\\u003c/p\\u003e \\u003cp\\u003eThe positive correlation between microplastic concentration and fecal cortisol levels further supports the hypothesis that environmental contamination contributes to stress responses in dugong populations. Conversely, seagrass protein content was positively associated with microbiome diversity and negatively with parasite prevalence. These relationships suggest that nutritionally rich seagrass habitats may promote healthier physiological conditions in dugongs. High-quality forage may support the growth of beneficial microbial taxa that enhance digestive efficiency and immune function. This may help reduce susceptibility to parasitic infections and maintain overall health.\\u003c/p\\u003e \\u003cp\\u003e \\u003cdiv class=\\\"gridtable\\\"\\u003e\\u003ctable float=\\\"Yes\\\" id=\\\"Tab3\\\" border=\\\"1\\\"\\u003e \\u003ccaption language=\\\"En\\\"\\u003e \\u003cdiv class=\\\"CaptionNumber\\\"\\u003eTable 3\\u003c/div\\u003e \\u003cdiv class=\\\"CaptionContent\\\"\\u003e \\u003cp\\u003ePearson correlation coefficients among selected variables.\\u003c/p\\u003e \\u003c/div\\u003e \\u003c/caption\\u003e \\u003ccolgroup cols=\\\"3\\\"\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c1\\\" colnum=\\\"1\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"char\\\" char=\\\".\\\" class=\\\"colspec\\\" colname=\\\"c2\\\" colnum=\\\"2\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c3\\\" colnum=\\\"3\\\"\\u003e\\u003c/div\\u003e \\u003cthead\\u003e \\u003ctr\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eVariable ipair\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003er i(Pearson)\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eDirection\\u003c/p\\u003e \\u003c/th\\u003e \\u003c/tr\\u003e \\u003c/thead\\u003e \\u003ctbody\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eMicroplastics \\u0026mdash; Microbiome Shannon\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e\\u0026minus;0.68\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eNegative\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eMicroplastics \\u0026mdash; Fecal cortisol\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e+\\u0026thinsp;0.72\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003ePositive\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eSeagrass protein \\u0026mdash; Microbiome\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e+\\u0026thinsp;0.72\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003ePositive\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eSeagrass protein \\u0026mdash; Parasite prev.\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e\\u0026minus;0.68\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eNegative\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eMicroplastics \\u0026mdash; Microbiome Shannon\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e\\u0026minus;0.68\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eNegative\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003c/tbody\\u003e \\u003c/colgroup\\u003e \\u003c/table\\u003e\\u003c/div\\u003e \\u003c/p\\u003e \\u003cp\\u003eThese relationships indicate that higher pollution is associated with dysbiosis and stress, while nutrient-rich seagrass habitats promote healthier physiological and microbial states in dugongs.\\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec15\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003eMultivariate Structure of Environmental Gradients\\u003c/h2\\u003e \\u003cp\\u003ePrincipal Component Analysis (PCA) reduced the dataset into two dominant axes explaining 78.4% of the total variance. The first component (PC1, 56.2%) represented a contamination gradient dominated by microplastics, fecal cortisol, and parasite prevalence, while PC2 (22.2%) represented habitat quality, driven by seagrass protein and microbiome diversity. Multivariate analysis provides valuable insights into the structure of complex ecological datasets by identifying underlying patterns that may not be apparent through univariate analyses. The first principal component represents a pollution-driven gradient that integrates multiple indicators of environmental stress and host physiological responses. High loadings of microplastics, fecal cortisol, and parasite prevalence on this axis suggest that these variables are strongly associated with anthropogenic disturbance.\\u003c/p\\u003e \\u003cp\\u003eThe second principal component reflects variation in habitat quality and biological resilience. Variables such as seagrass protein content and microbiome diversity contributed strongly to this axis, indicating their importance in shaping dugong ecological health. As shown in Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig2\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003e, protected stations clustered in the upper-left quadrant (high habitat quality, low stress), whereas port-adjacent sites clustered in the lower-right quadrant. This spatial separation highlights the strong influence of environmental conditions on the distribution of ecological indicators. The PCA results therefore reinforce the hypothesis that coastal pollution and habitat degradation can significantly alter the ecological conditions experienced by dugong populations.\\u003c/p\\u003e \\u003cp\\u003e \\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec16\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003eStructural Equation Model (SEM) Analysis\\u003c/h2\\u003e \\u003cp\\u003eThe conceptual Structural Equation Model (SEM) tested causal pathways linking habitat quality, microbiome diversity, and dugong health. The spatial variation in microplastic contamination and its association with seagrass nutritional quality and dugong health indicators are presented in Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig3\\\" class=\\\"InternalRef\\\"\\u003e3\\u003c/span\\u003e. The model fit was satisfactory (CFI = .93, RMSEA = .07, SRMR = .06). Structural equation modeling is particularly useful in ecological research because it allows researchers to evaluate complex causal relationships among multiple variables simultaneously. Microplastic load negatively affected microbiome diversity (β = \\u0026minus;0.71, 95% CI: \\u0026minus;1.02 to \\u0026minus;\\u0026thinsp;0.39, p\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.01), while microbiome diversity positively influenced dugong health (β\\u0026thinsp;=\\u0026thinsp;0.68, 95% CI: 0.21 to 1.04, p\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.01).\\u003c/p\\u003e \\u003cp\\u003eThese results indicate that environmental contamination may influence dugong health indirectly through its effects on gut microbial communities. Indirect effect of microplastic exposure on fecal cortisol via microbiome diversity was significant (βindirect\\u0026thinsp;=\\u0026thinsp;0.48, p\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.05), indicating partial mediation. This suggests that the relationship between pollution exposure and physiological stress is not purely direct but is partly mediated by changes in microbial composition within the digestive system. These findings support the hypothesis that environmental contamination indirectly impacts animal health through microbiome-mediated mechanisms. The SEM framework therefore provides a conceptual model linking pollution, habitat quality, microbial ecology, and physiological stress responses in dugongs. Such integrative models are valuable tools for understanding complex ecological processes and informing conservation strategies. Overall, the results highlight the importance of maintaining healthy coastal ecosystems to support the physiological and microbial health of marine megafauna such as dugongs.\\u003c/p\\u003e \\u003cp\\u003e \\u003c/p\\u003e \\u003c/div\\u003e\"},{\"header\":\"DISCUSSION\",\"content\":\"\\u003cp\\u003eUnderstanding the ecological context of dugong (\\u003cem\\u003eDugong dugon\\u003c/em\\u003e) health requires an integrated perspective that connects environmental stressors, physiological responses, and ecosystem interactions. Coastal ecosystems are complex socio-ecological systems where biological processes interact with anthropogenic activities, climate variability, and resource use patterns. Dugongs, as obligate marine herbivores highly dependent on seagrass meadows, occupy a unique ecological niche in tropical coastal ecosystems. Their ecological role as large grazers influences seagrass productivity, nutrient cycling, and habitat dynamics. Consequently, disturbances affecting seagrass habitats can rapidly propagate to dugong populations, altering their feeding ecology, physiological status, and population viability.\\u003c/p\\u003e \\u003cp\\u003eThis study underscores the interconnectedness of environmental degradation, wildlife health, and ecosystem services. Such interactions highlight the complexity of ecological relationships where anthropogenic pressures can cascade through food webs, affecting both wildlife and human communities dependent on coastal productivity (Lambert et al., \\u003cspan citationid=\\\"CR15\\\" class=\\\"CitationRef\\\"\\u003e2021\\u003c/span\\u003e; Tanaka et al., \\u003cspan citationid=\\\"CR31\\\" class=\\\"CitationRef\\\"\\u003e2023\\u003c/span\\u003e). Coastal pollution, habitat fragmentation, and climate-driven disturbances are increasingly recognized as major drivers of ecological transformation in tropical marine systems. These pressures can modify the structural and functional properties of seagrass ecosystems, thereby influencing the nutritional landscape available to marine herbivores such as dugongs.\\u003c/p\\u003e \\u003cp\\u003eSeagrass ecosystems provide critical ecosystem services including carbon sequestration, sediment stabilization, nutrient retention, and habitat provisioning for numerous marine species. However, these ecosystems are among the most threatened coastal habitats globally. Increasing coastal development, aquaculture expansion, agricultural runoff, and plastic pollution have contributed to the degradation of seagrass meadows in many tropical regions. The resulting ecological disturbances affect not only plant communities but also the associated fauna, including large herbivores that rely on these habitats as primary feeding grounds. Dugongs are particularly vulnerable to such changes because their dietary specialization limits their capacity to adapt to rapid environmental shifts.\\u003c/p\\u003e \\u003cp\\u003eFrom a One Health perspective, the health of dugongs serves as a sentinel indicator for marine ecosystem integrity. Changes in gut microbiota composition, oxidative stress levels, and body condition indices are not isolated phenomena but mirror the degradation of shared environmental resources (Delfino et al., \\u003cspan citationid=\\\"CR7\\\" class=\\\"CitationRef\\\"\\u003e2022\\u003c/span\\u003e; Noor et al., \\u003cspan citationid=\\\"CR20\\\" class=\\\"CitationRef\\\"\\u003e2024\\u003c/span\\u003e). Marine mammals often function as bioindicators due to their long lifespan, trophic position, and exposure to environmental contaminants. Physiological and microbiological changes observed in dugongs can therefore provide valuable insights into the broader ecological conditions of coastal habitats.\\u003c/p\\u003e \\u003cp\\u003eIntegrating ecological, veterinary, and environmental data can thus help identify early warning signals of ecosystem decline and inform adaptive management for conservation (Phothisat et al., \\u003cspan citationid=\\\"CR23\\\" class=\\\"CitationRef\\\"\\u003e2022\\u003c/span\\u003e). Multidisciplinary monitoring approaches that combine ecological surveys, microbiome analysis, and physiological biomarkers can provide a comprehensive understanding of marine health dynamics. Such approaches align with the growing recognition that wildlife health cannot be separated from environmental quality and ecosystem function.\\u003c/p\\u003e \\u003cp\\u003eFurthermore, linking the nutritional ecology of seagrass beds with dugong physiological status provides a tangible pathway to understand how climate-induced changes in coastal productivity influence marine herbivore populations (Kurniawan et al., \\u003cspan citationid=\\\"CR14\\\" class=\\\"CitationRef\\\"\\u003e2023\\u003c/span\\u003e; Supamattaya et al., \\u003cspan citationid=\\\"CR30\\\" class=\\\"CitationRef\\\"\\u003e2024\\u003c/span\\u003e). Climate change is expected to affect seagrass productivity through alterations in sea temperature, ocean acidification, and extreme weather events. These environmental changes may influence the biochemical composition of seagrass tissues, including protein, carbohydrate, and fiber content, thereby affecting the nutritional intake of herbivorous marine mammals.\\u003c/p\\u003e \\u003cp\\u003eThe nutritional quality of seagrass can influence digestive efficiency, microbial fermentation processes, and energy assimilation in dugongs. Variations in nutrient composition may also affect the structure of gut microbial communities responsible for fiber degradation and nutrient synthesis. Consequently, environmental changes that alter seagrass nutritional profiles can indirectly affect dugong metabolism and health status.\\u003c/p\\u003e \\u003cp\\u003eThis interdisciplinary framework reinforces the One Health paradigm by emphasizing that animal health, ecosystem function, and human well-being are interdependent and co-regulated by environmental stewardship (Wang et al., \\u003cspan citationid=\\\"CR33\\\" class=\\\"CitationRef\\\"\\u003e2024\\u003c/span\\u003e). Coastal communities often rely on the same marine ecosystems for fisheries, tourism, and cultural identity. Therefore, maintaining the ecological integrity of seagrass habitats contributes not only to wildlife conservation but also to the sustainability of coastal livelihoods.\\u003c/p\\u003e \\u003cp\\u003eThe subsequent sections elaborate on three interconnected mechanisms underpinning these findings: (1) the influence of microplastic pollution on dugong gut microbiota and immunity, (2) the mitigating role of seagrass nutritional diversity, and (3) behavioral adaptations to environmental perturbations. Together, these insights support the formulation of integrated conservation policies that align marine biodiversity protection with coastal resilience and sustainable community development (Kittiwattanawong et al., \\u003cspan citationid=\\\"CR12\\\" class=\\\"CitationRef\\\"\\u003e2023\\u003c/span\\u003e; Rizal et al., \\u003cspan citationid=\\\"CR25\\\" class=\\\"CitationRef\\\"\\u003e2025\\u003c/span\\u003e).\\u003c/p\\u003e \\u003cdiv id=\\\"Sec18\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003eMicroplastic Pollution and Gut Microbiota Alterations\\u003c/h2\\u003e \\u003cp\\u003eThe results align with emerging research showing that microplastic exposure disrupts gut microbial homeostasis in marine mammals, fishes, and invertebrates (Merrill et al., \\u003cspan citationid=\\\"CR18\\\" class=\\\"CitationRef\\\"\\u003e2023\\u003c/span\\u003e; Shi \\u0026amp; Li, \\u003cspan citationid=\\\"CR26\\\" class=\\\"CitationRef\\\"\\u003e2024\\u003c/span\\u003e). Microplastics have become ubiquitous pollutants in marine environments, originating from the breakdown of larger plastic debris and from primary microplastic sources such as cosmetic products, synthetic textiles, and industrial abrasives. Once released into the marine environment, these particles can be transported across long distances and accumulate in coastal habitats including seagrass beds.\\u003c/p\\u003e \\u003cp\\u003eDugongs may ingest microplastics indirectly through contaminated seagrass tissues or sediment particles attached to plant surfaces. Because dugongs feed by uprooting entire seagrass plants along with sediment, their feeding behavior increases the likelihood of ingesting particulate pollutants present in the benthic environment. Such ingestion can introduce microplastics into the digestive system, where they interact with gut tissues and microbial communities.\\u003c/p\\u003e \\u003cp\\u003eMicroplastics may physically damage intestinal tissues and act as vectors for toxic compounds, potentially leading to inflammation and altered microbial metabolism (Marcharla \\u0026amp; Singh, \\u003cspan citationid=\\\"CR16\\\" class=\\\"CitationRef\\\"\\u003e2024\\u003c/span\\u003e). The surfaces of microplastic particles often carry adsorbed contaminants including heavy metals, persistent organic pollutants, and pathogenic microorganisms. When ingested, these substances can be released into the digestive tract, creating a microenvironment that promotes microbial imbalance and oxidative stress.\\u003c/p\\u003e \\u003cp\\u003eMicroplastic-associated dysbiosis may impair short-chain fatty acid synthesis, reduce nutrient absorption efficiency, and disrupt mucosal immune regulation. Short-chain fatty acids such as acetate, propionate, and butyrate are important metabolites produced by gut bacteria during the fermentation of dietary fibers. These metabolites play key roles in maintaining intestinal health, regulating immune responses, and supporting host metabolism. Disruption of microbial fermentation pathways can therefore compromise digestive efficiency and immune stability.\\u003c/p\\u003e \\u003cp\\u003eSuch alterations could elevate systemic glucocorticoid levels and increase susceptibility to parasitic infections, thereby linking microbial imbalance to the elevated fecal cortisol and parasite prevalence observed in contaminated stations. Stress hormones such as cortisol are commonly used indicators of physiological stress in wildlife populations. Elevated cortisol levels may reflect chronic exposure to environmental stressors including pollution, habitat disturbance, and food scarcity.\\u003c/p\\u003e \\u003cp\\u003eChronic stress can suppress immune function, making individuals more vulnerable to infections and parasitic infestations. In the context of dugong populations, increased parasite prevalence may represent a secondary consequence of environmental stress and microbial dysbiosis. These dysbiotic shifts may reduce nutrient absorption and immune resilience, thereby increasing vulnerability to disease and stress.\\u003c/p\\u003e \\u003cp\\u003eFurthermore, microplastic ingestion may also influence the diversity and functional capacity of gut microbial communities. Studies in other marine organisms have demonstrated that exposure to microplastics can reduce microbial diversity and alter the relative abundance of key bacterial taxa involved in digestion and immunity. Reduced microbial diversity is often associated with decreased ecosystem stability and increased susceptibility to pathogen invasion.\\u003c/p\\u003e \\u003cp\\u003eIn marine herbivores such as dugongs, gut microbiota play a crucial role in breaking down complex plant fibers present in seagrass tissues. Alterations in microbial composition may therefore impair digestive efficiency and energy extraction from plant-based diets. Over time, reduced digestive performance could affect body condition, reproductive success, and population dynamics.\\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec19\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003eSeagrass Nutritional Quality as a Buffer\\u003c/h2\\u003e \\u003cp\\u003eHigher seagrass protein content appeared to mitigate the effects of pollution by supporting more stable gut microbiomes and reducing parasite prevalence (Fang et al., \\u003cspan citationid=\\\"CR8\\\" class=\\\"CitationRef\\\"\\u003e2022\\u003c/span\\u003e; Jupp et al., \\u003cspan citationid=\\\"CR11\\\" class=\\\"CitationRef\\\"\\u003e2023\\u003c/span\\u003e). Nutritional quality is a key determinant of herbivore health and resilience to environmental stress. In marine herbivores, the availability of high-quality forage can support metabolic processes, immune responses, and microbial stability within the digestive system.\\u003c/p\\u003e \\u003cp\\u003eNutrient-rich seagrass meadows offer better forage quality and may enhance host immunity through beneficial microbial pathways (Bass \\u0026amp; Duarte, \\u003cspan citationid=\\\"CR5\\\" class=\\\"CitationRef\\\"\\u003e2025\\u003c/span\\u003e). Protein-rich seagrass species may promote the growth of beneficial microbial taxa that contribute to nutrient synthesis and pathogen resistance. These microbial interactions can strengthen host immunity and reduce susceptibility to infections.\\u003c/p\\u003e \\u003cp\\u003eThe biochemical composition of seagrass varies widely across species and environmental conditions. Factors such as nutrient availability, water temperature, and light intensity can influence the concentration of proteins, carbohydrates, and secondary metabolites in seagrass tissues. Seagrass species with higher nutritional value may therefore provide dietary advantages for herbivorous marine mammals.\\u003c/p\\u003e \\u003cp\\u003eDugongs are known to selectively forage on seagrass species with favorable nutritional profiles. Selective feeding behavior allows them to optimize nutrient intake while minimizing the consumption of less digestible plant material. However, environmental disturbances that reduce seagrass diversity may limit the availability of preferred forage species.\\u003c/p\\u003e \\u003cp\\u003eLoss of seagrass biodiversity can therefore reduce the nutritional resilience of dugong populations. In degraded habitats dominated by low-quality seagrass species, dugongs may experience nutritional deficiencies that affect their growth, reproduction, and immune competence. Maintaining diverse seagrass communities is thus essential for sustaining healthy dugong populations.\\u003c/p\\u003e \\u003cp\\u003eIn addition to providing nutrients, seagrass ecosystems also influence sediment stability and water quality, which in turn affect the distribution of pollutants and pathogens. Healthy seagrass meadows can trap suspended particles and reduce turbidity, thereby limiting the spread of contaminants within coastal ecosystems. This ecological function may indirectly reduce the exposure of marine herbivores to harmful pollutants.\\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec20\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003eBehavioral Responses and Foraging Ecology\\u003c/h2\\u003e \\u003cp\\u003eReduced foraging time and feeding frequency in contaminated zones may reflect both behavioral avoidance and diminished food availability (Bassett \\u0026amp; Nguyen, \\u003cspan citationid=\\\"CR6\\\" class=\\\"CitationRef\\\"\\u003e2023\\u003c/span\\u003e). Behavioral adaptations are often the first line of response to environmental disturbances in wildlife populations. Animals may modify their movement patterns, feeding behavior, and habitat use in order to minimize exposure to harmful conditions.\\u003c/p\\u003e \\u003cp\\u003eReduced foraging time in contaminated areas suggests possible energetic trade-offs, where behavioral avoidance of polluted habitats may compromise caloric intake and reproductive investment. Dugongs must consume large quantities of seagrass to meet their daily energy requirements. If pollution reduces the quality or accessibility of seagrass meadows, dugongs may need to travel greater distances to locate suitable feeding grounds.\\u003c/p\\u003e \\u003cp\\u003eSuch behavioral changes can increase energetic expenditure and reduce overall energy balance. Over time, reduced energy acquisition could impair body condition, reproductive output, and long-term population viability, particularly in nutritionally sensitive marine herbivores. Female dugongs require substantial energy reserves for pregnancy and lactation, making reproductive success closely linked to forage availability.\\u003c/p\\u003e \\u003cp\\u003eIn degraded habitats, reproductive intervals may lengthen due to insufficient nutritional resources. Reduced reproductive rates can significantly slow population recovery, especially for species with long lifespans and low reproductive output such as dugongs.\\u003c/p\\u003e \\u003cp\\u003eSuch behavioral adaptations, while short-term survival strategies, could reduce energy intake and reproductive success over time\\u0026mdash;posing long-term threats to population viability (Mikkelsen \\u0026amp; Grech, \\u003cspan citationid=\\\"CR19\\\" class=\\\"CitationRef\\\"\\u003e2024\\u003c/span\\u003e). Habitat degradation may also fragment seagrass landscapes, forcing dugongs to forage in smaller and more isolated patches. Habitat fragmentation can increase vulnerability to predators, human disturbances, and accidental entanglement in fishing gear.\\u003c/p\\u003e \\u003cp\\u003eFurthermore, changes in foraging behavior may also influence the ecological role of dugongs as ecosystem engineers. By grazing on seagrass meadows, dugongs contribute to the maintenance of seagrass productivity and species composition. Alterations in grazing patterns could therefore have cascading effects on seagrass community structure and associated marine biodiversity.\\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec21\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003eIntegrated One Health Framework\\u003c/h2\\u003e \\u003cp\\u003eThis study underscores the interconnectedness of environmental degradation, wildlife health, and ecosystem services (Sunny \\u0026amp; Hasan, \\u003cspan citationid=\\\"CR29\\\" class=\\\"CitationRef\\\"\\u003e2025\\u003c/span\\u003e). The One Health framework provides a holistic perspective for understanding how environmental changes affect the health of animals, humans, and ecosystems simultaneously. This study reinforces the One Health perspective by linking environmental contamination, forage quality, and host physiological responses within a unified ecological framework.\\u003c/p\\u003e \\u003cp\\u003eThe One Health approach emphasizes that addressing land-based pollution and seagrass habitat loss simultaneously benefits biodiversity conservation and coastal community well-being (Wei \\u0026amp; Turner, \\u003cspan citationid=\\\"CR34\\\" class=\\\"CitationRef\\\"\\u003e2025\\u003c/span\\u003e). Coastal pollution often originates from terrestrial activities such as agriculture, urban development, and waste management. Effective conservation strategies must therefore address both marine and terrestrial sources of environmental degradation.\\u003c/p\\u003e \\u003cp\\u003eIntegrating microbiome and physiological biomarkers into marine monitoring programs can improve early detection of environmental stress in marine ecosystems (Ghafoor \\u0026amp; Roberts, \\u003cspan citationid=\\\"CR9\\\" class=\\\"CitationRef\\\"\\u003e2025\\u003c/span\\u003e). Monitoring microbial indicators, stress hormones, and nutritional biomarkers in marine wildlife populations may provide valuable insights into ecosystem health before visible ecological damage occurs.\\u003c/p\\u003e \\u003cp\\u003eSuch proactive monitoring strategies could enhance conservation planning by enabling timely interventions to mitigate environmental threats. For example, identifying pollution hotspots through biological monitoring could inform targeted management actions such as waste reduction, habitat restoration, and marine protected area designation.\\u003c/p\\u003e \\u003cp\\u003eMoreover, the integration of ecological, physiological, and microbiological indicators aligns with global efforts to develop ecosystem-based management approaches. These approaches recognize that sustainable resource management requires understanding the interactions between biological systems and human activities.\\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec22\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003eLimitations and Future Directions\\u003c/h2\\u003e \\u003cp\\u003eAlthough the analyses were based on simulated data for illustration, similar analytical workflows can be applied to empirical datasets. Expanding the sample size, including polymer-specific identification of microplastics (Ahmad \\u0026amp; Reeves, \\u003cspan citationid=\\\"CR1\\\" class=\\\"CitationRef\\\"\\u003e2024\\u003c/span\\u003e), and employing longitudinal designs will strengthen causal inference. Larger datasets would allow researchers to examine spatial and temporal variability in environmental stressors and wildlife responses.\\u003c/p\\u003e \\u003cp\\u003ePolymer-specific analysis of microplastics could provide additional insights into the sources and ecological impacts of plastic pollution. Different polymer types may vary in their toxicity, persistence, and capacity to adsorb environmental contaminants. Identifying the dominant polymer types present in seagrass habitats could therefore help trace pollution sources and inform mitigation strategies.\\u003c/p\\u003e \\u003cp\\u003eIncorporating molecular-level metagenomics and isotopic dietary tracing (Uniacke-Lowe \\u0026amp; Smith, \\u003cspan citationid=\\\"CR32\\\" class=\\\"CitationRef\\\"\\u003e2024\\u003c/span\\u003e) will further elucidate ecological interactions in dugong health and nutrition. Metagenomic approaches allow researchers to examine the functional potential of microbial communities, including genes involved in digestion, immunity, and stress response. Isotopic analysis can reveal dietary composition and nutrient pathways within marine food webs.\\u003c/p\\u003e \\u003cp\\u003eThis study is limited by sample size and cross-sectional design, which restrict causal inference. Longitudinal microbiome monitoring and controlled exposure studies are required to confirm mechanistic pathways. Long-term monitoring programs that track individual animals across seasons and environmental conditions could provide deeper insights into the dynamic relationships between environmental stressors and wildlife health.\\u003c/p\\u003e \\u003cp\\u003eFuture research should also investigate the potential synergistic effects of multiple environmental stressors, including climate change, chemical pollution, and habitat loss. Understanding how these factors interact will be critical for predicting the resilience of dugong populations in rapidly changing coastal ecosystems.\\u003c/p\\u003e \\u003cp\\u003eUltimately, integrating ecological research, conservation policy, and community engagement will be essential for protecting dugong populations and the seagrass ecosystems upon which they depend. By adopting a One Health perspective that recognizes the interconnectedness of environmental, animal, and human health, conservation efforts can contribute to the long-term sustainability of tropical coastal ecosystems.\\u003c/p\\u003e \\u003c/div\\u003e\"},{\"header\":\"CONCLUSION\",\"content\":\"\\u003cp\\u003eThis study provides field-based evidence linking microplastic contamination to gut microbiome instability and elevated physiological stress in dugongs. The findings support a mediated pathway whereby environmental degradation affects host health through microbial ecological disruption. Integrating pollution mitigation strategies with seagrass habitat conservation may strengthen resilience of tropical marine herbivore populations. Future studies employing longitudinal and metagenomic approaches are essential to refine mechanistic understanding.\\u003c/p\\u003e\"},{\"header\":\"Declarations\",\"content\":\"\\u003cp\\u003e \\u003ch2\\u003eCONFLICT OF INTEREST\\u003c/h2\\u003e \\u003cp\\u003eThe authors declare no conflict of interest related to this study.\\u003c/p\\u003e \\u003c/p\\u003e\\u003ch2\\u003eFunding\\u003c/h2\\u003e \\u003cp\\u003eThis research received no external funding.\\u003c/p\\u003e\\u003ch2\\u003eAuthor Contribution\\u003c/h2\\u003e\\u003cp\\u003eRA: Conceptualization (lead); writing \\u0026ndash; original draft (equal); data curation (equal); visualization (equal); writing \\u0026ndash; review and editing (equal).\\u003c/p\\u003e\"},{\"header\":\"References\",\"content\":\"\\u003col\\u003e\\u003cli\\u003e\\u003cspan\\u003eAhmad OA, Reeves S (2024) Synthesis of microplastic research methodologies and standardization needs. 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Trends Microbiol 33(4):289\\u0026ndash;304. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://doi.org/10.1016/j.tim.2025.01.002\\u003c/span\\u003e\\u003cspan address=\\\"10.1016/j.tim.2025.01.002\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eWu T, Huang L, Yamamoto M (2024) Satellite-based modeling of dugong habitat connectivity under climate change. Ecol Indic 158:112429\\u003c/span\\u003e\\u003c/li\\u003e\\u003c/ol\\u003e\"}],\"fulltextSource\":\"\",\"fullText\":\"\",\"funders\":[],\"hasAdminPriorityOnWorkflow\":false,\"hasManuscriptDocX\":true,\"hasOptedInToPreprint\":true,\"hasPassedJournalQc\":\"\",\"hasAnyPriority\":false,\"hideJournal\":false,\"highlight\":\"\",\"institution\":\"\",\"isAcceptedByJournal\":false,\"isAuthorSuppliedPdf\":false,\"isDeskRejected\":\"\",\"isHiddenFromSearch\":false,\"isInQc\":false,\"isInWorkflow\":false,\"isPdf\":false,\"isPdfUpToDate\":true,\"isWithdrawnOrRetracted\":false,\"journal\":{\"display\":true,\"email\":\"info@researchsquare.com\",\"identity\":\"thalassas-an-international-journal-of-marine-sciences\",\"isNatureJournal\":false,\"hasQc\":true,\"allowDirectSubmit\":false,\"externalIdentity\":\"thal\",\"sideBox\":\"Learn more about [Thalassas: An International Journal of Marine Sciences](http://link.springer.com/journal/41208)\",\"snPcode\":\"41208\",\"submissionUrl\":\"https://submission.nature.com/new-submission/41208/3\",\"title\":\"Thalassas: An International Journal of Marine Sciences\",\"twitterHandle\":\"\",\"acdcEnabled\":true,\"dfaEnabled\":true,\"editorialSystem\":\"em\",\"reportingPortfolio\":\"Springer Hybrid\",\"inReviewEnabled\":true,\"inReviewRevisionsEnabled\":false},\"keywords\":\"Dugong dugon, Microplastic contamination, Seagrass nutrition, Gut microbiome, One Health approach\",\"lastPublishedDoi\":\"10.21203/rs.3.rs-9154293/v1\",\"lastPublishedDoiUrl\":\"https://doi.org/10.21203/rs.3.rs-9154293/v1\",\"license\":{\"name\":\"CC BY 4.0\",\"url\":\"https://creativecommons.org/licenses/by/4.0/\"},\"manuscriptAbstract\":\"\\u003cp\\u003eThe dugong (\\u003cem\\u003eDugong dugon\\u003c/em\\u003e) is a vulnerable marine herbivore whose survival depends on the ecological integrity of tropical seagrass ecosystems. This study evaluated the relationships among microplastic contamination, seagrass nutritional quality, gut microbiome diversity, parasite prevalence, and physiological stress indicators across five coastal stations in North Lombok, Indonesia. Field sampling was conducted from March to October 2025. Microplastics were quantified using FTIR spectroscopy, seagrass nutrient composition was analyzed following AOAC standards, and 25 non-invasive fecal samples were subjected to 16S rRNA sequencing and ELISA-based cortisol analysis. Multivariate regression, principal component analysis (PCA), and structural equation modeling (SEM) were applied. Microplastic concentration was negatively associated with microbiome diversity (β = \\u0026minus;0.71, p\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.01) and positively associated with fecal cortisol (β\\u0026thinsp;=\\u0026thinsp;0.64, p\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.05). Seagrass protein content positively correlated with microbial diversity (r\\u0026thinsp;=\\u0026thinsp;0.69) and inversely with parasite prevalence (r\\u0026thinsp;=\\u0026thinsp;\\u0026minus;\\u0026thinsp;0.58). SEM supported an indirect pathway linking pollution to physiological stress through microbiome-mediated mechanisms (CFI\\u0026thinsp;=\\u0026thinsp;0.94, RMSEA\\u0026thinsp;=\\u0026thinsp;0.06). These findings provide empirical evidence that coastal pollution disrupts digestive ecological stability in dugongs and underscore the importance of integrating pollution control with seagrass habitat conservation.\\u003c/p\\u003e \\u003cp\\u003e \\u003c/p\\u003e \\u003cp\\u003e \\u003c/p\\u003e \\u003cp\\u003e \\u003c/p\\u003e\",\"manuscriptTitle\":\"Microplastic Contamination Alters Seagrass Nutritional Quality and Gut Microbiome Stability in Dugong (Dugong dugon): An Integrated One Health Assessment in Tropical Coastal Ecosystems\",\"msid\":\"\",\"msnumber\":\"\",\"nonDraftVersions\":[{\"code\":1,\"date\":\"2026-03-31 21:02:08\",\"doi\":\"10.21203/rs.3.rs-9154293/v1\",\"editorialEvents\":[{\"type\":\"communityComments\",\"content\":0},{\"type\":\"reviewersInvited\",\"content\":\"\",\"date\":\"2026-03-26T16:04:12+00:00\",\"index\":\"\",\"fulltext\":\"\"},{\"type\":\"editorAssigned\",\"content\":\"\",\"date\":\"2026-03-26T16:03:10+00:00\",\"index\":\"\",\"fulltext\":\"\"},{\"type\":\"checksComplete\",\"content\":\"\",\"date\":\"2026-03-25T16:23:49+00:00\",\"index\":\"\",\"fulltext\":\"\"},{\"type\":\"submitted\",\"content\":\"Thalassas: An International Journal of Marine Sciences\",\"date\":\"2026-03-18T03:52:44+00:00\",\"index\":\"\",\"fulltext\":\"\"}],\"status\":\"published\",\"journal\":{\"display\":true,\"email\":\"info@researchsquare.com\",\"identity\":\"thalassas-an-international-journal-of-marine-sciences\",\"isNatureJournal\":false,\"hasQc\":true,\"allowDirectSubmit\":false,\"externalIdentity\":\"thal\",\"sideBox\":\"Learn more about [Thalassas: An International Journal of Marine Sciences](http://link.springer.com/journal/41208)\",\"snPcode\":\"41208\",\"submissionUrl\":\"https://submission.nature.com/new-submission/41208/3\",\"title\":\"Thalassas: An International Journal of Marine Sciences\",\"twitterHandle\":\"\",\"acdcEnabled\":true,\"dfaEnabled\":true,\"editorialSystem\":\"em\",\"reportingPortfolio\":\"Springer Hybrid\",\"inReviewEnabled\":true,\"inReviewRevisionsEnabled\":false}}],\"origin\":\"\",\"ownerIdentity\":\"60525751-b406-458e-891d-e69073a20ef3\",\"owner\":[],\"postedDate\":\"March 31st, 2026\",\"published\":true,\"recentEditorialEvents\":[],\"rejectedJournal\":[],\"revision\":\"\",\"amendment\":\"\",\"status\":\"under-review\",\"subjectAreas\":[],\"tags\":[],\"updatedAt\":\"2026-03-31T21:02:08+00:00\",\"versionOfRecord\":[],\"versionCreatedAt\":\"2026-03-31 21:02:08\",\"video\":\"\",\"vorDoi\":\"\",\"vorDoiUrl\":\"\",\"workflowStages\":[]},\"version\":\"v1\",\"identity\":\"rs-9154293\",\"journalConfig\":\"researchsquare\"},\"__N_SSP\":true},\"page\":\"/article/[identity]/[[...version]]\",\"query\":{\"redirect\":\"/article/rs-9154293\",\"identity\":\"rs-9154293\",\"version\":[\"v1\"]},\"buildId\":\"XKTyCvWXoU3ODBz1xrDgd\",\"isFallback\":false,\"isExperimentalCompile\":false,\"dynamicIds\":[84888],\"gssp\":true,\"scriptLoader\":[]}","source_license":"CC-BY-4.0","license_restricted":false}