Search Results (92)

Coastal ecosystems are increasingly threatened by climate change and anthropogenic pressures, which can disrupt microbial communities and favor the emergence of pathogenic organisms. In this study, we applied metagenomic analysis to characterize fungal communities in sediment samples from an urban mangrove subjected to environmental stress. The results revealed a fungal community with reduced richness—28% lower than expected for similar ecosystems—likely linked to physicochemical changes such as heavy metal accumulation, acidic pH, and eutrophication, all typical of urbanized coastal areas. Notably, we detected an increase in potentially pathogenic genera, including Candida, Aspergillus, and Pseudoascochyta, alongside a decrease in key saprotrophic genera such as Fusarium and Thelebolus, indicating a shift in ecological function. The fungal assemblage was dominated by the phyla Ascomycota and Basidiomycota, and despite adverse conditions, symbiotic mycorrhizal fungi remained present, suggesting partial resilience. A considerable fraction of unclassified fungal taxa also points to underexplored microbial diversity with potential ecological or health significance. Importantly, this study does not aim to compare pristine and contaminated environments, but rather to provide a sanitary alert by identifying the presence and potential proliferation of pathogenic fungi in a degraded mangrove system. These findings highlight the sensitivity of mangrove fungal communities to environmental disturbance and reinforce the value of metagenomic approaches for monitoring ecosystem health. Incorporating fungal metagenomic surveillance into environmental management strategies is essential to better understand biodiversity loss, ecological resilience, and potential public health risks in degraded coastal environments. Full article

Mangroves form the dominant coastal plant community of low-energy tropical intertidal habitats and provide critical ecosystem services to humans and the environment. However, more frequent and increasingly powerful hurricanes and storm surges are creating additional pressure on the natural resilience of these threatened coastal ecosystems. Advances in remote sensing techniques and approaches are critical to providing robust quantitative monitoring of post-storm mangrove forest recovery to better prioritize the often-limited resources available for the restoration of these storm-damaged habitats. Here, we build on previously utilized spatial and temporal ranges of European Space Agency (ESA) Sentinel satellite imagery to monitor and map the recovery of the mangrove forests of the British Virgin Islands (BVI) since the occurrence of back-to-back category 5 hurricanes, Irma and Maria, on September 6 and 19 of 2017, respectively. Pre- to post-storm changes in coastal mangrove forest health were assessed annually using the normalized difference vegetation index (NDVI) and moisture stress index (MSI) from 2016 to 2023 using Google Earth Engine. Results reveal a steady trajectory towards forest health recovery on many of the Territory’s islands since the storms’ impacts in 2017. However, some mangrove patches are slower to recover, such as those on the islands of Virgin Gorda and Jost Van Dyke, and, in some cases, have shown a continued decline (e.g., Prickly Pear Island). Our work also uses a linear ANCOVA model to assess a variety of geospatial, environmental, and anthropogenic drivers for mangrove recovery as a function of NDVI pre-storm and post-storm conditions. The model suggests that roughly 58% of the variability in the 7-year difference (2016 to 2023) in NDVI may be related by a positive linear relationship with the variable of population within 0.5 km and a negative linear relationship with the variables of northwest aspect vs. southwest aspect, island size, temperature, and slope. Full article

Stand structural configuration dictates ecosystem functional performance. Mangrove ecosystems, located in ecologically sensitive coastal ecotones, require efficient acquisition of stand structure parameters and health assessments based on these parameters for practical applications. Effective assessment of mangrove ecosystem health, crucial for their functional performance in ecologically sensitive coastal ecotones, relies on efficient acquisition of stand structure parameters. This study developed a UAV (Unmanned Aerial Vehicle)-based framework for mangrove health evaluation integrating stand structure parameters, utilizing UAV visible-light imagery, field plot surveys, and computer vision techniques, and applied it to the assessment of a national nature reserve. We obtained the following results: (1) A deep neural network, combining UAV visible-light data with tree height constraints, achieved 88.29% overall accuracy in simultaneously identifying six dominant mangrove species; (2) Stand structure parameters were derived based on individual tree extraction results in seedling zones along forest edges (with canopy individual tree segmentation accuracy ≥ 78.57%), and a stand health evaluation model was constructed; (3) Health assessment revealed that the core zone exhibited significantly superior stand health compared to non-core zones. This method demonstrates high efficiency, significantly reducing the time and effort for monitoring, and offers robust support for future mangrove forest health assessments and adaptive conservation strategies. Full article

Mangrove forests serve as critical ecological barriers in coastal zones and play a vital role in global blue carbon sequestration strategies. In recent decades, China’s mangrove ecosystems have experienced complex interactions between degradation and restoration under intense coastal urbanization and systematic conservation efforts. However, the long-term spatiotemporal patterns and driving mechanisms of mangrove ecosystem health changes remain insufficiently quantified. This study developed a multi-temporal analytical framework using Landsat imagery (1986–2021) to derive kernel normalized difference vegetation index (kNDVI) time series—an advanced phenological indicator with enhanced sensitivity to vegetation dynamics. We systematically characterized mangrove growth patterns along China’s southeastern coast through integrated Theil–Sen slope estimation, Mann–Kendall trend analysis, and Hurst exponent forecasting. A Deep Forest regression model was subsequently applied to quantify the relative contributions of environmental drivers (mean annual sea surface temperature, precipitation, air temperature, tropical cyclone frequency, and relative sea-level rise rate) and anthropogenic pressures (nighttime light index). The results showed the following: (1) a nationally significant improvement in mangrove vitality (p < 0.05), with mean annual kNDVI increasing by 0.0072/yr during 1986–2021; (2) spatially divergent trajectories, with 58.68% of mangroves exhibiting significant improvement (p < 0.05), which was 2.89 times higher than the proportion of degraded areas (15.10%); (3) Hurst persistence analysis (H = 0.896) indicating that 74.97% of the mangrove regions were likely to maintain their growth trends, while 15.07% of the coastal zones faced potential degradation risks; and (4) Deep Forest regression id the relative rate of sea-level rise (importance = 0.91) and anthropogenic (nighttime light index, importance = 0.81) as dominant drivers, surpassing climatic factors. This study provides the first national-scale, 30 m resolution assessment of mangrove growth dynamics using kNDVI, offering a scientific basis for adaptive management and blue carbon strategies in subtropical coastal ecosystems. Full article

Mangrove wetlands, acting as significant traps for marine debris, have received insufficient attention in previous research. Here, we conduct the first comprehensive investigation into the magnitude, accumulation, source, and fate of marine debris across seven mangrove areas in the northern South China Sea (MNSCS) during 2019–2020. Systematic field surveys employed stratified random sampling, partitioning each site by vegetation density and tidal influence. Marine debris were collected and classified in sampling units by material (plastic, fabric, styrofoam), size (categorized into small, medium, and large), and origin (distinguishing between land-based and sea-based). Source identification and potential risk assessment were achieved through the integration of debris feature analysis. The results indicate relatively low debris levels in MNSCS mangroves, with plastics dominant. More than 70% of all debris weight with plastics (48.34%) and fabrics (14.59%) is land-based, and more than 70% comes from coastal/recreational activities. More than 90% of all debris items with plastics (52.50%) and Styrofoam (36.32%) are land-based, and more than 90% come from coastal/recreational activities. Medium/large-sized debris are trapped in mangrove wetlands under the influencing conditions of local tidal level, debris item materials, and sizes. Our study quantifies marine debris characteristics, sources, and ecological potential risks in MNSCS mangroves. From environmental, economic, and social sustainability perspectives, our findings are helpful for guiding marine debris management and mangrove conservation. By bridging research and policies, our work balances human activities with ecosystem health for long-term sustainability. Full article

This paper systematically reviews the progress of remote-sensing technology in coastal-ecosystem health assessment. Coastal ecosystems, as transitional zones between land and ocean, play vital roles in maintaining biodiversity, carbon sequestration, and coastal protection, but currently face severe challenges from climate change and human activities. Remote-sensing technology, with its capability for large-scale, long time-series observations, has become a key tool for coastal-ecosystem health assessment. This paper analyzes the technical characteristics and advantages of optical remote sensing, radar remote sensing, and multi-source data fusion in coastal monitoring; constructs a health-assessment framework that includes water-quality indicators, vegetation and ecosystem function indicators, and human disturbance and landscape change indicators; discusses the application of advanced technologies from traditional methods to machine learning and deep learning in data processing; and demonstrates the role of multi-temporal analysis in revealing coastal-ecosystem change trends through typical case studies of mangroves, salt marshes, and coral reefs. Research indicates that, despite the enormous potential of remote-sensing technology in coastal monitoring, it still faces challenges such as sensor limitations, environmental interference, and data processing and validation. Future development should focus on advanced sensor technology, platform innovation, data-processing method innovation, and multi-source data fusion, while strengthening the effective integration of remote-sensing technology with management practices to provide scientific basis for the protection and sustainable management of coastal ecosystems. Full article

The mud crab (Scylla paramamosain), also known as the mangrove crab, thrives in shallow mangrove forests, subtidal zones, and muddy intertidal habitats. It is a highly valuable species in the mangroves and estuaries of tropical regions and is in high demand in Vietnam’s coastal markets. This study provides a comprehensive analysis of the key factors influencing consumer behavior and purchasing decisions regarding mud crabs in Vietnam’s three largest cities: Can Tho City, Ho Chi Minh City, and Hanoi Capital. To achieve this, the research employs rigorous analytical methods, including Cronbach’s alpha reliability test, principal component analysis, and multivariate regression analysis, to identify the primary determinants of consumer behavior and purchasing preferences for fresh mud crabs. The multiple regression analysis reveals two key factors that significantly influence consumer choices: nutritional knowledge and convenience awareness. Most of consumers perceive fresh mud crabs as superior in quality, expecting them to offer greater freshness; higher levels of protein, amino acids, and minerals; as well as excellent flesh texture and enhanced palatability. Based on these findings, two strategic directions are proposed for the sustainable development of Vietnam’s crab industry: (1) improving the nutritional quality of crab products to align with consumer expectations for health benefits and (2) enhancing the distribution network and diversifying product offerings to improve accessibility and convenience for consumers. Full article

Mangrove forests are vital ecosystems along tropical coasts, playing crucial roles in water purification and biodiversity conservation. Diatoms, as sensitive ecological indicators, were employed in this study to evaluate the health of the mangrove forests along the Sanya River. The research involved analyzing the community structure of planktonic diatoms and water physicochemical properties during spring and winter, as well as carrying out a comprehensive assessment of the ecological health of the region in terms of four seasonal–spatial–environmental–biological indices. A total of 22 genera of planktonic diatoms were identified. In winter, Melosira sp. (34.94%), Skeletonema sp. (25.50%), and Chaetoceros sp. (15%) were dominant, with relative abundances of 34.94%, 25.50%, and 15.00%. In spring, Melosira sp. became the absolutely dominant species, averaging 70.16%. Diatom cell abundance shows both significant seasonal and spatial variation. In winter, it ranged from 0.53 to 17.4 × 10⁹ cells-L⁻¹, peaking in the midstream region, whereas in spring, it ranged from 2.48 to 21.0 × 10⁹ cells-L⁻¹, peaking at the mouth of the estuary. A higher abundance of diatoms in spring strengthens primary productivity and supports the subsequent functioning of the food chain. Diatom indices (Shannon–Wiener index H’, Pielou evenness index J, and Margalef richness index D) indicated an intermediate ecological health level for the Sanya River mangrove forests. Diversity was higher in winter than in spring, with the lowest values recorded in the midstream region. Redundancy analysis (RDA) indicated that T, pH, and PO₄³⁻ were the primary environmental drivers of diatom community succession. In spring, pH was positively correlated with T and PO₄³⁻, respectively. They drove the succession of diatom communities from diverse assemblages in winter to a single dominant species. Full article

Antimicrobial resistance (AMR) is a global health threat, with methicillin-resistant Staphylococcus aureus (MRSA) being one of the major resistant pathogens. This study reports the isolation of a novel mangrove-derived bacterium, Virgibacillus chiguensis FN33, as identified through genome analysis and the discovery of a new anionic antimicrobial peptide (AMP) exhibiting anti-MRSA activity. The AMP was composed of 23 amino acids, which were elucidated as NH₃-Glu-Gly-Gly-Cys-Gly-Val-Asp-Thr-Trp-Gly-Cys-Leu-Thr-Pro-Cys-His-Cys-Asp-Leu-Phe-Cys-Thr-Thr-COOH. The minimum inhibitory concentration (MIC) and minimum bactericidal concentration (MBC) for MRSA were 8 µg/mL and 16 µg/mL, respectively. FN33 AMP induced cell membrane permeabilization, suggesting a membrane-disrupting mechanism. The AMP remained stable at 30–40 °C but lost activity at higher temperatures and following exposure to proteases, surfactants, and extreme pH. All-atom molecular dynamics simulations showed that the AMP adopts a β-sheet structure upon membrane interaction. These findings suggest that Virgibacillus chiguensis FN33 is a promising source of novel antibacterial agents against MRSA, supporting alternative strategies for drug-resistant infections. Full article

Background: Bangladesh’s wetlands support fish genetic resources, biodiversity, and food security but face obstacles like habitat degradation, overfishing, and climate change. This research looks at the diversity, abundance, IUCN conservation status, and stakeholder views for sustainable wetland management in three major wetlands: the Sundarbans mangrove (brackish water), the Sylhet floodplain (freshwater), and the Meghna River basin (estuary). Methods: To assess ecosystem health and vulnerability, we assess fish biodiversity and conservation status using Margalef’s Species Richness Index and Shannon–Weaver Diversity Index. We also used structured questionnaires to interview and gain stakeholders’ perceptions. Results: A total of 165 fish species were identified and categorized based on the IUCN Red List. The Sundarbans exhibited the highest species richness and diversity. The proportion of vulnerable species was highest in Sylhet (15%), followed by the Sundarbans (12%) and Meghna (9%), while 54% of fish species in Sylhet, 36% in the Sundarbans, and 26% in Meghna were not threatened. This study emphasizes integrated wetland management solutions that support SDGs 2 (Zero Hunger), 14 (Life Below Water), and 15 (Life on Land) as outlined by the Food and Agriculture Organization (FAO). Conclusion: Findings provide a foundation for policymakers, researchers, and conservationists to develop sustainable wetland management frameworks that safeguard fish genetic resources, livelihoods, and ecological balance. Full article

Mangrove forests are intertidal ecosystems that harbor diverse microbial communities essential for biogeochemical cycles and energy flow. This study investigated the seasonal and spatial patterns of bacterial communities in the artificially introduced mangrove sediments of the Ao River estuary using 16S rRNA gene amplicon high-throughput sequencing. Alpha diversity analyses indicated that the bacterial community diversity in the mangrove sediments of the Ao River estuary was similar to those of natural-formed mangroves, with the Shannon index ranging from 5.16 to 6.54, which was significantly higher in winter compared to other seasons. The dominant bacterial phyla included Proteobacteria (43.65%), Actinobacteria (11.55%), Desulfobacterota (11.16%), and Bacteroidetes (5.52%), while beta diversity analysis revealed substantial differences in bacterial community structure across different seasons and regions. For instance, the relative abundance of Woeseiaceae and Bacteroidota during the summer was significantly higher than that observed in other seasons. And the relative abundance of Bacillaceae in autumn and winter increased by one order of magnitude compared to spring and summer. Woeseiaceae, Desulfobulbaceae, Thermoanaerobaculaceae, and Sva1033 (family of Desulfobacterota) exhibited significantly higher relative abundance in the unvegetated area, whereas Bacillaceae and S085 (family of Chloroflexi) demonstrated greater abundance in the mangrove area. Seasonal variations in bacterial community structure are primarily attributed to changes in environmental factors, including temperature and salinity. Regional differences in bacterial community structure are primarily associated with environmental stressors, such as wave action, fluctuations in salinity, and organic matter content, which are further complicated by seasonal changes. This study is significant for understanding the microbial diversity and seasonal dynamics of estuarine mangrove wetlands, and it contributes to the assessment of mangrove wetland restoration efforts in Zhejiang Province, providing important guidance for the development of strategies to maintain the health of mangrove ecosystems in the future. Full article

It has been well observed that accurate estimation of the aboveground biomass (AGB) of mangrove forests is critical for evaluating ecosystem health, carbon sink capacity, and sustainable development. This study utilizes UAV-LiDAR data and field measurements to develop an AGB inversion model based on 26 feature variables. We employed three machine learning algorithms—random forest (RF), extreme gradient boosting (XGBoost), and support vector machine (SVM)—to estimate mangrove AGB in the Xinyingwan region of Lingao County, Hainan Province, China. The key findings include that: (1) the SVM algorithm demonstrated the highest predictive accuracy, with an R² of 0.8853 and RMSE of 0.4766 kg/m², making it most suitable for this study; (2) the proposed zero-importance feature selection method based on mutual information (MI) outperformed traditional techniques, selecting more effective variables for model development; (3) in the SVM model, variables selected using the zero-importance feature selection method based on MI yielded the best prediction accuracy; and (4) the estimated AGB in the study area ranged from 1.97 to 5.23 kg/m², with an average of 3.83 kg/m². This study not only provides valuable data for mangrove ecosystem conservation and restoration but also offers a scientific basis and technical framework for future biomass estimation and carbon stock assessments. Full article

Resource conflicts constitute a major global issue in areas rich in natural resources. The modeling of factors influencing natural resource conflicts (NRCs), including environmental, health, socio-economic, political, and legal aspects, presents a significant challenge compounded by inadequate data. Quantitative research frequently emphasizes large-scale conflicts. This study presents a novel multilevel approach, SEFLAME-CM—Spatially Explicit Fuzzy Logic-Adapted Model for Conflict Management—for advancing understanding of the relationship between NRCs and drivers under territorial and rebel-based typologies at a community level. SEFLAME-CM is hypothesized to yield a more robust positive correlation between the risk of NRCs and the interacting conflict drivers, provided that the conflict drivers and input variables remain the same. Local knowledge from stakeholders is integrated into spatial decision-making tools to advance sustainable peace initiatives. We compared our model with spatial multi-criteria evaluation for conflict management (SMCE-CM) and spatial statistics. The results from the Moran’s I scatter plots of the overall conflicts of the SEFLAME-CM and SMCE-CM models exhibit substantial values of 0.99 and 0.98, respectively. Territorial resource violence due to environmental drivers increases coast-wards, more than that stemming from rebellion. Weighing fuzzy rules and conflict drivers enables equal comparison. Environmental variables, including proximity to arable land, mangrove ecosystems, polluted water, and oil infrastructures are key factors in NRCs. Conversely, socio-economic and political factors seem to be of lesser importance, contradicting prior research conclusions. In Third World nations, local communities emphasize food security and access to environmental services over local political matters amid competition for resources. The synergistic integration of fuzzy logic analysis and community perception to address sustainable peace while simultaneously connecting environmental and socio-economic factors is SEFLAME-CM’s contribution. This underscores the importance of a holistic approach to resource conflicts in communities and the dissemination of knowledge among specialists and local stakeholders in the sustainable management of resource disputes. The findings can inform national policies and international efforts in addressing the intricate underlying challenges while emphasizing the knowledge and needs of impacted communities. SEFLAME-CM, with improvements, proficiently illustrates the capacity to model intricate real-world issues. Full article

Mangroves play a crucial role in supporting the biodiversity of coastal wetlands, acting as a vital link between terrestrial and marine ecosystems. In mainland China, Sonneratia apetala, an invasive mangrove species, has recently become dominant in these environments. While it contributes to the stability of mangrove ecosystems and is widely used in coastal restoration efforts, its rapid growth poses a significant threat to the survival of native mangrove species. However, the spatiotemporal growth dynamics and landscape impacts of Sonneratia apetala remain underexplored in scholarly research. This study employs remote sensing and GIS techniques to analyze the growth patterns of Sonneratia apetala over a 14-year period along the eastern coast of the Leizhou Peninsula in China. The analysis revealed the following key findings: (1) The mangrove area expanded from 274.17 hm² to 383.42 hm², with an average annual growth rate of 2.84%. (2) The area of Sonneratia apetala increased from 115.15 hm² in 2010 to 254.81 hm² in 2023, with an average annual growth rate of 1.29%. The area of local mangrove species declined from 163.02 hm² to 125.06 hm² (a decrease from 22.11% to 16.96%), with an average annual growth rate of −1.66%. (3) The number of Sonneratia apetala patches increased from 139 to 324, while the area-weighted shape index rose from 3.4 to 7.81. The decline of native mangrove species, driven by the rapid spread of Sonneratia apetala, suggests that this species is encroaching on native mangrove habitats. Through geospatial analysis, this study provides valuable insights into how introduced species can reshape mangrove landscape structures and the broader implications for regional biodiversity. These findings clearly demonstrate that Sonneratia apetala is encroaching upon local mangrove habitats, highlighting the urgent need for strategic management and conservation efforts to mitigate the ecological impacts of the proliferation of this species. Furthermore, this research is important for coastal sustainability management strategies that balance ecological restoration with the preservation of native biodiversity, ensuring long-term ecosystem health and resilience. Full article