Coastal wetlands are a type of unique ecosystem, in which rhizosphere microorganisms of vegetation play a significant role in the overall ecology. Rhizosphere soil samples from the vegetation of Liaohekou Coastal Wetlands, Northeast China (40°54′44″ N, 121°47′51″ E), were collected in seven habitats (Suaeda and Phragmites community in different coverage, aquaculture ponds and farmland biotopes) to evaluate the diversity and structure of bacterial community using high throughput sequencing. Soil physicochemical characteristics and bacterial communities were found to be affected by vegetation coverage by ANOVA tests. As and Ni were the main heavy metal variables affecting the bacterial communities as demonstrated by RDA tests, while NO₃⁻-N were important variables in nutrient factors. Proteobacteria was the predominant phylum in all soils. Gillisia and Woeseia were the two most dominant genera peculiarly in Suaeda and Phragmites community. Meanwhile SparCC showed that Woeseia play a dominant role in wetland rhizosphere bacterial communities. The Chemoheterotrophic function was dominant in all communities with FAPROTAX results, while in wetland the cycle of Sulfur and Nitrogen were significantly affected by vegetation type and coverage. In conclusion, this study revealed the structural composition and diversity of rhizosphere bacterial communities under different vegetation types and coverage. This research could help deepen our understanding of the microbial ecology on the wetlands and provide information on bacterial communities in various habitats.

Cumulative effects assessment (CEA) should be conducted at ecologically meaningful scales such as large marine ecosystems to halt further ocean degradation caused by anthropogenic pressures and facilitate ecosystem-based management such as transboundary marine spatial planning (MSP). However, few studies exist at large marine ecosystems scale, especially in the West Pacific seas, where countries have different MSP processes yet transboundary cooperation is paramount. Thus, a step-wise CEA would be informative to help bordering countries set a common goal. Building on the risk-based CEA framework, we decomposed CEA into risk identification and spatially-explicit risk analysis and applied it to the Yellow Sea Large Marine Ecosystem (YSLME), aiming to understand the most influential cause-effect pathways and risk distribution pattern. The results showed that (1) seven human activities including port, mariculture, fishing, industry and urban development, shipping, energy, and coastal defence, and three pressures including physical loss of seabed, input of hazardous substances, nitrogen, and phosphorus enrichment were the leading causes of environmental problems in the YSLME; (2) benthic organisms, fishes, algae, tidal flats, seabirds, and marine mammals were the most vulnerable ecosystem components on which cumulative effects acted; (3) areas with relatively high risk mainly concentrated on nearshore zones, especially Shandong, Liaoning, and northern Jiangsu, while coastal bays of South Korea also witnessed high risk; (4) certain risks could be observed in the transboundary area, of which the causes were the pervasive fishing, shipping, and sinking of pollutants in this area due to the cyclonic circulation and fine-grained sediments. In future transboundary cooperation on MSP, risk criteria and evaluation of existing management measures should be incorporated to determine whether the identified risk has exceeded the acceptable level and identify the next step of cooperation. Our study presents an example of CEA at large marine ecosystems scale and provides a reference to other large marine ecosystems in the West Pacific and elsewhere.

According to the Thailand’s National Strategy (2017-2036) and National Reform Plans, various tools, techniques, or methods are necessary to collect and investigate data for the effective preservation and protection of the country’s natural resources. We aimed to apply various tools and methods for integrated coastal management in Thailand. This study used the InVEST models, including the habitat quality (HQ) and habitat risk assessment (HRA) models, to evaluate the natural habitat quality and cumulative human activity risk in the Sriracha district and Sichang Islands, Thailand. The HQ model revealed the presence of abundant ecological services and high quality natural habitats. We observed habitat degradation in the mooring zone, city area, forests, and coral reefs, with moderate risk to distinct habitats. Our findings identified two potential scenarios. Conservation scenarios exhibited a lower HRA ratio compared to current and development scenarios. Overall, the results showed the effectiveness of the InVEST model in evaluating habitat risk under both present and simulated conditions. Our study highlights the importance of informed management plans and policy-making processes to achieve planned coastal management goals.

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.