Mangrove forests are crucial wetland ecosystems located in tropical and subtropical intertidal zones, but they have become extensively degraded. As a viable ecological restoration strategy, the cultivation of native mangrove vegetation in these degraded areas has gained considerable attention. Mangroves’ unique environmental conditions make them suitable habitats for diverse microbial communities, including Archaea - one of the main microbial communities in mangrove sediments - which plays a pivotal role in biogeochemical cycles. However, little is known about the dynamics of archaeal communities during mangrove restoration through phytoremediation. In this study, we investigated the physicochemical properties of sediment profiles from natural and artificially restored mangrove ecosystems in Tieshan Bay. We utilized quantitative polymerase chain reaction (qPCR) and 16S rRNA gene sequencing techniques to explore differences in abundance, community structure, and composition of archaeal communities between sediment profiles in natural and artificially restored mangrove ecosystems. We also examined correlations between archaeal communities and environmental factors. Our results revealed that Crenarchaeota, Thermoplasmatota, Asgardarchaeota, Nanoarchaeota, and Euryarchaeota were the predominant archaeal phyla, with significant variation in sediment composition observed for Crenarchaeota and Thermoplasmatota in different depths. We also found significant differences in archaeal abundance and community composition between natural and restored mangrove sediments. Furthermore, C/N ratio and pH emerged as primary drivers of archaeal communities in wet and dry season sediments, respectively. Additionally, the study revealed seasonal disparities in seasonal differences in the relative abundance of certain archaeal groups (such as Asgardarchaeota, Nanoarchaeota). Network analysis demonstrated stronger interconnections among archaeal communities in sediments from natural mangroves than from artificially restored ones. These findings enhance our knowledge of archaeal community succession patterns in mangrove restoration, as well as provide fresh perspectives for the sustainable management of mangrove ecosystems.

Mangrove wetlands serve as pivotal ecotones at land-sea interfaces, where habitat stability is critically modulated by sediment mineralogical composition. However, the impacts of anthropogenic activities on mineralogical evolution and associated ecological consequences in these ecosystems remain poorly understood. This study examines these processes in a degraded mangrove ecosystem within Tieshangang Bay, China, to uncover engineering-induced perturbations and their implications for mangrove health. Integrated mineralogical analyses were employed, including X-ray diffraction (XRD) and automated mineralogical parameter analysis (MapsMin), to characterize sediment composition and discern anthropogenic influences. These techniques enabled high-resolution quantification of mineral distributions and associations across degraded and preserved zones. The results reveal pronounced kaolinite enrichment (48.97–66.35%) in surficial sediments (0–15 cm) of degraded zones, contrasting with stable mineralogical composition in preserved areas. Diagnostic mineral assemblages and spatial patterns implicate terrestrial clay inputs from port infrastructure expansion and dredging operations, exacerbated by tidal channel obstruction and hydrodynamic attenuation. Mechanistic analysis demonstrates that kaolinite’s platy morphology promotes pore occlusion, reducing sediment porosity and inducing rootzone hypoxia, which drives mangrove regression. Based on these findings, a synergistic remediation framework is proposed, integrating tidal hydrology restoration, sediment source regulation, and substrate permeability enhancement. These insights advance the understanding of anthropogenic-geochemical-ecological cascades in intertidal ecosystems and provide a mineralogical benchmark for nature-based mangrove conservation and coastal resilience management.

Leaf Area Index (LAI) is the total leaf area per unit of land surface area and is a crucial parameter for assessing vegetation growth and productivity. Machine learning regression algorithms are widely applied for LAI estimation. Due to spectral response variations among sensors and susceptibility of mangrove-derived variables to environmental noise suppression, obtaining sensitivity indices and optimal machine learning regression models is essential for retrieving mangrove LAI at the population scale. This study proposes a novel approach to processing and retrieving mangrove LAI data by integrating multispectral indices with machine learning methods. Box–Cox transformation and CatBoost-based feature selection were employed to obtain the optimal dataset. Random Forest (RF), Gradient Boosting Regression Trees (GBRT), and Categorical Boosting (CatBoost) algorithms were used to evaluate the accuracy of LAI retrieval from Unmanned Aerial Vehicle (UAV) and Gaofen-6 (GF-6) data. Results indicate that when LAI > 3, LAI does not immediately saturate as CVI, MTVI 2, and other indices increase, demonstrating higher sensitivity. UAV data outperformed GF-6 data in retrieving LAI for diverse mangrove populations; during model training, RF proved more suitable for small-sample datasets, while CatBoost effectively suppressed environmental noise. Both RF and CatBoost demonstrated higher robustness in estimating Avicennia marina (AM) (RF: R² = 0.704) and Aegiceras corniculatum (AC) (R² = 0.766), respectively. Spatial distribution analysis of LAI indicates that healthy AM and AC cover 85.36% and 96.67% of the area, respectively. Spartina alterniflora and aquaculture wastewater may be among the factors affecting the health of mangrove forests in the study area. LAI retrieval holds significant importance for mangrove health monitoring and risk early warning.