Influenced by human activities and natural interference, the worldwide distribution of coastal wetlands is now undergoing rapid evolution. The prediction on the locations of vegetation conversion is greatly important for the management of these coastal ecosystems in terms of early warning. In this paper, a series of waterlines extracted from multiple satellite images were used to generate a high-precision digital elevation model (DEM) in the intertidal zone of the Liaohe estuary. Based on the characteristics of the alternative stable states in elevation and normalized difference vegetation index (NDVI), the Logistic Regression Model was applied to predict the potential locations of vegetation expansion with geomorphic factors, such as elevation, slope, and annual changing rate of elevation. Before the prediction, the existence of two stable states in the landscape was confirmed in the study area, i.e., low-lying tidal flats and high-lying saltmarshes. When the geomorphic parameters exceeded the thresholds, the stable state transition would occur. By using the Logistic Regression Model, the elevation was the best explainer for determining the vegetation conversion in the single-factor simulation, while the slope was the worst. When multiple factors were integrated in simulations, the prediction with the elevation, slope, and annual elevation change rate was the best, with R² = 0.739, and the overall accuracy of prediction reached 88.6%. The prediction result indicated that the area of saltmarshes in the Liaohe estuary increased by 16.7 km² at a rate of 0.8% per year between 2011 and 2015. When considering the popularization in restoration practice, it is necessary to evaluate the reliability and flexibility of the Logistic Regression Model in predicting vegetation conversion in more types of estuarine wetlands.

The ecological implications of deep-sea mining, particularly the considerable discharge of suspended sediments during operational processes, have attracted substantial concerns. In order to reveal the metal regeneration dynamics in the polymetallic nodule area of the western Pacific Ocean, ex-situ sediment disturbance experiments were conducted on a research vessel. After two levels of regulated stirring disturbance were exerted for half an hour, the concentrations of 12 dissolved metals and physicochemical parameters, including Eh and pH, were monitored continuously in the overlying water for three days. Porewater samples were also collected at the starting and ending time of each experiment to detect the change of dissolved metal profiles within the sediments. The findings revealed that the sediment disturbance led to fluctuations in the concentrations of metals in the overlying water, with manganese exhibiting the most pronounced change at a coefficient of 208%. The temporal patterns of dissolved metal concentrations demonstrated a coherent behavior among certain metals, such as Li, V, Co, Ni, Rb, Mo, and Cs, which displayed an overall increase, ultimately surpassing the initial concentrations in the overlying water. In line with this, the metal concentration profiles in the porewater were also elevated. Spearman correlation analysis confirmed the synchronized behavior of these metals. The results suggested that the metals mobility might be governed by physicochemical factors in the overlying water. Moreover, the sedimentary features, such as grain size composition, and the morphological state of metals in sediments played pivotal roles in the differential responses of metal groups to sediment disturbance across stations. Conversely, the disturbance intensity was found to have a relatively minor impact on the dissolved metal behavior. The findings from the ex-situ experiments provided critical insights for predicting metal regeneration related to deep-sea mining, which are expected to be validated through rigorous monitoring protocols during future in-situ mining trials.

Microorganisms, as the foundation of deep-sea ecosystems, are crucial for maintaining the structure and stability of polymetallic nodule field environments. To investigate the community structure and distributional patterns of benthic microorganisms in such environments, this study used high-throughput sequencing to analyze the composition, diversity, and environmental correlations of bacteria, archaea, and fungi in the BPC (Beijing Pioneer Hi-tech Development Corporation Ltd., Beijing, China). Furthermore, microbial communities from BPC were compared with those from UK-1 (UK Seabed Resources, Southampton, UK) in terms of community structure and co-occurrence network characteristics. The results revealed that in the BPC, the bacterial communities were dominated by Proteobacteria and Chloroflexi, while Crenarchaeota represented the overwhelmingly dominant group. Fungal communities were primarily composed of Ascomycota and Basidiomycota. Correlation Analysis suggested that water depth, TOC (Total organic carbon), TN (Total nitrogen), and δ¹⁵N emerged as the key environmental drivers of microbial community variation. Comparative analysis showed microbial groups exhibited certain similarities but also some differences at the phylum, class, and order levels, with the differences becoming increasingly pronounced at finer taxonomic resolutions between BPC and UK-1. Co-occurrence network analyses indicated the microbial networks with higher density and node connectivity in the BPC, whereas the UK-1 exhibited greater modularity and clustering coefficients. Microbial interactions were weaker in the UK-1, but its resilience to benthic disturbance was expected to be higher than in the BPC.