Floodplain wetlands are among the most dynamic ecosystems on Earth, featuring high biodiversity and productivity. They are also sensitive to anthropogenic disturbances and are globally threatened. Understanding how flow regime drives the spatiotemporal dynamics of wetland habitats is fundamental to effective conservation practices. In this study, using Landsat imagery and the random forest (RF) machine learning algorithm, we mapped the winter distribution of four wetland habitats (i.e., Carex meadow, reedbed, mudflat, and shallow water) in East Dongting Lake, a Ramsar wetland in the middle to lower Yangtze Basin of China, for 34 years (1988–2021). The dynamics of wetland habitats were explored through pixel-by-pixel comparisons. Further, the response of wetland habitats to flow regime variations was investigated using generalized additive mixed models (GAMM). Our results demonstrated the constant expansion of reedbeds and shrinkage of mudflats, and that there were three processes contributing to the reduction in mudflat: (1) permanent replacement by reedbed; (2) irreversible loss to water; and (3) transitional swapping with Carex meadow. These changes in the relative extent of wetland habitats may degrade the conservation function of the Ramsar wetland. Moreover, the duration of the dry season and the date of water level withdrawal were identified as the key flow regime parameters shaping the size of wetland habitats. However, different wetland vegetation showed distinct responses to variations in flow regime: while Carex meadow increased with earlier water withdrawal and a longer dry season, reedbed continuously expanded independent of the flow regime corresponding to the increase in winter rainfall. Our findings suggested that flow regime acts in concert with other factors, such as climate change and sand mining in river channels, driving wetland habitat transition in a floodplain landscape. Therefore, effective conservation can only be achieved through diverse restoration strategies addressing all drivers.

Ecological restoration projects aim to recover ecosystems that have been degraded, damaged, or destroyed. The effective assessment of these projects is crucial for ensuring that restoration goals are met and for guiding future efforts. The Poplar Ecological Restoration Project (PERP), focused on removing floodplain poplar plantations, is a key environmental initiative in China to preserve the ecological functions and services of the Dongting Lake freshwater wetland complex. Understanding the dynamic changes in ecosystem services and their trade-offs/synergies before and after the project is crucial for ensuring that ecosystem services are maintained and enhanced. This study selected three key ecosystem services, water conservation, carbon storage, and habitat quality, as indicators to assess the PERP. Using the Integrated Valuation of Ecosystem Services and Trade-offs (InVEST) model and geostatistical methods, including the Spearman correlation analysis and spatial autocorrelation analysis, we explored the ecological outcomes of the project. The results indicated that since its implementation in 2014, the PERP has significantly altered the land use and land cover (LULC) in the lake. Most of the former plantations have been converted into reedbeds (17,404 ha), followed by sedgelands (3841 ha), mudflats (443 ha), shallow waters (382 ha), and deep waters (301 ha). During this period, water conservation and habitat quality initially decreased and then increased, while carbon storage continuously declined. Overall, all assessed services showed an increasing trend except for carbon storage, resulting in a slight decrease in the integrated ecosystem services. Moreover, there were strong bivariate associations among the three investigated ecosystem services, and the synergies were strengthened during the project. Importantly, these synergies became positive after the PERP, meaning that the improvement of one service enhanced others, potentially leading to a more sustainable and resilient ecosystem.

Extreme droughts are projected to become more frequent and severe under climate change, posing significant risks to wetland ecosystems and submerged macrophyte communities. We combined field surveys in West Dongting Lake, China, combined with controlled greenhouse experiments to examine how drought intensity (expressed as contrasting soil moisture conditions during drought) and drought timing affect submerged macrophyte species richness, biomass, as well as resilience, mediated through species response in their reproductive strategies. Field observations revealed a sharp decline in clonal species (Hydrilla verticillata, Ceratophyllum demersum, Vallisneria spinulosa) following an extreme drought, while the sexual species Najas marina emerged as dominant. Greenhouse experiments confirmed these patterns and elucidated underlying mechanisms: extreme drought suppressed biomass, leaf area, and seedling re-germination in clonal species, whereas N. marina maintained regeneration via a persistent soil seed bank. Moderate drought enhanced leaf area, consistent with the intermediate disturbance hypothesis, while early drawdowns were most detrimental to growth. Species-specific responses highlight the role of reproductive strategy in drought resilience. These findings underscore the need for climate-adaptive water-level management, including limiting early drawdowns, mitigating extreme drought, and conserving seed banks to sustain biodiversity and ecosystem function under increasing hydroclimatic variability.

Accurate species distribution modelling is essential for conservation planning, particularly for river-dependent species whose habitats are poorly captured by conventional distance-based predictors. The endangered Scaly-sided Merganser (Mergus squamatus) relies on linear freshwater habitats during winter, yet riverine habitat structure is often oversimplified in broad-scale species distribution models. Here, we developed a multi-algorithm modelling framework to map wintering habitat suitability for the species in the Dongting Lake Basin, China. Occurrence records were compiled primarily from systematic surveys and spatially thinned to reduce sampling bias. We compared MaxEnt, random forest, generalized additive models, and boosted regression trees under five-fold spatial block cross-validation, and generated an ensemble prediction across algorithms. The final predictor set emphasized hydrological and topographic attributes, including maximum water width, reservoir capacity, density of the river network, valley depth, landform type, and channel-network base level. Predictor importance and response curves indicated that wintering habitat suitability was shaped mainly by open-water scale, terrain position, valley structure, and regulated water storage. The ensemble prediction identified priority high-suitability areas mainly along the middle and lower Yuan River and the upper Xiang River, with smaller local patches along the Li and Zi Rivers and selected reservoir systems. These findings suggest that hydrological and topographic predictors can enhance the ecological representation of river-dependent waterbird habitats in SDMs and provide spatial evidence for targeted river-reach conservation.