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.

Large river floodplain systems (LRFS) are among the most diverse and dynamic ecosystems. Accurately monitoring the dynamics of LRFS over long time series is fundamental and essential for their sustainable development. However, challenges remain because the spatial distribution of LRFS is never static due to inter- and intra-annual changes in environmental conditions. In this study, we developed and tested a methodological framework to re-construct the long-term wetland dynamics in Dongting Lake, China, utilizing an unsupervised machine-learning algorithm (UMLA) on the basis of MODIS (Moderate Resolution Imaging Spectroradiometer) EVI (Enhanced Vegetation Index) time series. Our results showed that the UMLA achieved comparable performance to the time-consuming satellite image segmentation method with a Kappa coefficient of agreement greater than 0.75 and an overall accuracy over 85%. With the re-constructed annual wetland distribution maps, we found that 31.35% of wet meadows, one of most important ecological assets in the region, disappeared at an average rate of c.a. 1660 ha year⁻¹ during the past two decades, which suggests that the Dongting Lake is losing its ecological function of providing wintering ground for migratory water birds, and remediation management actions are urgently required. We concluded that UMLA offers a fast and cost-efficient alternative to monitor ecological responses in a rapidly changing environment.

Climate change has become an important cause of the loss of bird habitat and changes in bird migration and reproduction. The lesser white-fronted goose (Anser erythropus) has a wide range of migratory habits and is listed as vulnerable on the IUCN (International Union for Conservation of Nature) Red List. In this study, the distribution of suitable breeding grounds for the lesser white-fronted goose was assessed in Siberia, Russia, using a combination of satellite tracking and climate change data. The characteristics of the distribution of suitable breeding sites under different climate scenarios in the future were predicted using the Maxent model, and protection gaps were assessed. The analysis showed that under the background of future climate change, temperature and precipitation will be the main climatic factors affecting the distribution of breeding grounds, and the area associated with suitable breeding habitats will present a decreasing trend. Areas listed as an optimal habitat only accounted for 3.22% of the protected distribution; however, 1,029,386.341 km² of optimal habitat was observed outside the protected area. Obtaining species distribution data is important for developing habitat protection in remote areas. The results presented here can provide a basis for developing species-specific habitat management strategies and indicate that additional attention should be focused on protecting open spaces.

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.