Most migratory birds depend on stopover sites, which are essential for refueling during migration and affect their population dynamics. In the East Asian–Australasian Flyway (EAAF), however, the stopover ecology of migratory waterfowl is severely under-studied. The knowledge gaps regarding the timing, intensity and duration of stopover site usages prevent the development of effective and full annual cycle conservation strategies for migratory waterfowl in EAAF. In this study, we obtained a total of 33,493 relocations and visualized 33 completed spring migratory paths of five geese species using satellite tracking devices. We delineated 2,192,823 ha as the key stopover sites along the migration routes and found that croplands were the largest land use type within the stopover sites, followed by wetlands and natural grasslands (62.94%, 17.86% and 15.48% respectively). We further identified the conservation gaps by overlapping the stopover sites with the World Database on Protected Areas (PA). The results showed that only 15.63% (or 342,757 ha) of the stopover sites are covered by the current PA network. Our findings fulfil some key knowledge gaps for the conservation of the migratory waterbirds along the EAAF, thus enabling an integrative conservation strategy for migratory water birds in the flyway.

Waterbird monitoring is the foundation of conservation and management strategies in almost all types of wetland ecosystems. China’s improved wetland protection infrastructure, which includes remote devices for the collection of larger quantities of acoustic and visual data on wildlife species, increased the need for data filtration and analysis techniques. Object detection based on deep learning has emerged as a basic solution for big data analysis that has been tested in several application fields. However, these deep learning techniques have not yet been tested for small waterbird detection from real-time surveillance videos, which can address the challenge of waterbird monitoring in real time. We propose an improved detection method by adding an extra prediction head, SimAM attention module, and sequential frame to YOLOv7, termed as YOLOv7-waterbird, for real-time video surveillance devices to identify attention regions and perform waterbird monitoring tasks. With the Waterbird Dataset, the mean average precision (mAP) value of YOLOv7-waterbird was 67.3%, which was approximately 5% higher than that of the baseline model. Furthermore, the improved method achieved a recall of 87.9% (precision = 85%) and 79.1% for small waterbirds (defined as pixels less than 40 × 40), suggesting a better performance for small object detection than the original method. This algorithm could be used by the administration of protected areas or other groups to monitor waterbirds with higher accuracy using existing surveillance cameras and can aid in wildlife conservation to some extent.

Estimates of migratory waterbirds population provide the essential scientific basis to guide the conservation of coastal wetlands, which are heavily modified and threatened by economic development. New equipment and technology have been increasingly introduced in protected areas to expand the monitoring efforts, among which video surveillance and other unmanned devices are widely used in coastal wetlands. However, the massive amount of video records brings the dual challenge of storage and analysis. Manual analysis methods are time-consuming and error-prone, representing a significant bottleneck to rapid data processing and dissemination and application of results. Recently, video processing with deep learning has emerged as a solution, but its ability to accurately identify and count waterbirds across habitat types (e.g., mudflat, saltmarsh, and open water) is untested in coastal environments. In this study, we developed a two-step automatic waterbird monitoring framework. The first step involves automatic video segmentation, selection, processing, and mosaicking video footages into panorama images covering the entire monitoring area, which are subjected to the second step of counting and density estimation using a depth density estimation network (DDE). We tested the effectiveness and performance of the framework in Tiaozini, Jiangsu Province, China, which is a restored wetland, providing key high-tide roosting ground for migratory waterbirds in the East Asian–Australasian flyway. The results showed that our approach achieved an accuracy of 85.59%, outperforming many other popular deep learning algorithms. Furthermore, the standard error of our model was very small (se = 0.0004), suggesting the high stability of the method. The framework is computing effective—it takes about one minute to process a theme covering the entire site using a high-performance desktop computer. These results demonstrate that our framework can extract ecologically meaningful data and information from video surveillance footages accurately to assist biodiversity monitoring, fulfilling the gap in the efficient use of existing monitoring equipment deployed in protected areas.

The continuous increase in the number of reservoirs globally has raised important questions about the environmental impact of their greenhouse gases emissions. In particular, the littoral zone may be a hotspot for production of greenhouse gases. We investigated the spatiotemporal variation of CO₂ flux at the littoral zone of a Chinese reservoir along a wet-to-dry transect from permanently flooded land, seasonally flooded land to non-flooded dry land, using the static dark chamber technique. The mean total CO₂ emission was 346 mg m⁻² h⁻¹ and the rate varied significantly by water levels, months and time of day. The spatiotemporal variation of flux was highly correlated with biomass, temperature and water level. Flooding could play a positive role in carbon balance if water recession occurs at the time when carbon gains associated with plant growth overcomes the carbon loss of ecosystem. The overall carbon balance was analysed using cumulative greenhouse gases fluxes and biomass, bringing the data of the present study alongside previously published, simultaneously measured CH₄ and N₂O fluxes. For the growing season, 12.8 g C m⁻² was absorbed by the littoral zone. Taking CH₄ and N₂O into the calculation showed that permanently flooded sites were a source of greenhouse gases, rather than a sink. Our study emphasises how water level fluctuation influenced CO₂, CH₄ and N₂O in different ways, which greatly affected the spatiotemporal variation and emission rate of greenhouse gases from the littoral zone.

East Asian herbivorous waterfowl intensively use farmland in spring, next to their natural habitat. Accordingly, they might have expanded their migration strategy from merely tracking the green wave of newly emerging vegetation to also incorporating the availability of post-harvest agricultural seeds (here dubbed the seed wave). However, if and how waterfowl use multiple food resources to time their seasonal migration is still unknown. We test this migration strategy using 167 spring migration tracks of five East Asian herbivorous waterfowl species and mixed-effect resource selection function models. We found that all study species arrived at their core stopover sites in the Northeast China Plain after agricultural seeds became available, extended their stay after spring vegetation emerged and arrived at their breeding sites around the emergence of vegetation. At the core stopover sites, all study species used snowmelt as a cue to track seed availability, although smaller-bodied species tended to arrive later. At the breeding sites, swans tracked the onset of vegetation emergence and geese tracked the mid- or end phases of snowmelt. Our findings suggest that waterfowl track multiple resource waves to fine-tune their migration, highlighting new opportunities for conservation.

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.