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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.

The accelerated rate of human‐induced environmental change poses a significant challenge for wildlife. The ability of wild animals to adapt to environmental changes has important consequences for their fitness, survival, and reproduction. Behavioural flexibility, an immediate adjustment of behaviour in response to environmental variability, may be particularly important for coping with anthropogenic change. The main aim of this study was to quantify the response of two wintering goose species (bean goose Anser fabalis and lesser white‐fronted goose Anser erythropus) to poor habitat condition at population level by studying foraging behaviour. In addition, we tested whether behavioural plasticity could alter trophic niche. We characterised foraging behaviours and calculated daily home range (HR) of the geese using global positioning system tracking data. We calculated standard ellipse areas to quantify niche width using the δ13C and δ15N values of individual geese. We linked behavioural plasticity with habitat quality using ANCOVA (analysis of covariance) models. We also tested the correlation between standard ellipse areas and HR using ANCOVA model. We found significant differences in geese foraging behaviours between years in their daily foraging area, travel distance and speed, and turning angle. Specifically, the birds increased their foraging area to satisfy their daily energy intake requirement in response to poor habitat conditions. They flew more sinuously and travelled faster and longer distances on a daily basis. For the endangered lesser white‐fronted goose, all behaviour variables were associated with habitat quality. For bean goose, only HR and turning angle were correlated with habitat quality. The birds, especially the lesser white‐fronted goose, may have had a higher trophic position under poor conditions. Our findings indicate that wintering geese showed a high degree of behavioural plasticity. However, more active foraging behaviours under poor habitat condition did not lead to a broader trophic niche. Habitat availability could be responsible to the divergent responses of foraging HR and isotopic niche to human‐induced environmental change. Therefore, maintaining natural hydrological regimes during the critical period (i.e. September–November) to ensure that quality food resources are available is central to the future of populations of geese within the East Asian–Australasian Flyway.

As natural wetlands are degrading worldwide, artificial wetlands can operate as a substitute to provide waterbirds with refuge, but they cannot replace natural wetlands. Reservoirs, one of the most common artificial wetlands in China, can be of great importance to waterbirds. Miyun reservoir in Beijing, China, has undergone a process similar to a natural lake being constructed in a reservoir. In this study, we surveyed waterbird community composition and evaluated the corresponding land cover and land use change with satellite and digital elevation model images of both before and after the water level change. The results showed that in all modelled scenarios, when the water level rises, agricultural lands suffer the greatest loss, with wetlands and forests following. The water level rise also caused a decrease in shallow water areas and a decline in the number and diversity of waterbird communities, as the components shifted from a shallow-water preferring group (waders, geese and dabbling ducks) to a deep-water preferring group (most diving ducks, gulls and terns). Miyun reservoir ceased to be an important waterbird habitat in China and is no longer an important stopover site for white-naped cranes. A similar process is likely to occur when a natural lake is constructed in a reservoir. Therefore, we suggest that policymakers consider the needs of waterbirds when constructing or managing reservoirs.

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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.

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