East Asian migratory waterfowl have greatly declined since the 1950s, especially the populations that winter in China. Conservation is severely hampered by the lack of primary information about migration patterns and stopover sites. This study utilizes satellite tracking techniques and advanced spatial analyses to investigate spring migration of the greater white‐fronted goose (Anser albifrons) and tundra bean goose (Anser serrirostris) wintering along the Yangtze River Floodplain. Based on 24 tracks obtained from 21 individuals during the spring of 2015 and 2016, we found that the Northeast China Plain is far‐out the most intensively used stopover site during migration, with geese staying for over 1 month. This region has also been intensely developed for agriculture, suggesting a causal link to the decline in East Asian waterfowl wintering in China. The protection of waterbodies used as roosting area, especially those surrounded by intensive foraging land, is critical for waterfowl survival. Over 90% of the core area used during spring migration is not protected. We suggest that future ground surveys should target these areas to confirm their relevance for migratory waterfowl at the population level, and core roosting area at critical spring‐staging sites should be integrated in the network of protected areas along the flyway. Moreover, the potential bird–human conflict in core stopover area needs to be further studied. Our study illustrates how satellite tracking combined with spatial analyses can provide crucial insights necessary to improve the conservation of declining Migratory species.

Parental care in birds consists of elaborate forms across stages, including nest building, incubation, and offspring provision. Despite their evolutionary importance, knowledge gaps exist in the extent to which parents contribute disproportionately to these forms and factors that are associated with variations in care patterns between sexes. Here, we analyzed 1533 bird species and discovered remarkable variability in care patterns. We show that parental care should not be viewed as a unitary trait but rather as a set of integrated features that exhibit considerable temporal and sex-specific variation. Our analyses also reveal moderate consistency in care patterns between breeding stages, pointing towards shared intrinsic factors driving sex-specific care. Notably, we found that species experiencing strong sexual selection on males or species facing paternity uncertainty display a tendency towards female-biased care. This work advances our understanding of the temporal variations in sex-specific contributions to avian parental care and their potential evolutionary drivers.

Aim: Geographic structure is a fundamental organising principle in ecological and Earth sciences, and our planet is conceptually divided into distinct geographic clusters (e.g. ecoregions and biomes) demarcating unique diversity patterns. Given recent advances in technology and data availability, however, we ask whether geographically clustering diversity time‐series should be the default framework to identify meaningful patterns of diversity change.Location: North America.Taxon: Aves.Methods: We first propose a framework that recognises patterns of diversity change based on similarities in the behaviour of diversity time‐series, independent of their specific or relative spatial locations. Specifically, we applied an artificial neural network approach, the self‐organising map (SOM), to group time‐series of over 0.9 million observations from the North American Breeding Birds Survey (BBS) data from 1973 to 2016. We then test whether time‐series identified as having similar behaviour are geographically structured.Results: We find little evidence of strong geographic structure in patterns of diversity change for North American breeding birds. The majority of the recognised diversity time‐series patterns tend to be indistinguishable from being independently distributed in space.Main Conclusions: Our results suggest that geographic proximity may not correspond to shared temporal trends in diversity; assuming that geographic clustering is the basis for analysis may bias diversity trend estimation. We suggest that approaches that consider variability independently of geographic structure can serve as a useful addition to existing organising rules of biodiversity time‐series.