With the continued development of tracking technology and increasing interest in animal movement, our understanding of migration behavior has become more comprehensive. However, there are still many species that have not been well studied, particularly sea birds. Here, we present the first year-round Global Positioning System (GPS) tracking data of the Black-tailed Gull (Larus crassirostris) at the population level. We used solar-powered GPS-Global System for Mobile communication (GSM) loggers to successfully track 30 individuals breeding at Xingrentuo Islet, Liaoning Province, China, for 1–3 years. Except for one individual who roamed in the far north of the Yellow Sea during non-breeding period, all others did a directed southward migration. Migration routes and wintering sites differed among migrating gulls and between years for the birds tracked for two or more years. Additionally, during wintering, the migrating gulls were more likely to travel over a large body of water and shift sites, and some trajectories were quite complex, which was probably closely related to what we observed in the field about their boat-chasing behavior. Compared to wintering movements, the post-breeding movements ranged over a smaller area. Specifically, almost all of them had a long post-breeding period near the breeding islet (≥120 days, <220 ​km from the breeding islet), and 80% of the gulls who were tracked more than one year had at least one faithful post-breeding site. Compared to the post-breeding period, only approximately half of the migrating gulls had a pre-breeding period that was shorter (3–20 days) and closer to the breeding islet (≤80 ​km). Migration distance varied among migrating gulls (range 209–2405 ​km) and the gulls moved least distance during post-breeding period. Furthermore, we found that the southward movement of the migrating gulls occurred when the temperature near the breeding islet dropped; specifically, the gulls directly migrated southward away from the post-breeding site. Our results suggest that the Black-tailed Gull has a long post-breeding period but a short pre-breeding period near the breeding islet and high diversity of their migrating patterns (in especial migration routes and wintering sites).

Understanding the spatiotemporal patterns of animal movement is a central theme in the growing field of movement ecology. The Bridled Tern (Onychoprion anaethetus) is widely distributed across tropical and subtropical latitudes; however, knowledge of its annual movement is based only on relatively scarce recoveries of ringed birds. Studying the annual movement of pelagic seabirds is important to identify the impact of changes of marine habitats on them. We examined fine-scaled movement patterns of the species from two colonies in southern China by using GPS-GSM transmitters in 2018–2022. Twenty-three terns bred in Xichiyu Islet and Qilianyu Islet while wintered in four different sites: Gulf of Thailand, Natuna Islands, The Sulu Sea, and Makassar Strait. Bridled Terns made small detours and employed a fly-and-forage strategy with frequent stopovers to forage during autumn migration, but took more direct routes and reduced the need for stopovers during spring migration. Distance of migration between breeding and winter sites was significantly longer in autumn (average 3635 ​km) when compared with spring (2777 ​km). Ten birds with whole-year tracking data used the same breeding sites both years and four birds with tracking data of two consecutive years returned to the same wintering area, indicating that Bridled Terns are highly faithful to their breeding and wintering grounds.

Genetic diversity is one of the three dimensions of biodiversity and fundamental to various life forms on the Earth. Understanding the distribution pattern of genetic diversity and its driving forces has been an important topic in ecology, biogeography and conservation biology since the last decade. We investigated the genetic diversity pattern of passerine birds in the Mountains of Southwest China, a global biodiversity hotspot with the highest species richness of birds in the entire Eurasia, and explored the influencing forces of environmental variables on genetic diversity. We compiled 1189 Cytochrome b sequences of 27 passerine species from 152 geographic sites, covering the range of Mountains of Southwest China and its adjoining areas. We generated genetic diversity distribution maps using a grid-cell method based on nucleotide diversity and haplotype diversity indices. We further analyzed the variation pattern of the two indices along latitudinal, longitudinal, and elevational gradients. The correlations between the two indices and environmental variables were also evaluated. The nucleotide diversity hotspots were mostly located in the southern Hengduan Mountains, while for haplotype diversity, three hotspots were detected: the southeast edge of the Qinghai-Tibetan Plateau, the southern Hengduan Mountains and the Qinling Mountains. There was no monotonic increasing or decreasing pattern in nucleotide diversity or haplotype diversity along latitudinal, longitudinal or elevational gradients except for altitudinal range. Correlation and model selection analyses detected multiple environmental variables in driving genetic diversity patterns, including temperature, precipitation, vegetation, human influence, longitude and altitude range. Similar to the pattern of species richness, the nucleotide diversity pattern of passerine birds in the Mountains of Southwest China presents a decreasing trend from southwest to northeast, while the haplotype diversity pattern is more likely decreased from west to east. Our results indicate that the distribution pattern of genetic diversity may be derived from the complex topography and diverse microclimates in the Mountains of Southwest China.

The avifauna in Tajikistan has been widely studied for the last century, but specific work on species richness pattern along elevation gradients in Tajikistan is rarely investigated. Here, we reported the first study of bird species richness (BSR) in the high-altitude mountain systems (Tien Shan and Pamir-Alay) of Tajikistan which are very sensitive to the recent climate changes. We aim to explore the relationship of BSR pattern with elevation gradient and to determine the potential drivers underlying the patterns. We collected occurrence data from field surveys, published articles, and open access websites to compile a list of bird species along elevational gradients across the whole country. The BSR was counted by 100 ​m elevational bands ranging from 294 ​m to 5146 ​m. The patterns of BSR were calculated separately for five groups: all breeding birds, Passeriformes, Non-Passeriformes, large elevational range species, and small elevational range species. We calculated ecological and climatic factors of planimetric area, mid-domain effect (MDE), habitat heterogeneity (HH), mean annual temperature (MAT), temperature annual range (TAR), annual precipitation (AP), normalized difference vegetation index (NDVI), human influence index (HII), and human disturbance (HD) in each elevational band. A combination of polynomial regression, Pearson's correlation, and general least squares model analyses were used to test the effects of these factors on the BSR. A unimodal distribution pattern with a peak at 750–1950 m was observed for all breeding birds. The similar pattern was explored for Passeriformes and Non-Passeriformes, while species with different elevational range sizes had different shapes and peak elevations. For all the breeding birds and Passeriformes, BSR was significantly related to spatial, climate and human influence factors, while BSR of Non-Passeriformes positively correlated with all the given factors. First, second and fourth range classes of birds were significantly correlated with human influence factors. Moreover, large-ranged species had positive correlations with the mid-domain effect and weakly with habitat heterogeneity. We found that area, MAT and AP were the main factors to explain the richness pattern of birds, and the species richness increases with these three factors increasing. Multiple factors such as area and climate explain 84% of the variation in richness. Bivariate and multiple regression analyses revealed a consistent influence of spatial and climate factors in shaping the richness pattern for nearly all bird groups.