Birds have been widely considered crucial indicators of biodiversity. It is essential to identify bird species precisely for biodiversity surveys. With the rapid development of artificial intelligence, bird species identification has been facilitated by deep learning using audio samples. Prior studies mainly focused on identifying several bird species using deep learning or machine learning based on acoustic features. In this paper, we proposed a novel deep learning method to better identify a large number of bird species based on their call. The proposed method was made of LSTM (Long Short−Term Memory) with coordinate attention. More than 70,000 bird−call audio clips, including 264 bird species, were collected from Xeno−Canto. An evaluation experiment showed that our proposed network achieved 77.43% mean average precision (mAP), which indicates that our proposed network is valuable for automatically identifying a massive number of bird species based on acoustic features and avian biodiversity monitoring.

Climate change is affecting biodiversity by altering the geographical distribution range of species, and this effect is amplified in climate-sensitive areas. Studying the geographic distribution of flagship species in response to climate change is important for the long-term conservation of species and the maintenance of regional biodiversity. Therefore, we collected field survey records from 2016 to 2020 and conducted field surveys of black-necked cranes in the Shaluli Mountains (SLLMs) in May–June and August–October 2021; 103 breeding records were acquired totally, and the geographical distribution range under the current and four future climate scenarios was modeled with the MaxEnt model to predict the impact of climate change on its distribution and habitat quality. The results showed that 152 black-necked cranes were surveyed in seven counties of SLLMs in total; the estimated number of black-necked cranes in the entire SLLMs was about 200. The currently suitable habitat area is 27,122 km², mainly distributed in gentle meadows and wetland habitats along the lake where the Annual Mean Temperature is −1 °C and the Mean Diurnal Range (16 °C) and Precipitation Seasonality (105) are comparatively large. Furthermore, the breeding range would expand to varying degrees under future climate scenarios and showed a migration trend toward the northwest and higher elevation. Besides, as time goes by, the habitat for black-necked cranes in SLLMs would become more homogeneous and more suitable. The conservation effectiveness of the existing reserve network would keep stable with climate change, although there are large conservation gaps between protected areas, and these gaps will gradually expand over time. Overall, this study provides a preliminary understanding of the population and distribution and predicts the future distribution of black-necked cranes in the SLLMs. It also demonstrates the importance of SLLMs for protecting the central population of black-necked cranes and maintaining regional biodiversity. Therefore, we recommend long-term monitoring and conservation of the black-necked crane population and wetland resources in the region.

Tetraophasis szechenyii and T. obscurus are endangered phasianid birds endemic to China. Historically, the question of whether T. obscurus and T. szechenyii are valid species or subspecies has been controversial. In order to clarify their phylogenetic relationship, we sequenced the complete mitochondrial genome of T. obscurus and, using the complete mitochondrial genome of T. szechenyii, which our lab had already sequenced, conducted Bayesian and maximum-likelihood phylogenetic analyses based on the sequences of 12 concatenated heavy-strand encoded protein-coding genes. Genetic distance and divergence time between the two species were also calculated. The complete mitochondrial genome of T. obscurus was 16,707 bases (accession no.: NC_018034), and its structure was similar to mitochondrial genomes reported for other phasianids. The genetic distance between T. obscurus and T. szechenyii was 0.028, and the divergence time of T. obscurus and T. szechenyii was 1.75 Myr. Considering the genetic distance and divergence time, as well as geographical distribution and morphological differences, we suggest that T. obscurus and T. szechenyii are two valid species. The Pleistocene glacial events in the Hengduan Mountains region may have played an important role in the speciation of T. obscurus and T. szechenyii.

Understanding the distribution and the extent of suitable habitats is crucial for wildlife conservation and management. Knowledge is limited regarding the natural habitats of the Chinese monal (Lophophorus lhuysii), which is a vulnerable Galliform species endemic to the high-montane areas of southwest China and a good candidate for being an umbrella species in the Qionglai Mountains. Using ecological niche modeling, we predicted current potential suitable habitats for the Chinese monal in the Qionglai Mountains with 64 presence points collected between 2005 and 2015. Suitable habitats of the Chinese monal were associated with about 31 mm precipitation of the driest quarter, about 15 °C of maximum temperature of the warmest month, and far from the nearest human residential locations (>5,000 m). The predicted suitable habitats of the Chinese monal covered an area of 2,490 km², approximately 9.48% of the Qionglai Mountains, and was highly fragmented. 54.78% of the suitable habitats were under the protection of existing nature reserves and two conservation gaps were found. Based on these results, we provide four suggestions for the conservation management of the Chinese monal: (1) ad hoc surveys targeting potential suitable habitats to determine species occurrence, (2) more ecological studies regarding its dispersal capacity, (3) establishment of more corridors and green bridges across roads for facilitating species movement or dispersal, and (4) minimization of local disturbances.

The giant panda is an example of a species that has faced extensive historical habitat fragmentation, and anthropogenic disturbance and is assumed to be isolated in numerous subpopulations with limited gene flow between them. To investigate the population size, health, and connectivity of pandas in a key habitat area, we noninvasively collected a total of 539 fresh wild giant panda fecal samples for DNA extraction within Wolong Nature Reserve, Sichuan, China. Seven validated tetra‐microsatellite markers were used to analyze each sample, and a total of 142 unique genotypes were identified. Nonspatial and spatial capture–recapture models estimated the population size of the reserve at 164 and 137 individuals (95% confidence intervals 153–175 and 115–163), respectively. Relatively high levels of genetic variation and low levels of inbreeding were estimated, indicating adequate genetic diversity. Surprisingly, no significant genetic boundaries were found within the population despite the national road G350 that bisects the reserve, which is also bordered with patches of development and agricultural land. We attribute this to high rates of migration, with four giant panda road‐crossing events confirmed within a year based on repeated captures of individuals. This likely means that giant panda populations within mountain ranges are better connected than previously thought. Increased development and tourism traffic in the area and throughout the current panda distribution pose a threat of increasing population isolation, however. Maintaining and restoring adequate habitat corridors for dispersal is thus a vital step for preserving the levels of gene flow seen in our analysis and the continued conservation of the giant panda meta‐population in both Wolong and throughout their current range.