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The takin (Budorcas taxicolor) is one of the largest bovid herbivores in the subfamily Caprinae. The takin is at high risk of extinction, but its taxonomic status and genetic diversity remain unclear. In this study, we constructed the first reference genome of Bu. taxicolor using PacBio long High‐Fidelity reads and Hi‐C technology. The assembled genome is ~2.95 Gb with a contig N50 of 68.05 Mb, which were anchored onto 25+XY chromosomes. We found that the takin was more closely related to muskox than to other Caprinae species. Compared to the common ancestral karyotype of bovidae (2n = 60), we found the takin (2n = 52) experienced four chromosome fusions and one large translocation. Furthermore, we resequenced nine golden takins from the main distribution area, the Qinling Mountains, and identified 3.3 million single nucleotide polymorphisms. The genetic diversity of takin was very low (θπ = 0.00028 and heterozygosity =0.00038), among the lowest detected in domestic and wild mammals. Takin genomes showed a high inbreeding coefficient (FROH =0.217), suggesting severe inbreeding depression. The demographic history showed that the effective population size of takins declined significantly from ~100,000 years ago. Our results provide valuable information for protection of takins and insights into their evolution. [ABSTRACT FROM AUTHOR]

Using unmanned aerial vehicles (UAVs) for surveys on thermostatic animals has gained prominence due to their ability to provide practical and precise dynamic censuses, contributing to developing and refining conservation strategies. However, the practical application of UAVs for animal monitoring necessitates the automation of image interpretation to enhance their effectiveness. Based on our past experiences, we present the Sichuan snub‐nosed monkey (Rhinopithecus roxellana) as a case study to illustrate the effective use of thermal cameras mounted on UAVs for monitoring monkey populations in Qinling, a region characterized by magnificent biodiversity. We used the local contrast method for a small infrared target detection algorithm to collect the total population size. Through the experimental group, we determined the average optimal grayscale threshold, while the validation group confirmed that this threshold enables automatic detection and counting of target animals in similar datasets. The precision rate obtained from the experiments ranged from 85.14% to 97.60%. Our findings reveal a negative correlation between the minimum average distance between thermal spots and the count of detected individuals, indicating higher interference in images with closer thermal spots. We propose a formula for adjusting primate population estimates based on detection rates obtained from UAV surveys. Our results demonstrate the practical application of UAV‐based thermal imagery and automated detection algorithms for primate monitoring, albeit with consideration of environmental factors and the need for data preprocessing. This study contributes to advancing the application of UAV technology in wildlife monitoring, with implications for conservation management and research. [ABSTRACT FROM AUTHOR]