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Landslides have become a common global concern because of their widespread nature and destructive power. The Gaizi Valley section of the Karakorum Highway is located in an alpine mountainous area with a high degree of geological structure development, steep terrain, and severe regional soil erosion, and landslide disasters occur frequently along this section, which severely affects the smooth flow of traffic through the China-Pakistan Economic Corridor (CPEC). In this study, 118 views of Sentinel-1 ascending- and descending-orbit data of this highway section are collected, and two time-series interferometric synthetic aperture radar (TS-InSAR) methods, distributed scatter InSAR (DS-InSAR) and small baseline subset InSAR (SBAS-InSAR), are used to jointly determine the surface deformation in this section and identify unstable slopes from 2021 to 2023. Combining these data with data on sites of historical landslide hazards in this section from 1970 to 2020, we constructed 13 disaster-inducing factors affecting the occurrence of landslides as evaluation indices of susceptibility, carried out an evaluation of regional landslide susceptibility, and identified high-susceptibility unstable slopes (i.e., potential landslides). The results show that DS-InSAR and SBAS-InSAR have good agreement in terms of deformation distribution and deformation magnitude and that compared with single-orbit data, double-track SAR data can better identify unstable slopes in steep mountainous areas, providing a spatial advantage. The landslide susceptibility results show that the area under the curve (AUC) value of the artificial neural network (ANN) model (0.987) is larger than that of the logistic regression (LR) model (0.883) and that the ANN model has a higher classification accuracy than the LR model. A total of 116 unstable slopes were identified in the study, 14 of which were determined to be potential landslides after the landslide susceptibility results were combined with optical images and field surveys. These 14 potential landslides were mapped in detail, and the effects of regional natural disturbances (e.g., snowmelt) and anthropogenic disturbances (e.g., mining projects) on the identification of potential landslides using only SAR data were assessed. The results of this research can be directly applied to landslide hazard mitigation and prevention in the Gaizi Valley section of the Karakorum Highway. In addition, our proposed method can also be used to map potential landslides in other areas with the same complex topography and harsh environment. [ABSTRACT FROM AUTHOR]

The South China tiger (Panthera tigris amoyensis, SCT) is the most critically endangered subspecies of tiger due to functional extinction in the wild. Inbreeding depression is observed among the captive population descended from six wild ancestors, resulting in high juvenile mortality and low reproduction. We assembled and characterized the first SCT genome and an improved Amur tiger (P. t. altaica, AT) genome named AmyTig1.0 and PanTig2.0. The two genomes are the most continuous and comprehensive among any tiger genomes yet reported at the chromosomal level. By using the two genomes and resequencing data of 15 SCT and 13 AT individuals, we investigated the genomic signature of inbreeding depression of the SCT. The results indicated that the effective population size of SCT experienced three phases of decline, ~5.0–1.0 thousand years ago, 100 years ago, and since captive breeding in 1963. We found 43 long runs of homozygosity fragments that were shared by all individuals in the SCT population and covered a total length of 20.63% in the SCT genome. We also detected a large proportion of identical‐by‐descent segments across the genome in the SCT population, especially on ChrB4. Deleterious nonsynonymous single nucleotide polymorphic sites and loss‐of‐function mutations were found across genomes with extensive potential influences, despite a proportion of these loads having been purged by inbreeding depression. Our research provides an invaluable resource for the formulation of genetic management policies for the South China tiger such as developing genome‐based breeding and genetic rescue strategy. see also the Perspective by Anubhab Khan [ABSTRACT FROM AUTHOR]

Postnatal colonization and development of the gut microbiota is linked to health and growth. A comprehensive understanding of the postnatal compositional changes and development of the microbial community is helpful to understand the gut health and improve the survival rate of South China tiger cubs (Panthera tigris amoyensis). Fecal samples from three tiger cubs were collected on the day of birth in 2018 (June 17–21 [G0], July 18 [G1], July 31 [G2], and August 7 [G3]). The 16S rRNA genes of the fecal microflora were sequenced. Results showed that 38 phyla, 58 classes, 134 orders, 272 families, and 636 genera of bacteria from 3,059 operational taxonomic units were identified from 12 fecal samples. The diversity and abundance of species of group G0 were significantly higher (p < 0.05 or 0.01) than those of groups G2 and G3. The predominant phylum was Proteobacteria in groups G0 and G1 (38.85% and 48%, respectively) and Firmicutes in groups G2 and G3 (71.42% and 75.29%, respectively). At the phylum level, the abundance of Deinococcus-Thermus was significantly decreased in groups G1, G2, and G3 as compared to group G0 (p < 0.05), while that of Firmicutes was significantly increased in groups G2 and G3 (p < 0.05). At the genus level, the abundance of Faecalibacterium, Ralstonia, and unidentified Rickettsiales was significantly decreased in groups G1, G2, and G3 as compared with group G0 (p < 0.05), while that of Pseudomonas was significantly decreased in groups G2 and G3 (p < 0.05). The composition and structure of fecal microbiota of South China tiger cubs changed after birth. [ABSTRACT FROM AUTHOR]