Distribution Modeling and Factor Correlation Analysis of Landslides in the Large Fault Zone of the Western Qinling Mountains: A Machine Learning Algorithm

The area comprising the Langma-Baiya fault zone (LBFZ) and the Bailongjiang fault zone (BFZ) in the Western Qinling Mountains in China is characterized by intensive, frequent, multi-type landslide disasters. The spatial distribution of landslides is affected by factors, such as geological structure, landforms, climate and human activities, and the distribution of landslides in turn affects the geomorphology, ecological environment and human activities. Here, we present the results of a detailed landslide inventory of the area, which recorded a total of 2765 landslides. The landslides are divided into three categories according to relative age, area, and type of movement. Sixteen factors related to geological structure, geomorphology, materials composition and human activities were selected and four machine learning algorithms were used to model the spatial distribution of landslides. The aim was to quantitatively evaluate the relationship between the spatial distribution of landslides and the contributing factors. Based on a comparison of model accuracy and the Receiver Operating Characteristic (ROC) curve, RandomForest (RF) (accuracy of 92%, area under the ROC of 0.97) and GradientBoosting (GB) (accuracy of 96%, area under the ROC curve of 0.97) were selected to predict the spatial distribution of unclassified landslides and classified landslides, respectively. The evaluation results reveal the following. The vegetation coverage index (NDVI) (correlation of 0.2, and the same below) and distance to road (DTR) (0.13) had the highest correlations with the distribution of unclassified landslides. NDVI (0.18) and the annual precipitation index (API) (0.14) had the highest correlations with the distribution of landslides of different ages. API (0.16), average slope (AS) (0.14) and NDVI (0.1) had the highest correlations with the landslide distribution on different scales. API (0.28) had the highest correlation with the landslide distribution based on different types of landslide movement.

Endemism Patterns of Planthoppers (Fulgoroidea) in China

Studies on endemism are always of high interest in biogeography and contribute to better understanding of the evolution of species and making conservation plans. The present study aimed to investigate the endemism patterns of planthoppers in China by delimiting centers of endemism and areas of endemism. We collected 6,907 spatial distribution records for 860 endemic planthopper species from various resources. Centers of endemism were identified using weighted endemism values at 1° grid size. Parsimony analysis of endemicity and endemicity analysis were employed to detect areas of endemism at 1°, 1.5°, and 2° grid sizes. Six centers of endemism located in mountainous areas were identified: Taiwan Island, Hainan Island, eastern Yungui Plateau, Wuyi Mountains, western Qinling Mountains, and western Yunnan. We also delimited six areas of endemism, which were generally consistent with centers of endemism. Our findings demonstrated that mountainous areas have an essential role in facilitating the high level of endemism and formation of areas of endemism in planthoppers through the combined effects of complex topography, a long-term stable environment, and geological events. Dispersal ability and distribution of host plants also have important effects on the patterns of planthoppers’ endemism.