Incorporating ecosystem services into functional zoning and adaptive management of natural protected areas as case study of the Shennongjia region in China

Prediction of Potential Suitable Distribution Areas for an Endangered Salamander in China

Climate change has been considered to pose critical threats for wildlife. During the past decade, species distribution models were widely used to assess the effects of climate change on the distribution of species' suitable habitats. Among all the vertebrates, amphibians are most vulnerable to climate change. This is especially true for salamanders, which possess some specific traits such as cutaneous respiration and low vagility. The Wushan salamander (Liua shihi) is a threatened and protected salamander in China, with its wild population decreasing continuously. The main objective of this study was to predict the distribution of suitable habitat for L. shihi using the ENMeval parameter-optimized MaxEnt model under current and future climate conditions. Our results showed that precipitation, cloud density, vegetation type, and ultraviolet radiation were the main environmental factors affecting the distribution of L. shihi. Currently, the suitable habitats for L. shihi are mainly concentrated in the Daba Mountains, including northeastern Chongqing and western Hubei Provinces. Under the future climate conditions, the area of suitable habitats increased, which mainly occurred in central Guizhou Province. This study provided important information for the conservation of L. shihi. Future studies can incorporate more species distribution models to better understand the effects of climate change on the distribution of L. shihi.

Application of unsupervised clustering model based on graph embedding in water environment

Surface water monitoring data has spatiotemporal characteristics, and water quality will change with time and space in different seasons and climates. Data of this nature brings challenges to clustering, especially in terms of obtaining the temporal and spatial characteristics of the data. Therefore, this paper proposes an improved TADW algorithm and names it RTADW to obtain the spatiotemporal characteristics of surface water monitoring points. We improve the feature matrix in TADW and input the original time series data and spatial information into the improved model to obtain the spatiotemporal feature vector. When the improved TADW model captures watershed information for clustering, it can simultaneously extract the temporal and spatial characteristics of surface water compared with other clustering algorithms such as the DTW algorithm. We applied the proposed method to multiple different monitoring sites in the Liaohe River Basin, analyzed the spatiotemporal regional distribution of surface water monitoring points. The results show that the improved feature extraction method can better capture the spatiotemporal feature information between surface water monitoring points. Therefore, this method can provide more potential information for cluster analysis of water environment monitoring, thereby providing a scientific basis for watershed zoning management.