Biodiversity monitoring is essential for the conservation of biodiversity. Traditional methods of biodiversity monitoring are often time-consuming, costly, inefficient, and heavily influenced by human activity. These limitations hinder large-scale and long-term continuous monitoring, making it difficult to support the operational requirements and the goals of building a beautiful China. To address these challenges, this study proposes the concept of an intelligent biodiversity monitoring system, leveraging new equipment and advanced technologies. The system integrates ground-based and remote sensing monitoring, primarily utilizing passive survey equipment such as infrared cameras and audio recorders. It relies on cutting-edge technologies, including the Internet of Things (IoT), artificial intelligence (AI), and environmental DNA (eDNA), to achieve automated data collection and intelligent species identification. This system supports biodiversity surveys, monitoring, assessments, and early warning mechanisms. This paper summarizes the progress of the pilot project for the intelligent biodiversity monitoring system in Lishui City, Zhejiang Province. The project has successfully implemented intelligent monitoring of plants, mammals, birds, amphibians, reptiles, insects, and aquatic organisms. It provides practical case studies and theoretical support for the operational implementation of intelligent biodiversity monitoring systems.

With the rapid development of human society and economy, factors such as climate change have prompted the sharp loss of global biodiversity. Plants are a core component of biodiversity, and ex situ conservation to save endangered plants has become the core work of botanical gardens. At present, ex situ conservation based on protecting the genetic diversity of endangered plants remains a challenge. Genomic technologies have made it possible to accurately identify the taxonomic status of endangered plants, explore the population evolutionary dynamics and endangering causes of endangered plants and analyze genetic diversity at the whole-genome level and its adaptability to future climates. This paper summarizes the development of genomic sequencing technologies for plant conservation and the effectiveness and current status of ex situ conservation, as well as the research progress of genomics in helping protect endangered plants in terms of mining functional and adaptive variant genes, species identification, genetic risk assessment, and regulation of growth, development, and secondary metabolism. In response to how to promote future ex situ conservation in the genomic era, countermeasures, suggestions, and future development trends are proposed.

Multi-omics data, including genomics, transcriptomics, and epigenomics, contain a wealth of genetic information and are crucial for the study of biological genetic diversity and the conservation of biodiversity. However, the integration and reuse of multi-omics data remain challenging due to their massive volume and fragmented distribution across disparate databases and published literature. To facilitate timely submission and sharing of multi-omics data in life science, National Genomics Data Center (NGDC) has established multiple foundational data submission platforms. Additionally, to fully integrate and explore existing omics data resources, NGDC has developed several multi-omics integration resources and knowledge bases for domesticated animals and plants. Currently, NGDC has preliminarily formed a comprehensive multi-omics big data framework, which will significantly contribute to the conservation of biodiversity, including species identification, the protection of endangered species, breeding programs, and monitoring changes in habitats.

Basin ecological security is a major issue for human survival and development and has become an important branch in the field of social security research. This paper applies the principles and analytical methods of landscape ecology to evaluate the ecological security of the Chahannaoer Basin. Starting from the perspective of landscape structure, the ecological security status of the basin in 2010 and 2024 was evaluated using metrics such as Patch Density (PD), Landscape Shape Index (LSI), Landscape Division Index (DIVISION), and Landscape Aggregation Index (AI). Furthermore, a dynamic change analysis is conducted. Meanwhile, the study calculated the intensity of mutual transitions between ecosystem types and the overall dynamic degree of the watershed within the specified time period. The results indicate that between 2010 and 2024, the forest area and construction land in the basin showed an upward trend, while the grassland area showed a downward trend, and the saline-alkali land area increased significantly. A comprehensive analysis of the spatial distribution patterns of the indices PD, LSI, DIVISION, and AI reveals that the fragmentation of most landscapes decreased. Notably, the fragmentation of croplands and forests showed a significant decline, whereas the fragmentation of wetlands increased sharply. During this period, the land cover transition index and the comprehensive land cover change rate in the Chahannaoer Basin were 47.36% and 20.54%, respectively, indicating that land cover types are still transitioning towards a more favorable state.