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Advances in environmental DNA monitoring:standardization,automation,and emerging technologies in aquatic ecosystems

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摘要 Environmental DNA(eDNA)monitoring,a rapidly advancing technique for assessing biodiversity and ecosystem health,offers a noninvasive approach for detecting and quantifying species from various environmental samples.In this review,a comprehensive overview of current eDNA collection and detection technologies is provided,emphasizing the necessity for standardization and automation in aquatic ecological monitoring.Furthermore,the intricacies of water bodies,from streams to the deep sea,and the associated challenges they pose for eDNA capture and analysis are explored.The paper delineates three primary eDNA survey methods,namely,bringing back water,bringing back filters,and bringing back data,each with specific advantages and constraints in terms of labor,transport,and data acquisition.Additionally,innovations in eDNA sampling equipment,including autonomous drones,subsurface samplers,and in-situ filtration devices,and their applications in monitoring diverse taxa are discussed.Moreover,recent advancements in species-specific detection and eDNA metabarcoding are addressed,highlighting the integration of novel techniques such as CRISPR-Cas and nanopore sequencing that enable precise and rapid detection of biodiversity.The implications of environmental RNA and epigenetic modifications are considered for future applications in providing nuanced ecological data.Lastly,the review stresses the critical role of standardization and automation in enhancing data consistency and comparability for robust long-term biomonitoring.We propose that the amalgamation of these technologies represents a paradigm shift in ecological monitoring,aligning with the urgent call for biodiversity conservation and sustainable management of aquatic ecosystems.
出处 《Science China(Life Sciences)》 SCIE CAS CSCD 2024年第7期1368-1384,共17页 中国科学(生命科学英文版)
基金 supported by the National Natural Science Foundation of China(42330405,32200367) supported by the National Natural Science Foundation of China(32325034,U2340216) the National Key Research and Development Program of China(2022YFF0608200) the Special Project for Social Development of Yunnan Province(202103AC100001)to Meng Yao the Scientific Data Center,Institute of Hydrobiology,CAS the Wuhan Branch,Supercomputing Center of CAS for their support。
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