基于环境DNA宏条形码技术的南京不同林地陆生昆虫多样性差异分析初探

曹美伦; 石法贤; 陈亚如; 蔡琨; 孙长海; 王备新

引用本文:	曹美伦,石法贤,陈亚如,蔡琨,孙长海,王备新.基于环境DNA宏条形码技术的南京不同林地陆生昆虫多样性差异分析初探[J].环境监控与预警,2024,16(5):51-56
	CAO Meilun,SHI Faxian,CHEN Yaru,CAI Kun,SUN Changhai,WANG Beixin.Assessing Insect Biodiversity Under Different Forest Stands in Nanjing Based on Environmental DNA Metabarcoding Techniques[J].Environmental Monitoring and Forewarning,2024,16(5):51-56

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基于环境DNA宏条形码技术的南京不同林地陆生昆虫多样性差异分析初探
曹美伦¹，石法贤¹，陈亚如¹，蔡琨²，孙长海¹，王备新^1*
1.南京农业大学，江苏南京 210095； 2.江苏省环境监测中心，江苏南京 210019

摘要:

为探究环境DNA宏条形码技术在多种林分下昆虫生物多样性调查中的可行性，采用马氏网法，于2022年3—5月，对江苏省南京市六合区、高淳区和江宁区的人工林、杂木林以及天然次生林开展昆虫生物多样性调查。通过环境DNA宏条形码技术共注释到陆生昆虫11目100科330种465个分类操作单元（Operational Taxonomic Unit，OTUs）521 219条序列，其中鳞翅目、双翅目和半翅目为优势种群。Chao1指数和ACE指数结果显示，天然次生林的物种丰富度最高，其次是杂木林和人工林。香农-维纳（Shannon-Wiener）多样性指数表明天然次生林的生物多样性较好，杂木林次之，人工林较差。基于此，对环境DNA宏条形码技术在陆生昆虫多样性监测中的表现进行讨论，以期为陆生昆虫多样性监测方法提供新思路与技术参考。

关键词: 宏条形码条形码数据库生物多样性马氏网

DOI：DOI：10.3969/j.issn.1674-6732.2024.05.009

分类号:X835

基金项目:江苏省环保科研课题（2019002）

Assessing Insect Biodiversity Under Different Forest Stands in Nanjing Based on Environmental DNA Metabarcoding Techniques

CAO Meilun¹， SHI Faxian¹， CHEN Yaru¹， CAI Kun²， SUN Changhai¹， WANG Beixin^1*

1.Nanjing Agricultural University， Nanjing， Jiangsu 210095， China； 2.Jiangsu Environmental Monitoring Center， Nanjing， Jiangsu 210019， China

Abstract:

To investigate the feasibility of DNA metabarcoding technology in insect biodiversity surveys under different forest types， this study employed Malaise traps from March 2022 to May 2022 to conduct insect biodiversity surveys in artificial forests， mixed forests， and secondary forests in Luhe District， Gaochun District， and Jiangning District of Nanjing city， Jiangsu Province. Excluding data from typical aquatic insect orders such as Lepidoptera and Odonata， the eDNA metabarcoding technology annotated 11 orders， 100 families， 330 species， and 465 OTUs with 521，219 sequences of terrestrial insects， among which Lepidoptera， Diptera， and Hemiptera were the dominant groups. The Chao1 and ACE indices showed that species richness was highest in secondary forests， followed by mixed forests and artificial forests. The Shannon diversity index indicated that secondary forests had relatively good biodiversity， followed by mixed forests， while artificial forests had relatively low biodiversity. Based on this， we discuss the performance of environmental DNA metabarcoding technology in monitoring terrestrial insect diversity， aiming to provide technical reference for new methods of monitoring terrestrial insect biodiversity.

Key words: Metabarcoding Barcode reference database Biodiversity Malaise trap