Sino-American Biological Control Laboratory, Institute of Plant Protection, Chinese Academy of Agricultural Sciences, Beijing, China.
Centre for Biodiversity Genomics and Department of Integrative Biology, University of Guelph, Guelph, Ontario, Canada.
PeerJ. 2023 May 5;11:e15338. doi: 10.7717/peerj.15338. eCollection 2023.
DNA metabarcoding is rapidly emerging as a cost-effective approach for large-scale biodiversity assessment and pest monitoring. The current study employed metabarcoding to assess insect diversity in citrus orchards in Ganzhou City, Jiangxi, China in both 2018 and 2019. Insects were sampled using Malaise traps deployed in three citrus orchards producing a total of 43 pooled monthly samples.
The Malaise trap samples were sequenced following DNA metabarcoding workflow. Generated sequences were curated and analyzed using two cloud databases and analytical platforms, the barcode of life data system (BOLD) and multiplex barcode research and visualization environment (mBRAVE).
These platforms assigned the sequences to 2,141 barcode index numbers (BINs), a species proxy. Most (63%) of the BINs were shared among the three sampling sites while BIN sharing between any two sites did not exceed 71%. Shannon diversity index () showed a similar pattern of BIN assortment at the three sampling sites. Beta diversity analysis by Jaccard similarity coefficient () and Bray-Curtis distance matrix () revealed a high level of BIN similarity among the three sites ( = 0.67-0.68; = 0.19-0.20). Comparison of BIN records against all those on BOLD made it possible to identify 40% of the BINs to a species, 57% to a genus, 97% to a family and 99% to an order. BINs which received a species match on BOLD were placed in one of four categories based on this assignment: pest, parasitoid, predator, or pollinator. As this study provides the first baseline data on insect biodiversity in Chinese citrus plantations, it is a valuable resource for research in a broad range of areas such as pest management and monitoring beneficial insects in citrus gardens.
DNA 元条形码技术正在迅速成为一种具有成本效益的大规模生物多样性评估和害虫监测方法。本研究采用元条形码技术评估了 2018 年和 2019 年中国江西赣州柑橘园的昆虫多样性。在三个生产柑橘的果园中使用粘虫陷阱进行昆虫采样，共采集了 43 个每月混合样本。
用 Malaise 陷阱采集的样本按照 DNA 元条形码工作流程进行测序。生成的序列使用两个云数据库和分析平台，即生命条形码数据系统（BOLD）和多条形码研究和可视化环境（mBRAVE）进行整理和分析。
这些平台将序列分配到 2141 个条形码索引编号（BIN），这是一个物种代理。大多数（63%）的 BIN 在三个采样地点都有共享，而任何两个地点之间的 BIN 共享不超过 71%。Shannon 多样性指数（）在三个采样地点的 BIN 组合上显示出相似的模式。通过 Jaccard 相似系数（）和 Bray-Curtis 距离矩阵（）进行的β多样性分析表明，三个地点的 BIN 相似性很高（=0.67-0.68；=0.19-0.20）。将 BIN 记录与 BOLD 上的所有记录进行比较，可以确定 40%的 BIN 可以鉴定到物种，57%的 BIN 可以鉴定到属，97%的 BIN 可以鉴定到科，99%的 BIN 可以鉴定到目。在 BOLD 上获得物种匹配的 BIN 按照以下分类之一进行分类：害虫、寄生蜂、捕食者或传粉者。由于本研究提供了中国柑橘种植园中昆虫生物多样性的第一个基线数据，因此它是害虫管理和监测柑橘园有益昆虫等广泛领域研究的宝贵资源。
