Center for Integrative Biodiversity Discovery, Museum für Naturkunde, Leibniz Institute for Evolution and Biodiversity Science, Invalidenstraße 43, 10115 Berlin, Germany.
Institute of Biology, Humboldt University, 10115 Berlin, Germany.
Philos Trans R Soc Lond B Biol Sci. 2024 Jun 24;379(1904):20230120. doi: 10.1098/rstb.2023.0120. Epub 2024 May 6.
Holistic insect monitoring needs scalable techniques to overcome taxon biases, determine species abundances, and gather functional traits for all species. This requires that we address taxonomic impediments and the paucity of data on abundance, biomass and functional traits. We here outline how these data deficiencies could be addressed at scale. The workflow starts with large-scale barcoding (megabarcoding) of all specimens from mass samples obtained at biomonitoring sites. The barcodes are then used to group the specimens into molecular operational taxonomic units that are subsequently tested/validated as species with a second data source (e.g. morphology). New species are described using barcodes, images and short diagnoses, and abundance data are collected for both new and described species. The specimen images used for species discovery then become the raw material for training artificial intelligence identification algorithms and collecting trait data such as body size, biomass and feeding modes. Additional trait data can be obtained from vouchers by using genomic tools developed by molecular ecologists. Applying this pipeline to a few samples per site will lead to greatly improved insect monitoring regardless of whether the species composition of a sample is determined with images, metabarcoding or megabarcoding. This article is part of the theme issue 'Towards a toolkit for global insect biodiversity monitoring'.
整体昆虫监测需要可扩展的技术来克服分类偏见，确定物种丰度，并收集所有物种的功能特征。这要求我们解决分类障碍以及关于丰度、生物量和功能特征的数据缺乏问题。我们在这里概述了如何在大规模上解决这些数据缺陷。该工作流程从在生物监测点获得的大量样本中对所有标本进行大规模条形码（宏条形码）开始。然后，这些条形码用于将标本分为分子操作分类单位，随后使用第二个数据源（例如形态）对这些分类单位进行测试/验证为物种。使用条形码、图像和简短诊断来描述新物种，并为新物种和已描述物种收集丰度数据。用于物种发现的标本图像然后成为训练人工智能识别算法和收集特征数据（如体型、生物量和摄食方式）的原材料。通过分子生态学家开发的基因组工具，可以从凭证中获取其他特征数据。在每个站点应用该流水线只需对几个样本进行分析，就可以大大提高昆虫监测水平，而不管样本的物种组成是通过图像、代谢条形码还是宏条形码确定的。本文是“迈向全球昆虫生物多样性监测工具包”主题专刊的一部分。
