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基于形态学分类法与高通量测序技术检测幼鱼以早期发现入侵物种的比较

Comparison of Larval Fish Detections Using Morphology-Based Taxonomy versus High-Throughput Sequencing for Invasive Species Early Detection.

作者信息

Hoffman Joel Christopher, Meredith Christy, Pilgrim Erik, Trebitz Anett, Hatzenbuhler Chelsea, Kelly John Russell, Peterson Gregory, Lietz Julie, Okum Sara, Martinson John

机构信息

US Environmental Protection Agency Office of Research and Development, Great Lakes Toxicology and Ecology Division, 6201 Congdon Blvd, Duluth, Minnesota, 55804, USA.

Montana Department of Environmental Quality, 1520 E. 6th Avenue, Helena, Montana, 59601, USA.

出版信息

Can J Fish Aquat Sci. 2021 Jan 18;78(6):752-764. doi: 10.1139/cjfas-2020-0224.

Abstract

When first introduced, invasive species typically evade detection; DNA barcoding coupled with high-throughput sequencing (HTS) may be more sensitive and accurate than morphology-based taxonomy, and thereby improve invasive (or rare) species detection. We quantified the relative error of species detection between morphology-based and HTS-based taxonomic identification of ichthyoplankton collections from the Port of Duluth, Minnesota, an aquatic non-native species introduction 'hot-spot' in the Laurentian Great Lakes. We found HTS-based taxonomy identified 28 species and morphology-based taxonomy 30 species, of which 27 were common to both. Among samples, 76% of family-level taxonomic assignments agreed; however, only 42% of species assignments agreed. Most errors were attributed to morphology-based taxonomy, whereas HTS-based taxonomy error was low. For this study system, for most non-native fishes, the detection probability by randomized survey for larvae was similar to that by a survey that is optimized for non-native species early detection of juveniles and adults. We conclude that classifying taxonomic errors by comparing HTS results against morphology-based taxonomy is an important step toward incorporating HTS-based taxonomy into biodiversity surveys.

摘要

最初引入时,入侵物种通常难以被发现;与基于形态学的分类法相比,DNA条形码结合高通量测序(HTS)可能更灵敏、准确,从而改善入侵(或稀有)物种的检测。我们对明尼苏达州德卢斯港浮游鱼类样本基于形态学和基于HTS的分类鉴定之间的物种检测相对误差进行了量化,该港口是劳伦琴五大湖外来水生物种引入的“热点”地区。我们发现基于HTS的分类法鉴定出28个物种,基于形态学的分类法鉴定出30个物种,其中27个是两者共有的。在样本中,科级分类的一致性为76%;然而,物种分类的一致性仅为42%。大多数错误归因于基于形态学的分类法,而基于HTS的分类法错误率较低。对于本研究系统,对于大多数非本地鱼类,随机调查幼体的检测概率与针对非本地物种早期检测的幼体和成体优化调查的检测概率相似。我们得出结论,通过将HTS结果与基于形态学的分类法进行比较来分类分类错误,是将基于HTS的分类法纳入生物多样性调查的重要一步。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8d30/9132201/9988d5174351/nihms-1802938-f0001.jpg

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A reference inventory for aquatic fauna of the Laurentian Great Lakes.劳伦琴五大湖水生动物参考名录。
J Great Lakes Res. 2019 Dec 30;45(6):1036-1046. doi: 10.1016/j.jglr.2019.10.004.
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Invasion Science: A Horizon Scan of Emerging Challenges and Opportunities.入侵科学:新兴挑战与机遇的地平线扫描。
Trends Ecol Evol. 2017 Jun;32(6):464-474. doi: 10.1016/j.tree.2017.03.007. Epub 2017 Apr 7.

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