Environmental Technologies, Coastal and Freshwater Group, Cawthron Institute, Private Bag 2, Nelson, 7042, New Zealand.
School of Biological Sciences, University of Auckland, Private Bag 92019, Auckland, 1142, New Zealand.
Sci Rep. 2018 Nov 2;8(1):16290. doi: 10.1038/s41598-018-34541-1.
Marine infrastructure can favor the spread of non-indigenous marine biofouling species by providing a suitable habitat for them to proliferate. Cryptic organisms or those in early life stages can be difficult to distinguish by conventional morphological taxonomy. Molecular tools, such as metabarcoding, may improve their detection. In this study, the ability of morpho-taxonomy and metabarcoding (18S rRNA and COI) using three reference databases (PR2, BOLD and NCBI) to characterize biodiversity and detect non-indigenous species (NIS) in biofouling was compared on 60 passive samplers deployed over summer and winter in a New Zealand marina. Highest resolution of metazoan taxa was identified using 18S rRNA assigned to PR2. There were higher assignment rates to NCBI reference sequences, but poorer taxonomic identification. Using all methods, 48 potential NIS were identified. Metabarcoding detected the largest proportion of those NIS: 77% via 18S rRNA/PR2 and NCBI and 35% via COI/BOLD and NCBI. Morpho-taxonomy detected an additional 14% of all identified NIS comprising mainly of bryozoan taxa. The data highlight several on-going challenges, including: differential marker resolution, primer biases, incomplete sequence reference databases, and variations in bioinformatic pipelines. Combining morpho-taxonomy and molecular analysis methods will likely enhance the detection of NIS from complex biofouling.
海洋基础设施为非本地海洋生物附生物种的扩散提供了适宜的栖息地，从而有利于它们的繁殖。隐生生物或处于早期生命阶段的生物可能难以通过传统的形态分类学来区分。分子工具，如代谢组学，可以提高它们的检测能力。在这项研究中，我们比较了形态分类学和代谢组学（18S rRNA 和 COI）使用三个参考数据库（PR2、BOLD 和 NCBI）在新西兰码头的夏季和冬季部署的 60 个被动采样器上对生物污垢中的生物多样性和检测非本地物种（NIS）的能力。使用 PR2 分配的 18S rRNA 确定了后生动物分类群的最高分辨率。NCBI 参考序列的分配率更高，但分类鉴定效果较差。使用所有方法，共鉴定出 48 种潜在的 NIS。代谢组学通过 18S rRNA/PR2 和 NCBI 检测到最大比例的 NIS：77%，通过 COI/BOLD 和 NCBI 检测到 35%。形态分类学还检测到了另外 14%的所有鉴定出的 NIS，主要包括苔藓动物类群。这些数据突出了几个正在面临的挑战，包括：不同的标记分辨率、引物偏倚、不完整的序列参考数据库以及生物信息学管道的变化。结合形态分类学和分子分析方法可能会提高对复杂生物污垢中 NIS 的检测能力。
