Geraldi Nathan R, Acinas Silvia G, Alam Intikhab, Gasol Josep M, Fernández-de-Puelles María Luz, Giner Caterina R, Hernández León Santiago, Logares Ramiro, Massana Ramon, Sánchez Pablo, Bajic Vladimir, Gojobori Takashi, Duarte Carlos M
Red Sea Research Center, King Abdullah University of Science and Technology, Thuwal, Saudi Arabia.
Computational Bioscience Research Center, King Abdullah University of Science and Technology, Thuwal, Saudi Arabia.
R Soc Open Sci. 2024 Aug 14;11(8):240724. doi: 10.1098/rsos.240724. eCollection 2024 Aug.
Documenting large-scale patterns of animals in the ocean and determining the drivers of these patterns is needed for conservation efforts given the unprecedented rates of change occurring within marine ecosystems. We used existing datasets from two global expeditions, and , that circumnavigated the oceans and sampled down to 4000 m to assess metazoans from environmental DNA (eDNA) extracted from seawater. We describe patterns of taxonomic richness within metazoan phyla and orders based on metabarcoding and infer the relative abundance of phyla using metagenome datasets, and relate these data to environmental variables. Arthropods had the greatest taxonomic richness of metazoan phyla at the surface, while cnidarians had the greatest richness in pelagic zones. Half of the marine metazoan eDNA from metagenome datasets was from arthropods, followed by cnidarians and nematodes. We found that mean surface temperature and primary productivity were positively related to metazoan taxonomic richness. Our findings concur with existing knowledge that temperature and primary productivity are important drivers of taxonomic richness for specific taxa at the ocean's surface, but these correlations are less evident in the deep ocean. Massive sequencing of eDNA can improve understanding of animal distributions, particularly for the deep ocean where sampling is challenging.
鉴于海洋生态系统正在以前所未有的速度发生变化，为了开展保护工作，有必要记录海洋中动物的大规模分布模式并确定这些模式的驱动因素。我们使用了来自两次全球考察（即[考察名称1]和[考察名称2]）的现有数据集，这两次考察环绕了各大洋，并对深度达4000米的区域进行采样，以评估从海水中提取的环境DNA（eDNA）中的后生动物。我们基于宏条形码技术描述了后生动物门和纲内的分类丰富度模式，并利用宏基因组数据集推断各门类的相对丰度，还将这些数据与环境变量相关联。节肢动物在表层具有后生动物门中最大的分类丰富度，而刺胞动物在远洋区域具有最大的丰富度。宏基因组数据集中一半的海洋后生动物eDNA来自节肢动物，其次是刺胞动物和线虫。我们发现，表层平均温度和初级生产力与后生动物分类丰富度呈正相关。我们的研究结果与现有知识一致，即温度和初级生产力是海洋表层特定分类群分类丰富度的重要驱动因素，但这些相关性在深海中不太明显。对eDNA进行大规模测序可以增进对动物分布的了解，特别是在采样具有挑战性的深海区域。
