Marine protists have traditionally been assumed to be lowly diverse and cosmopolitan. Yet, several recent studies have shown that many protist species actually consist of cryptic complexes of species whose members are often restricted to particular biogeographic regions. Nonetheless, detection of cryptic species is usually hampered by sampling coverage and application of methods (e.g. phylogenetic trees) that are not well suited to identify relatively recent divergence and ongoing gene flow. In this paper, we show how these issues can be overcome by inferring phylogenetic haplotype networks from global metabarcoding datasets. We use the Chaetoceros curvisetus (Bacillariophyta) species complex as study case. Using two complementary metabarcoding datasets (Ocean Sampling Day and Tara Oceans), we equally resolve the cryptic complex in terms of number of inferred species. We detect new hypothetical species in both datasets. Gene flow between most of species is absent, but no barcoding gap exists. Some species have restricted distribution patterns whereas others are widely distributed. Closely related taxa occupy contrasting biogeographic regions, suggesting that geographic and ecological differentiation drive speciation. In conclusion, we show the potential of the analysis of metabarcoding data with evolutionary approaches for systematic and phylogeographic studies of marine protists.

传统上，海洋原生生物被认为是低多样性和世界性的。然而，最近的几项研究表明，许多原生生物实际上由神秘的物种复合体组成，其成员通常仅限于特定的生物地理区域。尽管如此，神秘物种的检测通常受到采样覆盖率和方法（例如系统发育树）的应用的阻碍，这些方法不太适合识别相对最近的分歧和正在进行的基因流动。在本文中，我们展示了如何通过从全球元条形码数据集中推断系统发育单倍型网络来克服这些问题。我们使用Chaetoceros curvisetus(Bacillariophyta) 物种复合体作为研究案例。使用两个互补的元条形码数据集（Ocean Sampling Day 和 Tara Oceans），我们同样解决了推断物种数量方面的神秘复合体。我们在两个数据集中检测到新的假设物种。大多数物种之间不存在基因流动，但不存在条形码缺口。一些物种的分布模式受到限制，而另一些则分布广泛。密切相关的分类群占据了对比鲜明的生物地理区域，这表明地理和生态分化推动了物种形成。总之，我们展示了使用进化方法分析元条形码数据在海洋原生生物系统和系统地理学研究中的潜力。