Double-digest restriction-site associated DNA sequencing of multiple Theromaster samples. Raw sequence reads. Theromaster

The diversity of biological and ecological characteristics of organisms, and the underlying genetic patterns and processes of speciation, makes the development of universally applicable genetic species delimitation methods challenging. Many approaches, like those incorporating the multispecies coalescent, sometimes delimit populations and overestimate species numbers. This issue is exacerbated in taxa with inherently high population structure due to low dispersal ability, and in cryptic species resulting from nonecological speciation. These taxa present a conundrum when delimiting species: analyses rely heavily, if not entirely, on genetic data which over split species, while other lines of evidence lump. We showcase this conundrum in the harvester Theromaster brunneus, a low dispersal taxon with a wide geographic distribution and high potential for cryptic species. Integrating morphology, mitochondrial, and sub-genomic (double-digest RADSeq and ultraconserved elements) data, we find high discordance across analyses and data types in the number of inferred species, with further evidence that multispecies coalescent approaches over split. We demonstrate the power of a supervised machine learning approach in effectively delimiting cryptic species by creating a custom training data set derived from a well-studied lineage with similar biological characteristics as Theromaster. This novel approach uses known taxa with particular biological characteristics to inform unknown taxa with similar characteristics, using modern computational tools ideally suited for species delimitation. The approach also considers the biology and natural history of organisms to make more biologically informed species delimitation decisions, and in principle is broadly applicable for taxa across the tree of life.

生物的生物学与生态学特征多样性，以及物种形成背后的遗传模式与演化过程，使得开发通用的遗传物种界定（species delimitation）方法颇具挑战。诸多方法（例如纳入多物种溯祖模型（multispecies coalescent）的方法）有时会将种群误界定为物种，从而高估物种数量。对于因扩散能力低下而固有种群结构复杂的类群，以及由非生态性物种形成所产生的隐存物种（cryptic species）而言，这一问题会更为突出。这类类群在物种界定过程中会陷入两难困境：即便分析并非完全依赖遗传数据，遗传数据也往往会过度拆分物种；而其他类型的证据则会将物种合并。我们以棕氏收获蛛（Theromaster brunneus）为例展示这一困境：该类群扩散能力低下、地理分布广泛，且具备极高的隐存物种形成潜力。本研究整合形态学数据、线粒体数据以及亚基因组数据（包括双酶切限制性位点相关DNA测序（double-digest RADSeq）与超保守元件（ultraconserved elements）数据），发现不同分析方法与数据类型在推断的物种数量上存在显著不一致，且进一步证实多物种溯祖模型方法存在过度拆分物种的问题。我们通过构建源自与棕氏收获蛛具有相似生物学特征的、已被充分研究的谱系（lineage）的定制训练数据集，证明了监督机器学习（supervised machine learning）方法在有效界定隐存物种方面的效能。这一全新方法借助专为物种界定设计的现代计算工具，利用具备特定生物学特征的已知类群，为具有相似特征的未知类群提供物种界定参考。该方法还会考量生物的生物学特性与自然历史，从而做出更符合生物学逻辑的物种界定决策，且原则上可广泛应用于生命之树的各类类群。