Cataloging the very large number of undescribed species of insects could be greatly accelerated by automated DNA based approaches, but procedures for large-scale species discovery from sequence data are currently lacking. Here, we use mitochondrial DNA variation to delimit species in a poorly known beetle radiation in the genus Rivacindela from arid Australia. Among 468 individuals sampled from 65 sites and multiple morphologically distinguishable types, sequence variation in three mtDNA genes (cytochrome oxidase subunit 1, cytochrome b, 16S ribosomal RNA) was strongly partitioned between 46 or 47 putative species identified with quantitative methods of species recognition based on fixed unique ("diagnostic") characters. The boundaries between groups were also recognizable from a striking increase in branching rate in clock-constrained calibrated trees. Models of stochastic lineage growth (Yule models) were combined with coalescence theory to develop a new likelihood method that determines the point of transition from species-level (speciation and extinction) to population-level (coalescence) evolutionary processes. Fitting the location of the switches from speciation to coalescent nodes on the ultrametric tree of Rivacindela produced a transition in branching rate occurring at 0.43 Mya, leading to an estimate of 48 putative species (confidence interval for the threshold ranging from 47 to 51 clusters within 2 logL units). Entities delimited in this way exhibited biological properties of traditionally defined species, showing coherence of geographic ranges, broad congruence with morphologically recognized species, and levels of sequence divergence typical for closely related species of insects. The finding of discontinuous evolutionary groupings that are readily apparent in patterns of sequence variation permits largely automated species delineation from DNA surveys of local communities as a scaffold for taxonomy in this poorly known insect group.

中文翻译：

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未描述昆虫 DNA 分类的基于序列的物种定界

通过基于 DNA 的自动化方法可以大大加速对大量未描述的昆虫物种进行编目，但目前缺乏从序列数据中发现大规模物种的程序。在这里，我们使用线粒体 DNA 变异来界定干旱澳大利亚 Rivacindela 属中鲜为人知的甲虫辐射中的物种。在从 65 个位点和多种形态可区分类型采样的 468 个个体中，三个 mtDNA 基因（细胞色素氧化酶亚基 1、细胞色素 b、16S 核糖体 RNA）的序列变异在 46 或 47 个假定物种之间强烈划分固定唯一（“诊断”）字符。组之间的界限也可以从受时钟约束的校准树中分枝率的显着增加中识别出来。随机谱系增长模型（Yule 模型）与聚结理论相结合，开发了一种新的似然方法，该方法确定了从物种水平（物种形成和灭绝）到种群水平（聚结）进化过程的过渡点。在 Rivacindela 的超度量树上拟合从物种形成到聚结节点的转换位置产生了发生在 0.43 Mya 处的分支率转变，导致估计有 48 个推定物种（阈值的置信区间从 47 到 51 个簇在 2 logL 单位）。以这种方式划定的实体表现出传统定义物种的生物学特性，显示出地理范围的连贯性，与形态学上公认的物种的广泛一致性，以及密切相关的昆虫物种的典型序列差异水平。在序列变异模式中很明显的不连续进化分组的发现允许从当地社区的 DNA 调查中进行很大程度上自动化的物种划分，作为这个鲜为人知的昆虫群体分类学的支架。