Statistical method for testing the neutral mutation hypothesis by DNA polymorphism.

Within uncharacterized groups, DNA barcodes, short DNA sequences that are present in a wide range of species, can be used to assign organisms into species. We propose an automatic procedure that sorts the sequences into hypothetical species based on the barcode gap, which can be observed whenever the divergence among organisms belonging to the same species is smaller than divergence among organisms from different species. We use a range of prior intraspecific divergence to infer from the data a model-based one-sided confidence limit for intraspecific divergence. The method, called Automatic Barcode Gap Discovery (ABGD), then detects the barcode gap as the first significant gap beyond this limit and uses it to partition the data. Inference of the limit and gap detection are then recursively applied to previously obtained groups to get finer partitions until there is no further partitioning. Using six published data sets of metazoans, we show that ABGD is computationally efficient and performs well for standard prior maximum intraspecific divergences (a few per cent of divergence for the five data sets), except for one data set where less than three sequences per species were sampled. We further explore the theoretical limitations of ABGD through simulation of explicit speciation and population genetics scenarios. Our results emphasize in particular the sensitivity of the method to the presence of recent speciation events, via (unrealistically) high rates of speciation or large numbers of species. In conclusion, ABGD is fast, simple method to split a sequence alignment data set into candidate species that should be complemented with other evidence in an integrative taxonomic approach.

ABGD, Automatic Barcode Gap Discovery for primary species delimitation

Motivation: Sequence-based methods to delimit species are central to DNA taxonomy, microbial community surveys and DNA metabarcoding studies. Current approaches either rely on simple sequence similarity thresholds (OTU-picking) or on complex and compute-intensive evolutionary models. The OTU-picking methods scale well on large datasets, but the results are highly sensitive to the similarity threshold. Coalescent-based species delimitation approaches often rely on Bayesian statistics and Markov Chain Monte Carlo sampling, and can therefore only be applied to small datasets. Results: We introduce the Poisson tree processes (PTP) model to infer putative species boundaries on a given phylogenetic input tree. We also integrate PTP with our evolutionary placement algorithm (EPA-PTP) to count the number of species in phylogenetic placements. We compare our approaches with popular OTU-picking methods and the General Mixed Yule Coalescent (GMYC) model. For de novo species delimitation, the stand-alone PTP model generally outperforms GYMC as well as OTU-picking methods when evolutionary distances between species are small. PTP neither requires an ultrametric input tree nor a sequence similarity threshold as input. In the open reference species delimitation approach, EPA-PTP yields more accurate results than de novo species delimitation methods. Finally, EPA-PTP scales on large datasets because it relies on the parallel implementations of the EPA and RAxML, thereby allowing to delimit species in high-throughput sequencing data. Availability and implementation: The code is freely available at www.

/pdf/a-general-species-delimitation-method-with-applications-to-32mb4uk65d.pdf

A general species delimitation method with applications to phylogenetic placements

Because many animal species are undescribed, and because the identification of known species is often difficult, interim taxonomic nomenclature has often been used in biodiversity analysis. By assigning individuals to presumptive species, called operational taxonomic units (OTUs), these systems speed investigations into the patterning of biodiversity and enable studies that would otherwise be impossible. Although OTUs have conventionally been separated through their morphological divergence, DNA-based delineations are not only feasible, but have important advantages. OTU designation can be automated, data can be readily archived, and results can be easily compared among investigations. This study exploits these attributes to develop a persistent, species-level taxonomic registry for the animal kingdom based on the analysis of patterns of nucleotide variation in the barcode region of the cytochrome c oxidase I (COI) gene. It begins by examining the correspondence between groups of specimens identified to a species through prior taxonomic work and those inferred from the analysis of COI sequence variation using one new (RESL) and four established (ABGD, CROP, GMYC, jMOTU) algorithms. It subsequently describes the implementation, and structural attributes of the Barcode Index Number (BIN) system. Aside from a pragmatic role in biodiversity assessments, BINs will aid revisionary taxonomy by flagging possible cases of synonymy, and by collating geographical information, descriptive metadata, and images for specimens that are likely to belong to the same species, even if it is undescribed. More than 274,000 BIN web pages are now available, creating a biodiversity resource that is positioned for rapid growth.

/pdf/a-dna-based-registry-for-all-animal-species-the-barcode-190e3osdti.pdf

A DNA-Based Registry for All Animal Species: The Barcode Index Number (BIN) System

Taxonomy is the biological discipline that identifies, describes, classifies and names extant and extinct species and other taxa. Nowadays, species taxonomy is confronted with the challenge to fully incorporate new theory, methods and data from disciplines that study the origin, limits and evolution of species. Integrative taxonomy has been proposed as a framework to bring together these conceptual and methodological developments. Here we review perspectives for an integrative taxonomy that directly bear on what species are, how they can be discovered, and how much diversity is on Earth. We conclude that taxonomy needs to be pluralistic to improve species discovery and description, and to develop novel protocols to produce the much-needed inventory of life in a reasonable time. To cope with the large number of candidate species revealed by molecular studies of eukaryotes, we propose a classification scheme for those units that will facilitate the subsequent assembly of data sets for the formal description of new species under the Linnaean system, and will ultimately integrate the activities of taxonomists and molecular biologists.

/pdf/the-integrative-future-of-taxonomy-mpi03p4szl.pdf

The integrative future of taxonomy.

High-throughput DNA sequencing has the potential to accelerate species discovery if it is able to recognize evolutionary entities from sequence data that are comparable to species. The general mixed Yule-coalescent (GMYC) model estimates the species boundary from DNA surveys by identifying independently evolving lineages as a transition from coalescent to speciation branching patterns on a phylogenetic tree. Applied here to 12 families from 4 orders of insects in Madagascar, we used the model to delineate 370 putative species from mitochondrial DNA sequence variation among 1614 individuals. These were compared with data from the nuclear genome and morphological identification and found to be highly congruent (98% and 94%). We developed a modified GMYC that allows for a variable transition from coalescent to speciation among lineages. This revised model increased the congruence with morphology (97%), suggesting that a variable threshold better reflects the clustering of sequence data into biological species. Local endemism was pronounced in all 5 insect groups. Most species (60-91%) and haplotypes (88-99%) were found at only 1 of the 5 study sites (40-1000 km apart). This pronounced endemism resulted in a 37% increase in species numbers using diagnostic nucleotides in a population aggregation analysis. Sample sizes between 7 and 10 individuals represented a threshold above which there was minimal increase in genetic diversity, broadly agreeing with coalescent theory and other empirical studies. Our results from >1.4 Mb of empirical data suggest that the GMYC model captures species boundaries comparable to those from traditional methods without the need for prior hypotheses of population coherence. This provides a method of species discovery and biodiversity assessment using single-locus data from mixed or environmental samples while building a globally available taxonomic database for future identifications. (Biodiversity; coalescent; DNA barcoding; DNA taxonomy; endemism; GMYC; Madagascar; turnover.)

https://www.jstor.org/stable/pdfplus/25677511.pdf

Miranda Elliot

Papers

Accelerated species inventory on Madagascar using coalescent-based models of species delineation.