In early online J Zool Syst Evol Res researchers from Natural History Museum and Imperial College, London, scrutinize “recent advances in DNA taxonomy…that follow the dramatic increase in data generation“. Authors Vogler and Monaghan provide a scientific update to
what has been learned so far: “a key finding from recent studies in animals is that variation in mitochondrial DNA is partitioned as tight clusters of closely related genotypes, which group specimens largely according to traditionally recognized species limits, and which are congruent with nuclear markers”,
the durability of clustering: “it can be expected that denser geographic and taxonomic sampling may result in the discovery of new clusters, and perhaps reduce their divergence from each other, but they are unlikely to erode the clustering altogether”,
the significance of incongruence between DNA-based and morphology-based methods for delimiting species: “the high degree of congruence of mtDNA groups and traditionally defined taxa appears to contradict the reported mismatch of established species boundaries…even well-studied groups may be in need of taxonomic revision before accurate tests of incongruence can be conducted”,
what the future holds: “a standard DNA taxonomic analysis will include broad sampling..followed by large-scale sequencing, and algorithmic procedures for delineating species limits. The taxonomic system will be derived from the data rather than expert opinion“,
and what is needed to harness DNA taxonomy in general and DNA barcoding in particular to speed description of the estimated 80% of earth’s biodiversity that is at yet undescribed: “a feedback loop that [uses] discrepancies between DNA and other data to refine species descriptions..founded in existing theory of evolutionary biology and phylogenetics”
I close with a pictorial analogy. The Coulter counter uses electrical sensing to gain the same information as morphologic diagnosis of blood smears, with dramatic improvements in speed, cost, and necessary expertise. In some situations, DNA sequencing may provide similar improvements over morphologic diagnosis for species-level identification.

Red seaweeds, kingdom Rhodophyta, are “weird, wonderful, and extremely ancient” organisms distantly related to plants (Tudge 2000 The Variety of Life). Multicellular red algae arose at least 1.2 billion years ago, predating the earliest multicellular animals by 600 million years. Visual identification is challenging, as “morphology can be highly variable within and between species, and conspicuous features with which they can be readily identified are often lacking. In addition, highly convergent morphology is commonly encountered. …Identification is further compounded by the complexities of red algal life histories, many of which have a heteromorphic alternation of generations. Different life history stages of species have frequently been described as separate species and have only been linked through observations of life histories in culture and use of molecular techniques” (

species names or boundaries, but that will not change DNA barcodes of specimens or the clustering patterns of barcode sequences. Thus it should be simple to use a specimen’s barcode sequence “name” to search a regularly revised public database for the current species name it corresponds to. A public database of sequences, specimens, and associated data as is
Researchers at the University of Frankfurt (
ALL crosses between individuals from the same MOTU population were viable, whereas NONE of crosses between individuals from different MOTU produced eggs. In morphometric analysis, Radix MOTU overlapped as shown at left, and in rearing experiments, shell shape changed in 4 of 5 populations, demonstrating phenotypic plasticity of putative morphologic characters. In northwestern European Radix snails, DNA trumps morphology.
Here I offer one possible way of visualizing differences in barcode data sets using as an example the
The histograms quickly show distances within most species are small and minimum distances between species are generally larger. Histograms are summaries with unlimited capacity. However, one might want to know more about individual species. For example, do species with higher intraspecific distances also show greater interspecific distances? One also wonders about the variation below 1% in both panels. In 
This graph is remarkably compressible, as shown by the small inset in the US county map above and in the figure at right. Here this is used to compare variation in Costa Rican skippers (278 species in 1 Family) to that in Australian fish (172 species in 1 Class) (