Area of Research: DNA Barcoding

Mitochondria are the power plants of the cell, consuming oxygen and breakdown products of sugars, amino acids, and fatty acids to produce energy as ATP and heat.  As originally proposed by Lynn Margulis in 1967, mitochondria, which have their own circular genome and replicate independently of the cell, are derived from an ancient symbiosis of an an alpha-proteobacterium related to gram-negative bacteria. 

In multicellular animals, most of the 100+ proteins in mitochondria are encoded by nuclear genes. The mitochondrial genome is only about 16 kb (vs about 2000 kb for nearest bacterial relatives Rickettsia sp) and encodes just 13 proteins, all of which function in the electron transport chain, plus 2 ribosomal RNAs and approximately 20 tRNAs.  Multiple protein-protein interactions (for example, complex I comprises 34 nuclear-encoded and 7 mitochondrial-encoded proteins) suggest there must be close co-evolution between nuclear and mitochondrial genomes; this might be one of the constraints on mitochondrial variation. Although an enormous amount of information on mitochondrial sequence differences among and within species has been been compiled (through DNA barcoding initiative and other efforts), there is surprisingly little study so far on whether mitochondrial differences among species reflect functional adaptation (although see Ruiz-Pesini et al 2004 Science 303:223, Bayona-Balfaluy et al 2004 Mol Biol Evol 22:716).  

In 25 February 2009 Proc R Soc B researchers from University of Groningen, The Netherlands; Max Planck Institute for Ornithology, Germany; and Ohio State University investigate whether mitochondrial differences modulate energy metabolism in birds. As mitochondria consume 90% of respired oxygen, mitochondrial activity presumably determines basal metabolism. Tieleman and colleagues performed crosses among 3 captive bred populations of stonechats (Saxicola torquata spp.) that differ in basal metabolic rate, which presumably reflects adaptation to different climates: Africa (Kenya, Saxicola torquata axillaris), Asia (Kazakhstan, Saxicola torquata maura), and Europe (Austria, Saxicola torquata rubicola). As an aside, I note that these three taxa are elevated to species status in current world checklists (Clements 2007, IOC Checklist; even 1992 edition of Birds of Europe notes “Siberian race may be a full species.”) This does not change the interpretation of the findings, but it does reflect the confused nature of taxonomic science that even for a group as well studied as birds, publication standards accept this laxity in taxonomic classification. Naming of bacteria in medical studies is more uniformly up to date than for multicellular animals; it seems that animal taxonomists have not found a way to establish a regularly updated consensus. In this regard the IOC Checklist suggests a way forward: “in this global world of wiki-style sharing of knowledge, we invite world birders and ornithologsts alike to help us keep the IOC list accurate, vital, and accessible.” 

Back to the paper. Tieleman and colleagues “tested for a genetic effect on BMR based on mitochondrial-nuclear coadaptation using hybrids between ancestral populations with high and low BMR (Europe-Africa and Asia-Europe), with different parental configurations (female-high x male-low or female-low x male-high). Hybrids with different parental configurations have on average identical mixtures of nuclear DNA, but differ in mitochondrial DNA because it is inherited only from the mother.”  The researchers found that metabolic rate differed between hybrids with contrasting parental configurations, “providing evidence for the importance of a match between mitochondrial and nuclear genomes to regulate metabolic rate.” So far so good. However, contrary to expectations, in both sets of crosses, metabolic rates in hybrids were more similar to that of the father than the mother! (see adapted figure). This result is a puzzler; it suggests there might be another factor such as genomic imprinting at work. 

Looking at the bigger picture, for those interested in mitochondrial evolution, there is a lot of opportunity: a large and growing database of COI sequences (>500,000 individuals, >50,000 species so far) that is waiting to be analyzed for evidence of purifying or positive selection, for example, or for limits to plasticity in COI amino acid sequence. I wonder if there might be convergent evolution of COI, such that diverse organisms in very cold or very hot environments environments, for example, might exhibit similar amino acid substitutions.

What do leaf beetles (Chrysomelidae) eat? In 11 Nov 2008 Proc R Soc B researchers from Spain, London, and Australia, apply DNA analysis to 76 species (1 individual/species) of Australian leaf beetles. Jurado-Rivera and colleagues extracted DNA from whole beetles using DNAeasy kit. To identify plant DNA in beetle extracts, they amplified chloroplast trnL intron (313 to 581 bp in analyzed samples). 70 (92%) of samples gave high quality reads after direct sequencing of the PCR products, consistent with ingestion of a single plant species; the remaining samples were sequenced from cloned PCR products; these gave 2 divergent clones in 3 of seven cases, for a total of 81 different trnL intron sequences. Why use trnL intron? The authors cite the large number of sequences in GenBank and favorable experience (ie successful amplification and good taxonomic resolution) in their prior work and that of others (eg Taberlet et al 2007 Nucleic Acids Res 35:e14). This certainly makes sense, but I hope a general agreement for plant barcode standards will be published shortly, otherwise the field will continue to be hobbled by having multiple incomplete and non-overlapping databases for the various markers. For example, according to the authors “only 14 and 15 of approximately 1000 and 800 described Australian species of Acacia and Eucalyptus, respectively, are represented in GenBank by trnL intron sequences. As to what resolution is possible with current trnL database, the authors found “reliable identification to plant family in every case and very frequently the inference is possible at lower taxonomic levels.” 

There also needs to be an agreement to have a curated plant barcode database. As the authors report, “in the course of this study, we found several examples of erroneous taxonomic assignments (e.g. Sapindaceae identified as Cypripedium, Cypripedioideae; Apocynaceae labelled as Sesamum, Pedaliaceae; one case of names switched between Pittosporum and Cheiranthera, both Pittosporaceae; suspicious generic assignment for Aesculus x carnea), and of sequencing artefacts (e.g. Tragopogon spp., Acacia usumatensis) and chimeras (e.g.Pentaphylax euryoides). Problems introduced by these sequences were only apparent after careful inspection of trees revealing suspicious relationships, and required phylogenetic re-evaluation after removing problematic sequence data.” 

This is helpful for the present study, but the problematic sequences remain in the reference databases, ready to trip up the next set of researchers who might not be so careful. To fix this problem, Jurado-Rivera and colleagues make what I think is the wrong suggestion, namely “all of the above would argue for the use of additional markers”. Adding markers may improve the ability to make species-level identifications in plants, but if the goal is to construct an error-free database, adding markers is an expensive and likely ineffective way to ferret out mislabeled or otherwise inaccurate sequences. What is needed is a stand-alone database, closely-linked to GenBank, in which problematic sequences can be weeded out or re-labeled (ie Barcode of Life Database (BOLD) www.barcodinglife.org).

To construct a beetle phylogeny, the authors amplified COI and EF1a from their specimens. They found strong concordance between the evolutionary histories of Australian Chrysomelinae beetles and their host plants, indicating long-term co-evolution. They conclude “our analysis not only shows the details of ecological associations for a dominant herbivore group but also offers the basis for their evolutionary interpretation.”

I am puzzled that the authors amplified a segment in the 3′ half of COI that does not overlap with the standard animal barcode region, making it impossible to combine their data with the 500,000+ COI sequences analyzed to date (www.barcodinglife.org). This important caveat aside, I look forward to many more studies that utilize DNA barcoding to join ecology and phylogenetics.

In 1 Oct 2008 Syst Entomol researchers from University of Alberta report on “Widespread decoupling of mtDNA variation and species integrity in Grammia tiger moths.” Authors Schmidt and Sperling analyzed COI sequences from 274 specimens representing 28 of 36 known Grammia species, collected across Canada and US. An NJ tree showed 13 haplogroups (loose clusters); 11 of these “largely or exclusively corresponded to nominal species,” while the other two, “designated the Western and Eastern haplogroups, contained polyphyletic asemblages of 13 and 10 species, respectively.” The researchers conclude that these two tangles of sequences and species represent historical or ongoing mating between species and “research on factors governing hybridization would be particularly informative in gaining an understanding of the role of isolating mechanisms in speciation” (ie DNA barcoding highlights an interesting group for further study). 

Like explorers mapping new territory, Schmidt and Sperling’s study creates a map that can be used by the next investigators studying these moths, whether as eggs, larva (according to Caterpillars of Eastern North America by David Wagner, Princeton University Press, 2005 “there are no keys that can be used to identify the [Grammia] caterpillars with reliability”), intact adults, or as fragments retrieved from droppings of predator species such as bats. Specimens with COI barcodes in the two polyphyletic tangles will at least be identifiable to a subset of species within the genus.       

This study brings to mind an analogy between GPS and DNA barcodes. Handheld GPS devices enable us to pinpoint our location on the earth’s surface within a few meters. We then use a map to translate the numerical coordinates into useful information. In areas where the mapping is incomplete or out of date, GPS coordinates are less informative and may be misleading. 

A DNA sequencer (handheld version soon perhaps) is a “biodiversity GPS” device, a DNA barcode is a set of biodiversity GPS coordinates, and a barcode reference library is a biodiversity map on which the specimen’s taxonomic identity can be located. In areas that have been mapped in detail (ie records from multiple specimens across the species range and from closely-related species), a barcode sequence will usually enable precise species-level identification with a high degree of certainty. In groups less-well surveyed or in which the taxonomy is unknown, there will be more uncertainty. Nonetheless, the general coherence of genera, families, and even orders in simple COI NJ trees (see figure below) suggests a DNA barcode will usually provide useful taxonomic information even in the absence of comprehensive taxonomic coverage.

I expect that in the future there will be good methods for defining species based on sequence data, including COI barcode records. While the importance of genetic data as an indicator of species status is informally recognized in science reporting (eg “DNA analysis confirmed it was a new species”), it is generally relegated to an ancillary role in species descriptions. It is remarkable to me that of all the mathematical tools of phylogeography, population genetics and phylogenetic reconstruction, none are designed to diagnose species. Just as a node in a ML tree may have 90% bootstrap support, why not apply the same rigor to species-level genetic data and say, for example, 90% confidence that this particular cluster represents a distinct species. I understand this would involve adopting a particular species concept, but at least it would be a place to start. If the data were only COI or other mtDNA sequences, then there might need be a warning about possible introgression as the above study demonstrates. I believe the flood of data from the barcode initiative, with multiple sequences from tens or hundreds of thousands of species, will help push development of such tools.

There are more bird species in the Neotropics than in any other biogeographic region (including many more as yet undescribed; see for example application of COI barcoding to cryptic diversity in Brazilian tyrant flycatchers Chaves et al Nov 2008 Mol Ecol Resources). Mapping this diversity with COI is both exciting and perhaps challenging. Some have wondered if the plethora of neotropical avian species might overwhelm the ability of a single mtDNA gene to resolve differences among species.

There is apparently no need to worry. In February 2009 PLoS ONE researchers from University of Guelph, Canada, and Museo Argentino de Ciencias Naturales, Argentina report on 1,594 COI barcode sequences from 500 species, which represents 51% of of Argentine birds. Kerr and colleagues find COI barcodes work here as elsewhere, ie most species show deep divergences from their “nearest neighbor” in the data set. Overall, 491 of 500 (98%) of species had distinct barcode(s). Of the nine species not resolved, six were Sporophila spp (common name “seedeaters”); these comprised a single cluster of shared barcodes. Although morphologically distinct, I wonder if these might be analogous to North America juncos, which have regional plumage variants without genetic differentiation. As recently as 1973 they were considered to be a complex of 5 species; these are now lumped into one, Junco hyemalis.  

21 Argentine birds showed 2 or more distinct clusters (maximum intraspecific distance 1.52-5.41%). Do these represent different species? The authors genuflect to taxonomic tradition, ie “levels of genetic differentiation do not dictate taxonomic status” and gently suggest “barcode analysis illuminates those taxa and those segments of their ranges where further research is justified.”

I am impressed with the ease of analyzing combined data sets; in this case the scientists merged their Neotropical COI data with that of Nearctic birds (2,615 sequences/659 species). With a click of a button so to speak, Kerr and colleagues were able to determine that 10 of 42 (24%) species shared between the 2 regions showed large genetic differences, which common sense tells us indicate long-standing reproductive isolation (ie species status).  Most of these North-South divergences were in plain-colored passerines or nocturnal species, and often with disjunct ranges consisting of a northern migratory and southern resident population. The former suggests why these might have been overlooked and the latter suggests how they arose. This work highlights how merging data sets amplifies the power of regional studies–one of the benefits of standardizing on a defined barcode mtDNA region, one that will grow with increasing size of barcode library.

In addition to enabling rapid and low-cost mapping of avian diversity including discovery of divergent lineages, which in most cases are indicators of new species, avian DNA barcoding establishes a genetic reference library with practical benefits to society, as in examples illustrated above. The DNA barcoding effort including All Birds Barcoding Initiative (ABBI) draws interest from many persons who live outside of museum walls.

In addition to their regular classes, most US high school students take and re-take a multitude of national standardized tests (and tests to practice for the tests) starting with the PSAT in 10th grade, then various SAT subject tests, AP tests, and the SAT or ACT achievement test (sometimes taken two or more times). Fortunately for students and their parents, this process usually comes to an end once they actually apply to college or university. For DNA barcoding, after six years and 500,000 sequences from 50,000 species, it seems it is still in midst of exams! 

In Nov 2008 Mol Phylogenet Evol, in “a test of the DNA barcoding approach,” researchers from University of New Orleans, USA; University of Antioquia, Colombia; and Natural History Museum, London; analyze barcode region COI sequences in a “hyperdiverse” genus (includes about 70 species) of butterflies endemic to Madagascar. They collected 109 specimens of 6 Heteropsis species, including 2 “undescribed species” and 1 species from a related genus. To confuse biological databases, Heteropsis is also a genus of flowering plant in family Aracaea. 

As an aside, and I know this is a commonplace observation, there needs to be a way of mapping biodiversity that gets around having “described” and “undescribed” species. For one, many of the “undescribed” species that are the focus of biological study, including perhaps those in this paper, will never be formally described. As an analogy of an alternate approach, in astronomical science, a first step is creating detailed sky maps based on particular wavelengths of the electromagnetic spectrum. Such sky maps are “just data,” in this case recordings of radiation-emitting stellar objects. Then, based on study, astronomers label certain objects as quasars, for example. Of course, this “annotation” does not change the underlying data, and astronomers may later change the labels on some objects based on new information or new understanding.

Following the suggestions of others, I believe some sort of sequence-based map of species-level biodiversity is a necessary way forward. Like the sky map, sequences are “just data” (the “just data” also include collection location, date, voucher specimen, and photographs).  Taxonomists would then annotate the “data map” with taxonomic interpretations, assigning species names to particular clusters for example. Species-level taxonomic revisions or conflicting taxonomies are easily accomodated–this simply involves re-labeling a cluster in the former case, or adding alternate names in the latter case. This sequence data map approach explicitly recognizes that species names are hypotheses. 

The present system is the inverse of the above: a taxonomic map (ie species names) is “annotated” with sequences. Under this system, there is no easy way to register biological information about organisms unless they have been already formally described as a species. Without a name or description of diagnostic characters, how does the next researcher know if they are studying the same “undescribed” species unless they examine the original specimens (in this case, stored in Natural History Museum, London)? On the other hand, one could easily report biological findings (eg coloration, larval morphology, food plants) associated with a specimen and its barcode sequence. 

Going back to the astronomical analogy, barcode-region COI is the appropriate “wavelength” for the species-level map of animals. This map will not be perfect. Just as gravitational lensing distorts the positions of some stellar objects, and others are obscured by intergalactic dust, the COI wavelength map will mislead in some areas and be obscured in others, not enabling one to “see” the existence of certain species–e.g. corals with slow mtDNA sequence evolution.

Would this be “DNA taxonomy?” No. First, community standards would ensure that the sequence map is not the arbiter of species status. Just as there are morphologically cryptic species and others that are phenotypically diverse, what we recognize as distinct species might be “hidden” within a single sequence cluster, and on the other hand, some named species might comprise a set of more distantly related sequences. Thus there would not be a fixed numerical determinant (eg distance, characters) of what constitutes a species cluster. Second, the COI-wavelength map would not establish higher-level relationships. Of course the shape and distinctness (or lack thereof) of clusters will change as new sequence data becomes available, as well as the taxonomic annotation, but that is the nature of biological diversity–we just don’t know everything yet! 

Why bother? Taking a sequence-mapping approach, I believe one can accelerate exploration of biodiversity and harness efforts of those outside the taxonomic priesthood. For example, one can predict much of the next ten years of species- and genus-level revisions in avian taxonomy simply on the basis of currently available COI and other mitochondrial DNA data (sequence plus specimen data). I suggest to collate and disseminate the available data in a publicly accessible form. This might even help harness “citizen science” by encouraging submission of birds that died of natural causes, or feathers naturally shed or collected in banding/ringing operations (or barcodes of feathers for those with resources and access to sequencing facilities), along with date, gps coordinates, and digital photo. If so, then legions of devoted birders could help with creating the genetic map, as they are already doing with observational records (see eBird). Because collecting sequence (and specimen-associated data) that establish the map is separate from the taxonomic process of “naming” this would not devolve into taxonomic chaos, rather, like astronomy amateurs, citizens could contribute to the observational database on which the sequence map is built. One utility that is needed is an easy graphical interface that collates available mtDNA on birds for example and highlights areas where information is missing either taxonomically or geographically; this sort of display would likely be of interest both to scientists and scientifically-minded amateurs.

Finally, going further out on this limb, depending on community standards, there might be agreement to consider a sufficiently divergent cluster a new species, until proven otherwise by more biological data. So a specimen plus a sequence could potentially be a “described species.” I prefer keeping the everyday designation of “species” rather than for example molecular operational taxonomic units (MOTU) or (evolutionarily significant units (ESU), but that is a discussion for another time!

Back to the paper. Linares and colleagues found that all 6 Heteropsis spp (including the 2 “undescribed” species) were evolutionarily distinct (ie formed reciprocally monophyletic lineages in Maximum Likelihood and Bayesian analysis of barcode-region COI), and that the mtDNA phylogeny was corroborated by nuclear DNA sequences. Given the large distances among and small within species, a neighbor-joining tree would likely have shown the same species clusters (although not necessarily the same branching pattern; the COI sequences do not appear to be public on GenBank yet, so I could not try NJ analysis). Unsurprisingly, one species pair showed less than “10X distance” (ie interspecies distance less than 10 times the average intra-species distance). Most barcode studies that include multiple congeneric species have sister species pairs that fall below this threshold. The results were initially confounded by amplification of Wolbachia (an intracellular parasitic bacteria of insects) DNA, leading them to design alternate primers. Wolbachia is unevenly distributed in tissues and often concentrated in reproductive tract, so perhaps the use of abdominal segments for DNA extraction is part of the reason this was a problem.

Almost 4 years ago, in October 2005 Philos Trans R Soc Lond B Biol Sci researchers from American Museum of Natural History examined the then nascent DNA barcoding effort, looking at what methods were best for integrating the growing pool of DNA barcode data into systematics, the science of classifying organisms based on evolutionary history. Using real-world examples, authors DeSalle, Egan, and Siddall argued strongly for “characters” and against “distances” when using DNA barcode data to identify species, ie assigning specimens to known species and discovering new species. Of course, sequence data was already the backbone of modern systematics but it had primarily been applied to reconstructing evolutionary branching patterns (eg what pattern of divergences led to the various orders of birds) and less so to the definition of species. For example, most phylogenetic work included single examplars of each species.  Analyzing sequence differences among and within closely-related species was more the domain of phylogeography which generally did not explicitly aim to define new species. 

Here a brief aside. In analyzing sequences, “characters” refer to specific nucleotides (eg guanine (G) at position 138 in COI gene) and “distances” refer to per cent differences between sequences. So right away you can see that “characters” are intrinsic to the specimen’s DNA, whereas distances are defined only in relation to sequences from other specimens. Systematists like characters; for one, this enables integrating sequence and morphologic data. Characters are the grist for the computational workhorses of systematics, Parsimony and Maximum Likelihood. Meanwhile, beginning with the first paper published in 2003, distances displayed in neighbor-joining trees have been the usual heuristic approach for analyzing DNA barcode differences among and within species. A crucial advantage of neighbor-joining distance analysis is speed. Creating a NJ distance tree from 1000 648 bp barcode sequences might take a minute on a desktop computer whereas Maximum Likelihood reconstruction might take several weeks. Unlike reconstructing the Tree of Life, DNA barcoding is a recurrent exercise that repeatedly involves submitting new data from multiple known and unknown specimens, so a fast analytic method is essential. 

Four years later, where are we? Most DNA barcoding analyses continue to rely on NJ distance trees, and this approach has proven to be a durable heuristic, enabling one to distinguish among most species analyzed so far. Regarding species discovery, NJ distance trees demonstrate continued value as a first step in flagging divergent lineages that may represent new species. Here there is something of a roadblock, in that defining new species is a human judgement, sort of like a medical diagnosis, while sequences differences are like medical laboratory results. Community standards do not accept divergent mtDNA sequences as sufficient evidence to define a new species, although at the same time it is generally acknowledged that such sequences do indicate it is new, albeit one that hasn’t been officially defined yet. For example, in Nov 2008 news item researchers confidently assert “DNA tests identify new dolphin species,” (based on published article in Nov 2008 Mol Phylogenet Evol), yet include statement “it is awaiting a scientific name after a formal description.” I expect the researchers knew they had a new species with the first mtDNA sequence from a single individual! For DNA barcoding effort it should not be necessary to wait for final taxonomic decisions; we can proceed with publicly-disseminating a broad-range, fine-scale map of biodiversity, which can then be annotated with taxonomic information as it arrives. Like sky surveys and the human genome project, we should aim to make the “barcode biodiversity map” public as quickly as possible.  

On the other side, it is now a commonplace observation that a 10X threshold (10 times the average intraspecific variation) is NOT a universal dividing line between intra- and inter-specific variation. To get technical, this was originally proposed as a screen for new species, but it has been taken as a dividing line between intra- and inter-specific distances, which it certainly is not; in the original 2004 paper (I am co-author) there are many sister species separated by distances less than the threshold. It has been a useful rhetorical target so maybe this issue won’t disappear just yet.

On the character front, there are more publications defining discriminatory DNA barcodes characters (eg Tavares and Baker 9 march 2008 BMC Evol Biol). It seems obvious to me that if, as is usually the case, sister species show large differences among and small differences within, then there must be diagnostic characters that distinguish them. The process of “translating” distances into characters should perhaps be a standard practice for nearest neighbor taxa in NJ trees; this would certainly give confidence (or not) as to whether one can reliably distinguish those species with less than 1% sequence difference. There is exciting development in character-based software tools (eg Ahrens et al 2007, Rosenberg 2007, Abdo and Golding 2007, Munch et al 2008) aimed at distinguishing the leaves (ie species) in addition to those already available for reconstructing the branches on the Tree of Life. I look forward to one that is friendly for non-specialists and works speedily on desktops!

Human filariasis, caused by various species of insect-transmitted parasitic nematodes, affects more than 120 million persons in Africa, South America, and Southeast Asia, and includes elephantiasis and river blindness. In 7 january 2009 Frontiers Zool, 10 researchers from 5 institutions in Italy, France, Japan, and Venezuela apply DNA barcoding and traditional morphologic taxonomy to identification of parasitic filarioid worms. According to the authors, a molecular tool for identification of filiaria is a “desirable goal for many reasons” including “parasites conferred to diagnostic laboratories are often of poor quality due to the difficult[y] of sampling adults and undamaged organisms,” as a “method for the identification of filarioid nematodes in vectors,” and “nematode biodiversity is still highly underestimated both at the morphological and molecular level.”

Ferri and colleagues analyze diagnostic utility of 12S and barcode-region COI sequences and morphologic examination by experts to an assemblage of data from 165 individual specimens (73 newly analyzed for this study) representing about 60 species. Their data set encompasses most of the important human and animal filarioid parasites, including Wuchereria bancrofti and Brugia malayi, agents of human tropical elephantiasis, Loa loa (human ocular filariasis), Onchocerca volvulus (human river blindness), and Dirofilaria immitis (dog and cat heartworm), plus specimens recovered from wild animals ranging from bats to toads.

The authors applied a medical test approach to the sequence data, looking at which distance cutoffs produced “minimum cumulative error,” in which they include type I false positive (failure to assign to correct species, analogous to oversplitting) and type II false negative (failure to distinguish between species; analogous to lumping). I find their approach refreshing in that it recognizes the uncertainty inherent in any identification method. Even “gold standard” tests have error rates. Just as a medical laboratory considers a range of factors when adopting a new test method–cost, speed, sensitivity, accuracy, replicability, and training requirements, for example, we might usefully look at methods for species identification, including traditional morphologic techniques, in a similar way. In taking such an approach, we can recognize there are often marked differences between the methods we use to detect something and the methods used to define it.

As a medical testing example, automated systems for rapid detection of bacteria in blood cultures rely on monitoring pressure changes in headspace gas in liquid culture bottles, as growing bacteria consume or produce gases. At the same time we do not define bacteria as “organisms that produce pressure changes in laboratory culture bottles,” for example. Similarly, percent differences between nucleotide sequences of the test specimen and those in a reference library might be a rapid way to “detect” a species, but this does not mean these are a defining characteristic of a species. We recognize species conceptually as independent evolutionary lineages, and practically on the basis of discriminatory characters (eg morphologic, behavioral, or nucleotide substitutions at specific sites). In the day-to-day work of specimen identification and detection of new species however, sequence distances may work just fine as diagnostic signatures. 

Back to the article. Ferri and colleagues report COI worked better than 12S as a diagnostic, primarily due to difficulty in finding a consistent algorithm for aligning 12S sequences. With COI, the minimum cumulative error was 0.62% at a K2P distance threshold of 4.8%. The errors were due to low interspecific distances between 2 congeneric pairs [Onchocerca volvulus (human host) and O. ochengi (cattle); Cercopithifilaria longa (Japanese serow, a goat-antelope) and C. bulboidea (Sika deer); might some of the morphologic differences between these species pairs represent phenotypic changes induced by the different hosts?]. More sampling within species will help determine if it is possible to molecularly discriminate among these species using a character- rather than distance-based method.

The authors call for an integrated taxonomic approach to solve discrepancies between morphologic and molecular methods, and conclude “we propose DNA barcoding as a reliable, consistent, and democratic tool for species discrimination in routine identification of parasitic nematodes.”

Just as new telescopes reveal previously hidden details of the universe, genetic surveys regularly reveal previously hidden (aka cryptic) species. Of course these species are cryptic only in the sense that morphological analysis is not the right tool to “see” them with. To my ear the word “cryptic” suggests camouflaged organisms that blend in with the environment, such as the Dead leaf butterfly Kallima inachus. Unlike camouflage, which is presumably a protection adaption, it is my impression there is nothing biologically special about morphologic crypsis except for the difficulty we have in recognizing it; that is, what we call cryptic species exhibit the same sorts of distinct ecological and behavioral adaptations found in those whose differences are more visible to the human eye.

To restate the above, when multiple individuals are examined for gene(s) that reflect species-level differences (this is the essence of DNA barcoding), many animal and at least some plant species are discovered to be comprised of two or more genetic clusters, each carrying diagnostic nucleotide substitutions. When appropriate analytic tools are applied, these within-species clusters are often found to be reciprocally monophyletic lineages that have been reproductively isolated for hundreds of thousands to millions of years. In studies where the painstaking work of natural history observation has been carried out, these genetic clusters usually show ecological and behavioral differences and sometimes previously overlooked morphological distinctions, consistent with species-level status. In short, DNA analysis speeds discovery of new species. In many cases, it reveals species that would otherwise probably remain unrecognized indefinitely.

The premise of DNA barcoding is that a very short segment (ie for animals 648 bp COI barcode region) is usually sufficient to screen for new species and to assign specimens to known species. Of course, more sequencing is always of interest, but the added discriminatory value for detecting species-level differences is small compared to the added cost. Moving backwards in evolutionary time, a neighbor joining tree constructed with 648 bp barcode sequences often groups genera and families correctly; however it generally does not contain enough information to establish branching order or uncover deeper-level associations that are the heart of phylogenetic study, so there is plenty for systematists to do.

Now for some data. In 25 december 2008 Mol Phylo Evol researchers from University of Gothenburg and University of Florida report on Lumbriculus variegatus Muller, 1774, a segmented freshwater worm widely distributed in Europe and North America, commonly used as a model laboratory organism, in environmental toxicology, and sold as pet food for fish and amphibians under name “blackworm.” Part of the laboratory interest in L. variegatus lies in its remarkable ability to re-generate after fragmentation; any of the approximately 200 segments can re-form a complete adult worm; most populations reproduce through auto-fragmentation. Given that L. variegatus is a common, widely-distributed organism described over 200 years ago and is regularly used in scientific study, one might not expect any taxonomic surprises.

Gustafsson and colleagues were initially studying a neuropeptide gene FMRFamide using L. variegatus purchased from a commercial supplier in California, with puzzling results suggesting polyploidy with multiple gene copies.  This lead them to further characterize approximately 50 individuals collected at multiple sites in Europe and North America.  Sequencing of COI, 16S, and ITS sorted the specimens into 2 phylogenetically distinct (maximum parsimony and Bayesian analysis) clades with 17% mean difference in COI, with the same genetic structure in mitochondrial COI/16S as nuclear ITS. Both clades were found in North America and Europe, sometimes at the same site. The authors conclude “it thus seems reasonable to regard these two main lineages within the L. variegatus complex as different species, regardless of which species concept one adheres to.”  Of course, it may be they have rediscovered a named species; they caution that more study needs to be done including sampling the other named species in genus Lumbriculus (see EOL page). 

DNA barcoding is an efficient instrument for revealing species-level differences. Routine application of DNA barcoding can enhance quality control in work with model organisms, cell lines, and collected specimens, and the long-term value of species descriptions.

The past year with the Barcode Blog has been exciting and challenging. Looking forward to 2009!

Mark Stoeckle

Program for the Human Environment
The Rockefeller University

In 26 November 2008 Mol Ecol researchers from University of British Columbia report on a meeting of 1200 plant specialists, entitled “Botany without Borders”, held on the campus in July 2008, which brought together the annual meetings of Botanical Society of America, the Canadian Botanical Association/L’Association Botanique du Canada, American Fern Society, and American Society of Plant Taxonomists. According to authors Kane and Cronk, DNA barcoding was a recurring theme of presentations and posters.

Plants continue to challenge a standardized approach to species identification using short DNA sequences from a uniform location on the genome, aka DNA barcoding. Genetic divergences among lineages make it difficult to design broad-range primers that amplify a desired target region across the diversity of plants and, at the same time, sequence differences among closely-related plant species are generally an order of magnitude fewer than those among animals, with the result that short sequences are often inadequate to assign specimens to species. Looking beyond these difficulties, the potential societal and scientific value of a standardized genetic identification method for plants is enormous. For one example cited in the meeting report, wild nutmeg trees of the genus Compsoneura can be identified by examining the tiny flowers on male trees, but trees are usually not in flower and female trees always lack these distinguishing characters. (It is remarkable that something as large as a tree can sometimes not be identified even by specialists!) In one study (Newmaster, Mol Ecol Notes 2007), a DNA barcoding approach using 2 short plastid sequences enabled identification of 94.7% of samples to species, compared to 40% using field characters. A standardized DNA-based approach should be a big boost to soil science by enabling the underground parts of plants, ie roots, to be readily named (Ridgway, BMC Ecol 2003). 

The authors conclude “DNA barcoding in plants is clearly here to stay and there is consequently an urgent need to rise to the scientific challenges it presents.” Some of those scientific challenges are explored in November 2008 Taxon by researchers from National Museum of Natural History, Washington, D.C., and National Center for Biotechnology Information, Bethesda, Maryland. Erickson and colleagues lay out a set of standard approaches to quantifying DNA barcoding success in plants.

The authors state “PCR amplification must be the primary criterion for selecting a DNA barcode,” i.e. the chosen region should have the best rate of successful amplification across the diversity of plants. They suggest 90% or greater rate of recovery as a guideline. Second, they suggest each or any additional markers should improve PCR success by reducing the number of non-recovered PCRs by 50% and improve identification by at least 10%, using a parameter they call “probability of correct identification (PCI),” which is defined pretty much as it sounds. Applying this statistic to existing plant studies indicates the best results are with 2 plastid barcodes in which case PCI approaches an average of 90%, which of course includes much lower rates among some groups. Nonetheless, in local flora successful identification to species level may often approach 100%, because closely-related congeneric species are not present. The effort to establish a standardized genetic library of DNA barcodes for world’s plants is moving ahead.