Figures
Abstract
Background
DNA barcoding based on the mitochondrial cytochrome oxidase subunit I gene (cox1 or COI) has been successful in species identification across a wide array of taxa but in some cases failed to delimit the species boundaries of closely allied allopatric species or of hybridising sister species.
Methodology/Principal Findings
In this study we extend the sample size of prior studies in birds for cox1 (2776 sequences, 756 species) and target especially species that are known to occur parapatrically, and/or are known to hybridise, on a Holarctic scale. In order to obtain a larger set of taxa (altogether 2719 species), we include also DNA sequences of two other mitochondrial genes: cytochrome b (cob) (4614 sequences, 2087 species) and 16S (708 sequences, 498 species). Our results confirm the existence of a wide gap between intra- and interspecies divergences for both cox1 and cob, and indicate that distance-based DNA barcoding provides sufficient information to identify and delineate bird species in 98% of all possible pairwise comparisons. This DNA barcoding gap was not statistically influenced by the number of individuals sequenced per species. However, most of the hybridising parapatric species pairs have average divergences intermediate between intraspecific and interspecific distances for both cox1 and cob.
Conclusions/Significance
DNA barcoding, if used as a tool for species discovery, would thus fail to identify hybridising parapatric species pairs. However, most of them can probably still assigned to known species by character-based approaches, although development of complementary nuclear markers will be necessary to account for mitochondrial introgression in hybridising species.
Citation: Aliabadian M, Kaboli M, Nijman V, Vences M (2009) Molecular Identification of Birds: Performance of Distance-Based DNA Barcoding in Three Genes to Delimit Parapatric Species. PLoS ONE 4(1): e4119. https://doi.org/10.1371/journal.pone.0004119
Editor: Neil John Gemmell, University of Otago, New Zealand
Received: August 22, 2008; Accepted: November 27, 2008; Published: January 7, 2009
Copyright: © 2009 Aliabadian et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Funding: This work was partially funded by the Ministry of Science, Research and Technology of the Islamic Republic of Iran to MA. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Competing interests: The authors have declared that no competing interests exist.
Introduction
Mitochondrial DNA (mtDNA) markers have been widely applied in molecular phylogenetic studies, but deciding which mtDNA genes to use for the identification of species remains an important issue [1]–[3] because different parts of the mtDNA genome evolve at different mutation rates [4], [5]. The choice of a suitable gene with high phylogenetic resolution will be more crucial when evaluating species delimitation of recently diverged species. MtDNA, with rapid pace of sequence changes, regularly shows pronounced divergences between closely related species [1], [6] but concern has been expressed that mtDNA sequence differences among such closely related species will often be too small to allow their discrimination, and the problem will be further accentuated by phenomena of ancient sharing of haplotype polymorphisms and by introgression (e.g. [7]).
Recent studies suggest that sequences of the mitochondrial cytochrome oxidase subunit I (cox1 or COI) could serve as a fast and accurate marker for the identification of animal species, and for the discovery of new species across the tree of life [8], a procedure for which the term DNA barcoding has been coined. A first major study investigated sequence variation of 25% of the species of North American breeding birds (260 species) [9]. Variation of cox1 sequences within species was an average of 20 times smaller than between species, and there was a clear gap between intra- and interspecific variation. Utilizing this barcoding gap, a standard sequence threshold was proposed to define species boundaries of around 10 times the mean intraspecific variation for the group under study [9]. DNA barcoding based on cox1 has been successful in species identification across a wide array of taxa [9]–[12]. For invertebrates, it has however been argued [13] that the barcoding gap may be an artifact of insufficient sampling across taxa.
In general, it is especially with pairs or complexes of closely related and potentially hybridising species where DNA barcoding can be expected to encounter problems [14]. In particular the existence of so-called parapatric species may pose a challenge for DNA barcoding. For birds, parapatric species are defined as species with contiguous or narrow overlap zones, excluding each other geographically; these species may or may not hybridise, and may or may not represent sister species, but phylogenetic data are incomplete thus far [15]. Only few DNA barcoding studies have focused on potentially hybridising bird species thus far [12], [16], which probably in part is due to the lack of cox1 sequences for crucial taxa. Especially the parapatric species pairs from the Palearctic have remained largely unstudied in this respect.
Data are available for 39 pairs of proven sister species of North American birds, most of which hybridise; these have K2P (Kimura two-parameter) mtDNA distances of 0.07% to 8.2%, with an average of 1.9% [12]. For 29 out of these 39 species pairs the sequence divergences was equal or lower than the suggested cox1 threshold (2.7%) [9]. Building upon this previous work, [9], Kerr et al. (2007) [16], working with a larger number of species and samples, found significantly smaller amounts of interspecific variation between closely related allopatric bird taxa (that often are known to hybridise), potentially compromising the universal applicability of cox1 DNA barcoding.
Besides the cox1 gene, other mitochondrial markers also have been widely sequenced across vertebrates for their utility in phylogenetics or to complement cox1 in DNA barcoding. In amphibians the 16S ribosomal RNA gene (16S) has been suggested as a complementary DNA barcoding marker [17]. Another protein coding gene, cytochrome b (cob), has also been suggested as a marker to determine species boundaries [18]–[20]. Birds are among the most intensively sequenced taxa for cox1 and cob, and there is a reasonable dataset available for 16S. Taken together, sequences of these three genes are available for a significant proportion of worldwide species diversity of birds. Furthermore, birds are taxonomically one of the best studied animal groups which indicates that a relatively low proportion of unknown, cryptic species is to be expected, and that taxonomic misidentifications are relatively rare, giving a reasonable degree of confidence in the specific identity of published DNA sequences.
We here make use of the availability of cob and 16S data to combine these with cox1 sequences into the largest taxon set that so far has been assessed for mitochondrial divergences at different taxonomic levels. We specifically aimed to provide novel data on (a) a possible dependence of the barcoding gap between intra- and interspecific divergences from the number of sequences per species, (b) a comparison of levels of pairwise divergences among species in the same genus vs. species in different genera, and, especially, (c) the utility of DNA barcoding to discern among mainly Palearctic hybridising parapatric species.
Results
For none of the three genes was mean divergence within species significantly related to the sample sizes per species, as revealed by regression analysis (cox1: R2 = 0.001, p = 0.465, 16S: R2 = 0.001, p = 0.465, cob: R2 = 0.001, p = 0.338) (Figure S1). In general, intraspecific K2P distances for the three genes ranged from zero to 17.9% (cox1: 0–7.3%, 16S: 0–6.2%, and cob: 0–17.9) and intrageneric K2P distances ranged from zero to 20.1% (cox1: 0–18.9%, 16S: 0–13.3%, and cob: 0–20.1). The lower range of values may be an effect of misnamed or misidentified taxa in GenBank, or may be real (as in the case of several taxa that form a so-called ring-species: [21]–[23]. Similarly, we strongly suspect that many of the the highest intraspecific distances are due to wrongly determined samples, or pseudogene sequences, recovered from Genbank. In total, only 134 out of 31,773 cob intraspecific K2P values were above 7.4% (and thus 10-fold higher than average intraspecific divergence, see below), indicating that these possibly wrong comparisons (affecting 63 species) will have a very limited effect on subsequent calculations.
Cox1 gene
Intraspecific K2P distances for species with ≥2 sequences (mean = 4.51, range = 2–122, n = 566 species) averaged 0.24% (SD = 0.59%). Intrageneric K2P distances are some 24-fold higher (5.97±3.55) than the mean intraspecific K2P distances (Figure 1a, Table S1). Mean divergences within families and orders were 11.46% (SD = 3.06%) and 15.80% (SD = 3.35%) respectively (Figure S2a).
Black bars are comparisons among intraspecific sequences (left axis) and grey bars represent comparisons among different species (right axis).
K2P distances within 64 parapatric species (comparing species occurring parapatrically with at least one other related bird species) with >2 sequences (mean = 3.1, range = 2–8) averaged 0.49±0.87% (Figure 2a). K2P distances between species in parapatric species pairs averaged 3.64±3.29% with significant differences between those species that do hybridise (3.35±3.35%) and those that do not hybridise (5.99±4.24%) (p>0.001 Table 1, 2). K2P distances between species in hybridising species pairs were significantly larger than intraspecific distances and smaller than intrageneric K2P distances for all species (p<0.001, Table 2).
White bars are comparisons between pairs of parapatric species and grey bars are comparisons within each of these species.
Cob gene
Intraspecific K2P distances for species with ≥2 sequences (mean = 4.64, range = 2–127, n = 656 species) averaged 0.74% (SD = 1.21%). Intrageneric K2P distances were some 11-fold higher (8.11±3.80) than the mean intraspecific K2P distances (Figure 1b, Table S1). Mean divergences within families and orders were 13.97% (S = 3.13%) and 19.50% (SD = 3.64%) respectively (Figure S2b).
K2P distances within 60 parapatric species with ≥2 sequences (mean = 3.51, range = 2–20) averaged 1.07±1.74% (Figure 2b). K2P distances between species in parapatric species pairs averaged 3.08±2.50, with a significant difference between those species that do hybridise and those that do not hybridise (p<0.001, Table 1, 2). Intrageneric K2P distances between species in hybrid species pairs were significantly different from intraspecific and intrageneric K2P distances for all species (p<0.001, Table 2).
16S gene
Intraspecific K2P distances for species with ≥2 sequences (mean = 2.67, range = 2–12, n = 125 species) averaged 0.48% (SD = 1.06%). Intrageneric K2P distances were some seven fold higher (3.13±1.92) than the mean intraspecific K2P distances (Figure 1c, Table S1). Mean divergences within families and orders were 6.51% (SD = 2.45%) and 10.69% (SD = 2.37%) respectively (Figure S2c).
K2P distancess within 46 parapatric species with ≥2 sequences (mean = 2.22, range = 2–12) averaged 0.33±0.82% (Figure 2c). K2P distances between species in parapatric species pairs averaged 1.30±1.17, with no significant difference between those species that do hybridise and those that do not hybridise (p = 0.078 Table 1, 2). K2P distances between species in hybridising species pairs were significantly different from intraspecific and intrageneric K2P distances for all species (p<0.001, Table 2).
Discussion
DNA barcoding efficiency of cox1 vs. cob and 16S
The accuracy of distance-based DNA barcoding depends particularly on the extent of the separation between intra- and interspecific divergence in the selected marker. The ideal world for barcoding lacks any overlap between these two values. By including cob and 16S in our analysis, besides cox1, we have been able to test the overlap between inter- and intraspecific mitochondrial distances in a much wider array of taxa than previous analysis [9], [16]. Furthermore, by specifically targeting parapatric and hybridising bird taxa which potentially are particularly problematic in DNA barcoding [14], we here provide a more stable basis to test the performance of mitochondrial DNA barcoding in these species.
As apparent from Figure 1 (and Figure S2abc, Supporting information for comparisons within higher taxonomy levels for each gene) a wide gap exist between intra- and interspecies divergences for both cox1 and cob genes if all taxa within genera are compared, whereas this gap is less apparent for 16S. This indicates that mitochondrial rRNA genes may be less suitable for bird species identification despite their many other advantages like universal primer applicability [17]. In the following we thus do not use the 16S data further to discuss the distribution of sequence divergences among birds, but report the cob data as a complement to cox1 in order to base our comparisons on the maximum available number of taxa.
Effects of sample size on DNA barcoding gap
The intrageneric distances of cox1, on average, were 24-fold higher that the sequence divergences within species (0.24 vs 5.97%). In cob the intrageneric distances were on average 11-fold higher than the sequence divergences within species (0.74 vs 8.11%). The slight differences between the two data sets may be due to a different composition of the data (intraspecific comparisons in cob possibly based on samples from more distant localities).
These values are not too different from those obtained in an initial effort to test cox1 DNA barcoding in birds [9], based on two or more individuals for 130 species (0.27% vs. 7.93%). It has been argued that this alleged gap will considerably lower down if more individuals per species are sampled and when a large proportion of closely related taxa are included [14], [24]. This effect was however not observed in a subsequent study [16] that analysed an average 4.1 individuals per species. Our study confirms this trend. The barcoding gap was apparent in the cox1 dataset with, on average, 4.51 sequences per species, and also in the independent cob dataset with 4.64 sequences per species. Furthermore, our regression analyses found no dependence of intraspecific divergences from the number of individuals per species included in the analysis, neither in cox1 nor in cob.
The present study compared the divergence of intraspecific sequences from specimens that in a high proportion originated from widely separated geographic regions, and confirmed that cox1 sequence variation was able to identify more than 98% of the pairwise sequence divergences correctly as corresponding to variation within species. In contrast, 20% of the pairwise comparisons within genera (intrageneric sequences distances) were lower than 0.24%. Intraspecific variation identified in this study was similar to that in North American breeding birds: 0.24% vs. 0.23% and 0.27% [9], [16]. These values are lower than in most other animal groups: e.g., 0.60% in Guyanese bats [25], 0.46% in Lepidoptera [26], 0.39% in marine fishes [27], 3–4% in Aneides salamanders and mantellid frogs [17].
DNA barcoding in parapatric bird species
While the barcoding gap appears to hold for overall comparisons among birds even if larger numbers of individuals are included, a more critical issue is that of distinguishing related combinations of species [14]. In such species complexes, the barcoding gap may not exist, and this effect may be diluted in overall comparisons of large numbers of taxa. Numerous DNA barcoding studies conducted thus far revealed that more than 90% of species under study could be identified by this method. For example, 93% of studied species of Guyanan bats and 95% of North American bird species could be allocated correctly [25], [16]. The cases where barcodes failed to separate species involved either closely allied allopatric taxa whose status, as distinct species, is uncertain, or sister taxa that hybridise. However, coalescent and character-based approaches are effective in closely related species, non-hybridising species of birds [10].
Our study showed that a high proportion of hybridising parapatric species cannot be distinguished by the suggested distance-based threshold value in DNA barcoding. The proportion was 48% (14/29) in the cox1 dataset and 78% (25/32) in the cob dataset. These different values probably were not due to different properties of the analysed genes but to different taxa included, and possibly to a higher degree of misidentified taxa (as taken from Genbank) in the cob dataset. Of the parapatric species pairs that do not hybridise, 14% (1/7) and 73% (19/26) did not meet the threshold for cox1 and cob genes respectively.
Based on published [e.g., 28] and unpublished estimates (own data) the percentage of parapatric species that hybridise in the Palearctic Region is 60%, which corresponds to 10–18% of all species: [29], [30] Using these values, plus the global number of parapatric species [15], [28] and the proportion of parapatric species that show only a small amount of genetic divergence, we can estimate that between 250 (based on cox1) and 650 (cob) parapatric species of birds are not distinguishable by the barcoding gap. This represents some 2.5–6.0% of the total number of species. If DNA barcoding would be used as a tool for species discovery, it would fail to identify these species.
However, especially in a well-known group such as birds, DNA barcoding is usually used for assigning individuals to known species. In these cases, most of the parapatric species could probably be still correctly identified, depending on the origin of the low divergences: (a) mitochondrial introgression due to hybridisation, or incomplete lineage sorting, which would cause some individuals in one species being closer to individuals of another species than to conspecifics; or (b) an origin of parapatric species pairs by recent speciation, and therefore overall low genetic divergences between them. Our data set does not allow distinguishing between these two causes, but further research into this question would be useful to understand the processes influencing the perspectives and reliability of DNA barcoding in birds. Furthermore, we should keep in mind that the named taxa also might be incorrectly classified, i.e., should be lumped into single species. If most of the problematic cases refer to introgression and incomplete lineage sorting, then nuclear markers need to be developed to reliably discern between the affected species [31]. If recent speciation and generally low genetic distances (but reciprocally monophyletic haplotype lineages) are involved, then character based DNA barcoding may be more appropriate and would allow to sidestep the problem to find appropriate threshold values by searching “barcoding gaps”. In any case, where not only species identification but species discovery is concerned, it is clear that DNA barcoding should be used as only one (in many groups the first preliminary) step in the recognition, diagnosis and description of species in terms of integrative taxonomy (e.g. [32]).
Materials and Methods
Data sampling
The study was carried out in compliance with the institutional guidelines on animal husbandry and experiments of the the Zoological Museum of the University of Amsterdam. In addition, the authorization for the experiments was given by the Iranian (permission number: 3–5360) and Moroccan (p.n.: 04666 DCRF/CPB/PFF) authorities.
We sequenced 210 individuals of 145 nominal species for DNA sequences of cox1 and 16S gene fragments, of which 31 and 46 species were parapatric species for cox1 and 16S respectively. Parapatric species are defined as species that have at least one other closely-related species which inhabits a continuous range, the two species excluding each other geographically [15], [33]. The range boundary between the two taxa has no dispersal barrier, allowing parapatric species to hybridise and display intergradation in their contact zones, yet they maintain distinct outside of these zones [34], [35].
DNA was extracted from tissue or blood samples using DNeasy Tissue Kits (QIAGEN) following the manufacturer's protocol. Polymerase chain reactions (PCR) and sequencing reactions follow protocols described by Aliabadian et al. (2007) [36] which can be summarized as follows. A fragment of cox1, was sequenced using two primer combinations that amplify a region of 612 bp starting from the 5′ terminus of the mitochondrial cox1 gene: BirdF1 (5′ -TTC TCC AAC CAC AAA GAC ATT GGC AC -3′), BirdR1 (5′ -ACG TGG GAG ATA ATT CCA EET CCT G- 3′), and BirdR2 (5′ -ACT ACA TGT GAG ATG ATT CCG AAT CCA G - 3′) [9]. A fragment of 16S was sequenced for the same individuals using 16SA-L (light chain; 5′-CGC CTG TTT ATC AAA AAC AT-3′) and 16SB-H (heavy chain; 5′-CCG GTC TGA ACT CAG ATC ACG T-3′) [37]. PCR products were cleaned using QIAquick PCR Purification Kit (Qiagen). Sequencing reactions were resolved on ABI 3100 or ABI 3730 automated DNA sequencers. Genbank accession numbers of newly determined sequences are FJ465179–FJ465383 and are listed in detail in Table S2).
Our data set was complemented by cox1 and 16S sequences from GenBank, as available on 1 July 2006). For cox1, additional sequences were included from the Barcode of Life Data Systems website (http://www.barcodinglife.org/, as accessed on 1 July 2006). Sequences were included provided they had a length of >612 (cox1) and >538 bp (16S) homologous to our sequences, with no more than 50 ambiguous or missing nucleotides. Cob sequences with a length of >1000 bp and no more than 50 ambiguous or missing nucleotides were retrieved from GenBank as well. Because of a probably high prevalence of misidentification, erroneous sequences or NUMTs in the Genbank sequences, we submitted these to a rigurous quality control. All sequences per gene were aligned and a Neighbor-joining tree produced. We identified, in this tree, all sequences clustering far from their known taxonomic or phylogenetic position, or characterized by extreme branch lengths, and omitted these sequences from further analysis. For cob, the gene were the largest data set (over 10,000 sequences) was initially downloaded, less than half of these were of sufficient length and quality.
All sequences were aligned using Muscle, a multiple alignment software for protein and nucleotide sequences which allows multiple sequence comparison by log-expectation [38]. Probably erroneous sequences (with highly unlikely positions or extreme branch lengths, based on a neighbour-joining tree calculated with all sequences) were identified by eye and omitted. A total of 2776 sequences (756 nominal species) were kept for cox1, 708 (498 species) for 16S, and 4614 (2087 species) for cob (Table 3), and altogether 2719 species were included for at least one of the genes (Table S2). Among conspecific sequences, we verified that for many species, samples from distant localities were included and our analysis is thus not based on including only samples from the same locality and population.
Data analysis
Genetic distances were calculated to quantify sequence divergences among individuals using Kimura's (1980) [39] two-parameter (K2P) models, theta, as implemented in MEGA 3.1 [40]. The K2P distance is the most effective model when genetic distances are low [8]. K2P distances were calculated at all taxonomic levels, intraspecific, intrageneric, intrafamilial, intraordinal, and, separately, between species in parapatric species pairs, following a published taxonomy [41] and unpublished data by CS Roselaar. For calculation involving higher taxonomic levels, pairwise comparisons of the previous levels were excluded (e.g., in comparisons of intrageneric, pairwise distances of intraspecific were removed, and only pairwise distances of those samples to other species were used).
Average K2P distances were computed based on pairwise comparisons of all sequences for each species, and each pair of parapatric species. To calculate intra- and interspecific pairwise distances, based on output matrix of MEGA 3.1, we wrote a converter program SPD 1.1) in C language (this program will be available from the authors upon request).
Altogether 3,823,995, 9,952,500, and 250,257 pairwise distances were compared in this study for, cox1, cob, and 16S respectively. NJ trees of K2P distances showing inter- and intra specific variations were constructed using MEGA 3.1 (not shown here). A regression analysis was employed to assess the effect of sample size on intraspecific divergences for each gene using SPSS for Windows, version 11.
Supporting Information
Figure S1.
The relationship between mean intraspecific variations (K2P) and the number of individuals analysed for each species. Black squares: cox1 (adjusted R2 = 0.001, P = 0.465). Grey dots: cob (adjusted R2 = 0.001, P = 0.338)
https://doi.org/10.1371/journal.pone.0004119.s001
(0.41 MB TIF)
Figure S2.
Comparisons of K2P pairwise distances in (A) cox1, (B) cob, and (C) 16S genes in birds. Mean (±SD). K2P distances are compared within various level of taxonomic hierarchy for three genes.
https://doi.org/10.1371/journal.pone.0004119.s002
(3.89 MB TIF)
Table S1.
K2P Mean intraspecific distances for cox1, cob, and 16S.
https://doi.org/10.1371/journal.pone.0004119.s003
(1.23 MB DOC)
Table S2.
List of all samples that have been sequenced in this study, with voucher numbers and collection localities
https://doi.org/10.1371/journal.pone.0004119.s004
(0.24 MB DOC)
Acknowledgments
We are grateful to the museums and curators that provided some of the samples used in this study: Jon Fjeldså and Niels Kaare Krabbe (Zoological Museum, University of Copenhagen); Per Ericson, Per Alström, and Göran Frisk (Swedish Museum of Natural History); Urban Olsson (University of Gothenburg): Kees Roselaar and Tineke Prins (Zoological Museum Amsterdam). Hans Breeuwer, Betsy Voetdijk, Peter Kuperus, and Wil Van Ginkel for their support and advice in the laboratory. Masoud Shirazian for writing SPD 1.1 program. Specimens were collected under permit authorization 3–5360 (26th February 2003; Iran) and No. 04666 DCRF/CPB/PFF (5th May 2004; Morocco), and we are grateful to the Iranian and Moroccan authorities for allowing us to do our fieldwork.
Author Contributions
Conceived and designed the experiments: MA VN MV. Performed the experiments: MA MV. Analyzed the data: MA VN MV. Contributed reagents/materials/analysis tools: MA MK VN MV. Wrote the paper: MA VN MV.
References
- 1. Brown WM, George M Jr, Wilson AC (1979) Rapid evolution of animal mitochondrial DNA. Proc Natl Acad Sci USA 76: 1967–1971.
- 2. Moore WS (1995) Inferring phylogenies from mtDNA variation: Mitochondrial-gene trees versus nuclear-gene trees. Evolution 49: 718–726.
- 3. Johns GC, Avise JC (1998) A comparative summary of genetic distances in the vertebrates from the mitochondrial cytochrome b gene. Mol Biol Evol 15: 1481–1490.
- 4. Avise JC (1986) Mitochodrial DNA and the evolutionary genetics of higher animals. Proc R Soc London B 312: 325–342.
- 5. Roques S, Fox CJ, Villasana MI, Rico C (2006) The complete mitochondrial genome of the whiting, Merlangius merlangus and the haddock, Melanogrammus aeglefinus: A detailed genomic comparison among closely related species of the Gadidae family. Gene 383: 12–23.
- 6. Avise JC, Arnold J, Ball RM, Bermingham E, Lamb T, et al. (1987) Intraspecific phylogeography: the mitochondrial DNA bridge between population genetics and systematics. Ann Rev Ecol Syst 18: 489–522.
- 7. Mallet J, Willmot K (2003) Taxonomy: renaissance or Tower of Babel? Trends Ecol Evol 18: 57–59.
- 8. Hebert PDN, Cywinska A, Ball SL, DeWaard JR (2003) Biological identifications through DNA barcodes. Proc R Soc London B 270: 313–321.
- 9. Hebert PDN, Stoeckle MA, Zemlak TS, Francis CM (2004) Identification of birds through DNA barcodes. PLoS Biol 2: 1657–1663.
- 10. Tavares ES, Baker AJ (2008) Single mitochondrial gene barcodes reliably identify sister-species in diverse clades of birds. BMC Evol Biol 8: 81.
- 11. Hogg ID, Hebert PDN (2004) Biological identification of springtails (Hexapoda: Collembola) from the Canadian arctic, using DNA barcodes. Can J Zool 82: 749–754.
- 12. Johnson NK, Cicero C (2004) New mitochondrial DNA data affirm the importance of Pleistocene speciation in North American birds. Evolution 58: 1122–1130.
- 13. Wiemers M, Fiedler K (2007) Does the DNA barcoding gap exist? – a case study in blue butterflies (Lepidoptera: Lycaenidae). Front Zool 4: 8.
- 14. Moritz C, Cicero C (2004) DNA barcoding: promise and pitfalls. PloS Biol 2: 1529–1531.
- 15. Haffer J (1992) Parapatric species of birds. Bull Brit Ornithol Club 112: 250–264.
- 16. Kerr KCR, Stoeckle MY, Dove CJ, Weigt L A, Francis CM (2007) Comprehensive DNA barcode coverage of North American birds. Mol Ecol Notes 7: 535–543.
- 17. Vences M, Thomas M, Bonett RM, Vieites DR (2005) Deciphering amphibian diversity through DNA barcoding: chances and challenges. Phil Trans Soc London B 360: 1859–1868.
- 18. Helbig AJ, Seibold I (1999) Molecular phylogeny of Palearctic–African Acrocephalus and Hippolais warblers (Aves: Sylviidae). Mol Phylogenet Evol 11: 246–260.
- 19. Bradley RD, Baker RJ (2001) A test of the genetic species concept: cytochrome b sequences and mammals. J Mammal 82: 960–973.
- 20. Lemer S, Aurelle D, Vigliola L, Durand JD, Borsa P (2007) Cytochrome b barcoding, molecular systematics and geographic differentiation in rabbitfishes (Siganidae). C R Biol 330: 86–94.
- 21. Crochet PA, Bonhomme F, Lebreton JD (2000) Molecular phylogeny and plumage evolution in gulls (Larini). J Evol Biol 13: 47–57.
- 22. Liebers D, de Knijff P, Helbig AJ (2004) The herring gull complex is not a ring species. Proc R Soc London B 271: 893–901.
- 23. Alström Per (2006) Species concepts and their application: insights from the genera Seicercus and Phylloscopus. Acta Zool Sinica 52: 429–434.
- 24. Meyer CP, Paulay G (2005) DNA barcoding: Error rates based on comprehensive sampling. PLoS Biol 3: e422.
- 25. Clare EL, Lim BK, Engstrom MD, Eger JL, Hebert PDN (2007) DNA barcoding of Neotropical bats: species identification and discovery within Guyana. Mol Ecol Notes 7: 184–190.
- 26. Hajibabaei M, Janzen DH, Burns JM, Hallwachs W, Hebert PDN (2006) DNA barcodes distinguish species of tropical Lepidoptera. Proc Natl Acad Sci USA 103: 968–971.
- 27. Ward RD, Zemlak TS, Innes BH, et al. (2005) DNA Barcoding Australia's fish species. Phil Trans Soc London B 360: 1847–1857.
- 28.
McCarthy EM (2006) Handbook of Avian Hybrids of the World. Oxford, UK: Oxford University Press.
- 29. Grant PR, Grant BR (1992) Hybridization of bird species. Science 256: 193–197.
- 30. Aliabadian M, Nijman V (2007) Avian hybrids: incidence and geographic distribution of hybridisation in birds. Contrib Zool 76: 59–61.
- 31. Tautz D, Arctander P, Minelli A, et al. (2003) A plea for DNA taxonomy. Trends Ecol Evol 18: 70–74.
- 32. Dayrat B (2005) Towards integrative taxonomy. Biol J Linn Soc 85: 407–415.
- 33. Aliabadian M, Roselaar CS, Nijman V, Sluys R, Vences M (2005) Identifying contact zone hotspots of passerine birds in the Palearctic Region. Biol Lett 1: 21–23.
- 34. Barton NH, Hewitt GM (1989) Adaptation, speciation and hybrid zones. Nature 341: 497–503.
- 35. Mallet J, Beltrán M, Neukirchen W, Linares M (2007) Natural hybridization in heliconiine butterflies: the species boundary as a continuum. BMC Evol Biol 7: 28.
- 36. Aliabadian M, Kaboli M, Prodon R, Nijman V, Vences M (2007) Phylogeny of Palaearctic wheatears (genus Oenanthe): Congruence between morphometric and molecular data. Mol Phylogenet Evol 42: 665–675.
- 37.
Palumbi SR, Martin A, Romano S, McMillan WO, Stice L, et al. (1991) The Simple Fool's Guide to PCR, Version 2.0. Honolulu: Privately published document compiled by S. Palumbi, Department of Zoology, University Hawaii.
- 38. Edgar RC (2004) MUSCLE: multiple sequence alignment with high accuracy and high throughput. Nuc Acids Res 32: 1792–97.
- 39. Kimura M (1980) A simple method for estimating evolutionary rates of base substitutions through comparative studies of nucleotid sequences. J Mol Evol 15: 111–120.
- 40. Kumar S, Tamura K, Nei M (2004) MEGA3: Integrated software for molecular evolutionary genetics analysis and sequence alignment. Brief Bioinforms 5: 150–163.
- 41.
Dickinson EC, editor. (2003) The Howard and Moore complete checklist of the birds of the world. London: Christopher Helm.