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Keywords:

  • Acacia;
  • biodiversity;
  • biogeographical pattern;
  • DNA barcoding;
  • sister species complex;
  • trees;
  • Vachellia

Abstract

  1. Top of page
  2. Abstract
  3. Introduction
  4. Methods
  5. Results
  6. Discussion
  7. Conclusion
  8. Acknowledgements
  9. Conflict of interest statement
  10. References

Acacia species are quite difficult to differentiate using morphological characters. Routine identification of Acacia samples is important in order to distinguish invasive species from rare species or those of economic importance, particularly in the forest industry. The genus Acacia is quite abundant and diverse comprising approximately 1355 species, which is currently divided into three subgenera: subg. Acacia (c. 161 species), subg. Aculiferum (c. 235 species), and subg. Phyllodineae (c. 960 species). It would be prudent to utilize DNA barcoding in the accurate and efficient identification of acacias. The objective of this research is to test barcoding in discriminating multiple populations among a sister-species complex in pantropical Acacia subg. Acacia, across three continents. Based on previous research, we chose three cpDNA regions (rbcL, trnH-psbA and matK). Our results show that all three regions (rbcL, matK and trnH-psbA) can distinguish and support the newly proposed genera of Vachellia Wight & Arn. from Acacia Mill., discriminate sister species within either genera and differentiate biogeographical patterns among populations from India, Africa and Australia. A morphometric analysis confirmed the cryptic nature of these sister species and the limitations of a classification based on phenetic data. These results support the claim that DNA barcoding is a powerful tool for taxonomy and biogeography with utility for identifying cryptic species, biogeograhic patterns and resolving classifications at the rank of genera and species.


Introduction

  1. Top of page
  2. Abstract
  3. Introduction
  4. Methods
  5. Results
  6. Discussion
  7. Conclusion
  8. Acknowledgements
  9. Conflict of interest statement
  10. References

DNA barcoding may provide a tool for identifying cryptic plant species. Hebert et al. (2003) developed DNA barcoding as a method of species identification and recognition using specific regions of DNA sequence data (Ratnasingham & Hebert 2007). He has developed barcoding in animals, which is well documented and can be reviewed online via the Canadian Barcode of Life (http://www.bolnet.ca) and the Consortium for the Barcode of Life (CBOL, http://www.barcoding.si.edu). Although the difficulties of plant barcoding have been debated (Chase et al. 2005; Kress et al. 2005; Cowan et al. 2006; Pennisi 2007), detailed studies (Newmaster et al. 2006, 2008; Kress & Erickson 2007, 2008; Fazekas et al. 2008; Lahaye et al. 2008) have demonstrated the utility of barcoding as an effective tool for plant identification.

One of the challenges for plant barcoding is the ability to resolve sister species within a large geographical range. It is expected that a system based on any one, or small number of chloroplast genes will fail in certain taxonomic groups with extremely low amounts of plastid variation while performing well in other groups. Newmaster et al. (2008) recently focused on a Neotropical group (Myristicaceae, or nutmeg family), with low molecular divergence containing some recently evolved species that might be expected to be at the limit of resolution for several of the proposed regions (Newmaster et al. 2008). Two of the regions (matK and trnH-psbA) had significant variation and show promise for barcoding in nutmegs. This research demonstrated that a two-gene approach utilizing a moderately variable region (matK) and a more variable region (trnH-psbA) provides resolution among all the Compsonuera species we sampled including the recently evolved C. sprucei and C. mexicana. Our research was limited to Central and South America and to our knowledge, there are no published comprehensive studies that test the ability of plant barcodes to discriminate multiple populations of sister species that span several contents such as a pantropical distribution.

The genus Acacia comprises approximately 1350 species of which there are many cryptic sister species with pantropical distributions (Maslin et al. 2003). In fact, many Acacia species are quite difficult to differentiate using morphological characters (Bentham 1842; Wardill et al. 2005). Identification is important in order to distinguish invasive species (Kriticos et al. 2003) from rare species (Byrne et al. 2001) or those of economic importance (Midgley & Turnbull 2003). Acacia species are well adapted to dry conditions (Ross 1981) and have great potential in agroforestry. They have wide-ranging utility as fuelwood, timber, fibre, medicine, food, handicrafts, domestic utensils, environmental amelioration, soil fertility, shade, game refuge, livestock fodder, ornamental planning, gum, and tannins (Wickens et al. 1995; McDonald et al. 2001; Midgley & Turnbull 2003).

The genus Acacia is divided into three subgenera: subg. Acacia (pantropical, c. 161 species), subg. Aculiferum (pantropical, c. 235 species) and subg. Phyllodineae (pantropical c. 960 species) (Maslin et al. 2003). However, current morphological and genetic differences separating the subgenera of Acacia and molecular evidence that the genus Acacia is polyphyletic necessitate transfer of many taxa to different genera (Maslin et al. 2003). This proposal is under debate among systematists. In the most likely scenario, the majority of the Australian taxa would remain as Acacia Mill. with a significant number of name changes to Senegalia (203 spp.) and Vachellia Wight & Arn. (161 spp.) in Asia, Africa, Australia and in the Americas (Maslin et al. 2003). Vachellia is actually the earliest legitimate generic name for species currently ascribed to Acacia subg. Acacia, based originally on morphological characters. The majority of the molecular support for this revision has been reported from populations in Australia, with a few from South Africa (Miller & Bayer 2001; Luckow et al. 2003; Miller & Bayer 2003; Seigler et al. 2006). No populations have been included from India or North Africa.

Our objective is to test plant barcoding in discriminating taxonomic affinities among species and multiple populations among a sister species complex in pantropical Acacia subg. Acacia, across three continents. Specifically, we will test the ability of DNA barcoding to: (i) distinguish the newly proposed genera of Vachellia from Acacia, (ii) discriminate sister species within the genera Acacia and Vachellia, and (iii) differentiate biogeographical patterns among populations from India, North Africa and Australia.

Methods

  1. Top of page
  2. Abstract
  3. Introduction
  4. Methods
  5. Results
  6. Discussion
  7. Conclusion
  8. Acknowledgements
  9. Conflict of interest statement
  10. References

Selecting sister species

Two pairs of pantropical sister species of Acacia were selected that have been well documented in several published systematic studies. The sister-species Acacia melanoxylon R. Br. and Acacia longifolia (Andrews) Willd. from the Mimosoideae gummiferae-spicateae are supported by a phylogenetic analysis of chloroplast sequence data (trnL-F, trnK, and matK: Luckow et al. 2003). The sister-species Vachellia farnesiana (L.) Wight & Arnott (sensu lato Acacia farnesiana (L.) Willd.) and Vachellia nilotica (L.) P. Hurter & Mabb. (sensu lato Acacia nilotica (L.) Willd. ex Delile) from the Mimosoideae gummiferae-globiferae are supported by a phylogenetic analysis chloroplast RFLP data (Bukhari et al. 1999).

Selecting barcode regions

Several DNA regions were selected for barcoding the sister species. Previous DNA barcoding analyses of molecular data (Newmaster et al. 2006, 2008; Kress & Erickson 2007, 2008; Fazekas et al. 2008; Lahaye et al. 2008) suggest that several DNA regions are suitable for barcoding plants. Based on these studies, we chose three regions (rbcL, trnH-psbA and matK) for barcoding Acacia.

Sampling

Representative specimens for all four sister species were collected from Australia, northeast Africa and India (Fig. 1). Five populations were collected for each sister species on each of the three contents (4–5 populations × 4 spp. × 3 continents = 56 voucher samples). Criteria for selecting a population include that they were at least 300 km from the next nearest collection site and that the population was robust and healthy (not diseased). Each of the 56 collection sites required the collection of a pressed herbarium voucher and leaf sample, which was stored in sealed plastic bags with silica gel to ensure rapid drying and minimal DNA degradation. The herbarium vouchers were used for a morphometric analysis and deposited at the Biodiversity Institute of Ontario Herbarium (OAC), University of Guelph, Ontario, Canada. Leaf samples from the respective 56 population vouchers were used for DNA barcoding.

image

Figure 1. Collection sites for Acacia and Vachellia in India, Africa and Australia (the names of the six localities are listed).

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DNA sequencing proto-cols

Total genomic DNA was isolated from approximately 10 mg of dried leaf material from each sample using the NucleoSpin 96 Plant II (MACHEREY-NAGEL). Extracted DNA was stored in sterile microcentifuge tubes at –20 °C. The selected loci were amplified by polymerase chain reaction (PCR; see primers in Table 1) on a PTC–100 thermocycler (Bio-Rad). DNA was amplified in 20-µL reaction mixtures containing 1 U AmpliTaq Gold Polymerase with GeneAmp 106 PCR Buffer II (100 mm Tris-HCl pH 8.3, 500 mm KCl) and 2.5 mm MgCl2 (Applied Biosystems), 0.2 mm dNTPs, 0.1 mm of each primer (0.5 mm for matK), and 20 ng/µL template DNA. Amplification products were sequenced directly in both directions with the primers used for amplification, following the protocols of the University of Guelph Genomics facility (http://www.uoguelph.ca/ib/facilities/Genomics/GenomicsFacility.shtml). Sequence products were cleaned from each specimen on Sephadex columns and ran the samples on an ABI 3730 sequencer (Applied Biosystems). Bidirectional sequence reads were obtained for all PCR products. Sequencher 4.7 (Gene Codes Corp.) was used to assemble and base-call sequences and alignment was completed manually using BioEdit version 7.0.9 (37). In order to obtain an estimate of variation in the regions examined, we calculated pairwise uncorrected p-distance for each region using mega 3.1 (Kumar et al. 2004). The sequences were submitted to BOLD and GenBank.

Table 1.  PCR primers used for amplification of plastid DNA sequences
Plastid locusPrimer nameSequences
matKmatK X FTAATTTACGATCAATTCATTC
matK 5rGTTCTAGCACAAGAAAGTCG
trnH-psbAtrnH-FCGCGCATGGTGGATTCACAATCC
psbA-RGTTATGCATGAACGTAATGCTC
rbcLrbcL-aFATGTCACCACAAACAGAGACTAAAGC
ajf634RGAAACGGTCTCTCCAACGCAT

DNA barcoding analyses

This analysis utilized the raw sequence data from each of the regions in a matrix with all 56 Acacia specimens. Bray–Curtis average linkage was used to create three distance matrices of the 56 specimens using all matK sequences. The relationship of classification structure in the species data to the molecular characters was analysed with nonmetric multidimensional scaling (NMS; Kruskal 1964; Primer 2002). In NMS, the Bray–Curtis distance measure was used because of its robustness for both large and small scales on the axes (Minchin 1987). Data were standardized by species maxima and two-dimensional solutions were appropriately chosen based on plotting a measure of fit (‘stress’) to the number of dimensions. Stress represents distortion in the data and a stress value over 0.15 is high enough that the results are invalidated (Primer 2002). One thousand iterations were used for each NMS run, using random start coordinates. The first two ordination axes were rotated to enhance interpretability with the different axes. As an independent check, detrended correspondence analysis (DCA; ter Braak 1998) was used to evaluate the NMS classification. In order to test whether accurate species assignments can be made among the samples in our data set, we used the ‘best match’ and ‘best close match’ functions of the program TaxonDNA (Meier et al. 2006). This program determines the closest match of a sequence from comparisons to all other sequences in an aligned data set. It establishes a similarity threshold based on the frequency distribution of the intraspecific pairwise distances. The threshold is set at a value below which 95% of all intraspecific pairwise distances are found (Meier et al. 2006). Unlike the ordinations we calculated, TaxonDNA ignores indels when calculating distance. These sequence identification methods were performed on the rbcL, matK, trnH-psbA, and all possible two-region combinations using uncorrected pairwise distances and a minimum sequence overlap of 300 bp. The inclusion of conspecific individuals is a key component of this type of analysis, as the query sequence is removed from the data set prior to determining its best or closest match.

Morphometric classification analysis

Twenty-six morphological variables were recorded from the 56 Acacia specimens used in the multivariate phenetic analysis. A matrix of 56 specimens and 26 morphological characters were used in a multivariate analysis using Canoco 4 (ter Braak 1998). Canonical ordination was used to detect groups of specimens and to estimate the contribution of each variable to the ordination. A principal component analysis (PCA; ter Braak 1998) was used to identify the length of the ordination axis. Unimodal, indirect ordination detrended correspondence analysis (DCA) was used to explore variation in species scores. A cluster analysis was used to classify the specimens, as it is better in representing distances among similar specimens, whereas DCA is better in representing distances among groups of specimens (Sneath & Sokal 1973). Cluster analysis was performed with ntsys (Rohlf 2000). A distance matrix was generated using an arithmetic average (upgma) clustering algorithm and standardized data based on average taxonomic distance subjected to the unweighted pair-group method. A discriminant function analysis (DFA; SPSS 1999) was used to rigorously test the classification of specimens provided in the cluster analysis. The object of DFA is to predict multivariate responses that best discriminate subjects among different groups (Ramsey & Schafer 1997). A total of 26 morphological characters for each of the 56 specimens were used as input for a DFA that were each coded as belonging to one group as designated a priori groups which: (i) determined if the classification was accurate, (ii) provided discriminant functions for the classification of the taxa and, (iii) indicated if there are important morphological characters for each of the canonical discriminate functions.

Results

  1. Top of page
  2. Abstract
  3. Introduction
  4. Methods
  5. Results
  6. Discussion
  7. Conclusion
  8. Acknowledgements
  9. Conflict of interest statement
  10. References

Barcoding supports the generic split in Acacia

A single-region DNA barcode using rbcL, matK or trnH-psbA can distinguish Vachellia species from that of the Acacia species. The ordination using matK sequence data clearly defines groups of taxa for each of the respective genera that are well separated (Fig. 2). Variation in rbcL could also be used to differentiate Vachellia species from that of the Acacia species. These results are supported by our phenetic analysis (defined below), which resulted in a morphometric ordination that clearly displays the generic split in Acacia; Vachellia species are grouped on the top and Acacia species grouped on the bottom of the ordination (Fig. 3).

image

Figure 2. Nonmetric multidimensional scaling ordination of 56 individuals of Acacia species using matK sequence data (33 variable sites). Grey circles represent intraspecific species variation (shading indicates geographic location: white, Australia; grey, Africa; black, India).

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image

Figure 3. Morphometric ordination of 56 individuals of Acacia species using 26 morphological variables. Dotted circles represent intraspecific species variation (shading indicates geographic location: white, Australia; grey, Africa; black, India).

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Barcoding discriminates sister species of Acacia

DNA barcoding identified the four species in question and the identity of 10 specimens that were misidentified as later verified by taxonomic experts. All three regions (rbcL, trnH-psbA and matK) had considerable interspecific divergence (Table 2). A considerable portion (67%) of the species can be distinguished using rbcL or trnH-psbA alone (Table 2). trnH-psbA was highly variable because of the presence of indels and homopolymer runs, which have the potential to overweight distance measures. Some of the indels are associated with the homopolymer runs and it is improbable that all indels of a given length in these regions are homologous. matK was quite variable, easy to align and could distinguish all of the Acacia species. We chose to use matK for the multivariate classification analysis. A single region DNA barcode (matK) discriminates the two sets of sister species among 56 of the specimens: Acacia melanoxylon, A. longifolia, Vachellia farnesiana and V. nilotica. The NMS sequence classification resulted in an ordination that discriminates 56 specimens into the two sets of sister-species species (Fig. 2). Interspecific variation (0.0023–0.0291) is greater than intraspecific variation (0–0.0026), which includes haplotypes from 4–5 populations per species distributed across three continents. This is consistent with other results that record nucleotide diversity for Acacia (Byrne et al. 2001). The slight overlap in inter/intraspecific variation found is our study is supported in other plant barcoding studies (Fazekas et al. 2008; Kress & Erickson 2008; Newmaster et al. 2008).

Table 2.  Summary statistics for coding and noncoding DNA (rbcL, matK and trnH-psbA) of acacias examined from 56 populations
ParametersrbcLmatKtrnH-psbA
  • *

    Resolution, number of species with haplotypes not found in any other species.

Resolution* 67%100%67%
Size (in bp)670–720 750–808630–700
No. of variable sites (S)253363
Mean interspecific p-distance0.0140.0150.201

A DNA barcode (using only matK) was used to make accurate species assignments and identify samples that were misidentified by the field taxonomists. We used the ‘best match’ and ‘best close match’ functions of the program TaxonDNA (Meier et al. 2006) to differentiate all 56 samples into six distinct groups of barcodes. In our study, no two individuals of different species share identical sequences and the percentage of correct identifications of all pairwise comparisons was 100% for six distinct groups of taxa. This identified a problem because only 83% of the samples matched the sister-species taxa that were targeted in our study: A. melanoxylon, A. longifolia, V. farnesiana and V. nilotica DNA barcodes identified 10 specimens from Africa that were misidentified; specimens from Africa thought to be A. melanoxylon and A. longifolia were not grouped with those respective taxa on the ordination (Fig. 2). These specimens were re-examined by taxonomist and sent to Acacia experts for determination. These are difficult species to identify morphologically, but a determination was confirmed by all the taxonomists that these misidentified specimens can be grouped into two species: Acacia saligna (Labill.) H.L. Wendl. and Acacia arabica (Lam.) Willd. In the ordination (Fig. 2), A. arabica is grouped on the left side of the ordination with all other species in the genus Vachellia. We are conducting further research, which suggests these taxa are closely allied and formal name change will be made separately (i.e. new combination Vachellia arabica (sensu lato Acacia arabica (Lam.) Willd.) following a more detailed examination of more populations.

Our morphometric analysis did not clearly discriminate all the Acacia samples. A discriminant function analysis (DFA) used 26 quantitative characters to classify heterogeneity in 56 specimens into what is currently considered six known taxa of Acacia and Vachellia (A. melanoxylon, A. longifolia, V. farnesiana, V. nilotica, A. saligna and A. arabica). The canonical correlation from the discriminant functions is the ratio of the between groups sums of squares to the total sums of squares. Thus, the first discriminant function is responsible for 46% of the between group differences (variability in the discriminant scores). The second function is responsible for an additional 11% of the between group variance and the third function is responsible for an additional 11% of the variance. Wilk's lambda was used to test the hypothesis that there are no differences in variance (P < 0.001) between the groups of taxa which represent different species (SPSS 1999). There were significant differences (P < 0.001) for first two canonical functions. Seventy-two per cent of the samples were correctly classified into six groups (representing the six species). The ordination analyses utilized DCA in the separation of six species from the 60 specimens that were analysed and provided a measure of the important morphological variables in the classification. Principal components analysis (PCA) provided a character gradient that was unimodal [3.2 standard deviations (SD)] violating the assumption of a linear model (ter Braak 1998). Consequently, a DCA was used to classify the 56 specimens into indistinct groups representing the six Acacia species (Fig. 1). The eigenvalues for the x-axis (0.713) and the y-axis (0.518) indicated that the gradient axes were of considerable length and justified the use of DCA.

Barcoding differentiates biogeographical patterns in Acacia

A single-region barcode (matK) discriminates all 56 specimens into there respective continental haplotypes. In the NMS ordination, all 56 samples are correctly classified by species and the continent where they were collected; India, North Africa or Australia (Fig. 1). We used the ‘best match’ and ‘best close match’ functions of the program TaxonDNA (Meier et al. 2006) to differentiate all 56 samples into six species and their respective haplotypes among three continents.

Discussion

  1. Top of page
  2. Abstract
  3. Introduction
  4. Methods
  5. Results
  6. Discussion
  7. Conclusion
  8. Acknowledgements
  9. Conflict of interest statement
  10. References

Barcoding and phenetic studies support the generic split in Acacia. Our research shows that a single-region DNA barcode can distinguish Vachellia species from that of the Acacia species. This was supported by our morphometric analyses, which resulted in an ordination with separation between Vachellia species and Acacia species. These results are supported by phylogenetic studies in which Vachellia species are placed in a separate clade (100% bootstrap support); all species other than those of Vachellia are placed in a different clade (66% bootstrap support), indicating that Vachellia is relatively distantly related to other members of Acacia s.l. (Luckow et al. 2003; Miller et al. 2003; Seigler et al. 2006). Early in the taxonomic Acacia literature, Bentham (1840) and later Wight & Arnott (1834) recognized Vachellia (Acacieae, Acacia subg. Acacia) as a distinct genus from the ‘true’Acacia (Acacieae, Acacia, subg. Phyllodineae). This distinction was based on several morphological characters including differences in the pods, phyllodes, involucre on peduncle, pollen, seeds and endosperm. Bentham (1864, 1875) later redefined Acacia into six series based on foliage characters and the nature of the spines, with inflorescence characters being of less importance. These six series are accepted by most taxonomists as the primary divisions of the genus (Ross 1973). Our barcoding results support the earlier classification that recognizes Vachellia, which is the earliest legitimate generic name for species currently ascribed to Acacia subg. Acacia. This supports a growing body of morphological and genetic differences separating the subgenera of Acacia s.l. and molecular evidence that the genus Acacia s.l. is polyphyletic, which requires several new generic combinations (Miller & Bayer 2001; Maslin et al. 2003; Miller et al. 2003; Seigler et al. 2006).

Barcoding discriminates sister species of Acacia

We used DNA barcoding to identify the four sister species and the identity of 10 voucher specimens that were misidentified as later verified by taxonomic experts. We chose to barcode cryptic sister species that are difficult for taxonomists to differentiate using morphological characters. The defining characters of many acacias are found in the small flowers that appear during short periods of time during the year. Vegetation characters are more variable and less reliable for identification. In our study, we found that the misidentification of specimens were those that only had vegetative characters, underscoring the difficulty of identifying these species. Given the important economic value of acacias, it would be very useful to have a reliable identification tool that can differentiate Acacia species using only the leaves, which are easily accessible.

Other studies have utilized diagnostic sequences to classify previously undetermined specimens due to lack of available morphological characters and as a classification tool where specimens have proven difficult to classify (Wardill et al. 2005). For example, ITS1 and trnL DNA fragments have been used to identify seven of the described subspecies of A. nilotica (Brenan 1983; Fagg & Greaves 1990). This has been particularly useful for identifying cryptic specimens previously undetermined by herbarium taxonomists. Wardill et al. (2005) created an ITS1 genotype library that was used as an identification tool to be matched exactly to genotypes of other herbarium specimens identified by taxonomists and provide a subspecies classification. Although this ITS1 genotype library is a useful tool for acacias, it would be desirable to have a standard region to build a library for all plants. We propose that DNA barcoding will be a universal tool used by taxonomists to identify cryptic species and therefore expedite the identification process.

We suggest that DNA barcoding may reveal biogeographical patterns due to intraspecific variation, particularly at large spatial scales. Our study revealed that intraspecific variation identifies haplotypes that are associated with a particular continent. Wardill et al. (2005) used DNA fragments to identify three distinct genotypes of Vachellia nilotica representing three distinct geographical regions: Pakistan, northern Africa and southern Africa. He suggested that Australian V. nilotica populations may have originated from India or Pakistan and recommend further analysis of Indian samples (not included in his study) to determine the genetic diversity profile and origins of Australian populations (Wardill et al. 2005). Our study did include India populations, which supports his claim that the origin of diversity may be within India or Pakistan. We are completing a more comprehensive barcoding campaign (including the Americas and Asia) of Acacia in order to resolve this and other hypotheses concerning the biogeography of Acacia.

At least in Acacia, a two-region barcode may be sufficient for identifying plants. Earlier research identified that a multiregion approach to barcoding plants will be required (Chase et al. 2005; Kress et al. 2005; Cowan et al. 2006; Newmaster et al. 2006). Kress & Erickson (2007) have proposed a two-locus barcode based on rbcL and the trnH-psbA intergenic spacer. Newmaster et al. (2008), in a study that investigated the utility of seven regions, demonstrated that a two-gene approach utilizing a moderately variable region (matK) and a more variable region (trnH-psbA) provides resolution for barcoding in nutmegs. Comprehensive studies of many temperate land plants revealed that combining more variable plastid markers provided clear benefits for resolving species (Fazekas et al. 2008; Ford et al. 2009). However, they found that all combinations assessed using four to seven regions had only marginally better success rates than some two or three region combinations. In our study of Acacia, we found that the rbcL region could distinguish subgenera groups (which we propose should be renamed as genera) and many (67%) of the species. The rbcL region could play an important role in barcoding. Previously (Chase et al. 2005; Newmaster et al. 2006), rbcL was evaluated as a possible region because of its universality, ease of amplification, ease of alignment, and because there is a significant body of data available for evaluation. It has also been shown to differentiate a large percentage of congeneric plant species (Newmaster et al. 2006). These studies discussed that some barcode applications will require a minimal complement of primers (e.g. ecology or applied projects) to identify cryptic plant material such as roots or leaves. When presented with a completely unknown sample, it will be highly desirable to run it with the smallest number of primer sets as possible in order to place the unknown sample in a genus. Newmaster et al. (2006) presented the ‘tiered approach’, which is based on the use of a common, easily amplified, and aligned region (such as rbcL) that can act as a scaffold on which to place data from a more variable region such as matK or a noncoding region. The utility of matK in barcoding has been explored in several studies (Fazekas et al. 2008; Kress & Erickson 2008; Lahaye et al. 2008; Newmaster et al. 2008). Although matK provides considerable interspecific variation it also requires at least two primer pairs, and sometimes up to 10 primers when considering a diverse group of taxa as in the study by Fazekas et al. (2008). A tiered approach allows an unknown sample to be placed in genus where a successful pair of primers can be targeted. The problems with noncoding regions, such as trnH-psbA, have been discussed previously (Kress et al. 2005; Cowan et al. 2006); alignment between species of different genera is the largest problem. In a tiered approach, an unknown sample can be aligned among a smaller group of taxa (i.e. within a genus). It has been shown that other noncoding regions such as atpF–atpH, and psbK–psbI provide no additional species resolution (Fazekas et al. 2008). In our study, matK alone could be use to distinguish all species. Other researchers have also successfully utilized DNA barcoding in plant studies, and the time has come for a unified approach (Kress & Erickson 2008; Lahaye et al. 2008; Newmaster et al. 2008). The Plant Working Group of the Consortium for the Barcode of Life (PWG-CBOL) is collaborating with an international body of researchers in proposing a ‘universal DNA barcode for flowering plants’, which will engage DNA barcoding as a tool for biologists.

Conclusion

  1. Top of page
  2. Abstract
  3. Introduction
  4. Methods
  5. Results
  6. Discussion
  7. Conclusion
  8. Acknowledgements
  9. Conflict of interest statement
  10. References

We are at the threshold of a taxonomy renaissance inspired by DNA barcoding (Hollingsworth 2007; Miller 2007). Already, we have seen many studies in animals that have revealed cryptic diversity, corrected classifications and aided in the discovery of new species (Hebert et al. 2004a, b; Webb et al. 2006; Costa & Carvalho 2007; Yassin et al. 2008). In plants, there are several reports, including this study, of the utility of barcoding for identifying cryptic species, new species, ethnotaxa, biogeograhic patterns and resolving classifications at the rank of genera and species (Newmaster et al. 2008, 2009; Kress & Erickson 2008; Lahaye et al. 2008; Ragupathy et al. 2009). The next move is toward Automated Identification Technology (AIT), a state-of-the-art system that will revolutionize biology and have considerable impacts on society (Newmaster et al. 2008). Advances in both molecular and bioinformatics technology have advanced to the point where AIT is available as a tool for biologists and eventually everyone in society who has a need for biodiversity information. Scientists (Tautz et al. 2002; Pennisi 2007) have discussed the application of computer identifications and recent innovations in animal and plant barcoding. Research has now shifted from an exploratory phase to a high throughput phase. Recent research indicates that the efficacy of an AIT system for plants equates with savings in time and funding allowing us to save resources for alpha taxonomy (Newmaster et al. 2008). We expect that field biologists in research studies or as environmental consultants in business will soon be using barcoding as a tool for quick surveys. Border control stations could search for invasive species within materials such as wood products entering the country. Industry could implement protocols to identify contaminants in food or health products. Farmers and gardeners could quickly identify a weed and learn how to control it. Naturalists could explore wetlands and children could explore their school yard. All of the biodiversity data from these applications could be fed into a data repository that would help us to understand, appreciate and conserve our natural world (Erickson et al. 2008; Newmaster et al. 2008; Pupulin et al. 2008).

Acknowledgements

  1. Top of page
  2. Abstract
  3. Introduction
  4. Methods
  5. Results
  6. Discussion
  7. Conclusion
  8. Acknowledgements
  9. Conflict of interest statement
  10. References

This research was supported by Genome Canada through the Ontario Genomics Institute and the Canadian Foundation for Innovation. We thank Trevor Wilson, Dr B. Jackes, E. Marry, Professor Ramadan Fauda, Dr M. Murugesan, Dr V. Balu and Professor Jana Janakiraman for helping to identify and collect the Acacia samples and voucher specimens for this study. We would like to thank Dr Bruce Maslin (Western Australian Herbarium, Australia) and Dr K. Thothathiri (Madras Herbarium, India) for their help in verification of the species. Dr K.T. is also acknowledged for reviewing an earlier version of the manuscript. We thank Drs A. Fazekas and P. Kesanakurti Canadian Plant Barcoding Group for reviewing the sequence results. Finally, we would like to thank members of the Floristic Diversity Research Group, Biodiversity Institute of Ontario for the support and assistance, particularly Royce Steeves, Carole Ann Lacroix, Neil Webster and the CBS sequencing facility.

Conflict of interest statement

  1. Top of page
  2. Abstract
  3. Introduction
  4. Methods
  5. Results
  6. Discussion
  7. Conclusion
  8. Acknowledgements
  9. Conflict of interest statement
  10. References

The authors have no conflict of interest to declare and note that the funders of this research had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

References

  1. Top of page
  2. Abstract
  3. Introduction
  4. Methods
  5. Results
  6. Discussion
  7. Conclusion
  8. Acknowledgements
  9. Conflict of interest statement
  10. References
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