The Thrips Family _________ Includes Most of the Species Regarded as Crop Pests.

• Loading metrics

Open Admission

Peer-reviewed

Inquiry Article

Deoxyribonucleic acid Barcode Analysis of Thrips (Thysanoptera) Diversity in Pakistan Reveals Cryptic Species Complexes

• Romana Iftikhar,
• Muhammad Ashfaq,
• Akhtar Rasool,
• Paul D. Due north. Hebert

10

• Published: January 7, 2016
• https://doi.org/x.1371/journal.pone.0146014

Figures

Abstract

Although thrips are globally important crop pests and vectors of viral affliction, species identifications are hard because of their small size and inconspicuous morphological differences. Sequence variation in the mitochondrial COI-5ʹ (DNA barcode) region has proven constructive for the identification of species in many groups of insect pests. We analyzed barcode sequence variation amongst 471 thrips from diverse institute hosts in north-primal Pakistan. The Barcode Index Number (BIN) system assigned these sequences to 55 BINs, while the Automatic Barcode Gap Discovery detected 56 partitions, a count that coincided with the number of monophyletic lineages recognized by Neighbour-Joining analysis and Bayesian inference. Congeneric species showed an average of nineteen% sequence divergence (range = 5.half-dozen% - 27%) at COI, while intraspecific distances averaged 0.6% (range = 0.0% - 7.vi%). BIN analysis suggested that all intraspecific departure >3.0% actually involved a species complex. In fact, sequences for 3 major pest species (Haplothrips reuteri, Thrips palmi, Thrips tabaci), and one predatory thrips (Aeolothrips intermedius) showed deep intraspecific divergences, providing evidence that each is a ambiguous species complex. The written report compiles the first barcode reference library for the thrips of Islamic republic of pakistan, and examines global haplotype diversity in four important pest thrips.

Citation: Iftikhar R, Ashfaq Grand, Rasool A, Hebert PDN (2016) Dna Barcode Assay of Thrips (Thysanoptera) Multifariousness in Pakistan Reveals Cryptic Species Complexes. PLoS ONE 11(1): e0146014. https://doi.org/10.1371/journal.pone.0146014

Editor: Owain Rhys Edwards, CSIRO, AUSTRALIA

Received: June 26, 2015; Accepted: December 12, 2015; Published: Jan 7, 2016

Copyright: © 2016 Iftikhar et al. This is an open access commodity distributed under the terms of the Creative Commons Attribution License, which permits unrestricted utilise, distribution, and reproduction in any medium, provided the original author and source are credited.

Data Availability: All information are available on the Barcode of Life Data Systems (www.boldsystems.org) nether the dataset DS-MATHR.

Funding: This study was partly supported by the College Education Commission Islamic republic of pakistan by fellowship grant to RI and inquiry grant (20-1403/R&D/09) to MA. PDNH was supported by International Development Research Eye (grant no. 106106-001), and a grant from the Government of Canada through Genome Canada and the Ontario Genomics Found in support of the International Barcode of Life (iBOL) project.

Competing interests: The authors accept declared that no competing interests exist.

Introduction

Thrips (Thysanoptera) are serious pests and illness vectors on many economically of import crops throughout the world [1,2]. Identification of most thrips to a species level is difficult because of their modest size, subtle morphological differentiation [3], intraspecific polymorphisms [4], and sexual dimorphisms [v]. Molecular identification of thrips has major advantages to morphology-based assay considering information technology overcomes the complexities introduced by morphological variation amidst life stages and the inconspicuous morphological differences among species [3,6]. Several gene regions accept been employed for species bigotry [7, 8] and phylogenetic analysis [9]. Crespi et al. [10] employed the nuclear 18S and mitochondrial factor cytochrome c oxidase I (COI) genes to examine phylogenetic relationships between two suborders Terebrantia and Tubulifera, while Buckman et al. [9] coupled four nuclear loci (18S & 28S ribosomal DNA (rDNA), Histone iii, Tubulin-alpha ane) with COI to define the phylogenetic relationships of 99 species of thrips from seven families. COI has besides been recognized as a especially suitable marker for thrips identification because it exhibits more consequent interspecific variation [xi] than other markers [12,13]. Its analysis has, for example, helped to reveal the number of thrips species inhabiting detail cropping systems [14]. The capacity of the COI-5ʹ (DNA barcode) region to discriminate cryptic species of insects has been well validated [fifteen–xviii] including for thrips [3,12].

Levels of COI sequence deviation often are helpful in deciding if two sequences derive from different species [19] equally most conspecifics show <2% divergence in the barcode region [15,20]. The Barcode Alphabetize Number (BIN) system now provides an interim taxonomic arrangement based on COI sequence clusters [21] for all animals and most BINs are coinciding with morphological species [22,23]. Barcode data has been used to advance species-level taxonomy in various animal groups [24], often revealing new species [25,26]. Researchers accept also employed DNA barcoding to identify pest thrips for quarantine [27,28].

Some pest thrips species are thought to be a circuitous of multiple cryptic species [29]. For example, COI analysis revealed 3 lineages of Thrips tabaci [6,xxx], while Thrips palmi has 2 clades [27,13]. Likewise, western flower thrips, Frankliniella occidentalis, is now viewed every bit two species [29]. Members of species complexes have been discriminated by sequence matches [31] or by PCR assay with species-specific primers, enabling a non-specialist to place the target species at whatever life stage [12].

The rapid increment in global trade warrants the development of a universal, anticipatory arrangement with the capacity to identify taxa that are newly encountered in a region considering invasive species can reduce local biodiversity and oft cause serious economic damage to crops [32]. The authentic identification of pest and invasive species is critical for both command [33,34] and quarantine [35] equally misidentifications may lead to ineffective control measures [33]. The varying capacity of thrips species to transmit viral diseases [36] provides an additional incentive to explore their genetic diversity. Prior taxonomic studies on the Thysanoptera of Pakistan [37–43] take been limited in scope, but 76 species have been reported including members of three families (Aeolothripidae, Phlaeothripidae, Thripidae) (Appendix ane). The present report analyzes patterns of COI sequence diversity amongst thrips from Pakistan, initiating the development of a regional barcode reference library for Thysanoptera. Furthermore, the study examines the broader geographic patterning of haplotype frequencies in four particularly important pest species.

Materials and Methods

Ideals Statement

No permits are required to collect thrips, but permission from the landowners was obtained whenever necessary. All the collection sites were in unprotected areas accessible to the public. The report did non involve endangered or protected species.

Specimen collections

Thrips were collected from 158 localities in Pakistan (Fig i) during 2009–2012 from shrubs, trees, crops, weeds and ornamentals following standard protocols [44]. Thrips were dislodged from plant foliage or inflorescences by beating vegetation over white paper sheets and transferring specimens with a fine camel hair brush to ane.5 ml Eppendorf tubes containing 95% ethanol. The name of the collector, engagement of collection, and locations with the GPS coordinates were recorded. 495 specimens were collected and stored at -20°C until analysis.

Databasing

Specimen data were submitted to the Barcode of Life Information Systems (BOLD) (http://www.boldsystems.org) [45] under the project, MATHR "Thrips Species of Pakistan". Prior to Dna extraction, each specimen was photographed and its image was uploaded to Bold.

Deoxyribonucleic acid extraction

Thrips were transferred individually, 1 specimen per well, to 96-well plates in accordance with the specimen data and images submitted to Assuming. DNA isolation was carried out at the Canadian Centre for Deoxyribonucleic acid Barcoding (CCDB) within the Centre for Biodiversity Genomics following protocols described past Porco et al. [46].

DNA polymerase concatenation reaction (PCR)

Amplification of the 658 bp COI-5ʹ barcode region was performed with the primers C_LepFolF and C_LepFolR (http://world wide web.ccdb.ca/docs/CCDB_PrimerSets.pdf) post-obit PCR conditions; 94°C (ane min), 5 cycles of 94°C (40 south), 45°C (xl s), 72°C (1 min); 35 cycles of 94°C (40 southward), 51°C (40 s), 72°C (1 min) and final extension of 72°C (5 min). These primers are mixtures of LepF1 (ATTCAACCAATCATAAAGATATTGG)/LCO1490 (GGTCAACAAATCATAAAGATATTGG) and LepR1 (TAAACTTCTGGATGTCCAAAAAATCA)/HCO2198 (TAAACTTCAGGGTGACCAAAAAATCA) [47], respectively. Amplification of a 439 bp segment of COI-3ʹ was performed with primer set C1-J-1751 (GGATCACCTGATATAGCATTYCC)/C1-N-2191 (CCCGGTAAAATTAAAATATAAACTTC) nether the PCR conditions outlined in a higher place. The internal transcribed spacer ane (ITS1) of the rDNA was amplified using primers CAS18sF1 (TACACACCGCCCGTCGCTACTA) and CAS5p8sB1d (ATGTGCGTTCRAAATGTCGATGTTCA) [48]. Amplifications involved 12.5 μL reactions containing standard PCR ingredients [49] and 2 μL of DNA template. PCR products were analyzed on ii% agarose Due east-gel® 96 system (Invitrogen Inc.). Amplicons were sequenced bidirectionally using the BigDye Terminator Cycle Sequencing Kit (v3.ane) (Applied Biosystems) on an Applied Biosystems 3730XL DNA Analyzer. The forward and reverse sequences were assembled, aligned and edited using CodonCode Aligner (CodonCode Corporation, Usa) and submitted to Bold. Sequences were too inspected and translated in MEGA5 [fifty] to verify that they were free of terminate codons. All sequences generated in this study were submitted to GenBank (S1 Table) and are attainable on Bold under the dataset DS-MATHR (dx.doi.org/x.5883/DS-MATHR).

Morphological identification

Specimens were retrieved afterwards DNA extraction [46] and mounted onto slides using Hoyer's medium. The mounted specimens were identified using descriptions at http://www.ozthrips.org, http://keys.lucidcentral.org/keys/v3/thrips_of_california and standard morphological keys [37–39,51]. Morphological characters were examined using a compound microscope (Olympus BX 41) at 40X, 100X and 400X. Identifications were verified by Stan Diffie (Department of Entomology, University of Georgia, USA) and Sueo Nakahara (USDA ARS, Beltsville, MD, USA). Species names were validated at ThripsWiki (http://thrips.info/wiki/) (accessed on 26 Apr 2014). Voucher specimens were deposited at the Insect Museum National Institute for Biotechnology and Genetic Engineering, Faisalabad, Pakistan.

Information assay

All sequences obtained in this study were compared with those on GenBank and BOLD using "BLASTn" (http://www.ncbi.nlm.nih.gov/blast/) or "Identification Request", respectively. Assuming assigns all barcode sequences with lengths >500bp to a BIN and so all thrips sequences from this report were assigned to a BIN.

ClustalW nucleotide sequence alignments [52] and Neighbor Joining (NJ) clustering analysis were performed using MEGA5. NJ copse employed the Kimura-2-Parameter (K2P) distance model [53] with pairwise deletion of missing sites and nodal back up was estimated using 500 bootstrap replicates. Altitude histograms were generated using the online version of Automatic Barcode Gap Discovery (ABGD) [54]. The 'Barcode Gap Analysis' (BGA) was performed using tools available on Bold. The presence or absence of a 'barcode gap' was determined for each species to define the reliability of its bigotry [55]. Using the barcode gap criterion, a species is distinct from its nearest neighbor (NN) if its maximum intraspecific altitude is less than the altitude to its NN sequence [55].

Unique haplotype sequences were extracted from the alignment with DnaSP 5.ten [56]. Phylogenetic trees were constructed from unique haplotype sequences using MrBayes v3.2.0 [57] and the Markoff chain Monte Carlo (MCMC) technique. Rhopalosiphum padi (HQ979401) was included every bit outgroup. The data was partitioned in two ways; i) a single segmentation with parameters estimated across all codon positions, 2) a codon-partition in which each codon position was allowed different parameter estimates. The analyses were run for x meg generations using four chains with sampling every 1000 generations. The evolution of sequences was modelled by the GTR+Γ model independently for the 2 partitions using the ''unlink" command in MrBayes. The model selection was made using FindModel (world wide web.hiv.lanl.gov/cgi-bin/findmodel/findmodel.cgi). Bayesian posterior probabilities were calculated from the sample points one time the MCMC algorithm converged. Convergence was determined when the standard deviation of dissever frequencies was less than 0.002 and the PSRF (potential calibration reduction factor) approached 1, and both runs converged to a stationary distribution after the burn-in phase (by default, the showtime 25% of samples were discarded). Each run produced 100001 samples of which 75001 samples were included. The trees generated through this process were visualized using FigTree v1.4.0.

Haplotype and distribution analysis

Barcode sequences from Pakistan for four species (Scirtothrips dorsalis, T. flavidulus, T. palmi, T. tabaci), which are of import pests and virus vectors, were combined with records from other countries and aligned in MEGA5. For this analysis each morphological species was treated as one taxon regardless of the number of lineages/BINs among its barcode sequences. GenBank and BOLD sequences with clearly wrong species assignments or potential contaminants (returning unexpected alignments or distances) were removed from the analysis. Sequence alignment (fasta file), for each species, was imported into DnaSP five.ten to reconstruct haplotypes and generate a haplotype data file (nexus). The nexus file was edited to add haplotype-country links and a minimum spanning network (MSN) [58] was constructed in PopART (http://popart.otago.ac.nz) to examine the relationships among haplotypes for each thrips species from unlike locations. The MSN is based on a minimum spanning tree where a set of sequence types connects all given types without creating any cycles or inferring additional (ancestral) nodes, such that the full length (i.e., the sum of distances betwixt linked sequence types) is minimal, allowing construction of the union of all minimum spanning trees [58].

Results

Morphological identification

Morphological identifications assigned 461 specimens to 43 species, and 14 others to five genera, including two Thrips, two Aeolothrips and i Haplothrips. The remaining 20 specimens could only be identified to a family; seven were members of the Phlaeothripidae while 13 were Thripidae. Morphology successfully identified the four major pests and illness vectors (Thrips flavidulus, T. palmi, T. tabaci, Scirtothrips dorsalis) in the region.

DNA barcode analysis of thrips species

Barcode sequences greater than 500bp were recovered from 471 of the 495 specimens (95%), providing at least i sequence for 42 of the 43 identified species with just Megalurothrips distalis defective coverage. Comparison of the sequences with those in GenBank and Bold revealed shut sequence matches (>98% nucleotide identify) for only 13 of these 42 species. Tabular array 1 shows the sequence divergences among 1) the 31 species with >two specimens, 2) the five genera with ii or more species and 3) the ii families with two or more genera. Intra- and interspecific distances averaged 0.half-dozen% and 19%, respectively (ranges = 0–7.6% and v.6–27.0%) (Table 1). Intraspecific distances could not be determined for the 16 species with a unmarried representative, but Nearest-Neighbor (NN) distances for all morphological species were more 5%. This pattern supported the presence of a barcode gap (the maximum intraspecific distances were less than the distances to the NN sequences). Sequence divergence increased with the taxonomic rank with overlap (ii%) betwixt conspecific and congeneric distances detected in simply one morphological species, T. palmi. However, the distance between T. palmi and its NN (Thrips florum) was 12.62%. Distances between the genera in each family averaged 22.iii% (range = 6.6–35.vi%). Altitude (K2P) histograms are presented in Fig 2A. These analyses revealed a gap between maximum intraspecific and minimum interspecific distances in barcodes from the morphological species, supporting the being of a "barcode gap" (Fig 2A). The ABGD assay showed 55 initial and 56 recursive partitions at a prior maximal distance of 0.0215. The barcode gap analysis showed that the maximum and mean intraspecific distances for all the species were lower than the distances to their nearest-neighbors (Fig 2B). Maximum intraspecific distances were less than 2% in all species excepting Aeolothrips intermedius (3.7%), Haplothrips reuteri (3.vii%), T. palmi (7.vi%) and T. tabaci (3.7%).

Fig 2. Distribution of pairwise (K2P) distances (A) and barcode gap analysis (B) of thrips from Islamic republic of pakistan.

The gap betwixt intraspecific and interspecific distances is indicated by an arrow. Three peaks in the altitude bars reflect the sequence divergence among the iii families of thrips represented in the dataset. NN = nearest neighbour.

https://doi.org/10.1371/journal.pone.0146014.g002

Table 1. Percentage sequence divergence (K2P) at the COI barcode region for the 31 thrips species with two or more specimens, the v genera with ii or more species and the two families with ii or more genera.

https://doi.org/10.1371/journal.pone.0146014.t001

The 471 sequences were assigned to 55 BINs. Morphological identifications and BINs were coinciding for 39 species, while sequences for the other 3 species (A. intermedius, H. reuteri, T. palmi) were divide into two BINs. The remaining BINs represented 10 unidentified lineages. Maximum intra-BIN distances were <2% for all 39 species assigned to ane BIN except for T. flavidulus (2.7%) and T. tabaci (3.7%). The BINs and maximum intraspecific distances for thrips species from Islamic republic of pakistan and other countries are shown in Table 2.

NJ analysis, which included all 471 sequences, showed that sequences from each of the 55 BINs formed a monophyletic clade (Fig 3). Although all specimens of T. tabaci were assigned to i BIN, the NJ analysis showed two distinct clusters with a mean divergence of 3.four% and Bayesian analysis confirmed the reciprocal monophyly of these two clusters (Fig 4). As both un-partitioned and codon-partitioned trees produced like topology, merely the un-partitioned tree is presented (Fig 4). No prior study has examined sequence variation in the barcode region for the 2 virtually important species (T. palmi, T. tabaci) of pest Thysanoptera in Islamic republic of pakistan, impeding analysis of the broader geographical distribution of the two COI-5ʹ lineages in these two species. Considering more data was available for COI-3ʹ, we sequenced this cistron region from both lineages of these two species. NJ analysis of COI-3ʹ from T. palmi revealed 2 divergent (min K2P = 7.9% versus half dozen.4% at COI-5ʹ) lineages; i (BIN: AAE7913) matched (>99% nucleotide identity) specimens from the Dominican Republic, India, Japan, and Thailand, while the other (BIN: AAN2747) matched records from the Dominican Democracy and India (Fig 5). Analysis of the COI-3ʹ from T. tabaci also showed ii lineages (min K2P = three.6%); one matched a lineage from the UK and Bosnia-Herzegovina, while the other matched specimens from Israel (Fig v).

Fig 3. NJ analysis of COI-5′ sequences from species of thrips from Pakistan.

Bootstrap values (%) (500 replicates) are shown above the branches (values <50% are non shown) while the scale bar shows K2P distances. The node for each species with multiple specimens was complanate to a vertical line or triangle, with the horizontal depth indicating the level of intraspecific divergence. BIN numbers are shown for species with but family-level identification or those dissever into two BINs.

https://doi.org/10.1371/journal.pone.0146014.g003

Fig 4. Phylogenetic analysis of thrips species from Islamic republic of pakistan based on COI-5ʹ sequences.

The tree was estimated using Bayesian inference. Posterior probabilities are indicated at nodes. Taxa are followed by the BINs and haplotype numbers. Rhopalosiphum padi (HQ979401) was employed as outgroup.

https://doi.org/x.1371/periodical.pone.0146014.g004

Fig five. NJ analysis based on COI-3′ sequences from Thrips palmi and Thrips tabaci from Pakistan (bold letters) with their closest matches (>99% nucleotide identity) from GenBank and other species of Thrips in this study.

The land of collection follows the species names. K2P divergence inside T. palmi and T. tabaci clusters is indicated betwixt arrows.

https://doi.org/10.1371/periodical.pone.0146014.g005

ITS1 was sequenced from the representative BINs of A. intermedius, H. reuteri and T. palmi and the two barcode lineages of T. tabaci to determine if sequence variation in this nuclear cistron region supported the presence of cryptic species. This analysis revealed substantial length variation in ITS1 among the 4 species—A. intermedius 889 bp, H. reuteri 513 bp, T. palmi 840 bp and T. tabaci 965 bp. The minimum K2P divergence (pairwise deletions) betwixt the BINs of A. intermedius (AAU0552 vs. AAZ8618), H. reuteri (AAI6863 vs. ACA2784) and T. palmi (AAE7913 vs. AAN2747) was 1.iv%, iv.3% and 2.3% respectively, while difference between the 2 barcode lineages of T. tabaci was ane.v%.

Global haplotype diversity

COI-5ʹ sequences from iv pest thrips (T. tabaci, T. palmi, T. flavidulus, S. dorsalis) were combined with those from GenBank to construct haplotype networks (Figs 6 and 7). Analysis focused on these four taxa considering also few records were available for the other species to generate meaningful conclusions. The 36 sequences of T. tabaci from Pakistan combined with 246 from GenBank from 4 geographic regions (Asia, Europe, Australia and America; 23 countries) revealed 28 haplotypes which clustered into three groups connected by the haplotype from the United Kingdom (Fig 6A). The commonest haplotype (n = 135) occurred in 15 countries including Pakistan, while three low frequency haplotypes were only institute in Switzerland [vi] and comprised a unmarried cluster in the network. In a similar fashion, 47 sequences of T. palmi from Islamic republic of pakistan were combined with 165 from GenBank (xi countries) revealing 23 haplotypes, 1 known from all the countries, except for Singapore (Fig 6B). The near common haplotype (due north = 133) was shared betwixt India and Pakistan. A majority of the haplotypes were amassed into ii groups with records from Pakistan establish in both groups. Even so, one haplotype from the Dominican Democracy [xiii] and one from India [xiv], with eight and ten nucleotide substitutions from their corresponding NNs, represented singleton clusters (Fig 6B). There were 5 haplotypes of T. palmi in Pakistan and two were only found in this country. The 114 records for T. flavidulus (106 from Pakistan) included xv haplotypes (Fig 6C) in two clusters. Thirteen were but represented past 1–three records, near detected only in Pakistan. The dominant haplotype (due north = 92) was in Pakistan. Among the half-dozen haplotypes of T. flavidulus from China, just one was shared with Pakistan. The x records of S. dorsalis from Islamic republic of pakistan combined with 239 from GenBank (14 countries) included 99 haplotypes forming five large (>10 sequences) and six pocket-sized (<three sequences) clusters (Fig 7). The largest haplotype cluster included three subclusters; the starting time 2 subclusters included haplotypes from Asia (China, India, Israel, Nihon, Pakistan, Thailand, Vietnam) and Africa (Kenya), while the third included haplotypes from Asia (Cathay, Japan, Singapore, Taiwan, Thailand), and N America (USA). The second and third largest clusters were comprised of haplotypes predominantly from Japan and China. 7 haplotypes of S. dorsalis were plant in Pakistan with three shared with Republic of india (Fig 7). The 11 haplotype clusters corresponded to the same number of BINs for this species on BOLD (AAC9747, AAC9749, AAC9750, AAC9751, ACQ0434, ACQ0435, ACQ4218, ACV6509, ACV6510, ACV6511, ACV7644) (Fig seven).

Fig half dozen. Haplotype networks for three species of pest thrips from Pakistan based on COI-5ʹ sequences including presumptive conspecifics from GenBank.

A) Thrips tabaci; B) Thrips palmi; C) Thrips flavidulus. Different pie colors in the circles indicate the country of haplotype origin with pie size proportional to the number of records, while the circle sizes are proportional to the haplotype frequency in the dataset. Perpendicular tick marks on the lines represent the number of nucleotide substitutions between the linked haplotypes. Corresponding BINs are provided likewise each haplotype cluster. NA = BIN not assigned.

https://doi.org/10.1371/journal.pone.0146014.g006

Fig vii. Haplotype network for Scirtothrips dorsalis from Pakistan based on COI-5ʹ sequences including conspecifics from GenBank.

Different pie colors in the circles point the country of haplotype origin with pie size proportional to the number of records, while the circle sizes are proportional to the haplotype frequency in the dataset. Perpendicular tick marks on the lines represent the number of nucleotide substitutions between the linked haplotypes. Corresponding BINs are provided besides each haplotype cluster.

https://doi.org/10.1371/periodical.pone.0146014.g007

Discussion

The thrips fauna of Pakistan is poorly known and no molecular data was available earlier this written report which coupled morphological identifications with subsequent barcode analysis. Morphological study resolved most thrips to a presumptive species, simply 34 specimens could only be identified to a genus or family unit. Because thrips are so difficult to identify morphologically, the development of DNA-based identification system is an attractive option [59]. Still, the use of molecular approaches requires a reference sequence library from well-identified specimens; its lack inevitably compromises efforts to make taxonomic assignments by molecular analysis [sixty]. Efforts to build a Dna barcode library for thrips have begun [61], but just 263 of the 6000 known species of Thysanoptera have a barcode record and nearly x% of these records derive from the present study. Consequently, sequence matches were simply found for thirteen of the 56 species analyzed in this report and none of these reference sequences derived from Pakistan. This gap highlights the need to further populate databases with reference sequences from carefully identified specimens. This report provides barcode data for 56 lineages of Thysanoptera, 42 identified to the species-level. Bated from providing the first barcode records for Thysanoptera from Pakistan, this study raised the species count for the country from 77 to 87.

Barcode gap assay showed that the maximum intraspecific distance was invariably lower than the NN distance, even for those species (A. intermedius, H. reuteri, T. palmi, T. tabaci) with high intraspecific divergence (>2%). Two of these cases (T. palmi, T. tabaci) were expected as prior studies [14,31] have reported loftier intraspecific distances and multiple COI clusters in these species. For case, in a multi-land analysis of COI, Kadirvel et al. [fourteen] plant a maximum K2P distance of 19.9% for T. palmi and x.4% for T. tabaci with the former species showing iv and the latter three COI variants. In fact, Rebijith [31] reported maximum barcode divergences of 12.3% and thirteen.8% for T. palmi and T. tabaci in Indian populations, much college values than those detected in these species from Pakistan. We did not perform NJ or phylogenetic assay with the global information for these species, but nosotros performed distance analysis and constructed MSN of haplotypes. The distance analysis showed a maximum divergence of 13% in T. palmi and 12% in T. tabaci, and the MSN showed four haplotype clusters for T. palmi and iii for T. tabaci. The two haplotype clusters for T. tabaci were assigned to a unmarried BIN by BOLD, while sequences from the most divergent cluster for this species remain without a BIN because they were <500 bp. Similarly, two principal haplotype clusters for T. palmi were assigned to ii BINs, while 2 additional clusters, each with 1 sequence (<500 bp) were not assigned to a BIN because they did non meet quality criteria (e.g., >500bp, <1% ambiguous bases), and the sequences without a BIN, in the haplotype network analysis, fall under the short-length category. However, the number of haplotype clusters recognized in our analysis corresponds with the number of NJ clusters reported for T. palmi and T. tabaci by Kadirvel et al. [14]. The gap betwixt maximum intraspecific and minimum interspecific distances has been oft used for species delimitation in various animal groups [54,62,63]. Prevot et al. [64] used barcode gap analysis, with several other species delimitation methods, to identify species in a complex group of snails. Also, Roy et al. [65] and Ashfaq et al. [66] employed barcode gap analysis as a tool to discriminate cryptic species of termites and collywobbles, respectively. However, other reports have shown the absence of a barcode gap in well-differentiated [67] and recently diverged [68] species.

The present study gathered barcode records from 42 of the 43 morphological species that were collected. Specimens from 39 of these species were assigned to a single BIN, indicating perfect congruence with morphological identifications. The other three species (A. intermedius, H. reuteri, T. palmi) showed BIN splits. The number of BINs likely signals the number of species in our collection every bit except for T. tabaci where the merely BIN was split into two clusters, similar number of clusters was recovered by both NJ and Bayesian assay. Researchers have used BINs to delimit species in barcode datasets and take plant them congruent with the morphospecies [22].

The barcode sequences from A. intermedius, H. reuteri, T. palmi and T. tabaci showed deep divergences suggesting that each is a cryptic species complex. This inference was supported by the divergences observed in ITS1. Although inter-BIN ITS1divergence was high in H. reuteri (4.3%) and T. palmi (2.three%), it was relatively low in A. intermedius (1.4%) and T. tabaci (ane.5%). Although the utility of ITS1 for differentiating cryptic species has been documented [69], some taxa show lower divergence in ITS1 than COI [70] limiting the usefulness of this marker in discriminating closely related species. For example, in a group of Leptaxini gastropods where COI revealed high divergence, ITS1 and ITS2 showed little variation [71]. The NJ assay of COI-3ʹ sequences for T. palmi and T. tabaci from Pakistan confirmed that these species matched their corresponding counterparts in GenBank with each species including at least two genetic clusters, a outcome supporting the like departure observed in barcode sequences from these two species. While earlier morphological studies suggested that T. tabaci is a complex of several species [72], color and size variation has hampered their discrimination [four]. Similar suggestions for the presence of a species circuitous accept been made for T. palmi [27] and Due south. dorsalis [73,74]. Revelation of cryptic species by barcode data has been documented in other insects including sphingid moths [75], leaf-mining micromoths [76], aphids [77] and whiteflies [78]. However, farther studies are needed to confirm the reproductive isolation of these cryptic thrips lineages, as has been done for F. occidentalis [29].

The present study has indicated that barcode haplotypes for the 4 vector species from sites effectually the globe were clustered in groups, data that may be useful in analyzing vector-virus relationships and affliction epidemiology as virus transmission capacity is known to vary among species populations [79]. Several thrips species, including of import virus vectors, are now thought to exist species complexes [31], just there is little information on the role of each lineage in viral transmission. For instance, a recent study [74] suggests that South. dorsalis is a complex of at least ix species, merely that just i member of this complex, Southern asia 1, is involved in virus manual. In this study, the MSN for S. dorsalis revealed 11 divergent haplotype clusters corresponding to the 11 BINs for this species on Assuming. If the BINs are species proxies, S. dorsalis is a complex of 11 cryptic species, ii more than reported by Dickey et al. [74]. This result highlights the need to gain an agreement of both the thrips species complexes and their genetic lineages through development of a comprehensive global DNA barcode library for Thysanoptera. This study helps to accost this demand by employing Deoxyribonucleic acid barcodes to examine thrips diversity in Pakistan.

Supporting Information

Acknowledgments

Nosotros are very grateful to Professor Dr. Waseem Akram, Department of Entomology, University of Agriculture, Faisalabad, for his help in thrips identification. Nosotros as well thank staff at the CCDB for aiding this piece of work.

Author Contributions

Conceived and designed the experiments: RI MA. Performed the experiments: RI MA. Analyzed the data: RI MA AR. Contributed reagents/materials/analysis tools: MA PDNH. Wrote the paper: RI MA PDNH.

References

1. 1. Mound LA. Thysanoptera: multifariousness and interactions. Ann Rev Entomol. 2005;fifty: 247–269.
   • View Article
   • Google Scholar
2. 2. Mandal B, Jain RK, Krishnareddy K, Krishna Kumar NK, Ravi KS, Pappu 60 minutes. Emerging problems of Tospoviruses (Bunyaviridae) and their direction in the Indian Subcontinent. Constitute Dis. 2012;96: 468–479.
   • View Commodity
   • Google Scholar
3. 3. Brunner PC, Fleming C, Frey JE. A molecular identification cardinal for economically of import thrips species (Thysanoptera: Thripidae) using direct sequencing and a PCR-RFLP-based approach. Agric Forest Entomol. 2002;4: 127–136.
   • View Article
   • Google Scholar
4. four. Murai T, Toda South. Variation of Thrips tabaci in colour and size. In: Thrips and Tospoviruses: in Proceedings of the 7th International Symposium on Thysanoptera. 1–viii July 2001, Reggio Calabria, Italy 377–378.
5. five. Tyagi M, Kumar V, Mound LA. Sexual dimorphism among Thysanoptera Terebrantia, with a new species from Malaysia and remarkable species from India in Aelothripidae and Thripidae. Insect Syst Evol. 2008;39: 155–170.
   • View Article
   • Google Scholar
6. six. Brunner PC, Chatzivassiliou EK, Katis NI, Frey JE. Host-associated genetic differentiation in Thrips tabaci (Insecta; Thysanoptera), as determined from mtDNA sequence data. Heredity 2004;93: 364–370. pmid:15241445
   • View Article
   • PubMed/NCBI
   • Google Scholar
7. seven. Toda S, Komazaki S. Identification of thrips species (Thysanoptera: Thripidae) on Japanese fruit trees by polymerase chain reaction and restriction fragment length polymorphism of the ribosomal ITS2 region. Balderdash Entomol Res. 2002;92: 359–363. pmid:12191445
   • View Commodity
   • PubMed/NCBI
   • Google Scholar
8. viii. Walsh K, Boonham N, Barker I, Collins DW. Development of a sequence-specific existent-time PCR to the melon thrips Thrips palmi (Thysan., Thripidae). J Appl Entomol. 2005;129: 272–279.
   • View Article
   • Google Scholar
9. 9. Buckman RS, Mound LA, Whiting MF. Phylogeny of thrips (Insecta: Thysanoptera) based on five molecular loci. Syst Entomol. 2013;38: 123–133.
   • View Article
   • Google Scholar
10. ten. Crespi BJ, Carmean D, Vawter L, Von Dohlen C. Molecular phylogenetics of Thysanoptera. Syst Entomol. 1996;21: 79–87.
    • View Article
    • Google Scholar
11. eleven. Savolainen V, Cowan RS, Vogler AP, Roderick GL, Lane R. Towards writing the encyclopaedia of life: an introduction to Dna barcoding. Philosophical Transactions of the Royal Society B: Biol Sci. 2005;360: 1805–1811.
    • View Article
    • Google Scholar
12. 12. Asokan R, Krishna Kumar NK, Kumar 5, Ranganath HR. Molecular differences in the mitochondrial cytochrome oxidase I (mtCOI) factor and evolution of a species-specific marking for onion thrips, Thrips tabaci Lindeman, and melon thrips, T. palmi Karny (Thysanoptera: Thripidae), vectors of tospoviruses (Bunyaviridae). Bull Entomol Res. 2007;97: 461–470. pmid:17916265
    • View Article
    • PubMed/NCBI
    • Google Scholar
13. 13. Glover RH, Collins DW, Walsh K, Boonham Due north. Assessment of loci for DNA barcoding in the genus Thrips (Thysanoptera: Thripidae). Mol Ecol Resour. 2010;x: 51–59. pmid:21564990
    • View Article
    • PubMed/NCBI
    • Google Scholar
14. xiv. Kadirvel P, Srinivasan R, Hsu YC, Su FC, De La Pena R. Awarding of cytochrome oxidase I sequences for phylogenetic analysis and identification of thrips species occurring on vegetable crops. J Econ Entomol. 2013;106: 408–418. pmid:23448058
    • View Article
    • PubMed/NCBI
    • Google Scholar
15. xv. Hebert PDN, Cywinska A, Ball SL, de Waard JR. Biological identifications through DNA barcodes. Phil Trans R Lond B: Biol Sci Series 2003;270: 313–321.
    • View Commodity
    • Google Scholar
16. 16. Burns JM, Janzen DH, Hajibabaei M, Hallwachs W, Hebert PDN. DNA barcodes of closely related (merely morphologically and ecologically singled-out) species of skipper collywobbles (Hesperiidae) can differ past just ane to three nucleotides. J Lepid Soc. 2007;61: 138–153.
    • View Article
    • Google Scholar
17. 17. Foottit RG, Maw HEL, Von Dohlen CD, Hebert PDN. Species identification of aphids (Insecta: Hemiptera: Aphididae) through DNA barcodes. Mol Ecol Resour. 2008;8: 1189–1201. pmid:21586006
    • View Article
    • PubMed/NCBI
    • Google Scholar
18. 18. Ashfaq Thou, Hebert PDN, Mirza JH, Khan AM, Zafar Y, Mirza MS. Analyzing musquito (Diptera: Culicidae) diversity in Pakistan by DNA barcoding. PLOS One 2014;9: e97268. pmid:24827460
    • View Commodity
    • PubMed/NCBI
    • Google Scholar
19. 19. Hernandez-Triana LM, Chaverri LG, Rodriguez-Perez MA, Prosser SWJ, Hebert PDN, Gregory TR, et al. DNA barcoding of Neotropical black flies (Diptera: Simuliidae): species identification and discovery of cryptic diverseness in Mesoamerica. Zootaxa 2015;3936: 093–114.
    • View Article
    • Google Scholar
20. twenty. Hebert PDN, Ratnasingham S, de Waard JR. Barcoding animal life: cytochrome c oxidase subunit 1 divergences among closely related species. Phil Trans R Lond B: Biol Sci. 2003;270: S96–S99.
    • View Article
    • Google Scholar
21. 21. Ratnasingham South, Hebert PDN. A DNA-based registry for all animal species: The Barcode Index Number (BIN) System. PLOS Ane 2013;8: e66213. pmid:23861743
    • View Article
    • PubMed/NCBI
    • Google Scholar
22. 22. Zahiri R, Lafontaine JD, Schmidt BC, deWaard JR, Zakharov EV, Hebert PDN. A transcontinental challenge—A test of Deoxyribonucleic acid barcode operation for 1,541 species of Canadian Noctuoidea (Lepidoptera). PLOS ONE 2014;9: e92797. pmid:24667847
    • View Commodity
    • PubMed/NCBI
    • Google Scholar
23. 23. Pentinsaari M, Hebert PDN, Mutanen Thousand. Barcoding beetles: A regional survey of 1872 species reveals high identification success and unusually deep interspecific divergences. PLOS ONE 2014;9: e108651. pmid:25255319
    • View Article
    • PubMed/NCBI
    • Google Scholar
24. 24. Hausmann A, Godfray HC, Huemer P, Mutanen M, Rougerie R, van Nieukerken EJ, et al. Genetic patterns in European geometrid moths revealed by the Barcode Index Number (BIN) system. PLOS ONE 2013;17: e84518.
    • View Article
    • Google Scholar
25. 25. Landry JF, Hebert PDN. Plutella australiana (Lepidoptera, Plutellidae), an overlooked diamondback moth revealed by Deoxyribonucleic acid barcodes. Zookeys 2013;29: 43–63.
    • View Article
    • Google Scholar
26. 26. Mutanan M, Hausmann A, Hebert PDN, Landry JF, de Waard JR, Huemer P. Allopatry equally a Gordian knot for taxonomists: patterns of DNA barcode deviation in arctic-alpine Lepidoptera. PLOS ONE 2013;7: e47214.
    • View Commodity
    • Google Scholar
27. 27. Karimi J, Hassani-Kakhki M, Awal MM. Identifying thrips (Insecta: Thysanoptera) using Dna barcodes. J Jail cell Mol Res. 2010;2: 35–41.
    • View Commodity
    • Google Scholar
28. 28. Wei-Na Q, Fang-Hao W, Ai-Bing Z, Liang Thou, Gui-Fen Z. Application of DNA barcoding technology for species identification of mutual thrips (Insecta: Thysanoptera) in Communist china. Acta Entomol Sinica 2012;55: 344–356.
    • View Article
    • Google Scholar
29. 29. Rugman-Jones PF, Hoddle MS, Stouthamer R. Nuclear-mitochondrial barcoding exposes the global pest Western blossom thrips (Thysanoptera: Thripidae) every bit two sympatric cryptic species in its native California. J Econ Entomol. 2010;103: 877–886. pmid:20568635
    • View Article
    • PubMed/NCBI
    • Google Scholar
30. 30. Toda Due south, Murai T. Phylogenetic analysis based on mitochondrial COI cistron sequences in Thrips tabaci Lindeman (Thysanoptera: Thripidae) in relation to reproductive forms and geographic distribution. Appl Entomol Zool. 2007;42: 309.
    • View Article
    • Google Scholar
31. 31. Rebijith KB, Asokan R, Krishna V, Ranjitha HH, Kumar NKK, Ramamurthy VV. DNA barcoding and elucidation of cryptic diversity in thrips (Thysanoptera). Fla Entomol. 2014;97: 1328–1347.
    • View Article
    • Google Scholar
32. 32. Vitousek PM, D'Antonio CM, Loope LL Westbrooks R. Biological invasions as global ecology change. Am Scient. 1996;84: 468–478.
    • View Article
    • Google Scholar
33. 33. Rosen D. The role of taxonomy in effective biological control programs. Agric Ecosyst Environ. 1986;15: 121–129.
    • View Commodity
    • Google Scholar
34. 34. Armstrong KF, Ball SL. DNA barcodes for biosecurity: invasive species identification. Phil Trans R Soc B: Biol Sci. 2005;360: 1813–1823.
    • View Article
    • Google Scholar
35. 35. Przybylska A, Fiedler Z, Kucharczyk H, Obrepalska-Steplowska A. Detection of the quarantine species Thrips palmi by loop-mediated isothermal amplification. PLOS I 2015;10: e0122033. pmid:25793743
    • View Article
    • PubMed/NCBI
    • Google Scholar
36. 36. Ananthakrishnan TN, Annadurai RS. Thrips-tospovirus interactions: Biological and molecular implications. Current Sci. 2007;92: 1083–1086.
    • View Article
    • Google Scholar
37. 37. Akram W, Oh SW, Lee JJ. Taxonomic review of the genus Mycterothrips (Terebrantia: Thysanoptera) and its species from Pakistan. Entomol Res. 2002;32: 251–254.
    • View Commodity
    • Google Scholar
38. 38. Akram W, Koh JB, Lee JJ. Genus Thrips (Thysanoptera: Thripidae) from Pakistan with virtually usually recorded species. Korean J Entomol. 2003;33: 277–286.
    • View Article
    • Google Scholar
39. 39. Akram Westward, Shin BS, Lee JJ. Description of ii genera of Idolothripinae (Phlaeothripidae: Tubulifera: Thysanoptera) from Pakistan. Entomol Res. 2003;33: 53–57.
    • View Article
    • Google Scholar
40. forty. Ali R. Studies on Thripidae of Lyallpur. K.Sc. thesis, University of Agriculture, Lyallpur, Pakistan 1976;247.
41. 41. Palmer JM. Thrips (Thysanoptera) from Pakistan to the Pacific: A review. Bull Br Mus (Nat Hist) Entomol Series 1992;61.
    • View Article
    • Google Scholar
42. 42. Saeed M, Yousuf One thousand. Checklist of Thysanoptera of the Punjab (Pakistan). Pak Entomol J. 1994;xvi: 59–61.
    • View Article
    • Google Scholar
43. 43. Umar M, Akram W, Choi JK, Lee JJ. Description of Neolimothrips binervis (Kobus) (Thysanoptera: Thripidae: Chirothripini) from Pakistan, with a new locality record. Entomol Res. 2004;34: 55–58.
    • View Article
    • Google Scholar
44. 44. Bradley SJ, Mayer DF. Evaluation of monitoring methods for western blossom thrips, Frankliniella occidentalis (Thysanoptera: Thripidae), during the blossom period of Granny Smith apples. J Entomol Soc B C. 1994;91: 63–68.
    • View Article
    • Google Scholar
45. 45. Ratnasingham South, Hebert PDN. Bold: the Barcode of Life Information Arrangement (www.barcodinglife.org). Mol Ecol Notes 2007;vii: 355–364. pmid:18784790
    • View Article
    • PubMed/NCBI
    • Google Scholar
46. 46. Porco D, Rougerie R, Deharveng Fifty, Hebert PDN. Coupling non-destructive Deoxyribonucleic acid extraction and voucher retrieval for pocket-sized soft-bodied arthropods in a high throughput context: the example of Collembola. Mol Ecol Resour. 2010;10: 942–945. pmid:21565103
    • View Commodity
    • PubMed/NCBI
    • Google Scholar
47. 47. Folmer O, Black K, Hoeh W, Lutz R, Vrijenhoek R. DNA primers for amplification of mitochondrial cytochrome c oxidase subunit I from diverse metazoan invertebrates. Mol Mar Biol Biotech. 1994;iii: 294–299.
    • View Article
    • Google Scholar
48. 48. Ji Y-J, Zhang D-10, He L-J. Evolutionary conservation and versatility of a new set of primers for amplifying the ribosomal internal transcribed spacer regions in insects and other vertebrates. Mol Ecol Notes 2003;iii: 581–585.
    • View Article
    • Google Scholar
49. 49. Hebert PDN, Dewaard JR, Zakharov EV, Prosser SWJ, Sones JE, McKeown JTA, et al. A Deoxyribonucleic acid "barcode blitz": rapid digitization and sequencing of a natural history collection. PLOS 1 2013;8: e68535. pmid:23874660
    • View Article
    • PubMed/NCBI
    • Google Scholar
50. 50. Tamura K, Peterson D, Peterson Due north, Stecher K, Nei Chiliad, Kumar S. MEGA5: molecular evolutionary genetics assay using maximum likelihood, evolutionary distance, and maximum parsimony methods. Mol Biol Evol. 2011;28: 2731–2739. pmid:21546353
    • View Article
    • PubMed/NCBI
    • Google Scholar
51. 51. Moritz G, Morris DC, Mound LA. Thrips ID–Pest Thrips of the Earth. CD-ROM Published for ACIAR by CSIRO Publishing, Melbourne 2001.
52. 52. Thompson JD, Higgins DG, Gibson TJ. Clustal Westward: improving the sensitivity of progressive multiple sequence alignment through sequence weighting, position-specific gap penalties and weight matrix pick. Nucleic Acids Res. 1994;22: 4673–4680. pmid:7984417
    • View Commodity
    • PubMed/NCBI
    • Google Scholar
53. 53. Kimura One thousand. A simple method for estimating evolutionary rates of base substitutions through comparative studies of nucleotide sequences. J Mol Evol. 1980;16: 111–120. pmid:7463489
    • View Article
    • PubMed/NCBI
    • Google Scholar
54. 54. Puillandre Due north, Lambert A, Brouillet S, Achaz G. ABGD, Automatic Barcode Gap Discovery for main species delimitation. Mol Ecol. 2012;21: 1864–1877. pmid:21883587
    • View Article
    • PubMed/NCBI
    • Google Scholar
55. 55. Meyer CP, Paulay G. DNA barcoding: mistake rates based on comprehensive sampling. PLOS Biology 2005;3: e422. pmid:16336051
    • View Article
    • PubMed/NCBI
    • Google Scholar
56. 56. Librado P, Rozas J. DnaSP v5: a software for comprehensive analysis of DNA polymorphism data. Bioinformatics 2009;25: 1451–1452. pmid:19346325
    • View Article
    • PubMed/NCBI
    • Google Scholar
57. 57. Ronquist F, Teslenko M, van der Mark P, Ayres DL, Darling A, Höhna S, et al. MrBayes 3.2: efficient Bayesian phylogenetic inference and model choice across a large model space. Syst Biol. 2012;61: 539–542. pmid:22357727
    • View Commodity
    • PubMed/NCBI
    • Google Scholar
58. 58. Bandelt H- J, Forster P, Rohl A. Median-joining networks for inferring intraspecific phylogenies. Mol Biol Evol. 1999;xvi: 37–48. pmid:10331250
    • View Commodity
    • PubMed/NCBI
    • Google Scholar
59. 59. Mainali BP, Shrestha S, Lim UT, Kim Y. Molecular markers of two sympatric species of the genus Frankliniella (Thysanoptera: Thripidae). J Asia Pacific Entomol. 2008;11: 45–48.
    • View Article
    • Google Scholar
60. 60. Virgilio M, Backeljau T, Nevado B, Meyer MD. Comparative performances of DNA barcoding across insect orders. BMC Bioinformatics 2010;11: 206. pmid:20420717
    • View Commodity
    • PubMed/NCBI
    • Google Scholar
61. 61. Kumar V, Seal DR, Osborne LS, McKenzie CL. Coupling scanning electron microscopy with DNA barcoding: a novel approach for thrips identification. Appl Entomol Zool. 2014;49: 403–409.
    • View Article
    • Google Scholar
62. 62. Hebert PDN, Penton EH, Burns JM, Janzen DH, Hallwachs Westward. 10 species in i: DNA barcoding reveals cryptic species in the Neotropical skipper butterfly Astraptes fulgerator. Proc Natl Acad Sci USA. 2004;101: 14812–14817. pmid:15465915
    • View Commodity
    • PubMed/NCBI
    • Google Scholar
63. 63. Dellicour S, Flot J-F. Delimiting species-poor information sets using unmarried molecular markers: a written report of barcode gaps, haplowebs and GMYC. Syst Biol. 2015;64: 900–908. pmid:25601944
    • View Article
    • PubMed/NCBI
    • Google Scholar
64. 64. Prevot V, Jordaens Thou, Sonet Chiliad, Backeljau T. Exploring species level taxonomy and species delimitation methods in the facultatively self-fertilizing land snail genus Rumina (Gastropoda: Pulmonata). PLOS ONE 2013;eight: e60736. pmid:23577154
    • View Article
    • PubMed/NCBI
    • Google Scholar
65. 65. Roy V, Constantino R, Chassany Five, Stephanie G-M, Diouf M, Mora P, et al. Species delimitation and phylogeny in the genus Nasutitermes (Termitidae: Nasutitermitinae) in French Guiana. Mol Ecol. 2014;23: 902–920. pmid:24372711
    • View Article
    • PubMed/NCBI
    • Google Scholar
66. 66. Ashfaq G, Akhtar Southward, Khan AM, Adamowicz SJ, Hebert PDN. DNA barcode analysis of butterfly species from Pakistan points towards regional endemism. Mol Ecol Resour. 2013;thirteen: 832–843. pmid:23789612
    • View Article
    • PubMed/NCBI
    • Google Scholar
67. 67. Wiemers 1000, Fiedler Yard. Does the Deoxyribonucleic acid barcoding gap exist?–a case written report in blueish collywobbles (Lepidoptera: Lycaenidae). Front Zool. 2007;4: eight. pmid:17343734
    • View Article
    • PubMed/NCBI
    • Google Scholar
68. 68. van Velzen R, Weitschek Due east, Felici G, Bakker FT. Deoxyribonucleic acid barcoding of recently diverged species: relative performance of matching methods. PLOS 1 2012;vii: e30490. pmid:22272356
    • View Article
    • PubMed/NCBI
    • Google Scholar
69. 69. Yong HS, Lim P-Eastward, Tan J, Ng YF, Eamsobhana P, Suana IW. Molecular phylogeny of Orthetrum dragonflies reveals cryptic species of Orthetrum pruinosum. Sci Rep. 2014;4: 5553. pmid:24989852
    • View Article
    • PubMed/NCBI
    • Google Scholar
70. seventy. Schon I, Gandolfi A, Masso East, Rossi V, Griffiths HI, Martens K. Persistence of asexuality through mixed reproduction in Eucypris virens (Crustacea, Ostracoda). Heredity 2000;84: 161–169. pmid:10762385
    • View Article
    • PubMed/NCBI
    • Google Scholar
71. 71. Riel PV, Jordaens K, Houtte NV, Martins AM, Verhagen R, Backeljau T. Molecular systematics of the endemic Leptaxini (Gastropoda: Pulmonata) on the Azores islands. Mol Phylog Evol. 2005;37: 132–143.
    • View Article
    • Google Scholar
72. 72. Zawirska I. Untersuchungen u¨ ber zwei biologische Typen von Thrips tabaci Lind. Thysanoptera, Thripidae) in der VR Polen. Arch Phytopathol P Prot. 1976;12: 411–422.
    • View Article
    • Google Scholar
73. 73. Hoddle MS, Heraty JM, Rugman-Jones PF, Mound LA, Stouthamer R. Relationships amongst species of Scirtothrips (Thysanoptera: Thripidae, Thripinae) using molecular and morphological information. Ann Entomol Soc Am. 2008;101: 491–500.
    • View Article
    • Google Scholar
74. 74. Dickey AM, Kumar V, Hoddle MS, Funderburk JE, Morgan JK, Jara-Cavieres A, et al. The Scirtothrips dorsalis species complex: endemism and invasion in a global pest. PLOS ONE 2015;10: e0123747. pmid:25893251
    • View Article
    • PubMed/NCBI
    • Google Scholar
75. 75. Vaglia T, Haxaire J, Kitching IJ, Meusnier I, Rougerie R. Morphology and Dna barcoding reveal three ambiguous species within the Xylophanes neoptolemus and loelia species-groups (Lepidoptera: Sphingidae). Zootaxa 2008;1923: eighteen–36.
    • View Commodity
    • Google Scholar
76. 76. Nieukerken EV, Doorenweerd C, Stokvis FR, Groenenberg DSJ. DNA barcoding of the leaf-mining moth subgenus Ectoedemia south. str. (Lepidoptera: Nepticulidae) with COI and EF1-α: 2 are amend than one in recognizing cryptic species. Contrib Zool. 2012;81: 1–24.
    • View Article
    • Google Scholar
77. 77. Carletto J, Blin A, Vanlerberghe-Masutti F. DNA-based discrimination between the sibling species Aphis gossypii Glover and Aphis frangulae Kaltenbach. Syst Entomol. 2009;34: 307–314.
    • View Article
    • Google Scholar
78. 78. Ashfaq M, Hebert PDN, Mirza MS, Khan AM, Mansoor S, Shah GS, et al. Deoxyribonucleic acid barcoding of Bemisia tabaci complex (Hemiptera: Aleyrodidae) reveals southerly expansion of the dominant whitefly species on cotton wool in Islamic republic of pakistan. PLOS ONE 2014;9: e104485. pmid:25099936
    • View Article
    • PubMed/NCBI
    • Google Scholar
79. 79. Wijkamp I, Almarza Northward, Goldbach R, Peters D. Singled-out levels of specificity in thrips transmission of tospoviruses. Phytopathology 1995;85: 1069–1074.
    • View Article
    • Google Scholar

garzasupoed.blogspot.com

Source: https://journals.plos.org/plosone/article?id=10.1371%2Fjournal.pone.0146014

Share this post