https://orcid.org/0000-0001-9585-4241
Figures
Abstract
The correct identification of mosquito species is important for effective mosquito vector control. However, the standard morphological identification of mosquito species based on the available keys is not easy with specimens in the field due to missing or damaged morphological features during mosquito collections, often leading to the misidentification of morphologically indistinguishable. To resolve this problem, we collected mosquito species across Thailand to gather genetic information, and evaluated the DNA barcoding efficacy for mosquito species identification in Thailand. A total of 310 mosquito samples, representing 73 mosquito species, were amplified using mitochondrial cytochrome c oxidase subunit I (COI) primers. The average maximum intraspecific genetic variation of the 73 mosquito species was 1% ranged from 0–5.7%. While, average minimum interspecific genetic variation (the distance to the nearest neighbour) of the 73 mosquito species was 7% ranged from 0.3–12.9%. The identification of success rates based on the “Best Match,” “Best Close Match,” and “All Species Barcodes” methods were 97.7%, 91.6%, and 81%, respectively. Phylogenetic analyses of Anopheles COI sequences demonstrated a clear separation between almost all species (except for those between An. baimaii and An. dirus), with high bootstrap support values (97%–99%). Furthermore, phylogenetic analyses revealed potential sibling species of An. annularis, An. tessellatus, and An. subpictus in Thailand. Our results indicated that DNA barcoding is an effective molecular approach for the accurate identification of mosquitoes in Thailand.
Citation: Chaiphongpachara T, Changbunjong T, Laojun S, Nutepsu T, Suwandittakul N, Kuntawong K, et al. (2022) Mitochondrial DNA barcoding of mosquito species (Diptera: Culicidae) in Thailand. PLoS ONE 17(9): e0275090. https://doi.org/10.1371/journal.pone.0275090
Editor: Bi-Song Yue, Sichuan University, CHINA
Received: February 21, 2022; Accepted: September 9, 2022; Published: September 22, 2022
Copyright: © 2022 Chaiphongpachara 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.
Data Availability: All relevant data are within the manuscript and its Supporting Information files.
Funding: This work was supported by a Grant from the Suan Sunandha Rajabhat University, Thailand. The funders had no role in study design, data collection and analysis, and preparation of the manuscript.
Competing interests: The authors have declared that no competing interests exist.
Introduction
Mosquitoes are small flying insects that belong to the order Diptera (two-winged flies), and the family Culicidae. Currently, 3,611 species of mosquitoes are formally recognized, which can be classified into two subfamilies and 113 genera, with numerous further species awaiting confirmation [1]. Several mosquito species are important from the perspective of tropical medicine and public health, because they are vectors for infectious pathogens, including nematodes, protozoans, and arboviruses, that cause dangerous diseases in humans [2]. Mosquito-borne infectious diseases are considered public health threats in several countries, especially those located in tropical and temperate regions [3]. The World Health Organization estimated that in humans, vector-borne diseases account for >17% of all infectious diseases and 700,000 deaths annually [4]. Thailand is endemic to mosquito-borne diseases, such as dengue, chikungunya, Zika, malaria, and Japanese encephalitis [5]. Annually, >100,000 cases and >100 deaths occur in Thailand due to mosquito-borne diseases [6].
The epidemiology of mosquito-borne infectious diseases is strongly related to the mosquito species. Each mosquito species exhibits biological and ecological differences related to their resting and biting behavior, vectorial capacity, and habitat [7,8]. Therefore, a clear identification of the mosquito species is essential for developing species-specific vector controls. The correct identification of the mosquito species helps provide accurate vector information, leading to the effective control of mosquito-borne diseases [7,9]. However, the standard morphological identification of mosquito species based on the available keys is not easy with specimens in the field due to missing or damaged morphological features during mosquito collections, often leading to the misidentification of morphologically indistinguishable [10]. Furthermore, several Anopheles mosquito species form complex groups, comprising morphologically indistinguishable sibling species [11]. Our knowledge in molecular biology has undergone significant progress, leading to the development of techniques useful for species identification [12]. Therefore, the use of modern molecular biology techniques for the species-based identification of mosquito vectors is an extremely attractive alternative to standard morphological methods [13].
DNA barcoding is a molecular biological approach that has gained wide attention because of its efficiency and accuracy in identifying species of mammals [14], birds [15], reptiles, amphibians [16], fishes [17], and arthropods [18]. This diagnostic molecular assay is based on the principle that each animal species displays its own unique set of short DNA fragments that differ among species, similar to the barcodes of items in a department store that show information about the items [19,20]. DNA barcoding relies on amplifying a highly conserved and standardized short region of DNA (approximately 400–800 base pairs [bp]) using PCR for species-level taxonomy [21]. Mitochondrial cytochrome c oxidase subunit I (COI), a conserved gene and the first standard genetic region used for animal DNA barcoding [22], is the most popular barcode marker. However, this technique requires reliable reference sequences stored in international DNA barcoding libraries to identify unknown species. This often represents a significant problem with the absence of nucleotide sequences of a given species in the database for genetic comparison or the presence of insufficient sequences; leading to a lack of credibility [23,24].
In medical entomology, DNA barcoding is used to confirm insect species that are difficult to identify by morphological methods, such as black flies [25], biting midges [26], sand flies [27], horse flies [28], deer flies [29], flesh flies [30], and mosquitoes [31–34]. Mosquitoes have been widely and successfully investigated by this molecular technique in various countries [23,35–38]. However, this technique might not be efficient in distinguishing certain mosquito species because of insufficient differences in their mitochondrial nucleotide sequences, such as for Anopheles deaneorum and Anopheles marajoara [39] or Anopheles albertoi and Anopheles strodei [40]. Although several studies report using DNA barcoding to solve the problem of identifying mosquito vectors in Thailand, most focus only on the main mosquito vectors. To address these problems, we collected mosquito species across Thailand to gather genetic information, and submitted it to an international reference database for facilitating taxonomic studies in mosquitoes. In addition, we analyzed mitochondrial COI sequence to evaluate the DNA barcoding efficacy for mosquito species identification in Thailand.
Materials and methods
Ethics statement
This study was strictly conducted according to the animal care and use guidelines of the Suan Sunandha Rajabhat University, Thailand. Animal care and experimental procedures were reviewed and approved by the Institutional Animal Care and Use Committee of the Suan Sunandha Rajabhat University, Bangkok, Thailand (Animal Ethics Permission number: IACUC 64-004/2021).
Study sites and mosquito collection
Mosquito samples were collected from all regions of Thailand in 2021. The sites for mosquito collections in each province were selected based on the reports on endemic areas of mosquito-borne infectious diseases from Thailand’s National Disease Surveillance (available at http://doe.moph.go.th/surdata/index.php) (Fig 1A). To ensure species richness of the samples, two mosquito-collection methods, including adult mosquito trapping and larvae dipper, were used (Fig 1B and 1C). Adult mosquitoes were captured using the BG-Pro CDC-style mosquito trap (BioGents, Regensburg, Germany), with the BG-lure cartridge (BioGents, Regensburg, Germany) and solid carbon dioxide (dry ice) at night between 6:00 pm and 6:00 am. Mosquito traps were hung above the ground at approximately 1.5 and 10 m of residential houses. Mosquito larvae were collected using plastic dippers from various water sources at the study site. The collected larvae were placed in plastic trays and their collection dates and sources were labeled on the lids. The samples were transported to the laboratory at the College of Allied Health Sciences, Suan Sunandha Rajabhat University, Thailand. The larvae were reared in white plastic trays (25 × 30 × 5 cm) under laboratory conditions (25°C–28°C, a 12–12-h light–dark cycle, and 50%–60% relative humidity). At the pupal stage, they were transferred into mosquito cages (30 × 30 × 30 cm) to facilitate adult stage emergence. All adult female mosquitoes, identified based on their morphological features under a stereomicroscope using several standard taxonomic keys, were killed in the freezer at −20°C [41–47]. Then, the mosquito samples identified were placed in 1.5-mL microcentrifuge tubes (one sample per tube) with 95% ethanol and stored in the freezer at −20°C until DNA extraction.
Map of the study sites (A) and mosquito-collection methods used, including adult mosquito trapping (B) and larvae dipper (C). Mosquito samples were collected from 22 provinces from six geographic regions of Thailand, comprising the Northern region (green), including (1) Mae Hong Son, (2) Chiang Mai, and (3) Nan; the Western region (pink) including (4) Tak, (5) Kanchanaburi, (6) Ratchaburi, and (7) Phetchaburi; the Central region (gray), including (8) Nakhon Pathom and (9) Samut Songkhram; the Eastern region (red), including (10) Chachoengsao, (11) Chanthaburi, and (12) Trat; the Northeastern region (yellow), including (13) Chaiyaphum, (14) Nakhon Ratchasima, (15) Surin, and (16) Ubon Ratchathani; and the Southern region (blue), including (17) Phang Nga, (18) Surat Thani, (19) Nakhon Si Thammarat, (20) Krabi, and (21) Narathiwat. Free map provided by USGS National Map Viewer (public domain): http://viewer.nationalmap.gov/viewer/.
DNA extraction, PCR amplification, and sequencing
Total genomic DNA was extracted from 2 to 4 legs of individual female mosquito specimens using the FavorPrep™ Mini Kit (Favorgen Biotech, Ping-Tung, Taiwan), according to the manufacturer’s protocol. All final DNA products were suspended in 50 μL elution buffer and stored at −20°C until PCR amplification.
We amplified an approximately 707-bp-long mitochondrial COI fragment using PCR with universal barcode primers COI_F (forward primer: 5′-GGA TTT GGA AAT TGA TTA GTT CCT T-3′) and COI_R (reverse primer: 5′-AAA AAT TTT AAT TCC AGT TGG AAC AGC-3′) recommended by Kumar et al. [48]. Each 25-μL PCR reaction mixture contained 4 μL of DNA template, 1× reaction buffer, 1.5 mM MgCl2, 0.2 mM dNTPs, 0.2 μM forward primer, 0.2 μM reverse primer, 5% dimethyl sulfoxide, 1.5 U Platinum Taq DNA polymerase (Invitrogen), and distilled water up to 25 μL. Negative controls were used in every PCR reaction by adding all reagents except genomic DNA. The thermal cycling conditions for PCR were set as follows: 5 min for initial denaturation at 95°C, followed by five cycles of denaturation for 40 s at 94°C, annealing for 60 s at 45°C, and extension for 1 min at 72°C; 35 cycles of denaturation for 40 s at 94°C, annealing for 60 s at 54°C, and extension for 1 min at 72°C, with final extension for 10 min at 72°C.
The quality of the PCR products was examined by electrophoresing the samples on 1.5% agarose gels and TBE buffer and staining with the Midori Green DNA stain (Nippon Gene, Tokyo, Japan). DNA was visualized using the ImageQuant LAS 500 imager (GE Healthcare Japan Corp., Tokyo, Japan) before nucleotide sequencing. The PCR products that showed clear DNA bands in a volume of 1 μL were sent to SolGent, Inc. (Daejeon, Korea) for nucleotide sequencing in both directions using the PCR primers.
Sequence analyses
We manually edited the obtained trace files of the COI sequences, removed ambiguous bases, and created consensus sequences of each specimen based on forward and reverse sequences using the BioEdit software [49]. We excluded the samples with abnormalities in the nucleotide sequences, including ambiguities between sequences and double peaks, from genetic analyses to prevent problems from nuclear mitochondrial pseudogenes [50]. Multiple sequence alignments were performed using Clustal W [51] incorporated into MEGA X [52], with adjustment manually. Next, the consensus sequences, with detailed specimen field data, were uploaded to the Barcode of Life Data Systems (BOLD) database. The Refined Single Linkage (RESL) algorithm in BOLD was automatically used to assign the barcode sequences of mosquito species to Barcode Index Numbers (BINs) for assigning Operational Taxonomic Units (OTUs) (the proxy for species) [53]. Specimens assigned in the same BIN were presumed to belong to the same mosquito species.
For genetic distances, the intraspecific sequence divergence for each species was estimated using the nucleotide substitution model Kimura 2 Parameters (K2P) under MEGA X [50]. The distribution of the genetic distance to the nearest neighbor (NN) of each species was estimated based on K2P using the barcode gap analysis function in BOLD. Sequence polymorphic sites and number of haplotypes were estimated using DNA Sequences Polymorphism software version 6 [54]. The presence or absence of the “barcoding gap” of the mosquito species was evaluated using intraspecific values and the minimum interspecific distance (NN distance) [55].
The maximum likelihood (ML) tree—based on the general time-reversible model, with gamma-distribution rates plus invariant sites (GTR + G + I)—was constructed using MEGA X with bootstrapping (1,000 replicates) to visualize the genetic relationships between mosquito species belonging to the Anophelinae and Culicinae subfamilies, as described by Harbach [1]. All phylogenetic trees were edited and graphically prepared using FigTree v.1.4.3. (http://tree.bio.ed.ac.uk/software/Figtree/).
The assemble species by automatic partitioning (ASAP) method based on the simple distance (p-distances) [56] and the BIN-RESL algorithm method were used for molecular species delimitation. The TaxonDNA software [57] was used to diagnose identification success, ambiguous sequences, and misidentified sequences in the COI barcode dataset based on three query identification analyses to investigate the frequency of successful identification. These included the “Best Match” (considered from sequences with the smallest genetic distance to query all conspecifics [the closest match]), “Best Close Match” (considered from sequences with the smallest genetic distance to query all conspecifics and under the threshold value of 95% for all intraspecific genetic distances), and “All Species Barcodes” (considered from all conspecific sequences topping the list of the best matches and within the same threshold value as the best close match) methods [57]. In this study, the thresholds for “Best Close Match” and “All Species Barcodes” were set at 1% based on the standard cutoff value fixed by the BOLD database [58]. After that “Identification Engine” in BOLD was used to test the identification efficiency of the COI sequences obtained from the present study. This tool can help assess the identification efficiency of the COI sequences once compare with available COI sequences reported in the public database.
Results
A total of 73 identified species from 14 genera were collected from several provinces in Thailand: Aedes (7 species), Aedeomyia (1 species), Anopheles (30 species), Armigeres (4 species), Collessius (1 species), Coquillettidia (2 species), Culex (15 species), Lutzia (3 species), Mansonia (5 species), Mimomyia (1 species), Ochlerotatus (1 species), Rhinoskusea (1 species), Toxorhynchites (1 species), and Uranotaenia (1 species) (S1 Table). A total of 310 mosquito samples, representing 73 mosquito species, were amplified using COI primers. These COI barcode sequences (not revealing insertions, deletions, stop codons, and pseudogenes) were submitted to the GenBank and BOLD databases.
DNA sequence analyses
All COI sequences of the mosquito specimens were found to be adenosine- and thymine-rich (AT-rich) (an average of 68.4%), with an average nucleotide composition of thymine (T) = 38.5%, adenine (A) = 29.9%, cytosine (C) = 15.9%, and guanine (G) = 15.7%. DNA polymorphism analyses of the 310 COI sequences showed 417 monomorphic variable, 277 polymorphic variable, 271 parsimony informative, and 6 singleton variable sites. Haplotype analyses revealed 225 haplotypes from the 310 COI sequences (Table 1).
BIN analysis
A total of 310 barcode sequences were assigned to the existing BINs in BOLD based on OTUs by the RESL algorithm. BIN analysis revealed that our COI dataset contained 75 BINs, representing 73 mosquito species (Table 2). Sixty-six species perfectly clustered into a single BIN. Four species were split into two BINs, namely: An. annularis (BOLD: AAR3272 and AEG7105), An. dirus (BOLD: AAC7100 and ABZ2357), An. subpictus (BOLD: AAA4215 and ABY5601), and An. tessellatus (BOLD: AAT9116 and ACW0305). Two species pairs including An. baimaii and An. dirus (BOLD: ABZ2357), and Lt. fuscana and Lt. chiangmaiensis (BOLD: AAG3834) shared the same BINs.
In this study, four mosquito species were identified as new COI sequence records: An. pseudojamesi, Collessius macfarlanei, Mimomyia aurea, and Rhinoskusea longirostris and were assigned unique BINs: AEQ7311, AEP6081, AEP3699, and AEP6082, respectively.
Sequence divergence
The average intraspecific genetic variation of the 73 mosquito species, based on the K2P distance method, was 0.6% (range = 0–2.6%). The highest average intraspecific divergence was observed in An. annularis at 2.6% (range = 0–4.3%), followed by An. tessellatus at 2.5% (range = 0.3–5.7%) (Table 1). While, average minimum interspecific genetic variation (the distance to the nearest neighbour) of the 73 mosquito species was 7% ranged from 0.3–12.9%. The greatest minimum interspecific divergence was found in Tx. splendens (closest to Ae. poicilius, 12.9%), followed by Mi. aurea (closest to Lt. chiangmaiensis, 12.4%) (Table 2). The lowest minimum interspecific divergence was found in An. baimaii (closest to An. dirus, 0.3%) and An. dirus (closest to An. baimaii, 0.3%), followed by Lt. fuscana (closest to Lt. chiangmaiensis, 1.6%) and Lt. chiangmaiensis (closest to Lt. fuscana, 1.6%).
Barcoding gap analysis and species identification efficiency
To estimate the presence or absence of a “barcode gap,” intraspecific genetic distance was compared with the minimum interspecific genetic distance. Barcode gap analysis revealed that An. baimaii and An. dirus had overlap (absence of a barcode gap) (Fig 2). The identification success rates of our COI barcode sequences based on the “Best Match,” “Best Close Match,” and “All Species Barcodes” methods were 97.7%, 91.6%, and 81%, respectively; those of ambiguous identification were 0%, 0%, and 11.6%, respectively; and those of incorrect identification were 2.3%, 1%, and 0%, respectively (Fig 3). The percentages of sequences without any match closer than 1% in the “Best Close Match” and “All Species Barcodes” methods were the same (at 7.4%).
(A) Maximum intraspecific distances compared with minimum interspecific distances (distance to the nearest species), and (B) Mean intraspecific distances compared with minimum interspecific distances. Species dots above the 1:1 line show the presence of a “barcode gap,” whereas those on and below the 1:1 line show the absence of a “barcode gap”.
The threshold value for “Best Close Match” and “All Species Barcodes” was set at 1%.
Comparisons of the COI sequences in this study with all available COI sequences reported in the public database were performed using the BOLD Identification Engine. Sixty-six species were correctly identified (based on top matches, Table 3). Whereas three mosquito species were ambiguous including Ae. vittatus (confused with Ae. cogilli), An. dirus (confused with An. baimaii) and An. baimaii (confused with An. dirus). In addition, high levels of genetic diversity based on the gap between maximum and minimum values in some mosquito species indicated that they are species complex such as An. annularis (93.3%–100%), An. culicifacies (94.8%–99.8%), An. maculatus (93%–100%), An. subpictus (91.1%–100%), and An. tessellatus (92.1%–99.8%).
Phylogenetic analysis and species delimitations
Maximum likelihood phylogenetic analysis in Fig 4 reveals the genetic relationships between 112 COI sequences, representing 30 morphologically identified species in the subfamily Anophelinae and Tx. splendens (OL743111) as an outgroup. Phylogenetic analyses of Anopheles COI sequences demonstrated a clear separation between almost all species, with high bootstrap support values (97%–99%), except for those between An. dirus and An. baimaii. Results from species delimitation of Anopheles mosquitoes based on the BIN-RESL algorithm were consistent with those of ML phylogenetic analysis (Fig 4). Anopheles baimaii and An. dirus are clustered together. Three species: An. annularis, An. tessellatus, and An. subpictus were split into two subclusters based on BIN assignment. By contrast, ASAP based on the simple distance failed to separate many Anopheles species pairs, including An. minimus and An. harrisoni, An. nitidus and An. pursati, and An. dissidens + An. wejchoochotei + An. saeungae (Fig 4).
Bootstrap values (1000 replicates) are shown near each branch (numbers in red). Vertical bars indicate species delimited using the assemble species by automatic partitioning (ASAP) (green bars) and the BIN-refined single linkage analysis (RESL) algorithm (blue bars) methods. Toxorhynchites splendens (OL743111) was used as an outgroup to root the tree. The pink branches showed subgrouping in the same mosquito species.
The phylogenetic tree of the mosquito species in the subfamily Culicinae based on 198 COI sequences representing 43 morphologically identified species is presented in Fig 5. All Culicinae mosquitoes showed a clear separation between species, with high bootstrap support values (98–100%), whereas Phlebotomus papatasi (MN086383), an outgroup species, was distinct from other mosquito species. The BIN-RESL algorithm and ASAP delimited mosquito species of the subfamily Culicinae into 42 taxa, as Lt. fuscana and Lt. chiangmaiensis were not separated, which is not consistent with the results of the ML phylogenetic analysis (Fig 5).
Bootstrap values (1000 replicates) are shown near each branch (numbers in red). Vertical bars indicate species delimited using the assemble species by automatic partitioning (ASAP) (green bars) and the BIN-refined single linkage analysis (RESL) algorithm (blue bars) methods. Phlebotomus papatasi (MN086383).
BIN analysis indicated that An. annularis, An. tessellatus, and An. subpictus could be different cryptic species in our samples collected because they were clustered into two BINs. Therefore, we compared their sequences with published COI sequences from the GenBank database (Figs 6–8). The ML-based phylogenetic analysis of the An. annularis complex revealed that the three specimens from Ratchaburi province, Western Thailand (OL744383-84 and OL742837), were the closest to An. annularis species B from Sri Lanka (MH330210 and KX599416) and India (AY917197), whereas one specimen from Ratchaburi province, western Thailand (OL742836), and two specimens from Narathiwat province, Southern Thailand (OL74382 and OL72838), were separated from An. annularis species A and B (Fig 6). Phylogenetic analyses of the An. tessellatus complex revealed that four of our specimens from Nan province, Northern Thailand (OL742926-29), were the closest to An. tessellatus species C from China (JQ728051) and Vietnam (MT380511) (Fig 7). By contrast, one specimen from Phang Nga province, Southern Thailand (OL742930), was the closest to An. tessellatus species A from Singapore (KF564697 and KF564699). The phylogenetic tree of the An. subpictus complex revealed that all our specimens were separated from the An. subpictus species A in India (DQ267688 and DQ310146) and Sri Lanka (KC191814) and species B in India (DQ310149), Sri Lanka (KC191820), and Myanmar (HQ609031) (Fig 8).
This tree was constructed using the Tamura 3-parameter substitution model with gamma distribution. Bootstrap values (1000 replicates) are shown near each branch (numbers in red). Anopheles pallidus (MH330200) was used as an outgroup to root the tree. The bold font is samples in this study.
This tree was constructed using the Tamura 3-parameter substitution model with gamma distribution. Bootstrap values (1000 replicates) are shown near each branch (numbers in red). Anopheles sinensis (OL742920) was used as an outgroup to root the tree. The bold font is samples in this study.
This tree was constructed using the Tamura 3-parameter substitution model with gamma distribution. Bootstrap values (1000 replicates) are shown near each branch (numbers in red). Anopheles minimus (KX668149) was used as an outgroup to root the tree. The bold font is samples in this study.
Discussion
The correct identification of mosquito species is important for choosing the optimal vector control approach for the target mosquito species [9]. This study represents a comprehensive mosquito survey in Thailand to compile the COI barcode data in an international reference library. We analyzed 310 COI nucleotide sequences of mosquitoes, classified into 73 species in 12 genera. Our results provide evidence supporting DNA barcoding as a genetic approach for identifying mosquito species in Thailand.
Our DNA barcode sequences of mosquito specimens had 68.4% AT-richness on average, which is consistent with the results of other studies describing the AT-richness of COI sequence in mosquitoes [36,59]. Phylogenetic analyses revealed that almost all mosquito species (71 of 73 species) in the subfamilies Anophelinae and Culicinae were classified according to morphologically identified species (bootstrap support > 97%). COI sequence differences at the nucleotide level between groups were used to identify distinct mosquito species in studies from several countries, including Canada [60], China [36], Pakistan [37], Singapore, Sri Lanka [38], the United Kingdom [61], and Mexico [62]. However, in this study, we grouped An. baimaii and An. dirus genetically into the same clade. This was consistent with the results from BIN assignment. Similarly, previous studies have reported that nucleotide sequences of An. baimaii and An. dirus COI exhibit low interspecific differences [63,64]. Chaiphongpachara et al. [65] recently used DNA barcoding to separate An. dirus and An. baimaii in the Thai-Cambodia border, and resulted in the same failure based on low interspecific differences for the mtDNA COI gene. They described both Anopheles as sibling species which are genetically close species.
In addition, the results of BOLD identification engine based on the comparison with available COI sequences in the public database revealed that Ae. vittatus and Ae. cogilli were unsuccessfully identified by DNA barcoding because of overlap between genetic distances of the Ae. vittatus and Ae. cogilli sequences. Similarly, Díez-Fernández et al. [66] recently reported that both Aedes mosquitoes had low interspecific differences in their COI nucleotide sequences. However, both species could be distinguished based on their morphology using available taxonomic keys and Ae. cogilli has not been reported in Thailand.
For COI sequence-based species delimitation, the BIN-RESL algorithm method was more accurate than the ASAP method. The results of the BIN-RESL algorithm method was almost all consistent with those of the ML tree, except for those between Lt. fuscana and Lt. chiangmaiensis. By contrast, the ASAP method does not efficiently distinguish between species members of groups that are genetically close, including the Minimus complex (An. minimus and An. harrisoni), Nigerrimus subgroup (An. nitidus and An. pursati), Barbirostris complex (An. dissidens, An. wejchoochotei, and An. saeungae), and Lutzia genera (Lt. fuscana and Lt. chiangmaiensis). However, more than one method must be used for species delimitation, because each method has its own advantages [56]. The BIN-RESL algorithm method involves clustering based on the assignment of OTUs and putative species from sequence data using RESL [53], whereas ASAP in this study was based on a simple distance (p-distances) substitution model that uses threshold values to distinguish between inter- and intraspecific divergence for building species partitions from barcode data sets [56]. However, we checked our COI sequences with other substitution models and found that the Jukes-Cantor (JC69) model gave better results (see the S1 Fig). This Jukes-Cantor is a DNA substitution model, in which each base is substituted by any other at an equal rate [67]. Thus, Jukes-Cantor (JC69) model for ASAP can be used to distinguish mosquitoes that are genetically close species such as Lt. chiangmaiensis, Lt. fuscana, An. minimus, An.harrisoni, An.wejchoochotei, An.saeungae and An.dissidens.
The investigation results of Lt. fuscana and Lt. chiangmaiensis by the BIN-RESL algorithm and ASAP methods were not consistent with those of the ML tree. Because their COI nucleotide sequences were low interspecific differences (the distance to NN = 1.6%). Similarly, a previous study in Thailand reported that nucleotide sequences of Lt. fuscana and Lt. chiangmaiensis COI exhibited low interspecific variations (genetic distances = 0.2–2.4%) [68]. However, ML phylogenetic analysis can assist in the assessment of both species based on this study. Thus, phylogenetic analysis remains critical in species assessment and should be applied to DNA barcoding.
Although the results of the identification success rates based on “Best Match,” “Best Close Match,” and “All Species Barcodes” of our COI barcode dataset revealed quite high values, intra- and interspecific genetic divergence showed the absence of the “barcoding gap” between mosquito species because of the slight overlap between An. baimaii and An. dirus (the distance to NN is less than the maximum intraspecific distance [55]). The barcode gap, a hiatus between COI intraspecific and interspecific genetic distances, is important for DNA barcoding and is effective in species identification [69,70]. Several studies have shown the absence of the barcode gap in mosquitoes found in very close species or species complexes, containing closely related taxa [35,70,71]. However, finding two mosquito species with an overlap between intra- and interspecific divergence values does not mean that DNA barcoding failed to identify mosquito species, with error accounting for only 2.7% (2 out of 73 species).
Furthermore, An. paraliae samples were classified as An. lesteri based on BIN assignment. This contradiction can be explained on the basis of a study by Taai et al. [72], who reported that both Anopheles mosquito species could represent the same species based on crossing experiments, morphological variations, and genetic relationships, which are based on second internal transcribed spacer (ITS2) of ribosomal DNA and COI and COII of mitochondrial DNA [72]. The taxonomic changes in mosquitoes are frequently due to the discovery of new biological evidence, such as morphological and genetic evidence [1]. In this study, new scientific names of three mosquito species, namely, Ochlerotatus vigilax, Rh. longirostris, and Co. macfarlanei were used [1]. However, the BOLD database remains unmodified, with all three mosquito species still Aedes vigilax, Aedes longirostris, and Aedes macfarlanei. Therefore, BOLD users should have basic taxonomic knowledge to avoid confusion.
For species investigation, DNA barcoding cannot separate mosquito species with low/no interspecific differences in their COI nucleotide sequences. However, several studies have reported that DNA barcoding can reveal the cryptic species of some mosquito complexes [62,71]. Usually, insects display approximately ≤2% intraspecific genetic divergence [71,73,74]. The assessment of COI nucleotide sequence difference within species showed that mosquito species with the high intraspecific divergence (>2%) included An. annularis (2.6%), with two BINs (BOLD: AAR3272 and AEG7105) and An. tessellatus (2.5%) with two BINs (BOLD: AAT9116 and ACW0305). Additionally, ML tree analysis of mosquito species in the subfamily Anophelinae showed subgroup separations in An. annularis, An. tessellatus, and An. subpictus groups. Subsequently, phylogenetic analyses of our An. annularis and An. tessellatus specimens against previous COI sequences of sibling species within their species complexes revealed that they were potentially composed of two sibling species within both species groups.
Currently, the An. annularis complex contains two sibling species A and B [75]. In this study, phylogenetic analysis revealed that the three An. annularis specimens correspond to An. annularis species B, whereas the other three specimens were distinct from species A and B. It is possible that it may be a new An. annularis species “C”. However, sibling species assessment at this time is preliminary. Further investigations must be performed in detail using several DNA markers, such as ITS2 16S rDNA and 18S rDNA [76]. Anopheles annularis is an important malaria vector in Nepal and Bangladesh and a secondary malaria vector in India and Sri Lanka [77]. In Thailand, An. annularis is an important malaria vector in the Tak province, based on the detection of both Plasmodium falciparum and P. vivax in this mosquito species [7]. However, the sibling species of this complex investigation in several countries remains unclear, except for those in India, Sri Lanka, and Pakistan [77].
A recent study reported that the An. tessellatus complex includes at least six sibling species: A, B, C, D, E, and F [78]. In addition, these species can transmit Plasmodium parasites in Sumatra, Indonesia [79], and Sri Lanka [80]. Our analysis of phylogenetic relationships showed that the specimens correspond to An. tessellatus species A and C. Specimens from Nan province, Northern Thailand, were grouped into An. tessellatus species C, which is consistent with previous studies that described that they are widely spread in mainland Southeast Asia, such as China and Laos, a territory near Northern Thailand [78]. One specimen from the Phang Nga province, Southern Thailand, was grouped into An. tessellatus species A, which is consistent with previous studies that found that this species was distributed in Singapore, where it was nearly located in the Southern region of Thailand [78].
Likewise, An. subpictus was investigated because this mosquito is reported to be species complex. The An. subpictus complex contain four sibling species: A, B, C, and D [75]. In this study, phylogenetic analyses revealed that no An. subpictus specimen belonged to species A or B. Consistently, a study reported that An. subpictus is distinct from species A and B in Thailand [81]. We could not assess the sibling species of the An. Subpictus complex, because COI or ITS2 can be used to identify only species A and B [81]. Therefore, we recommend performing crossing experiments and cytogenetic analysis, which were used to identify mosquito species in this complex [77].
Conclusions
Our results confirm that DNA barcoding is an effective molecular approach for the accurate identification of mosquitoes in Thailand. However, some mosquitoes that are genetically closely related cannot be identified using this technique based on our findings on An. baimaii and An. dirus. Other modern techniques are needed to support true species identification of two Anopheles mosquitoes. We submitted all COI nucleotide sequences of the mosquitoes obtained in this study to the GenBank and BOLD databases. Our results can be used for species identification and investigating genetic variations related to the geographical distribution of mosquito vectors. Furthermore, this study is the first to report the COI sequences of four mosquito species: An. pseudojamesi, Co. macfarlanei, Mi. aurea, and Rh. longirostris, which were added to the GenBank and BOLD databases.
Supporting information
S1 Fig. Result of ASAP based on Jukes-Cantor (JC69) model.
https://doi.org/10.1371/journal.pone.0275090.s001
(PDF)
S1 Table. Detailed data of mosquito specimens.
https://doi.org/10.1371/journal.pone.0275090.s002
(PDF)
Acknowledgments
We would like to thank the Suan Sunandha Rajabhat University, Thailand for supporting this research.
References
- 1. Harbach RE. Mosquito Taxonomic Inventory. 2022. Available from: https://mosquito-taxonomic-inventory.myspecies.info.
- 2. Killick-Kendrick R. Medical entomology for students. Transactions of the Royal Society of Tropical Medicine and Hygiene. 1996. https://doi.org/10.1016/S0035-9203(96)90345-4.
- 3. Rocklöv J, Dubrow R. Climate change: an enduring challenge for vector-borne disease prevention and control. Nature Immunology. 2020; 21, 479–483 pmid:32313242
- 4.
World Health Organization. WHO Factsheet Vector-borne diseases. Factsheet number 387. 2014; 10. Available from: http://www.who.int/kobe_centre/mediacentre/ vbdfact sheet.pdf.
- 5. Raksakoon C, Potiwat R. Current arboviral threats and their potential vectors in Thailand. Pathogens. 2021; 10(1): 80. pmid:33477699
- 6.
Ministry of Public Health, Thailand. Annual Report. Bureau of Epidemiology: MOPH; 2016. Available from: http://doe.moph.go.th/surdata/index.php.
- 7. Sriwichai P, Samung Y, Sumruayphol S, Kiattibutr K, Kumpitak C, Payakkapol A, et al. Natural human Plasmodium infections in major Anopheles mosquitoes in western Thailand. Parasit Vectors. 2016; 9: 17. pmid:26762512
- 8. Chaiphongpachara T, Laojun S. Wing morphometric variability of the malaria vector Anopheles (Cellia) epiroticus Linton et Harbach (Diptera: Culicidae) for the duration of the rainy season in coastal areas of Samut Songkhram, Thailand. Folia Parasitol (Praha). 2020; 67: 2020.007. pmid:32350157
- 9. Sumruayphol S, Chaiphongpachara T, Samung Y, Ruangsittichai J, Cui L, Zhong D, et al. Seasonal dynamics and molecular differentiation of three natural Anopheles species (Diptera: Culicidae) of the Maculatus group (Neocellia series) in malaria hotspot villages of Thailand. Parasit Vectors. 2020; 13(1): 574. pmid:33176862
- 10. Chaiphongpachara T. Comparison of landmark- and outline-based geometric morphometrics for discriminating mosquito vectors in Ratchaburi Province, Thailand. Biomed Res Int. 2018; 6170502. pmid:30533435
- 11. Müller P, Pflüger V, Wittwer M, Ziegler D, Chandre F, Simard F, et al. Identification of cryptic Anopheles mosquito species by molecular protein profiling. PLoS One. 2013; 8(2): e57486. pmid:23469000
- 12. Walton C, Handley JM, Kuvangkadilok C, Collins FH, Harbach RE, Baimai V, et al. Identification of five species of the Anopheles dirus complex from Thailand, using allele-specific polymerase chain reaction. Med Vet Entomol. 1999; 13: 24–32. pmid:10194746
- 13. Sumruayphol S, Apiwathnasorn C, Ruangsittichai J, Sriwichai P, Attrapadung S, Samung Y, et al. DNA barcoding and wing morphometrics to distinguish three Aedes vectors in Thailand. Acta Trop. 2016; 159: 1–10. https://doi:10.1016/j.actatropica.2016.03.010.
- 14. Ivanova NV, Clare EL, Borisenko AV. DNA barcoding in mammals. Methods Mol Biol. 2012; 858. pmid:22684956
- 15. Bilgin R, Ebeoǧlu N, Inak S, Kirpik MA, Horns JJ, Şekercioǧlu ÇH. DNA barcoding of birds at a migratory hotspot in eastern Turkey highlights continental phylogeographic relationships. PLoS One. 2016; 11(6): e0154454. pmid:27304877
- 16. Vences M, Nagy ZT, Sonet G, Verheyen E. DNA barcoding amphibians and reptiles. Methods Mol Biol. 2012; 858: 79–107. pmid:22684953
- 17. Bhattacharya M, Sharma AR, Patra BC, Sharma G, Seo EM, Nam JS, et al. DNA barcoding to fishes: Current status and future directions. Mitochondrial DNA. 2016; 27(4): 2744–2752. pmid:26057011
- 18. Pentinsaari M, Blagoev GA, Hogg ID, Levesque-Beaudin V, Perez K, Sobel CN, et al. A DNA barcoding survey of an arctic arthropod community: Implications for future monitoring. Insects. 2020; 11(1): 46. pmid:31936447
- 19. Kress WJ, Erickson DL. DNA barcodes: genes, genomics, and bioinformatics. Proc Natl Acad Sci. 2008; 105(8): 2761–2762. pmid:18287050
- 20. Wilson J, Wales S, Kingdom U. DNA barcoding: Bioinformatics work flows for beginners. 2019; 3: 985–995.
- 21. Yang F, Ding F, Chen H, He M, Zhu S, Ma X, et al. DNA barcoding for the identification and authentication of animal species in traditional medicine. Evidence-Based Complement Altern Med. 2018; 2018: 5160254. pmid:29849709
- 22. Adeniran AA, Hernández-Triana LM, Ortega-Morales AI, Garza-Hernández JA, Cruz-Ramos J de la, Chan-Chable RJ, et al. Identification of mosquitoes (Diptera: Culicidae) from Mexico State, Mexico using morphology and COI DNA barcoding. Acta Trop. 2021; 213: 105730. pmid:33096064
- 23. Chan A, Chiang LP, Hapuarachchi HC, Tan CH, Pang SC, Lee R, et al. DNA barcoding: Complementing morphological identification of mosquito species in Singapore. Parasit Vectors. 2014; 7: 569. pmid:25498759
- 24. Rodrigues MS, Morelli KA, Jansen AM. Cytochrome c oxidase subunit 1 gene as a DNA barcode for discriminating Trypanosoma cruzi DTUs and closely related species. Parasit Vectors. 2017; 10(1): 488. pmid:29037251
- 25. Pramual P, Wongpakam K, Adler PH. Cryptic biodiversity and phylogenetic relationships revealed by DNA barcoding of Oriental black flies in the subgenus Gomphostilbia (Diptera: Simuliidae). Genome. 2010; 54(1): 1–9. https://doi:10.1139/G10-100.
- 26. Bakhoum MT, Sarr M, Fall AG, Huber K, Fall M, Sembène M, et al. DNA barcoding and molecular identification of field-collected Culicoides larvae in the Niayes area of Senegal. Parasit Vectors. 15: 1–10. https://doi:10.1186/s13071-018-3176-y.
- 27. Dokianakis E, Tsirigotakis N, Christodoulou V, Poulakakis N, Antoniou M. Identification of wild-caught phlebotomine sand flies from Crete and Cyprus using DNA barcoding. Parasit Vectors. 2018; 11(1): 94. pmid:29454363
- 28. Changbunjong T, Bhusri B, Sedwisai P, Weluwanarak T, Nitiyamatawat E, Chareonviriyaphap T, et al. Species identification of horse flies (Diptera: Tabanidae) in Thailand using DNA barcoding. Vet Parasitol. 2018; 259: 35–43. pmid:30056981
- 29. Changbunjong T, Weluwanarak T, Sedwisai P, Ruangsittichai J, Duvallet G, Chareonviriyaphap T. New records and DNA barcoding of deer flies, Chrysops (Diptera: Tabanidae) in Thailand. Acta Trop. 2020; 210: 105532. pmid:32497542
- 30. Buenaventura E, Valverde-Castro C, Wolff M, Triana-Chavez O, Gómez-Palacio A. DNA barcoding for identifying synanthropic flesh flies (Diptera, Sarcophagidae) of Colombia. Acta Trop. 2018; 182: 291–297. pmid:29408406
- 31. Murugan K, Vadivalagan C, Karthika P, Panneerselvam C, Paulpandi M, Subramaniam J, et al. DNA barcoding and molecular evolution of mosquito vectors of medical and veterinary importance. Parasitol Res. 2016. 115(1): 107–121. pmid:26358100
- 32. Gao B, Fang Y, Zhang J, Wu R, Xu B, Xie L. A DNA barcoding based study to identify main mosquito species in Taiwan and its difference from those in mainland China. Comb Chem High Throughput Screen. 2017. 20(2): 147–152. pmid:28215143
- 33. Karthika P, Vadivalagan C, Thirumurugan D, Ravi R, Murugan K, Canale A, et al. DNA barcoding of five Japanese encephalitis mosquito vectors (Culex fuscocephala, Culex gelidus, Culex tritaeniorhynchus, Culex pseudovishnui and Culex vishnui). Acta Trop. 2018; 183: 84–91. pmid:29625090
- 34. Batovska J, Blacket MJ, Brown K, Lynch SE. Molecular identification of mosquitoes (Diptera: Culicidae) in southeastern Australia. Ecol Evol. 2016; 6(9): 3001–3011. pmid:27217948
- 35. Cywinska A, Hunter FF, Hebert PDN. Identifying Canadian mosquito species through DNA barcodes. Med Vet Entomol. 2006; 20: 413–424. pmid:17199753
- 36. Wang G, Li C, Guo X, Xing D, Dong Y, Wang Z, et al. Identifying the Main Mosquito Species in China Based on DNA Barcoding. 2012; 7: 1–11.
- 37. Ashfaq M, Hebert PDN, Mirza JH, Khan AM, Zafar Y, Mirza MS. Analyzing mosquito (Diptera: Culicidae) diversity in Pakistan by DNA barcoding. PLoS One. 2014; 9(5): e97268. pmid:24827460
- 38. Weeraratne TC, Surendran SN, Parakrama Karunaratne SHP. DNA barcoding of morphologically characterized mosquitoes belonging to the subfamily Culicinae from Sri Lanka. Parasit Vectors. 2018; 11(1): 266. pmid:29695263
- 39. Motoki MT, Wilkerson RC, Sallum MAM. The Anopheles albitarsis complex with the recognition of Anopheles oryzalimnetes Wilkerson and Motoki, n. sp. and Anopheles janconnae Wilkerson and Sallum, n. sp. (Diptera: Culicidae). Mem Inst Oswaldo Cruz. 2009; 104(6): 823–850. pmid:19876554
- 40. Bourke BP, Oliveira TP, Suesdek L, Bergo ES, Sallum MAM. A multi-locus approach to barcoding in the Anopheles strodei subgroup (Diptera: Culicidae). Parasit Vectors. 2013; 6. pmid:23597081
- 41. Taai K, Harbach RE. Systematics of the Anopheles barbirostris species complex (Diptera: Culicidae: Anophelinae) in Thailand. Zool J Linn Soc. 2015.
- 42. Rattanarithikul R, Harrison BA, Panthusiri P, Coleman RE. Illustrated keys to the mosquitoes of Thailand. I. Background; geographic distribution; lists of genera, subgenera, and species; and a key to the genera. Southeast Asian J Trop Med Public Health. 2005; 36: 1–80.
- 43. Rattanarithikul R, Harbach RE, Harrison BA, Panthusiri P, Jones JW, Coleman RE. Illustrated keys to the mosquitoes of Thailand. II. Genera Culex and Lutzia. Southeast Asian J Trop Med Public Health. 2005.
- 44. Rattanarithikul R, Harrison BA, Panthusiri P, Peyton EL, Coleman RE. Illustrated keys to the mosquitoes of Thailand: III. Genera Aedeomyia, Ficalbia, Mimomyia, Hodgesia, Coquillettidia, Mansonia, and Uranotaenia. Southeast Asian J Trop Med Public Health. 2006.
- 45. Rattanarithikul R, Harbach RE, Harrison BA, Panthusiri P, Coleman RE, Richardson JH. Illustrated keys to the mosquitoes of Thailand. VI. Tribe Aedini. Southeast Asian J Trop Med Public Health. 2010.
- 46. Rattanarithikul R, Harrison BA, Harbach RE, Panthusiri P, Coleman RE. Illustrated keys to the mosquitoes of Thailand IV. Anopheles. Southeast Asian J Trop Med Public Health. 2006.
- 47. Rattanarithikul R, Harbach RE, Harrison BA, Panthusiri P, Coleman RE. Illustrated keys to the mosquitoes of Thailand V. Genera Orthopodomyia, Kimia, Malaya, Topomyia, Tripteroides, and Toxorhynchites. Southeast Asian J Trop Med Public Health. 2007; 38 Suppl 2.
- 48. Kumar NP, Rajavel AR, Natarajan R, Jambulingam P. DNA barcodes can distinguish species of Indian Mosquitoes (Diptera: Culicidae). J Med Entomol. 2007. 44(1): 1–7. pmid:17294914
- 49. Hall TA. BIOEDIT: a user-friendly biological sequence alignment editor and analysis program for Windows 95/98/ NT. Nucleic Acids Symp Ser. 1999; 41.
- 50. Leite LAR. Mitochondrial pseudogenes in insect DNA barcoding: differing points of view on the same issue. Biota Neotrop. 2012; 12.
- 51. Larkin MA, Blackshields G, Brown NP, Chenna R, Mcgettigan PA, McWilliam H, et al. Clustal W and Clustal X version 2.0. Bioinformatics. 2007. 23(21): 2947–2948. pmid:17846036
- 52. Kumar S, Stecher G, Li M, Knyaz C, Tamura K. MEGA X: Molecular evolutionary genetics analysis across computing platforms. Mol Biol Evol. 2018; 35(6): 1547–1549. pmid:29722887
- 53. Ratnasingham S, Hebert PDN. A DNA-based registry for all animal species: The Barcode Index Number (BIN) System. PLoS One. 2013; 8(7): e66213. pmid:23861743
- 54. Rozas J, Ferrer-Mata A, Sanchez-DelBarrio JC, Guirao-Rico S, Librado P, Ramos-Onsins SE, et al. DnaSP 6: DNA sequence polymorphism analysis of large data sets. Mol Biol Evol. 2017; 34(12): 3299–3302. pmid:29029172
- 55. Čandek K, Kuntner M. DNA barcoding gap: Reliable species identification over morphological and geographical scales. Mol Ecol Resour. 2015; 15(2): 268–277. pmid:25042335
- 56. Puillandre N, Brouillet S, Achaz G. ASAP: assemble species by automatic partitioning. Mol Ecol Resour. 2021; 21(2): 609–620. pmid:33058550
- 57. Meier R, Shiyang K, Vaidya G, Ng PKL. DNA barcoding and taxonomy in diptera: A tale of high intraspecific variability and low identification success. Syst Biol. 2006; 55(5): 715–728. pmid:17060194
- 58. Ratnasingham S, Hebert PDN. BOLD: The Barcode of Life Data System: Barcoding. Mol Ecol Notes. 2007; 7(3): 355–364. https://doi:10.1111/j.1471-8286.2007.01678.x.
- 59. Hanemaaijer MJ, Houston PD, Collier TC, Norris LC, Fofana A, Lanzaro GC, et al. Mitochondrial genomes of Anopheles arabiensis, An. gambiae and An. coluzzii show no clear species division. F1000Research. 2019; 7: 347.
- 60. Cywinska A, Hunter FF, Hebert PDN. Identifying Canadian mosquito species through DNA barcodes. Med Vet Entomol. 2006; 20(4): 413–424. pmid:17199753
- 61. Hernández-Triana LM, Brugman VA, Nikolova NI, Ruiz-Arrondo I, Barrero E, Thorne L, et al. DNA barcoding of British mosquitoes (Diptera, Culicidae) to support species identification, discovery of cryptic genetic diversity and monitoring invasive species. Zookeys. 2019; 832: 57–76. pmid:30930645
- 62. Chan-Chable RJ, Martínez-Arce A, Mis-Avila PC, Ortega-Morales AI. DNA barcodes and evidence of cryptic diversity of anthropophagous mosquitoes in Quintana Roo, Mexico. Ecol Evol. 2019; 9(8): 4692–4705. pmid:31031936
- 63. Takenaka Takano K, Thi Hong Nguyen N, Thi Huong Nguyen B, Sunahara T, Yasunami M, Duc Nguyen M, et al. Partial mitochondrial DNA sequences suggest the existence of a cryptic species within the Leucosphyrus group of the genus Anopheles (Diptera: Culicidae), forest malaria vectors, in northern Vietnam. Parasit Vectors. 2010; 3: 41. pmid:20433694
- 64. Sallum MAM, Foster PG, Li C, Sithiprasasna R, Wilkerson RC. Phylogeny of the Leucosphyrus group of Anopheles (Cellia) (Diptera: Culcidae) based on mitochondrial gene sequences. Ann Entomol Soc Am. 2007; 100.
- 65. Chaiphongpachara T, Changbunjong T, Sumruayphol S, Laojun S, Suwandittakul N, Kuntawong K. Geometric morphometrics versus DNA barcoding for the identification of malaria vectors Anopheles dirus and An. baimaii in the Thai‑Cambodia border. Sci. Rep. 2022; 12: 13236. https://doi.org/10.1038/s41598-022-17646-6.
- 66. Díez-Fernández A, Martínez-De La Puente J, Ruiz S, Gutiérrez-López R, Soriguer R, Figuerola J. Aedes vittatus in Spain: Current distribution, barcoding characterization and potential role as a vector of human diseases. Parasit Vectors. 2018; 11: 297. https://doi.org/10.1186/s13071-018-2879-4.
- 67.
Jukes TH, Cantor CR. Evolution of Protein Molecules. In: Munro H.N., Ed., Mammalian Protein Metabolism, Academic Press, New York. 1969; 21–132. http://dx.doi.org/10.1016/ B978-1-4832-3211-9.50009–7.
- 68. Phanitchakun T, Namgay R, Miyagi I, Tsuda Y, Walton C, Harbach RE, et al. Morphological and molecular evidence for a new species of Lutzia (Diptera: Culicidae: Culicini) from Thailand. Acta Trop. 2019; 191: 77–86. pmid:30593818
- 69. Sheraliev B, Peng Z. Molecular diversity of Uzbekistan’s fishes assessed with DNA barcoding. Sci Rep. 2021; 11: 1–12.
- 70. Meier R, Zhang G, Ali F. The use of mean instead of smallest interspecific distances exaggerates the size of the “barcoding gap” and leads to misidentification. Systematic Biology. 2008; 57(5): 809–813. pmid:18853366
- 71. Versteirt V, Nagy ZT, Roelants P, Denis L, Breman FC, Damiens D, et al. Identification of Belgian mosquito species (Diptera: Culicidae) by DNA barcoding. Mol Ecol Resour. 2015; 15(2): 449–457. pmid:25143182
- 72. Taai K, Baimai V, Saeung A, Thongsahuan S, Min GS, Otsuka Y, et al. Genetic compatibility between Anopheles lesteri from Korea and Anopheles paraliae from Thailand. Mem Inst Oswaldo Cruz. 2013; 108(3): 312–320
- 73. Madeira S, Duarte A, Boinas F, Costa Osório H. A DNA barcode reference library of Portuguese mosquitoes. Zoonoses Public Health. 2021; 68: 926–936. pmid:34398521
- 74. Bourke BP, Justi SA, Quiroga LC, Pecor DB, Wilkerson RC, Linton YM. Phylogenetic analysis of the neotropical albitarsis complex based on mitogenome data. Parasit Vectors. 2021; 14(1): 589. pmid:34838107
- 75. Harbach RE. Anopheles Classification. 2021. Available from: http://www.mosquito-taxonomic-inventory.info/node/11358.2019; 11358.
- 76. Lv J, Wu S, Zhang Y, Chen Y, Feng C, Yuan X, et al. Assessment of four DNA fragments (COI, 16S rDNA, ITS2, 12S rDNA) for species identification of the Ixodida (Acari: Ixodida). Parasites and Vectors. 2014; 7: 93. pmid:24589289
- 77. World Health Organization. Anopheline species complexes in South and South-East Asia. 2007. Available from: http://203.90.70.117/PDS_DOCS/B2406.pdf.
- 78. Bourke BP, Wilkerson RC, Linton YM. Molecular species delimitation reveals high diversity in the mosquito Anopheles tessellatus Theobald, 1901 (Diptera, Culicidae) across its range. Acta Trop. 2021; 215: 105799.
- 79. Elyazar IRF, Sinka ME, Gething PW, Tarmidzi SN, Surya A, Kusriastuti R, et al. The distribution and bionomics of Anopheles malaria vector mosquitoes in Indonesia. Advances in Parasitology. 2013; 83: 173–266. pmid:23876873
- 80. Amerasinghe FP, Amerasinghe PH, Peiris JSM, Wirtz RA. Anopheline ecology and malaria infection during the irrigation development of an area of the Mahaweli Project, Sri Lanka. Am J Trop Med Hyg. 1991; 45(2): 226–235. 45. pmid:1877717
- 81. Wilai P, Ali RSM, Saingamsook J, Saeung A, Junkum A, Walton C, et al. Integrated systematics of Anopheles subpictus (Diptera: Culicidae) in the Oriental Region, with emphasis on forms in Thailand and Sulawesi, Indonesia. Acta Trop. 2020; 208: 105503. pmid:32407791