Research Article
Research Article
DNA barcoding of British mosquitoes (Diptera, Culicidae) to support species identification, discovery of cryptic genetic diversity and monitoring invasive species
expand article infoLuis M. Hernández-Triana, Victor A. Brugman§, Nadya I. Nikolova|, Ignacio Ruiz-Arrondo, Elsa Barrero, Leigh Thorne, Mar Fernández de Marco, Andreas Krüger#, Sarah Lumley¤, Nicholas Johnson«, Anthony R. Fooks»
‡ Animal and Plant Health Agency, Surrey, United Kingdom
§ London School of Hygiene and Tropical Medicine, London, United Kingdom
| University of Guelph, Guelph, Canada
¶ Center for Rickettsiosis and Arthropod-Borne Diseases, Logroño, Spain
# Bunderswehr Hospital Hamburg, Hamburg, Germany
¤ Public Health England, Salisbury, United Kingdom
« University of Surrey, Guildford, United Kingdom
» University of Liverpool, Liverpool, United Kingdom
Open Access


Correct mosquito species identification is essential for mosquito and disease control programs. However, this is complicated by the difficulties in morphologically identifying some mosquito species. In this study, variation of a partial sequence of the cytochrome c oxidase unit I (COI) gene was used for the molecular identification of British mosquito species and to facilitate the discovery of cryptic diversity, and monitoring invasive species. Three DNA extraction methods were compared to obtain DNA barcodes from adult specimens. In total, we analyzed 42 species belonging to the genera Aedes Meigen, 1818 (21 species), Anopheles Meigen, 1818 (7 species), Coquillettidia Theobald, 1904 (1 species), Culex Linnaeus, 1758 (6 species), Culiseta Felt, 1904 (7 species), and Orthopodomyia Theobald, 1904 (1 species). Intraspecific genetic divergence ranged from 0% to 5.4%, while higher interspecific divergences were identified between Aedes geminus Peus, 1971/Culiseta litorea (Shute, 1928) (24.6%) and Ae. geminus/An. plumbeus Stephens, 1828 (22.5%). Taxonomic discrepancy was shown between An. daciae Linton, Nicolescu & Harbach, 2004 and An. messeae Falleroni, 1828 indicating the poor resolution of the COI DNA barcoding region in separating these taxa. Other species such as Ae. cantans (Meigen, 1818)/Ae. annulipes (Meigen, 1830) showed similar discrepancies indicating some limitation of this genetic marker to identify certain mosquito species. The combination of morphology and DNA barcoding is an effective approach for the identification of British mosquitoes, for invasive mosquitoes posing a threat to the UK, and for the detection of hidden diversity within species groups.


DNA extraction methods, hidden genetic diversity, molecular identification, vector species


The family Culicidae includes approximately 112 genera and 3,547 described species (Harbach 2017, 2018). Several species are biting pests playing an important role as vectors of pathogens of humans and livestock (Becker et al. 2010). These include chikungunya, dengue, Japanese encephalitis, yellow fever, West Nile, Rift Valley fever, and Zika viruses, as well as several nematodes and protozoans such as Plasmodium Marchiafava & Celli, 1885 (Becker et al. 2010, Medlock et al. 2007). In addition to their medical and veterinary importance, mosquitoes are significant nuisance biters of humans and within the environment may serve additional roles such as key indicators of landscape degradation (Dorvillé 1996, Guedes and Navarro-Silva 2014, Montagner et al. 2018). As a result, mosquitoes are one of the principal target groups within surveillance and control programs worldwide (Hernández-Triana et al. 2017).

Current approaches to species identification still rely heavily upon morphology-based procedures, which typically require substantial training and may not always provide a good resolution on a specimen’s identity due to homogeneity between life stages of different species and the presence of species complexes (Cywinska et al. 2006, Hernández-Triana et al. 2012, 2014, 2015, Linton et al. 2005, Packer et al. 2009, Versteirt et al. 2015). To overcome this obstacle, a small region (658 bp) of the mitochondrial cytochrome c oxidase unit I (COI) gene was proposed as a standardized DNA marker in support of species identification for animal barcodes, in a process commonly referred to as DNA barcoding (Hebert et al. 2003a, b).

Until recently, thirty-four mosquito species have been recorded in the United Kingdom (UK) (Medlock et al. 2015, Medlock and Vaux 2009, 2015). However, Medlock et al. (2017a) detected the presence of Ae. albopictus (Skuse, 1895) in southern England, and Dallimore et al. (2017) collected a single male Ae. aegypti (Linnaeus, 1762) in Merseyside in north west England, although these two invasive species are not believed to be locally established. Nonetheless, these findings demonstrated that the UK is at risk of introduction by invasive species of Aedes (Dallimore et al., 2017). In addition, Harbach et al. (2017) discovered Ae. nigrinus (Eckstein, 1918) in the New Forest, southern England, which brings the total count of named species to 37 [35 native species plus two records of invasive species]. In addition, the occurrence of certain species has been very sporadic as in the case of Ae. vexans (Meigen, 1830); however, Medlock et al. (2017b) reported a resident population of this species at Marston Marshes, Norwich. Although no mosquito-borne pathogen affecting humans or livestock is presently thought to circulate in the UK, there is potential for future pathogen emergence (Medlock and Leach 2015; Medlock et al. 2017a, b; Vaux et al. 2015) and there remains continuing mosquito nuisance in various parts of the country (Brugman et al. 2017a, b). Collectively, these discoveries highlight the need for continued research on the native mosquito fauna of the UK, taking into account the potential incursion of invasive species.

There is, however, a paucity of data on the utility of molecular methods for species identification of the British mosquito fauna. During the first development of a molecular assay for the identification of hybrids and sibling species within Culex pipiens s.l., Smith and Fonseca (2004) used specimens from England and Scotland. Golding et al. (2012) subsequently employed the COI marker to compare sequences of Cx. modestus Ficalbi, 1890 with other Culex Linnaeus, 1758 species in southeast England, and Danabalan et al. (2012) employed a combination of the internal transcribed spacer gene-2 (ITS-2) and COI markers in their assessment of molecular identification tools to determine the status of Cx. pipiens s.l. The same approach was used by Danabalan et al. (2014) to confirm the occurrence of species within the Anopheles maculipennis complex Theobald, 1911 in England and Wales. Recently, Hernández-Triana et al. (2015) employed an integrated approach to determine mosquito host feeding preferences (via identification of blood meal origin), as well as the molecular characterization of mosquito species carrying pathogens such as myxoma virus (Brugman et al. 2015, 2017a, b) and Theileria orientalis Yakimoff & Soudatschenkoff, 1931 within their bloodmeal (de Marco et al. 2016).

In the present paper, we apply the COI DNA barcoding approach in support of the identification of native British mosquitoes and known invasive species in continental Europe. In addition, we assessed the DNA barcode variability using genetic distance methods to detect cryptic diversity across the taxa.

Materials and methods

Collection of specimens

Ten locations were visited between March and October in the years 2012 to 2015 and specimens were collected following the protocols of Brugman et al. (2015, 2017a, b) (see Table 1, Fig. 1). Further samples were obtained by collecting mosquitoes alighting on the collectors and by standard larval dipping procedures followed by laboratory rearing according to Brugman et al. (2015, 2017a, b). All specimens were kept either at -20 °C or dry-pinned, and were morphologically identified using the key of Cranston et al. (1987). We followed the classification of Wilkerson et al. (2015) for taxa in Aedini. The subgeneric placement for all species can be found in Harbach (2017) and Harbach et al. (2017).

The source of specimens from invasive species is as follows: Ae. albopictus – Luke Alphey, UK (colony from Malaysia); Aleksandra Ignjatović-Ćupina, Serbia (wild caught); Ae. aegypti – Shahida Begum, UK (colony from West Africa); Ae. atropalpus (Coquillet, 1902), Ae. japonicus (Theobald, 1901), Ae. koreicus (Edwards, 1917) – Norbert Becker and Daniel Hoffman, Germany, and Ignacio Justicia-Ibáñez, Holland (all wild caught); Culex tritaeniorhynchus Giles, 1901, Filiz Gunay, Turkey (wild caught); Cx. quinquefasciatus Say, 1823 [for sequences from NCBI and further details Suppl. material 1].

Figure 1. 

Location of study sites in the United Kingdom. Key: 1 ADAS Arthur Rickwood; 2 Church Farm; 3 Coombelands Farms; 4 Elmley Nature Reserve; 5 Glendell Livery, Mill Lane; 6 Frimley; 7 Mudchute Farm; 8 Northney Farm, Hayling Island; 9 White Lodge, Bisley; 10 Bartley Heath; 11 Dee Marsh.

Table 1.

Description of key collecting sites with reference to habitat and the main livestock present. Further information can be found in Brugman et al. (2017b).

Locality/Farms County Coordinates Habitat Main livestock types present
1. ADAS Arthur Rickwood Cambridgeshire 52.422560, -0.098302 Grazing farm Sheep
2. Church Farm Oxfordshire 51.715807, -1.380813 Rural area Cattle, sheep
3. Coombelands Farm Surrey 51.360241, -0.652256 Mixed farm Cattle, sheep, pigs, horses
4. Elmley Nature Reserve Kent 51.377587, 0.783954 Grazing marsh Cattle, sheep
5. Glendell Livery, Mill Lane Surrey 51.290499, -0.652256 Mixed woodland Horses
6. Frimley Surrey 51.313037, -0.745237 Peri-urban n/a
7. Mudchute Farm Greater London 51.491732, -0.009367 City farm Cattle, sheep, pigs, horses
8. Northney Farm, Hayling Island Hampshire 50.828166, -0.962151 Arable farm Cattle
9. White Lodge, Bisley Surrey 51.322255, -0.637692 Mixed woodland Cattle
10. Bartley Heath Hampshire 50.919701, -1.565337 Woodland Cattle, horses, deer
11. Dee Marsh Cheshire 52.8322, -3.7656 Salt marsh n/a

DNA extraction methods

Three methods were used for DNA extraction from two mosquito tissue types (Brugman et al. 2015, 2017). Firstly, 1–2 legs of specimens were placed in 100 µl of molecular grade water in a 96-well plate, which was then sonicated at room temperature for 10 min to release DNA (Hunter et al. 2008). Secondly, we employed a modified Hotshot technique (Montero-Pau et al. 2008). In this case, 1–2 legs were placed directly into 50 µl of alkaline lysis buffer in a 96-well plate, which was then sonicated in a water bath for 10 min. The plate was subsequently incubated in a thermocycler for 30 min at 94 °C, cooled for 5 min at 4 °C, and then centrifuged for 3 min at 3000 rpm, after which 50 µl of the neutralizing buffer was added to each sample. The plate was then centrifuged again for 10 min at 3000 rpm, and stored at -80 °C until analysis. Thirdly, engorged female abdomens were processed using Qiagen DNeasy Blood and Tissue kits following the procedures detailed in Brugman et al. (2015, 2017a, b) and the manufacturer’s instructions.

COI DNA barcoding region amplification

For molecular species identification using the COI DNA barcoding region, the protocols of Hernández-Triana et al. (2012, 2014) and Hebert et al. (2003a, b) were followed. We used the primers developed by Folmer et al. (1994), which amplify the 658 bp long target region of the COI gene. PCR products were obtained using a Qiagen PCR system following the reaction mix of Hernández-Triana et al. (2017).

Data analysis

Paired bi-directional sequence traces were combined to produce a single consensus sequence (i.e., the full-length 658 bp barcode sequence). To achieve this, individual forward and reverse traces were oriented, edited, and aligned using the Sequencer (v.4.5; Genes Codes Corporation, Ann Harbour, MI), GenDoc (v. 2.6.02) and ClustalX sequence analysis programs (Hernández-Triana et al. 2017). Full details for each specimen and sequence information can be found at the Barcode of Life Database (BOLD) within the “Human Pathogens and Zoonoses Initiative”, Working Group 1.4. The Digital Object Identifier (DOI) for the publically available projects in BOLD is and Accession numbers for all sequences were obtained from NCBI (accession numbers: MK403007-MK403548). For certain species, we used COI barcode sequences deposited at NCBI due to the lack of available material from UK populations (Table 3; Suppl. material 1). The dataset was analyzed in MEGA v.6 (Tamura et al. 2007). The Neighbor Joining (NJ) analysis was performed using the Kimura 2-Parameter distance metric to determine their distribution pattern. The tree robustness was measured by the bootstrap approach using 1000 pseudoreplicates (Hernández-Triana et al. 2012, 2014). To barcode sequences larger than 500 bp, a Barcode Index Number (BIN) was assigned and each BIN was mapped according to species (see Fig. 2). The taxonomic discordance in the dataset (Hernández-Triana et al. 2017) was analyzed using BOLD as detailed in Ratnasingham and Hebert (2013).

Figure 2. 

Neighbor joining tree of COI DNA barcodes (658 bp) for mosquito species. A divergence of > 2% may be indicative of separate operational taxonomic units. Only bootstrap values higher than 70% are shown.


Assessment of DNA extraction methodologies

In general, adding 1–2 legs to molecular grade water and then sonicating them for 10 min proved to be an effective method for obtaining DNA (30 min total time); however, only 41 barcodes (43.1%) yielded sufficient sequence data for inclusion in our analysis (Table 2). The Hotshot technique also proved to be an efficient approach (1 hour per plate) for processing 1–2 legs with high percentages of target DNA amplification and COI DNA barcode sequences (429 sequences, 90.3%). In terms of cost, reagents for the preparation of the Hotshot working stock buffers were only 200 GBP, one purchase of which we estimate can last up to one year. DNA extraction from blood-engorged abdomens processed using the Qiagen DNeasy Blood and Tissue kit also provided barcodes for 306 specimens (64.4%), but this approach was time consuming, with a sample processing rate of 32 specimens per four hour session of DNA extraction. The time for the DNA extraction was also increased by the limitation of the number of wells in the centrifuge available (30 wells). In addition, non-target PCR product was also encountered as vertebrate DNA present was amplified from cows, chicken, sheep, rabbits and birds [169 samples] (see Table 2).

Table 2.

DNA extraction methods and percentage of PCR amplification success in obtaining COI DNA barcodes from mosquitoes.

Extraction method No. plates / samples Time per plate Amplification success n (%) Observations
1. Legs directly into molecular grade water and sonicated for 10 min 1 plate / 95 samples 30 min 41 (43.1%) High sequencing failure (54 samples)
2. Legs directly into alkaline lysis buffer and sonicated for 10 min (Hotshot) 5 plates / 475 samples 1hr each plate 429 (90.3%) Target length barcodes obtained
3. Abdomen processed using Qiagen kit 5 plates / 475 samples Only 32 samples per 4hr session for DNA extraction for each plate 306 (64.4%) Target length barcodes obtained. Vertebrate DNA amplified

Mosquito species identification using DNA barcoding

In total, we analyzed DNA barcode sequences for 42 species belonging to the genera Aedes (21 species), Anopheles (7 species), Coquillettidia (1 species), Culex (6 species), Culiseta (7 species), and Orthopodomyia (1 species) (Table 3). Of these, we analyzed sequences for 23 of the 37 species of mosquito that have been recorded in the UK (62%) (Harbach et al. 2017, Medlock et al. 2007a, b). In addition, we also analyzed representatives of invasive Aedes species (Ae. aegypti, Ae. albopictus, Ae. japonicus, Ae. koreicus, Ae. atropalpus) and two Culex species (Cx. quinquefasciatus, Cx. tritaeniorhynchus), which are of epidemiological relevance in Europe (Medlock et al. 2017a, b). Three or more representatives were available for 38 morphospecies in the dataset (see Table 3). In total, 1198 barcode sequences were analyzed.

Even though in most cases individuals of the same species clustered together, this was not the case for all species. Within the genus Aedes, the first incongruence was identified between Ae. sticticus (Meigen, 1838) and Ae. nigrinus. Although the majority of specimens from Belgium and the two UK specimens identified as Ae. sticticus (voucher number APHA-4-2015G06, APHA-4-2015G07) grouped together in a separate cluster with 100% bootstrap support, the only two available COI sequences of Ae. nigrinus in NCBI (KP942769, KP942770) grouped with the two specimens collected in Belgium, identified as Ae. sticticus (CULBE-833009, CULBE-833008) (Fig. 2). To further support our identification of the two UK specimens as Ae. sticticus, we obtained ITS-2 sequences (data not shown) and searched the NCBI database using the BLAST algorithm; both queries retrieved Ae. sticticus with 96% match (KF535079) [this relative low percentage could be due to the low coverage of the ITS-2 sequences we obtained (338 bp and 369 bp, respectively)]. Similar results have been obtained by Versteirt et al. (2015) (see Fig. 2). Certain specimens grouped only as Ae. cantans or Ae. annulipes, but another group was composed of Ae. cantans and Ae. annulipes (Fig. 1) with 100% bootstraps support values. Similarly, no successful identification was reached between Ae. cinereus (Meigen, 1818) and Ae. geminus, species which are morphologically similar.

Within Anopheles maculipennis s.l. (Linton et al. 2002, 2005), no accurate identification was achieved between An. messeae [also molecularly identified by ITS-2 in our laboratory; see also Kronefeld et al. 2012] and An. daciae (sensu Nicolescu et al. 2004), although An. atroparvus was clearly separated from the aforementioned species (Fig. 2). This is not surprising as all members of the An. maculipennis complex are phylogenetically related, and cannot be readily identified based on adult morphological traits or only using the COI genetic marker (Linton et al. 2002, Kronefeld et al. 2014, Ruiz-Arrondo et al. 2017). In the genus Culex, COI was not able to separate Culex pipiens s.l. (including both forms pipiens and molestus) and Cx. quinquefasciatus, in agreement with results by Gunay et al. (2015).

Our DNA barcodes dataset from the genus Culiseta separated certain species with high support bootstrap values such as Cs. alaskaensis (Ludlow, 1906), Cs. annulata (Schrank, 1776), Cs. longiareolata (Macquart, 1838) and Cs. subochrea (Edwards, 1921) (Fig. 2). However, we could not achieve the same resolution for Cs. fumipennis (Stephens, 1825), Cs. litorea (Shute, 1928) and Cs. morsitans (Theobald, 1901). The specimens identified as Cs. fumipennis (Versteirt et al. 2015) from Belgium grouped separately from one specimen (KU748471) collected in the UK (de Marco et al. 2016) previously identified as Cs. fumipennis, which clustered with specimens from Belgium identified as Cs. morsitans (CULBE-816017, CULBE-816018, CULBE-997001, CULBE-997002, CULBE-9972101, CULBE-972103) with 99% bootstrap values. Therefore, we now consider the UK specimen to be Cs. morsitans. In addition, seven specimens from the UK identified as Cs. morsitans in de Marco et al. (2016) (KU748440, KU748443, KU748450, KU748453, KU748460, KU748500, KU748488), grouped with 99% bootstraps values with two males recently collected from Spain identified as Cs. litorea. Subsequent dissection of the genitalia of these specimens confirmed their identification as Cs. litorea based on the key of Becker et al. (2010); therefore, we now considered these seven specimens from the UK as Cs. litorea.

The levels of sequence divergence were variable across the taxa, with conspecific individuals collected from a single site often exhibiting zero, or 0.07% to 0.1% divergence values, while other specimens showed higher percentages (see Table 3). The intraspecific genetic divergence measured 1.3%, ranging from 0% to 5.4% (Table 3) (Ae. aegypti, An. plumbeus, Cx. modestus and Cx. quinquefasciatus), while the interspecific divergence ranged between 0.19% to 24.6% (Suppl. material 2). Interspecific genetic divergence values were higher between species from different genera. The pairs Ae. geminus/Cs. litorea (24.6%) and Ae. geminus/An. plumbeus (22.5%) were the most divergent species. As known, the smallest values of genetic divergence were found among species in the same genus, for example Cx. pipiens s.l./Cx quinquefasciatus (0.19%), Ae. cantans/Ae. annulipes (1.2%) and Ae. geminus/Ae. cinereus (0.65%) (Suppl. material 2).

Table 3.

List of mosquito species (in alphabetical order), country of collection, and number of specimens with DNA barcodes. Mean (%) intraspecific values of sequence divergence (Kimura2-Parameter distance) are shown with missing entries indicating that less than two specimens were analyzed. Asterisks indicate species complexes (*) and taxa with deep splits (**) in the Neighbor Joining tree; (***) taxa with above 2% genetic divergence. Invasive species in Europe are in Bold.

Species Collection Country n mean %
Aedes aegypti West Africa 10 0
Aedes albopictus Malaysia; Montenegro 12 0.12
Aedes annulipes Belgium 12 0.89
Aedes atropalpus Holland, USA, Canada 11 0.69
Aedes cantans Belgium; UK 44 0.80
Aedes caspius Belgium; UK 40 0.78
Aedes cinereus Sweden; UK 30 0.61
Aedes communis Belgium 13 0.14
Aedes detritus Belgium; UK 44 0.66
Aedes dorsalis USA; Canada 8 0.16
Aedes flavescens UK 10 0.18
Aedes geminus Germany 4 0.58
Aedes geniculatuss Belgium 16 0.25
Aedes japonicus Germany 14 0.32
Aedes koreicus **;*** Belgium; Holland; Hungary 6 2.19
Aedes leucomelas Sweden 2 0.40
Aedes nigrinus Sweden 2 0.77
Aedes punctor Belgium; UK 47 0.67
Aedes rusticus Belgium; UK 31 0.07
Aedes sticticus Belgium; UK 10 1.29
Aedes vexans** Belgium; Spain; Holland; Sweden; UK 38 1.46
Anopheles algeriensis Spain 6 0.41
Anopheles atroparvus UK; Belgium 91 0.92
Anopheles claviger s.l. Belgium; UK 26 0.65
Anopheles daciae Romania; UK 28 0.76
Anopheles messeae UK 35 1.01
Anopheles plumbeus Belgium; UK 17 0
Coquillettidia richiardii Belgium; UK 42 0.07
Culex modestus Germany; Romania; Turkey; UK 49 0
Culex pipiens s.l.* Belgium; UK 187 0.06
Culex quinquefasciatus Pakistan; Turkey 12 0
Culex territans Belgium; Germany 5 2.05
Culex torrentium Belgium; Germany; UK 66 0.43
Culex tritaeniorhynchus Turkey 5 0.65
Culiseta alaskaensis Canada 3 1.13
Culiseta annulata Belgium; UK 192 0.05
Culiseta fumipennis Belgium 2 0.30
Culiseta litorea*** Spain; UK 9 5.35
Culiseta longiareolata Spain 5 0.12
Culiseta morsitans Belgium; UK 7 0.34
Culiseta subochrea Spain; UK 6 0.34
Orthopodomyia pulcripalpis Austria 1 n/a

In this study, we analyzed three species which are known, or suspected to be, part of species complexes [species which can only be distinguished either by cytotaxonomy or molecular methods (Danabalan et al. 2012, 2014; Linton et al. 2001)]: An. maculipennis s.l., An. claviger s.l. (Meigen, 1804), and Cx. pipiens s.l. All specimens grouped together in either Cx. pipiens s.l. or An. claviger s.l., and we did not detect high levels of genetic diversity or deep splits in the NJ tree as found in other studies (Gunay et al. 2015, Versteirt et al. 2015). This might be due to specimens originating from localities in relatively close proximity to one another in England (Fig. 1). Specimens of Cx. pipiens s.l. in this study originated from the study of Brugman et al. (2015, 2017), in which the CQ11 PCR assay was conducted to separate the forms molestus and pipiens. Only specimens from the typical pipiens form of Cx. pipiens were detected in the aforementioned studies, with 0.06% genetic diversity in our dataset. Nonetheless, not all morphologically identified species clustered as expected. Certain species exhibited higher levels of divergence above 2% (see Table 1) and other showed deep splits in the NJ tree (Fig. 2). For example, intraspecific genetic divergence averaged 1.46% in Ae. vexans, but the specimens separated into two defined clusters (I and II) (Fig. 2). Similarly, Ae. koreicus showed a deep split in the NJ with 2.19% genetic divergence.

The BIN counts in our dataset in BOLD of 721 full length barcode sequences from 1006 records in BOLD datasets found 21 BINs. The BIN analysis did not include sequences downloaded from the NCBI database. In general, 487 barcodes were assigned a BIN number, which represented 14 concordant BINs, three BINs were singletons (Cs. fumipennis BOLD:AAR2210, Ae. geniculatus BOLD:AAM5898, and Cs. subochrea BOLD:AAV90 75), and only four BINs (231 records) were discordant. The discordant BINs occurred at the species level, mainly because of the discrepancy in taxonomic information assigned to certain specimens, for example Ae. cinereus versus Ae. nr. cinereus, and Ae. caspius versus Ae. nr. caspius. Another discordance was in a single specimen identified as Cx. torrentium, which appears to be close to a BIN within Cx. pipiens s.l. (BOLD:AAA4751; Process ID:MSEMV855-15); however, morphological traits in the male genitalia and other analysis (CQ11 PCR) showed that it does belongs to Cx. torrentium (Manley et al. 2015), and it did cluster with this species when further 66 barcode sequences of Cx. torrentium were added to the dataset.


This study assessed minimally destructive approaches that retained a significant part of the sample as a referenced voucher and the development of a COI DNA barcoding library in mosquitoes, and assessed the use of the variability within COI in support of species identification. Overall, the three extraction methods used provided sufficient DNA for subsequent analysis; however the modified Hotshot technique of Montero-Pau et al. (2008) proved to be the most efficient and inexpensive method for obtaining COI DNA barcode sequences. This method has been applied to other groups such as the Hymenoptera (Guzmán-Larralde et al. 2016) with good results as assessed by DNA yield and PCR amplification success. The amplification of vertebrate DNA from engorged abdomens using the Qiagen DNeasy Blood and Tissue extraction kit highlights the need to use insect specific primers, for example LepF/LepR (see Public_Primer_PrimerSeach) instead of the standard Folmer primers (Folmer et al. 1994). In terms of cost, considering that we did not have to buy a DNA extraction kit to perform the DNA extraction for processing the legs, the Hotshot technique represented savings of around 500 GBP per 96-well plate to our laboratory, making it a cost-effective method for performing DNA extractions.

The majority of morphologically identified species in this study formed defined groups in the NJ analysis based on DNA barcodes (Fig. 2), supporting the use of COI DNA barcoding in combination with morphological observation as a suitable approach for species identification. Genetic divergence between morphospecies ranged from 0.19% to 24.6%, whereas intraspecific genetic divergences within distinct species ranged from 0% to 5.4% (average 1.30%; Table 1, Suppl. material 2). Most of the specimens within a morphospecies were resolved in the NJ tree (Fig. 2). However, some individuals in certain taxa such as Ae. annulipes/Ae. cantans, Ae. cinereus/Ae. geminus, Ae. sticticus/Ae. nigrinus, An. daciae/An. messeae, and Cx. quinquefasciatus/Cx. pipiens s.l. clustered together (Fig. 2), indicating some limitations of the COI gene as a marker to separate these species. This finding is not surprising as these taxa are phylogenetically, as it has been highlighted in the literature (Harbach 2017, Harbach et al. 2017).

With regard to Anopheles maculipennis s.l., although some morphological traits in egg structure provide an effective method to separate some members of this group, there is some dispute regarding the taxonomic status of An. daciae (e.g. Linton et al. 2002, 2005, Kronefeld et al. 2012). This species was described by Nicolescu et al. (2004) based on all life stages collected from the Black Sea region in Romania. The authors stated that An. daciae and An. messeae have been misidentified in the past because of similar morphology. However, they showed that An. daciae eggs are generally smaller than those of An. messeae, with patches of larger deck tubercles that contrast more sharply with patches of smaller tubercles to impart greater definition to the mottled surface of the deck (see Nicolescu et al. 2004; fig. 3 A, C). In contrast, the deck of An. messeae eggs has a more diffuse or weakly mottled appearance (see Nicolescu et al. 2004; fig. 3 B, D). In the same study, molecular analysis of ITS-2 sequences identified single nucleotide polymorphisms and unique haplotypes which, in the authors’ views, confirmed the specific status of An. daciae. However, other authors have queried the specific status of An. daciae, and stated that COI offered poor resolution and advocated for further work to determine the status of An. daciae. Similarly, in their study of Belgian mosquitoes, Versteirt et al. (2015) reported that specimens identified as Ae. annulipes and Ae. cantans grouped together in their NJ tree, and stated that COI cannot separate these two species; the same results have been obtained in our study (Fig. 2).

Moreover, COI DNA barcoding highlighted mis-identifications within the genus Culiseta (Cs. fumipennis, Cs. litorea and Cs. morsitans). These species are placed in the subgenus Culicella, and the females are difficult to identify because of their morphological similarity and absence of reliable diagnostic characteristics (Becker 2010, Cranston et al. 1987, Medlock and Vaux 2010). All of our females were collected in traps and were in relatively poor condition. In addition, no COI sequences of reliably identified material of Cs. litorea were available in the NCBI or BOLD databases to compare with our specimens (Ruiz-Arrondo et al. 2017). Because the sequences of females of Cs. morsitans matched the two males identified as Cs. litorea from Spain, we considered all our specimens to be Cs. litorea. In the literature (Medlock et al. 2007), Cs. morsitans is considered a widespread species in the UK, found in permanent waters, while Cs. litorea has a more restricted distribution in coastal regions of southern England. Both species feed mainly on birds, but can also bite humans, and they are considered bridge vectors of arboviruses (Medlock et al. 2007). Our findings highlight the need for careful examination of material obtained from traps in combination with the application of molecular techniques for a reliable identification of these species. Even though in our dataset Ae. vexans showed a low genetic divergence of approximately 2%, specimens of this species separated into two distinct groups in the NJ tree (Fig. 2), which may be an indication of some genetic differentiation within the population. This agrees with the findings of Lilja et al. (2018) in which the authors reported a distinct genotype of Ae. vexans in Europe.

Regarding non-indigenous mosquito species, although the adults of certain species are easily identified using morphological keys, for example Ae. aegypti and Ae. albopictus (Becker 2010, Cranston et al. 1987), the development of a molecular library for species identification is important, in particular when specimens are found in a poor stage of preservation. This is an essential step for the establishment of control measures (Versteirt et al. 2015) in the event of a recent introduction, as in the case of the detection of Ae. albopictus in the UK (see Medlock et al. 2017a, b).

In our dataset, Ae. koreicus and Cs. litorea showed higher intraspecific genetic divergences (Table 1, Fig. 2), which may indicate the presence of cryptic diversity. For all other species, the variation in intra- and interspecific genetic values reported in this study fall within the range for DNA barcoding studies of European mosquitoes (Engdahl et al. 2014, Gunay et al. 2015, Versteirt et al. 2015) or other zoogeographical regions such as the Nearctic and Oriental Regions (Cywinska et al. 2006, Kumar et al. 2007, Murugan et al. 2016). Nonetheless, we advocate the combination of the COI DNA barcoding with other genetic markers such as the Elongator Complex Protein 1 gene (ECP1) (Low et al. 2016, Pangjanda and Pramual 2016, Senatore et al. 2014) and ITS-2 sequences from a larger number of specimens across the species distribution range in order to resolve some of the taxonomic problems highlighted in this study.


This study provides COI DNA barcoding data to support the molecular identification of mosquito species in the UK as well as invasive mosquito species, many of which are currently expanding their geographical range in continental Europe. We augment the barcoding data for anthropophilic species such as Ae. cinereus, Ae. detritus, Ae. sticticus, Ae. vexans, and Cx. modestus, as well as other species of veterinary importance such as the bridge vector Cs. annulata. Even though the majority of specimens were separated by COI, certain taxa could not be distinguished using this genetic marker within the genera Aedes, Anopheles and Culex. The use of COI also underlined identification problems in Culiseta species (Cs. fumipennis, Cs. litorea and Cs. morsitans) within the BOLD and NCBI databases. This finding supports the need for continuing research combining the use of molecular methodologies with morphological traits for species delineation in the Culicidae.


We thank the kind assistance of each of the field site employees without whom the work could not have been completed. Thanks are given to our colleagues at Public Health England (Jolyon Medlock, Alex Vaux, and Ben Cull), The Pirbright Institute (Simon Carpenter) and the Natural History Museum (Ralph Harbach) for discussions on mosquito ecology and distribution. Luis M. Hernández-Triana gives special thanks to Quetzaly Siller (Juarez University, Durango Estate) for the preparation of the distribution map. In addition, we also thank Luke Alphey (The Pirbright Institute, UK), Aleksandra Ignjatović-Ćupina (University of Novi Sad, Faculty of Agriculture, Serbia), Jenny Hesson (Liverpool School of Tropical Medicine, UK), Gale Chapman (School of Veterinary Medicine and Science, University of Nottingham, UK), Shahida Begum (London School of Hygiene and Tropical Medicine, UK), Norbert Becker and Daniel Hoffman (University of Heidelberg, Heidelberg, Germany), Renke Lühken (Bernhard Nocht Institute for Tropical Medicine, WHO Collaborating Centre for Arbovirus and Hemorrhagic Fever Reference and Research, Hamburg, Germany), Adolfo Justicia-Ibáñez (National Centre for Monitoring of Vector, Netherlands Food and Consumer Product Safety Authority, Ministry of Economic Affairs, Wageningen, Holland), and Filiz Gunay (Hacettepe University, Faculty of Science, Department of Biology, Ecology Section, ESRL Laboratories Turkey) for providing material for invasive species analyzed in this study. Funding for LMHT was provided by the UK Department for Environment Food and Rural Affairs (DEFRA), Scottish Government and Welsh Government through grants SV3045, and SE4113, and the EU Framework Horizon 2020 Innovation Grant, European Virus Archive (EVAg, grant no. 653316). Funding for VAB was additionally provided by the Biotechnology and Biological Sciences Research Council (BBSRC), grant BB/F016492/1 and the Pirbright Institute as part of his PhD project. All German collections between 2011 and 2014 were part of projects conducted by the Bernhard Nocht Institute for Tropical Medicine, Hamburg, Germany, and collaborators. This work was funded by the Leibnitz Association, grant no. SAW-2011-BNI-3, and the German Federal Ministry for Environment, Nature Conservation, Building and Nuclear Safety (BMUB) through the Federal Environment Agency (UBA), grant no. FKZ371148404.


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Supplementary materials

Supplementary material 1 

Accession number(s) of COI DNA barcode sequences used in this study downloaded from the NCBI database or provided by colleagues

Luis M. Hernández-Triana, Victor A. Brugman, Nadya I. Nikolova, Ignacio Ruiz-Arrondo, Elsa Barrero, Leigh Thorne, Mar Fernández de Marco, Andreas Krüger, Sarah Lumley, Nicholas Johnson, Anthony R. Fooks

Data type: molecular data

This dataset is made available under the Open Database License ( The Open Database License (ODbL) is a license agreement intended to allow users to freely share, modify, and use this Dataset while maintaining this same freedom for others, provided that the original source and author(s) are credited.
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Supplementary material 2 

Percentage of Interspecific (between groups) pairwise K2P genetic divergence of unique DNA barcodes (658 bp), representing 42 species of mosquitoes

Luis M. Hernández-Triana, Victor A. Brugman, Nadya I. Nikolova, Ignacio Ruiz-Arrondo, Elsa Barrero, Leigh Thorne, Mar Fernández de Marco, Andreas Krüger, Sarah Lumley, Nicholas Johnson, Anthony R. Fooks

Data type: molecular data

Explanation note: Highest pairwise distances (most divergent taxa) and lowest pairwise distances (most closely related taxa) are highlighted in yellow/bold, and green, respectively.

This dataset is made available under the Open Database License ( The Open Database License (ODbL) is a license agreement intended to allow users to freely share, modify, and use this Dataset while maintaining this same freedom for others, provided that the original source and author(s) are credited.
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