Wing Geometric Morphometrics As a Tool for the Identification of Culex

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Article Wing Geometric Morphometrics as a Tool for the Identiﬁcation of Culex Subgenus Mosquitoes of Culex (Diptera: Culicidae)

1, 2, 3,4 Roseli França Simões †, André Barretto Bruno Wilke † , Carolina Romeiro Fernandes Chagas , Regiane Maria Tironi de Menezes 5, Lincoln Suesdek 1,6, Laura Cristina Multini 7, Fabiana Santos Silva 1,5 , Marta Gladys Grech 8, Mauro Toledo Marrelli 1,7 and Karin Kirchgatter 1,5,*

1 Institute of Tropical Medicine, School of Medicine, University of São Paulo, São Paulo, SP 05403-000, Brazil; [email protected] (R.F.S.); [email protected] (L.S.); [email protected] (F.S.S.); [email protected] (M.T.M.) 2 Department of Public Health Sciences, Miller School of Medicine, University of Miami, Miami, FL 33136, USA; [email protected] 3 Institute of Ecology, Nature Research Centre, Vilnius 08412, Lithuania; [email protected] 4 Applied Research Department, Zoological Park Foundation, São Paulo, SP 04301-905, Brazil 5 Department of Specialized Laboratories, Superintendence for Endemic Disease Control, SUCEN, São Paulo, SP 01027-000, Brazil; [email protected] 6 Butantan Institute, São Paulo, SP 05503-900, Brazil 7 Department of Epidemiology, School of Public Health, University of São Paulo, São Paulo, SP 01246-904, Brazil; [email protected] 8 Centro de Investigación Esquel de Montaña y Estepa Patagónica (CIEMEP), CONICET and UNPSJB, Facultad de Ciencias Naturales y Ciencias de la Salud, Sede Esquel, Esquel 9200, Chubut, Argentina; [email protected] * Correspondence: [email protected] These authors contributed equally to this work. † Received: 18 July 2020; Accepted: 21 August 2020; Published: 25 August 2020

Simple Summary: Different mosquito species have different ecology and behaviors. Therefore, the correct identification of vector mosquito species is essential for the development of targeted mosquito control operations. Traditionally, the identification of mosquitoes to species relies on differences in their external morphological characters. Identifying mosquitoes can be challenging if the specimen is either damaged or if only a few morphological characters can be used to sort them apart. For this reason, this study focused on the use of wing geometric morphometrics to identify Culex species from the subgenus Culex that are not easily identified by their external morphology. We analyzed the wing shape variation of 11 different species. Our results indicated that the species in this study were identified with high degrees of confidence based on their wing shape variation. From all possible comparisons in the cross-validated reclassification test, 87 yielded values higher than 70%, with 13 comparisons yielding 100% reclassification scores. Overall, our results are suggesting that wing geometric morphometrics is a reliable tool to identify Culex species of the subgenus Culex.

Abstract: Culex is the largest subgenus within the genus Culex that includes important vectors of diseases. The correct identification of mosquitoes is critical for effective control strategies. Wing geometric morphometrics (WGM) has been used to identify mosquito species alongside traditional identification methods. Here, WGM was used for eleven Culex species from São Paulo, Brazil, and one from Esquel, Argentina. Adult mosquitoes were collected using CDC (Centers for Disease Control) traps, morphologically identified and analyzed by WGM. The canonical variate analysis (CVA) was performed and a Neighbor-joining (NJ) tree was constructed to illustrate the patterns of species segregation. A cross-validated reclassification test was also carried out.

Insects 2020, 11, 567; doi:10.3390/insects11090567 www.mdpi.com/journal/insects Insects 2020, 11, 567 2 of 13

From 110 comparisons in the cross-validated reclassification test, 87 yielded values higher than 70%, with 13 comparisons yielding 100% reclassification scores. Culex quinquefasciatus yielded the highest reclassification scores among the analyzed species, corroborating with the results obtained by the CVA, in which Cx. quinquefasciatus was the most distinct species. The high values obtained at the cross-validated reclassification test and in the NJ analysis as well as the segregation observed at the CVA made it possible to distinguish among Culex species with high degrees of confidence, suggesting that WGM is a reliable tool to identify Culex species of the subgenus Culex.

Keywords: Culex; mosquitoes; morphometry; Atlantic Forest; Patagonia

1. Introduction Vector-borne diseases (VBD) constitute a significant burden on human society, with millions of people infected every year. Moreover, there is growing evidence indicating that VBDs are responsible not only for the morbidity and mortality associated with the acute infection of the diseases they cause, such as malaria, dengue, and yellow fever but also for persistent long-term morbidity in the form of severe neurologic complications and fetus malformations associated to Zika virus infections [1–3]. Mosquitoes from the Culex genus are considered primary vectors for many pathogens, including different arboviruses (Table1). It is widely accepted that controlling mosquitoes is the most e ffective way to prevent VBDs. However, controlling mosquitoes is a complex task that relies on multi-sectoral collaborations between local government, scientists, and the community, in which, many critical steps must logically build on each other in order to achieve effective mosquito control [4,5]. Effective mosquito control strategies have to take into account and base their action on the targeted mosquito vector and develop the control actions based on its ecology and behavior. In this context, the correct identification of mosquito species is critical for planning and guiding effective long-term mosquito control operations. The correct identification of mosquitoes is exceptionally challenging, mainly because there is a lack of evident anatomical differences in many species and the distinguishing features are often restricted to male genitalia, in addition to the fact that the number of taxonomists is dwindling. Culex is by far the largest genus of tribe Culicini (Diptera: Culicidae: Culicinae) with 768 species allocated to 26 subgenera [6]. The subgenus Culex by itself comprises 198 species with unique ecology and behavior that should be taken into account for control actions [6]. Identification of mosquitoes is predominantly carried out through the taxonomic key based on the external morphology, and to a lesser extent by molecular techniques [7–10]. Both the traditional morphological and molecular mosquito identification methods have advantages and disadvantages. Morphological identification is inexpensive and quick and can be done in the field, but since the correct identification relies on morphological structures, damaged mosquitoes often cannot be identified. Moreover, cryptic species and species complexes are, in some cases, indistinguishable using only their morphology. Molecular identification of mosquitoes does not require intact specimens, only a fraction of the body is enough to provide reliable identification. However, this technique is expensive and requires a specialized laboratory. Wing geometric morphometrics (WGM) has been successfully used to correctly identify mosquito species [11–14], being proven an efficient tool to be used alongside traditional morphological and molecular mosquito identification. It is relatively inexpensive and only requires that at least one wing is intact. The identification of mosquitoes can be made in the field in a rudimentary lab equipped only with a stereo microscope, a digital camera, and a computer. Mosquito wings are especially suitable to be used for mosquito identification due to its bidimensional characteristic and wide availability of anatomical landmarks that can be used to distinguish species [15]. Here we hypothesize that the variation of the anatomical landmarks based on wing venation will make it possible to successfully distinguish among the species of the Culex Insects 2020, 11, 567 3 of 13 Insects 2020, 11, x FOR PEER REVIEWInsects 2020 , 11, x FOR PEER REVIEW 3 of 14 3 of 14 subgenus of Culexsubgenus. Therefore,subgenus of Culexthe objective of. Therefore,Culex. ofTherefore, this the study objective the wa objectives ofto thisuse WGM studyof this wastostudy identify to wa uses WGM11to usespecies toWGM identify of theto identify 11 species 11 species of the of the Culex subgenus. Culex subgenus.Culex subgenus.

2. Materials and Methods2. Materials2. Materials and Methods and Methods

2.1. Mosquito Sampling2.1. Mosquito and2.1. Identification Mosquito Sampling Sampling and Identiﬁcation and Identification Mosquitoes were MosquitoescollectedMosquitoes in were three collected differentwere collected in sites: three (i) diinﬀ Municipalthreeerent sites:different parks (i) Municipal sites: on the (i) Municipalcity parks of on São the parksPaulo, city of on Sã theo Paulo, city of Brazil: São Paulo, Brazil: AnhangueraAnhanguera (23°25Brazil:′05.8 (23Anhanguera″ S,◦25 46°46005.8′0056.1S, (23°25″ 46 W),◦46′ 05.80Barragem56.1″ 00S,W), 46°46 Barragem(23°40′56.1′40.1″ W), (23″ S,Barragem◦ 4046°42040.1′59.000 S,(23°40″ 46W),◦42′ 40.1Burle059.0″ 00S, W),46°42 Burle′59.0 Marx″ W), Burle Marx (23°38′00.2″(23 S, ◦46°43380Marx00.2′20.600 (23°38S,″ W), 46◦ 43′Buenos00.2020.6″ S,00 Aires46°43W), Buenos ′(23°3220.6″ W),′43.7 Aires Buenos″ S, (23 46°39◦32 Aires043.7′31.5 00(23°32″ S,W), 46 ′Castelo◦43.739031.5″ S, (23°420046°39W),′′31.5 Castelo48.2″″ W), (23 Castelo◦42048.2 (23°4200 S, ′48.2″ S, 46°42′57.6″ W),46 Cidade◦42057.6S, 46°4200TorontoW),′57.6 Cidade ″(23°30 W), Toronto′Cidade19.1″ S, Toronto (2346°43◦30′030.719.1 (23°30″00 W),S,′19.1 46São◦″43 S,Domingos030.7 46°4300 W),′30.7 (23°30 S″ã oW), Domingos′ 02.3São″ DomingosS, (23◦30 002.3(23°3000 ′S,02.3″ S, 46°44′12.4″ W), Eucaliptos46◦44012.446°44 (23°3600 W),′12.4′ Eucaliptos59.5″ W),″ S,Eucaliptos 46°45 (23◦′06.536 059.5(23°36″ W),00 S, ′59.5Ganhembu 46◦″45 S,0 06.546°45 00(23°43W),′06.5 Ganhembu″′47.2 W),″ GanhembuS, 46°40 (23◦′59.543 047.2(23°43″ W),00 S, ′47.2 46◦″40 S,0 59.546°4000 W),′59.5″ W), Guarapiranga (23°40Guarapiranga′33.1Guarapiranga″ S, 46°44 (23′◦03.7400 33.1(23°40″ W),00 S,Ibirapuera′33.1 46◦″44 S,003.7 46°44 (23°3500 W),′03.7′ Ibirapuera16.9″ W),″ S, Ibirapuera 46°39 (23′◦30.3350 16.9(23°35″ W),00 S,Jacinto′16.9 46◦″39 S, Alberto0 30.346°3900 W),′30.3 Jacinto″ W), Jacinto Alberto Alberto (23°28′49.4″ S, 46°43(23◦′41.0280(23°2849.4″ W),00 ′S, 49.4Jardim 46″◦ 43S,0 41.0Felicidade46°4300 ′W),41.0 Jardim″ (23°29W), Jardim Felicidade′42.2″ S,Felicidade 46°43 (23◦′32.529 0(23°2942.2″ W),00 S,′ 42.2Jardim 46″◦ 43S,0 32.5Herculano46°4300 ′W),32.5 Jardim″ W), Jardim Herculano Herculano (23°41′33.9″ S, 46°45(23◦′15.7410(23°4133.9″ W),00 S,′ 33.9Lina 46″◦ 45eS, 0 Paula15.746°4500 ′W),Raia15.7 Lina″ (23°38W), e Lina Paula′08.2 e″ RaiaPaulaS, 46°38 (23 Raia◦′3834.10 08.2(23°38″ W),00 S,′ 08.2Luz 46◦″ 38 (23°31S,0 34.146°38′0058.8W),′34.1″ S, Luz″ W), (23 Luz◦31 0(23°3158.800 S,′58.8″ S, 46°38′06.9″ W), M.46 Boi◦38 0Mirim06.946°3800 W),′ 06.9(23°42M.″ W), Boi′22.8 M. Mirim″ S,Boi 46°47 (23Mirim◦′00.1420 22.8(23°42″ W),00 S, ′22.8Nabuco 46◦″47 S,0 00.1 46°47(23°3900 W),′00.1′48.1 Nabuco″ ″W), S, 46°39Nabuco (23◦′37.4390 48.1(23°39″ W),00 S, ′48.1 46◦″39 S,0 37.446°3900 W),′37.4″ W), Nove de Julho (23°43Nove′14.1 deNove Julho″ S, de 46°43 (23Julho◦43′00.40 14.1(23°43″ W),00 S,′14.1 Previdência 46◦″43 S,0 00.446°4300 W),′(23°3400.4 Previd″ ′W),51.1 ″êPrevidência nciaS, 46°43 (23◦34′37.9 051.1(23°34″ W),00 S,′51.1 Cohab 46″◦ 43S,0 37.946°4300 W),′37.9 Cohab″ W), Cohab Raposo Tavares (23°35Raposo′05.5Raposo Tavares″ S, 46°48 Tavares (23′03.3◦35 0(23°35″05.5 W),00 Rodrigo′S,05.5 46″◦ S,48 46°480de03.3 Gasperi00′03.3W),″ RodrigoW),(23°28 Rodrigo′54.9 de″ GasperiS, de 46°43 Gasperi′ (2310.7◦ (23°2828″ W),054.9 Vila′54.900 S, ″ 46S,◦ 46°4343010.7′10.700 W),″ W), Vila dos Remédios (23°30Vila′47.9 dosdos″ Rem S, Remédios 46°45édios′00.7 (23 (23°30″◦ 30W),047.9 ′47.9Raposo00 ″S, S, 46 46°45Tavares◦45000.7′00.7 (23°3500″ W),W),′ Raposo18.5Raposo″ S, 46°45 TavaresTavares′22.3 (23(23°35″ ◦W),350 ′18.518.5Santos00″ S, 46°45 46◦45′022.322.3″00 W),W), Santos Dias (23°39′46.8″ SantosS, 46°46Dias Dias′22.6 (23°39 (23″ W),◦39′ 046.8Colina46.8″00 S,S, de46°46 46 São◦46′22.60 22.6Francisco″ 00W),W), Colina Colina(23°33 de′32.4 de São S″ã oS, FranciscoFrancisco 46°45′36.1 (23°33 (23″ W),◦33′ 032.4Severo32.4″00 S,S, 46°45 46◦45′36.1036.1″ 00W),W), Severo Gomes (23°38′18.2Severo″ S, 46°42Gomes Gomes′12.3 (23°38″ (23W),◦38 Shangrilá′18.2018.2″ S,00 46°42S, (23°45 46◦′12.342′43.4012.3″ W),″00 S, W), Shangrilá46°40 Shangril′08.2 (23°45″ W),á (23 São′43.4◦45 José″0 43.4S, 46°40(23°4300 S,′08.2 46′48.9◦40″″ W),0 08.2S, São00 W), José S ã(23°43o José′48.9″ S, 46°43′00.1″ W), Senhor(23◦43 046°43do48.9 Vale00′00.1S, 46(23°26″ ◦W),43000.1′ 39.8Senhor00″ W),S, do46°44 Senhor Vale′14.3 do(23°26″ W), Vale′ 39.8Orlando (23″◦ 26S, 039.846°44 Villas-Boas00 ′S,14.3 46″◦ 44W), (23°31014.3 Orlando00′11.5W),″ Orlando Villas-BoasS, Villas-Boas (23°31′11.5″ S, 46°44′20.7″ W) [16,17];(23◦31 (ii)046°4411.5 Zoo00′S,20.7 in 46 the″◦ 44W) city020.7 [16,17]; of00 W)São (ii) [ 16Paulo Zoo,17]; (Fundaçãoin (ii) the Zoo city in theofParque São city Paulo ofZoológico São (Fundação Paulo de (Fundaç São Parque Paulo—ão Parque Zoológico Zool deógico São de Paulo— FPZSP) (23°39′03″S ãS,o 46°37 Paulo—FPZSP)FPZSP)′14″ W)[18]; (23°39 (23 ′and03◦39″ S,0(iii)03 46°3700 inS, a 46′14 forest-steppe◦″37 W)[18];01400 W)[ and18 ecotone]; (iii) and in (iii) neara forest-steppe in Esquel a forest-steppe city, ecotone Southern ecotone near near Esquel Esquel city, city, Southern Argentina (42°55′Southern00.0” S,Argentina 71°21 Argentina′00.0 (42°55″ W) (42 (Figure◦′00.0”55000.0 S, 1).00 71°21S, 71′◦00.021000.0″ W)00 (FigureW) (Figure 1). 1).

Figure 1. CollectionFigure sites. AnhangueraFigure 1. Collection 1. Collection (AN), sites. Barragem sites. Anhanguera Anhanguera (BA), (AN),Burle (AN), Ma Barragemrx Barragem (BM),(BA), Buenos (BA), Burle AiresBurle Marx (BU),Marx (BM), (BM), Buenos Buenos Aires Aires (BU), Castelo (CS), Cidade(BU), Toronto CasteloCastelo (CT), (CS), (CS), CidadeSão Cidade Domi Toronto ngosToronto (CT),(DO), (CT), SãEucaliptoso São Domingos Domi (EC),ngos (DO), Ganhembu(DO), Eucaliptos Eucaliptos (GN), (EC), Ganhembu(EC), Ganhembu (GN), (GN), Guarapiranga (GU),Guarapiranga IbirapueraGuarapiranga (IB), (GU), Jacinto Ibirapuera (GU), Albert Ibirapuera (IB),o (JA), Jacinto Jardim(IB), Alberto Jacinto Felicidade (JA), Albert Jardim (JF),o (JA), FelicidadeJardim Jardim Herculano (JF),Felicidade Jardim (JF), Herculano Jardim (JH), Herculano (JH), Lina e Paula LinaRaia e(LP), Paula(JH), Luz RaiaLina (LZ), (LP),e Paula M. Luz Boi Raia (LZ), Mirim (LP), M. Boi (MM),Luz Mirim (LZ), Nabuco (MM), M. Boi (NB), Nabuco Mirim Nove (NB), (MM), de Nove Julho Nabuco de (NJ), Julho (NB), (NJ), Nove Previd deê nciaJulho (NJ), Previdência (PV), Cohab(PV), Cohab RaposoPrevidência Raposo Tavares (PV), Tavares (RA), Cohab (RA)Rodr Raposo,igo Rodrigo de TavaresGasperi de Gasperi (RG),(RA), (RG), VilaRodr Viladosigo dosdeRemédios Gasperi Remédios (RM), (RG), (RM), Vila Raposo dos Remédios Tavares (RM), Raposo Tavares (RT),(RT), Santos SantosRaposo Dias Dias (SD),Tavares (SD), Colina Colina (RT), de Santos de São Sã oFrancisco Dias Francisco (SD), (SF), (SF),Colina Severo Severo de SãoGomes Gomes Francisco (SG), (SG), Shangrilá Shangril(SF), Severoá (SH), Gomes São Jos(SG),é (SJ), Shangrilá (SH), São José (SJ), SenhorSenhor do(SH), do Vale Vale São (SV), José Orlando (SJ), Senhor Villas-Boas do Vale (VB), (SV), SãoSOrlandoão Paulo Villas-Boas ZOO ( ✩),), and(VB), Esquel—ArgentinaEsquel— São Paulo ZOO (✩ (),). and Esquel— Argentina (□). Argentina (□).

Insects 2020, 11, 567 4 of 13

The collections were carried out with the use of CDC (Centers for Disease Control) Miniature light traps [19] baited with CO2 (dry ice). The CDC traps were installed 1.5 m above the ground before down for 12 h. All collected mosquitoes were identiﬁed using taxonomic keys [9,20], then the right wing of each specimen was detached from the body and mounted between a microscope slide and coverslip with Canada balsam (Sigma-Aldrich, St. Louis, MO, USA) (Table1).

Table 1. Collection data and epidemiological importance of sampled mosquito species.

Taxon N * Collection Site Collection Year Epidemiological Importance Culex (Cux.) acharistus Root 26 Esquel 2016/2017 EEEV [21] Culex (Cux.) ameliae Casal 30 FPZSP 2015 Unknown Culex (Cux.) bidens Dyar 27 FPZSP 2015 EEEV [21] Municipal Parks of Culex (Cux.) chidesteri Dyar 30 2010/2011 Unknown São Paulo City ** Culex (Cux.) coronator Dyar 43 FPZSP 2015 SLEV [22], VEEV [23], WNV [24] and Knab Culex (Cux.) declarator Dyar 13 FPZSP 2015 SLEV [25], Diroﬁlaria immitis [26] and Knab Culex (Cux.) dolosus (Lynch Municipal Parks of 32 2010/2011 Unknown Arribálzaga) São Paulo City ** Culex (Cux.) eduardoi Casal Municipal Parks of 15 2010/2011 Unknown and Garcia São Paulo City ** Culex (Cux.) habilitator Dyar 32 FPZSP 2015 WNV [27] and Knab EEEV [22], EVEV [22], KEYV [22], Culex (Cux.) Municipal Parks of 34 2010/2011 ROCV [22], SLEV [22], TENV [22], nigripalpus Theobald São Paulo City ** VEEV [22], WNV [24] CHIKV [22], EEEV [22], MAYV [28], OROV [22], ROCV [22], Culex (Cux.) Municipal Parks of 57 2010/2011 SLEV [22], VEEV [22], WNV [22], quinquefasciatus Say São Paulo City ** ZIKV [22], Wuchereria bancrofti [9], Diroﬁlaria immitis [26] * Number of females used in this study; ** [16,17]. Fundação Parque Zoológico de São Paulo (FPZSP) [18]; Eastern Equine Encephalitis Virus (EEEV), Saint Louis Encephalitis Virus (SLEV), Venezuelan Equine Encephalitis Virus (VEEV), West Nile Virus (WNV), Chikungunya Virus (CHIKV), Everglades virus (EVEV), Keystone Virus (KEYV), Mayaro Virus (MAYV), Oropouche Virus (OROV), Rocio Virus (ROCV), Zika Virus (ZIKV), and Tensaw (TENV).

This study was performed according to the Ethical Principles in Animal Research. It was approved by the Ethics Committee of Institute of Tropical Medicine, University of Sao Paulo (CPE-IMT/193 and CPE-IMT/371A), and the Brazilian Ministry of Environment (SISBIO 34605-4). The datasets analyzed during the current study are available in the Mendeley Data repository (https://data.mendeley.com/ datasets/nzsxjnng63/1).

2.2. Data Acquisition and Morphometrics Analysis Each wing was photographed under 40 magnification with a Leica DFC320 digital camera (Leica × Camera AG, Solms, Germany) coupled to a Leica S6 stereomicroscope (Leica Camera AG, Solms, Germany), and subsequently, 18 type II landmarks were digitized by one of the authors (RFS) using TpsDig V1.40 software [29], as in Wilke et al. [14] (Figure2). The selected landmarks are homologous and can be found in all representatives of the Culicidae family [14]. We calculated the allometry (influence of the wing size in the wing shape) by multivariate regression of the Procrustes coordinates against centroid size using a permutation test with 10,000 randomizations. Discriminant analysis in a morphospace defined by a canonical variate analysis (CVA) was performed to determine the degree of dissimilarity between populations and to calculate the Mahalanobis distances. All specimens were reclassified according to its wing similarity to the average shape of each group (group 1 vs. group 2 and group 2 vs. group 1) using cross-validation tests based on Mahalanobis distances. All the analyses were carried out in MorphoJ 1.02 [30]. Neighbor-Joining (NJ) trees were constructed to display the Mahalanobis distances between populations using PAST 1.89 [31] with 1000 bootstrap replicates (30 specimens of Aedes aegypti were used as outgroup). Insects 2020, 11, x FOR PEER REVIEW 5 of 14

2.2. Data Acquisition and Morphometrics Analysis Each wing was photographed under 40× magnification with a Leica DFC320 digital camera (Leica Camera AG, Solms, Germany) coupled to a Leica S6 stereomicroscope (Leica Camera AG, Solms, Germany), and subsequently, 18 type II landmarks were digitized by one of the authors (RFS) Insects 2020, 11, 567 5 of 13 using TpsDig V1.40 software [29], as in Wilke et al. [14] (Figure 2). The selected landmarks are homologous and can be found in all representatives of the Culicidae family [14].

FigureFigure 2. 2. RightRight wing wing of of CulexCulex specimenspecimen showing showing the the 18 18 landmarks landmarks used used in in this this study. study.

3. ResultsWe calculated the allometry (influence of the wing size in the wing shape) by multivariate regressionThe allometry of the Procrustes was minor coor butdinates significant against 0.72% centroid (p = 0.031) size andusin wasg a notpermutation removed fromtest with the analysis 10,000 randomizations.since the influence Discriminant of size in the analysis shape ofin wingsa morphospace were considered defined evolutionarily by a canonical relevant variate and, analysis thus, (CVA)can be was informative performed for to the determine species identification the degree of process. dissimilarity between populations and to calculate the MahalanobisThe results distances. from the All CVA specimens for all Culexwere reclasspeciessified displayed according substantial to its wing segregation similarity to from the averageCx. quinquefasciatus shape of eachfrom group all other (group species, 1 vs. beinggroup partially 2 and group overlapped 2 vs. group solely 1) with usingCx. cross-validation eduardoi. Similar testsresults based were on also Mahalanobis found for Cx. distances. dolosus, partiallyAll the overlappinganalyses were with carriedCx. eduardoi out in andMorphoJ in a much 1.02 lesser[30]. Neighbor-Joiningextent to Cx. chidesteri (NJ). Culextrees nigripalpuswere constructedwas completely to display segregated the Mahalanobis from Cx. acharistus distances, Cx. coronatorbetween , populationsCx. quinquefasciatus using PAST, Cx. eduardoi, 1.89 [31]and withCx. 1000 dolosus bootstrap, and was replicat onlyes partially (30 specimens overlapped of Aedes with aegyptiCx. chidesteri were . usedCulex as acharistus outgroup).greatly overlapped with Cx. chidesteri and Cx. declarator, and to a lesser extent to Cx. habilitator and Cx. eduardoi (Figure3). 3. Results A pairwise CVA analysis for all species showed that from the 55 possible comparisons, none presentedThe allometry major wing was shape minor pattern but significant overlaps 0.72% and only (p = 0.031) 12 had and minor was overlaps not removed (Figure from4). the analysis since Thethe influence NJ tree analysisof size in resultedthe shape in of highwings bootstrap were considered values evolutionarily (>80) for all relevant species (Figureand, thus,5). canCulex be quinquefasciatusinformative for theyielded species the identification highest value process. (100), being segregated from all other species in a singleThe branch.results fromCulex the nigripalpus CVA for, Cx.all declaratorCulex speciesand Cx.displayed acharistus substantialwere also segregation segregated from in single Cx. quinquefasciatusbranches with high from bootstrap all othervalues species, (> 94).being Some partially species overlapped cluster together solely withwith highCx. eduardoi bootstrap. Similar values results(>95): Cx.were dolosus also foundand Cx. for eduardoiCx. dolosus, Cx., partially ameliae andoverlappingCx. bidens, withand Cx.Cx. eduardoi coronator andand in aCx. much habilitator lesser extent(Figure to5 ).Cx. chidesteri. Culex nigripalpus was completely segregated from Cx. acharistus, Cx. coronator,

Insects 2020, 11, x FOR PEER REVIEW 6 of 14

Insects 2020, 11,Cx. 567 quinquefasciatus, Cx. eduardoi, and Cx. dolosus, and was only partially overlapped with Cx. 6 of 13 chidesteri. Culex acharistus greatly overlapped with Cx. chidesteri and Cx. declarator, and to a lesser extent to Cx. habilitator and Cx. eduardoi (Figure 3).

Figure 3. MorphologicalFigure 3. Morphological space producedspace produced by by the the ﬁrstfirst two two canonical canonical variates variates for Culex for speciesCulex basedspecies on based on 18 wing landmarks. 18 wing landmarks. Insects 2020, 11, x FOR PEER REVIEW 7 of 14 A pairwise CVA analysis for all species showed that from the 55 possible comparisons, none presented major wing shape pattern overlaps and only 12 had minor overlaps (Figure 4).

Figure 4. Wing shape diagram of the first canonical variable from the pairwise comparison for Culex Figure 4. Wingspecies shape based on diagram 18 wing landmarks. of the ﬁrst X-axis: canonical first canonical variable variable; from Y-axis: the frequency. pairwise comparison for Culex species based on 18 wing landmarks. X-axis: ﬁrst canonical variable; Y-axis: frequency. The NJ tree analysis resulted in high bootstrap values (>80) for all species (Figure 5). Culex quinquefasciatus yielded the highest value (100), being segregated from all other species in a single branch. Culex nigripalpus, Cx. declarator and Cx. acharistus were also segregated in single branches with high bootstrap values (>94). Some species cluster together with high bootstrap values (>95): Cx. dolosus and Cx. eduardoi, Cx. ameliae and Cx. bidens, and Cx. coronator and Cx. habilitator (Figure 5).

Insects 2020, 11, 567 7 of 13 Insects 2020, 11, x FOR PEER REVIEW 8 of 14

FigureFigure 5. 5.Neighbor-Joining Neighbor-Joining tree tree for forCulex Culexspecies species based based on Mahalanobis on Mahalanobis distances distances with 1000 with bootstrap 1000 bootstrap replicates.replicates.Aedes Aedes aegypti aegypti(n =(n30) = 30) was was used used as an as outgroup. an outgroup.

TheThe cross-validatedcross-validated reclassification reclassification results results were high, we withre high, the overall with meanthe overall value of 82%mean accuracy value of 82% accuracy(Table2). From(Table the 2). 110From possible the 110 tests, possible 13 achieved tests, 13 100% achieved accuracy, 100% 45 accuracy, were higher 45 thanwere 90% higher and than 90% andonly only six yielded six yielded values values below 50%below accuracy. 50% accuracy.Culex quinquefasciatus Culex quinquefasciatusyielded the yielded highest the scores highest in the scores in cross-validated reclassification test, with an average of 94% accuracy, followed by Cx. dolosus with 89% the cross-validated reclassification test, with an average of 94% accuracy, followed by Cx. dolosus with accuracy and Cx. acharistus with 87%. Culex declarator had the lowest reclassification average, yielding 89% accuracy and Cx. acharistus with 87%. Culex declarator had the lowest reclassification average, an average of 73% accuracy. The lowest reclassification scores were obtained by the comparison between yieldingCx. bidens vs.an Cx.average ameliae yieldingof 73% 44%accuracy. accuracy. The The lowest comparison reclassification between Cx. declarator scores vs.wereCx. acharistusobtained, by the comparisonCx. declarator vs.betweenCx. bidens Cx., andbidensCx. vs. habilitator Cx. ameliaevs. Cx. yielding declarator 44%yielded accuracy. 46% accuracy The comparison scores. between Cx. declarator vs. Cx. acharistus, Cx. declarator vs. Cx. bidens, and Cx. habilitator vs. Cx. declarator yielded 46% accuracy scores.

Insects 2020, 11, 567 8 of 13

Table 2. Results of pairwise cross-validated species reclassiﬁcation tests (%).

Group 2 Culex Culex Culex Culex Culex Culex Culex Culex Culex Culex Culex acharistus ameliae bidens chidesteri coronator declarator dolosus eduardoi habilitator nigripalpus quinquefasciatus Culex acharistus - 90 89 89 83 46 97 73 91 88 91 Culex ameliae 88 - 44 83 74 77 100 100 66 74 91 Culex bidens 84 57 - 79 81 46 100 87 69 62 94 Culex chidesteri 92 87 96 - 93 62 91 80 94 91 89 Culex coronator 88 83 67 72 - 62 84 67 47 88 91 Group 1 Culex declarator 64 80 48 59 81 - 94 73 59 82 94 Culex dolosus 100 93 93 90 91 77 - 53 97 100 94 Culex eduardoi 72 97 81 86 91 92 63 - 81 94 71 Culex habilitator 96 73 67 86 58 46 88 67 - 85 94 Culex nigripalpus 100 77 63 79 81 85 100 93 78 - 97 Culex quinquefasciatus 92 100 100 100 91 100 100 60 100 97 - Values below the diagonal correspond to mosquitoes from group 1 compared with group 2 and correctly identiﬁed. Each pairwise comparison results in two values, one for species A in comparison to species B and then species B in comparison to species A; values above the diagonal correspond to mosquitoes from group 2 compared with group 1 and correctly identiﬁed. p-value (parametric): <0.0001. Insects 2020, 11, 567 9 of 13

4. Discussion The species phylogeny and taxonomy of the Culex genus is intricate and has been a matter of debate over the last decades [32]. Microevolutionary and speciation processes add an increased level of complexity to the correct identification of Culex species [33]. Cryptic species, such as the species complex formed by Cx. quinquefasciatus and Cx. pipiens and the Cx. coronator complex, pose as an increasing need for additional taxonomic tools for the correct identification of Culex species [34,35]. Here, we were able to identify 11 sympatric species from the Culex subgenus with high degrees of confidence using WGM technique based on quantitative analyses of mosquito wing venation characters. It is important to note that, with the exception of Cx. quinquefasciatus, all Culex species used in this study are not easy to recognize even for entomologists with some experience. However, cryptic species were not analyzed here. Although WGM is also a very useful tool for identifying populations, in some cases it is unable to find clearly differentiating patterns [36] and this can also happen with cryptic species, in which, to obtain an accurate identification, the use of WGM alongside other taxonomical techniques, substantially increasing the accuracy scores, can be necessary. In this study, we analyzed 73% (11/15) of the Culex subgenus species collected in the study areas [16–18]. Considering that all analyses were made for species from the same subgenus and therefore a reduced variation would be expected, the classification power of the WGM analyses was consistently reliable. Both the cross-validated reclassification test and the bootstrap values from the NJ analysis resulted in high values, even when compared to the ones found for species from different genera [14]. The results obtained by the cross-validated reclassification test were high, with the overall mean value of 82% accuracy. From all comparisons, approximately 50% of the results from the cross-validated reclassification tests yielded values above 90%. Furthermore, considering that the lowest reclassification average among all comparisons was found for Cx. declarator (73% accuracy), and only a few punctual comparisons resulted in values lower than 50% we strongly believe these results are reliable enough to be used in routine mosquito control operations. In many studies of interspecific variation, WGM has been used to identify species based on the reclassification scores. Analysing three Aedes species from Thailand, values of 73 to 93% were found [37] and in two Haemagogus species from Brazil, values were from 67 to 81% [38]. Still in Brazil, Cx. quinquefasciatus and Cx. nigripalpus could be distinguished by wing shape with accuracy rates ranging from 85 to 100% [39] and for four Culex (Culex) species from Argentina, re-classification was 100% in Cx. bidens and Cx. mollis, 89% in Cx. interfor and 94% in Cx. tatoi [13]. Cross-validated reclassification showed that WGM is an effective analytical method to distinguish between Anopheles (Kerteszia) bellator, An. cruzii and An. homunculus with a reliability rate varying from 78 to 88% [40]. However, when larger numbers of species were analyzed together (11 Anopheles species from Colombia), like here in this study, reclassification scores were quite lower, reaching >80% for six species and >75% for eight species, but with scores of 46%, 66%, and 69%, for An. benarrochi, An. oswaldoi and An. strodei [41]. The lowest bootstrap value on the NJ analysis was 80 for the Cx. dolosus and Cx. eduardoi. These species are notably challenging to identify by traditional morphologic characters since specimens in the adult life stage are difficult to distinguish and larval stages have only a few morphological differences that can be used for their correct identification [32]. The females of Cx. dolosus and Cx. eduardoi, sister to one another in the NJ tree, are also morphologically similar. Very few morphological characters differentiate them: The presence of basal white bands in the abdominal tergites, pleural integument with dark areas, and yellowish scales antealar patches, which also distinguish them from other species of the subgenus Culex. Moreover, it is worth considering that the smaller size of Cx. eduardoi sample (n = 15) and its high heterogeneity in the morphospace may be limiting factors for interpretations involving this species. Females of Cx. bidens with legs without evident clear marking, as in the case of this study, present morphological characters very similar to Cx. ameliae. Appendices of the head, parts of the abdomen and thorax are similar, in both species, in terms of its scales. Such similarities result in greater difficulty Insects 2020, 11, 567 10 of 13 in separating these species, most likely explaining the proximity between them in the NJ tree. However, in this study, the morphological differentiation of these females was possible due to the appressed scales present on the occiput. The proximity of the Culex females of the Coronator complex to the females of Cx. habilitator in the NJ tree could be explained mainly in the case of Cx. habilitator presenting well-marked tarsi similar to the Cx. coronator. In this condition, the two species present morphological similarities related to the covering of scales of appendices of the head, such as the maxillary and proboscis palps and parts of the shield on the thorax. Most of the females from Cx. habilitator, collected for the elaboration of this article, presented tarsi little marked with clear color, a characteristic that includes these specimens in the taxonomic key together with Cx. pseudojanthinosoma, and not with Culex from the Coronator complex, in dichotomy with Cx. scimitar. However, it is a single character. Other similar characters for Cx. habilitator and Cx. pseudojanthinosoma species refer to the scales in the proboscis and thorax, being the same as those of the Coronator complex. Culex quinquefasciatus and Cx. nigripalpus are the primary vectors for West Nile virus (WNV), Eastern Equine encephalitis (EEE), and lymphatic filariasis (LF) [42–47]. Together, these diseases affect millions of people worldwide and endanger almost half of the world’s population [48,49]. Culex quinquefasciatus and Cx. nigripalpus can be found in high numbers in urban environments. Developing countries that have undergone chaotic urbanization processes often lack basic sanitation, resulting in polluted rivers and untreated sewage. These features are intensely explored by Culex mosquitoes which are able to thrive in such conditions, in which they feed on blood on widely available human hosts and oviposit their eggs in highly polluted breeding habitats, absent from predators and abundant in organic matter [4,5,8,9,50]. Culex subgenus species are also capable of transmitting avian malaria to birds of various species throughout the globe including endangered species [51]. Many studies have demonstrated full or partial (part of the life cycle has not been demonstrated experimentally) vectorial competence of different Culex (Culex) spp. transmitting avian Plasmodium spp. such as Cx. annulirostris, Cx. annulus, Cx. antennatus, Cx. gelidus, Cx. nigripalpus, Cx. pipiens, Cx. pseudovishnui, Cx. salinarius, Cx. sitiens, Cx. stigmatosoma, Cx. tarsalis, Cx. tritaeniorhynchus, Cx. univittatus, Cx. restuans, and Cx. quinquefasciatus [50]. However, only Cx. nigripalpus and Cx. quinquefasciatus are listed among the 25 different Culex (Culex) species reported in Brazil (http://www.mosquitocatalog.org/). Previous studies from our group have detected avian malaria transmission in the São Paulo Zoo [52]. In this study, we used five Culex (Culex) species collected in this active transmission site. However, so far, there is no record in the literature of Plasmodium spp. detected in any of these five species. Although the analysis to test these mosquito species for Plasmodium presence is still in progress, considering that these mosquitoes were collected in a short period of time, we recommend that further research be conducted to enable long-term mosquito sampling and screening. Thus, in addition to identifying all Culex subgenus species that are present in a given area, it is essential to detect the Plasmodium infection in these mosquitoes to precisely establish their roles in the transmission of avian malaria. The correct identification of Culex mosquitoes to species level is critical to enable the better mapping of infection risks, the development of more effective mosquito control strategies, and increase our knowledge on the evolutionary relationships of host-pathogen interactions. Even though more studies using WGM to identify mosquito species are available some obstacles still have to be overcome before this technique can be used in routine mosquito control operations. The further development and maintenance of reliable databases would greatly help to develop and validate WGM guidelines and protocols directly impacting its usefulness and effectiveness in correctly identifying mosquito species on a global scale. Moreover, additional validation using more specimens from each species, as well as more species, are needed to increase the identification reliability and guide future WGM efforts in the identification of mosquito vectors. However, in its current form, WGM identification standards can be considerably helpful to identify species of which the traditional Insects 2020, 11, 567 11 of 13 identification is difficult. WGM can also be used alongside traditional and molecular techniques serving as an option rather than an antagonizing strategy.

5. Conclusions The results obtained in this study suggest the WGM technique is a reliable tool to identify Culex species of the subgenus Culex. The high values obtained at the cross-validated reclassiﬁcation test and in the NJ analysis as well as the segregation observed at the canonical variate analysis made it possible to distinguish among the Culex species used in this study with high degrees of conﬁdence for most specimens.

Author Contributions: Conceptualization, A.B.B.W. and K.K.; formal analysis, R.F.S. and A.B.B.W.; investigation, R.F.S., A.B.B.W., R.M.T.d.M., L.C.M., F.S.S.; resources, C.R.F.C., R.M.T.d.M., L.S., M.G.G., M.T.M., and K.K.; writing—original draft preparation, A.B.B.W. and K.K.; writing—review and editing, R.F.S., A.B.B.W., C.R.F.C., R.M.T.d.M., L.S., L.C.M., F.S.S., M.G.G., M.T.M., and K.K.; supervision, K.K.; project administration, K.K.; funding acquisition, K.K. All authors have read and agreed to the published version of the manuscript. Funding: This research was funded by Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP 2012/51427-1). RFS had a FAPESP scholarship (2017/23407-0). KK and LS are CNPq research fellows, grants #308678/2018-4 and #311984/2018-5, respectively. Acknowledgments: We thank the São Paulo Zoo Foundation (Fundação Parque Zoológico de São Paulo) for the support provided to this research. Conﬂicts of Interest: The authors declare no conﬂict of interest.

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