A study of Anopheles monticola of the Baileyi Complex, Culex sasai, and Lutzia vorax (Diptera: Culicidae) in Yunnan Province, China.

This article deals with the morphological and molecular assessments of 3 species of mosquitoes collected in Yunnan Province, China, i.e Anopheles monticola Somboon, Namgay & Harbach, the only species of the Baileyi Complex identified, Culex sasai Kano, Nitahara & Awaya, and Lutzia vorax Edwards. Interestingly, some adult specimens of An. monticola exhibited a variant form in having pale fringe spots, as found in An. simlensis James, another species of the Baileyi Complex. Culex sasai was recorded for the first time in Yunnan and mainland China. The prevalence of Lt. vorax was confirmed, and previous records of Lt. halifaxii in China are regarded as misidentifications of Lt. vorax.

Aedes pertinax, Anopheles perplexens, Culex declarator, and Cx. interrogator: An Update of Mosquito Species Records for Charlotte County, Florida.

Understanding the geographic occurrence of mosquito species is an important element to addressing public health and nuisance mosquito-related issues, particularly as changing climates and increased global connectivity is likely to facilitate changes in the distribution of mosquitoes and other species. In Charlotte County, FL, routine surveillance of mosquito species for public health in 2019-21 identified 4 mosquito species not previously documented in the county. Aedes pertinax, Anopheles perplexens, Culex declarator, and Cx. interrogator adults were collected and verified to species level. Aedes pertinax and Cx. declarator and were collected in 2019, whereas An. perplexens and Cx. interrogator were documented from collections in 2021. All 4 species were initially visually identified by external morphology and confirmed by sequencing the DNA barcoding region of the cytochrome c oxidase subunit I gene. Apart from native An. perplexens, in which only 1 specimen has been confirmed to date, the 3 newly documented nonnative species are now recognized throughout the county.

Redescription of Wyeomyia (Decamyia) felicia (Dyar amp; Nez Tovar, 1927) and description of a new species of the subgenus from Trinidad (Diptera: Culicidae).

Mosquito species of the subgenus Decamyia Dyar, 1919 of genus Wyeomyia Theobald, 1901 are known to have immature stages mostly associated with Heliconia plants. Currently, the subgenus includes five species, some of which are poorly characterized. Here, we redescribe and illustrate the larval, pupal and adult life stages of Wy. felicia (Dyar Nez Tovar, 1927). In addition, Wyeomyia monticola, a new species of Decamyia from Trinidad, close to Wy. felicia, is described. The new species can be distinguished by morphological characters of the male genitalia, larva and pupa.

Description of Larva and Pupa of Psorophora (Grabhamia) dimidiata (Diptera: Culicidae) and Redescription of the Adult Female and Male.

Psorophora (Grabhamia) dimidiata Cerqueira (1943) was described based on the adult female and male. Later, descriptions of the male and female genitalia were published by Lane (in Neotropical Culicidae, vols. I & II. Editora da Universidade de São Paulo, S. P., Brazil, 1953) and Guedes et al. (in Catalogo ilustrado dos mosquitos da coleção do Instituto Nacional de Endemias Rurais. I. Gênero Psorophora Robineau-Desvoidy, 1827. Rev. Bras. Malariol. Doencas Trop. 12: 3-24; 1965), respectively. Here we describe the pupa and fourth-instar larva and redescribe the adult male and female genitalia and female cibarium. All stages (except the egg) are illustrated. Unambiguous recognition is provided. Distinctions from Ps. cingulata (Fabricius) and possible phylogenetic relationships are discussed.

First description of the mitogenome and phylogeny:Aedes vexansand Ochlerotatus caspius of the Tribe Aedini (Diptera: Culicidae).

Culicidae, the mosquito family, includes more than 3600 species subdivided into the subfamilies Anophelinae and Culicinae. One-third of mosquitoes belong to the Aedini tribe, which is subordinate to the subfamily Culicinae, which comprises common vectors of viral zoonoses. The tribe of Aedini is extremely diverse in morphology and geographical distribution and has high ecological and medical significance. However, knowledge about the systematics of the Aedini tribe is still limited owing to its large population and the similar morphological characteristics of its species. This study provides the first description of the complete mitochondrial (mt) genome sequence of Aedes vexans and Ochlerotatus caspius belonging to the Aedini tribe. The mt genomes of A. vexans and O. caspius are circular molecules that are 15,861 bp and 15,954 bp in size, with AT contents of 78.54% and 79.36%, respectively. Both the circular mt genomes comprise 37 functional subunits, including 13 protein-coding genes (PCGs), two ribosomal RNA genes, 22 transfer RNA genes (tRNAs), and a control region (also known as the AT-rich region). The most common start codons are ATT/ATG, apart from cox1 (TCG) and nad5 (GTG), while TAA is the termination codon for all PCGs. All tRNAs have a typical clover leaf structure, except tRNA Ser1. Phylogenetic analysis of the concatenated, aligned amino acid sequences of the 13 PCGs showed that A. vexans gathered with Aedes sp. in a sister taxon, and O. caspius gathered with Ochlerotatus sp. in a sister taxon. The findings from the present study support the concept of monophyly of all groups, ratify the current taxonomic classification, and provide vital molecular marker resources for further studies of the taxonomy, population genetics, and systematics of the Aedini tribe.

Influence of seasonality on wing morphological variability in populations of Mansonia amazonensis (Theobald) (Diptera: Culicidae).

Mansonia amazonensis (Theobald, 1901) is one of 15 species of the subgenus Mansonia Blanchard, 1901. It is essentially a Neotropical species, recorded in Bolivia, Peru and Brazil. In the highly seasonal Amazon region, Mansonia species find ideal environmental conditions for reproduction, development and establishment. Considering that climate significantly influences the size and behavior of mosquitoes, and insects in general, we analyzed the influence of seasonality on wing morphological variability in populations of Ma. amazonensis. Captures were carried out near the banks of the Madeira River in Rondnia State, Brazil during the dry, rainy and transition periods between seasons during 2018 and 2019. Morphometric characters of 181 wings were analyzed using morphological methods. The results show that wing size of Ma. amazonensis increases following a relatively gradual trend, from smaller wings during the dry period to larger ones in the rainy season. This study provides the first evidence, detected using geometric morphometry, of seasonally associated phenotypic variability in the wing conformation of Ma. amazonensis.

Rediscovering the mosquito fauna (Diptera: Culicidae) of Estonia: an annotated checklist with distribution maps and DNA evidence.

Female mosquitoes (Diptera: Culicidae) affect their hosts in numerous negative ways and are crucial to the spread of vector-borne pathogens. It is, therefore, important to have a detailed overview of regional mosquitoes, to be able to detect changes in species diversity and identify possible health threats. The aim of this study was to update the checklist of the mosquito fauna of Estonia for the first time since 1957. For this purpose, 24,344 adult mosquitoes (94% females) were collected in Estonia from 2008 to 2020 using various trapping methods. Specimens were primarily identified by morphological characteristics, but DNA barcoding based on the partial cytochrome c oxidase subunit 1 gene (COI) was also used. Species were included in the checklist based on historical records as well as new collections, while also considering reports from neighboring countries. Species records are supplemented with voucher specimens, distribution maps and DNA evidence. The updated checklist includes 34 species, 27 of which were confirmed with recently collected material. All in all, Aedes communis (de Geer, 1776) proved to be the most common mosquito in Estonia, accounting for 30.1% of the specimens collected. This is noteworthy, as this species has been implicated in the transmission of multiple disease agents present in the area. New evidence revealed the presence of Ae. hexodontus Dyar, 1916, Ae. sticticus (Meigen, 1838), Anopheles messeae Falleroni, 1926 and Culiseta bergrothi (Edwards, 1921) in Estonia.

Geometric morphometric wing analysis as a tool to discriminate female mosquitoes from different suburban areas of Chiang Mai province, Thailand.

Mosquitoes are hematophagous insects that transmit parasites and pathogens with devastating effects on humans, particularly in subtropical regions. Different mosquito species display various behaviors, breeding sites, and geographic distribution; however, they can be difficult to distinguish in the field due to morphological similarities between species and damage caused during trapping and transportation. Vector control methods for controlling mosquito-borne disease epidemics require an understanding of which vector species are present in the area as well as the epidemiological patterns of disease transmission. Although molecular techniques can accurately distinguish between mosquito species, they are costly and laborious, making them unsuitable for extensive use in the field. Thus, alternative techniques are required. Geometric morphometrics (GM) is a rapid and inexpensive technique that can be used to analyze the size, shape, and shape variation of individuals based on a range of traits. Here, we used GM to analyze the wings of 1,040 female mosquitoes from 12 different species in Thailand. The right wing of each specimen was removed, imaged microscopically, and digitized using 17 landmarks. Wing shape variation among genera and species was analyzed using canonical variate analysis (CVA), while discriminant function analysis was used to cross-validate classification reliability based on Mahalanobis distances. Phenetic relationships were constructed to illustrate the discrimination patterns for genera and species. CVA of the morphological variation among Aedes, Anopheles, Armigeres, Culex, and Mansonia mosquito genera revealed five clusters. In particular, we demonstrated a high percentage of correctly-distinguished samples among Aedes (97.48%), Armigeres (96.15%), Culex (90.07%), and Mansonia (91.67%), but not Anopheles (64.54%). Together, these findings suggest that wing landmark-based GM analysis is an efficient method for identifying mosquito species, particularly among the Aedes, Armigeres, Culex, and Mansonia genera.

Mosquito species identification using convolutional neural networks with a multitiered ensemble model for novel species detection.

With over 3500 mosquito species described, accurate species identification of the few implicated in disease transmission is critical to mosquito borne disease mitigation. Yet this task is hindered by limited global taxonomic expertise and specimen damage consistent across common capture methods. Convolutional neural networks (CNNs) are promising with limited sets of species, but image database requirements restrict practical implementation. Using an image database of 2696 specimens from 67 mosquito species, we address the practical open-set problem with a detection algorithm for novel species. Closed-set classification of 16 known species achieved 97.04 ± 0.87% accuracy independently, and 89.07 ± 5.58% when cascaded with novelty detection. Closed-set classification of 39 species produces a macro F1-score of 86.07 ± 1.81%. This demonstrates an accurate, scalable, and practical computer vision solution to identify wild-caught mosquitoes for implementation in biosurveillance and targeted vector control programs, without the need for extensive image database development for each new target region.

Oviposition behavior of wild yellow fever vector mosquitoes (Diptera: Culicidae) in an Atlantic Forest fragment, Rio de Janeiro state, Brazil.

Although there are many studies on the control of mosquito vectors of the yellow fever virus (YFV) in tropical forests, there are still few ecological studies regarding abiotic factors effect on these mosquitoes. Here we characterize these effects on oviposition behavior, abundance, and diversity of mosquito vectors of YFV. The study was conducted in Córrego da Luz Municipal Park, in Casimiro de Abreu, Rio de Janeiro state, Brazil, from July 2018 to December 2019. Ovitraps were placed at ground level and 3 m high. The data were tested for normality using the Shapiro-Wilk test, followed by an independent sample analysis, the Mann-Whitney test. The Shannon Diversity Index was used to evaluate the abundance of mosquitos' eggs collected at both ground level and 3 m high. We highlight the presence of Haemagogus janthinomys and Hg. leucocelaenus, primary YFV vectors in forest areas. The abundance of Hg. leucocelaenus (63%), Hg. janthinomys (75%), and Aedes terrens (58%) was higher at the height of 3 m, while Ae. albopictus (52%) was higher at ground level. Aedes albopictus was positively correlated with temperature. Culicidae monitoring is essential for assessing the YFV transmission cycle in Atlantic forest fragments.

Deep learning identification for citizen science surveillance of tiger mosquitoes.

Global monitoring of disease vectors is undoubtedly becoming an urgent need as the human population rises and becomes increasingly mobile, international commercial exchanges increase, and climate change expands the habitats of many vector species. Traditional surveillance of mosquitoes, vectors of many diseases, relies on catches, which requires regular manual inspection and reporting, and dedicated personnel, making large-scale monitoring difficult and expensive. New approaches are solving the problem of scalability by relying on smartphones and the Internet to enable novel community-based and digital observatories, where people can upload pictures of mosquitoes whenever they encounter them. An example is the Mosquito Alert citizen science system, which includes a dedicated mobile phone app through which geotagged images are collected. This system provides a viable option for monitoring the spread of various mosquito species across the globe, although it is partly limited by the quality of the citizen scientists' photos. To make the system useful for public health agencies, and to give feedback to the volunteering citizens, the submitted images are inspected and labeled by entomology experts. Although citizen-based data collection can greatly broaden disease-vector monitoring scales, manual inspection of each image is not an easily scalable option in the long run, and the system could be improved through automation. Based on Mosquito Alert's curated database of expert-validated mosquito photos, we trained a deep learning model to find tiger mosquitoes (Aedes albopictus), a species that is responsible for spreading chikungunya, dengue, and Zika among other diseases. The highly accurate 0.96 area under the receiver operating characteristic curve score promises not only a helpful pre-selector for the expert validation process but also an automated classifier giving quick feedback to the app participants, which may help to keep them motivated. In the paper, we also explored the possibilities of using the model to improve future data collection quality as a feedback loop.

Wyeomyia shannoni Lane amp; Cerqueira, 1942, a taxonomic puzzle (Diptera: Culicidae): synonymy, genus transfer, homonymy, and description of a new species of Sabethes Robineau-Desvoidy, 1827.

We discovered the immature stages of Wyeomyia shannoni Lane Cerqueira, 1942, unknown for almost 80 years, and found that they, as well as the adult male, are identical with those previously described for Sabethes (Peytonulus) paradoxus Harbach, 2002. Consequently, the species described by Lane and Cerqueira is here considered a senior synonym of Sa. paradoxus and is transferred to the genus Sabethes, hereafter named Sabethes (Peytonulus) shannoni (Lane Cerqueira, 1942), comb. n. As the name shannoni is preoccupied in the genus Sabethes, the species known as Sabethes (Sabethes) shannoni Cerqueira, 1961 is here renamed Sabethes (Sabethes) cerqueirai Nascimento-Pereira, Neves, Loureno-de-Oliveira Motta, nom. n. We improved the morphological characterization of Sa. (Pey.) shannoni (Lane Cerqueira) by including an illustration of the female genitalia, larval mouthparts and the female genital lobe of the pupa. Finally, we describe and illustrate a new species of Sabethes closely related to Sa. (Pey.) shannoni, named Sabethes (Peytonulus) harbachi Nascimento-Pereira, Guimares, Loureno-de-Oliveira Motta, sp. n.

Identification of mosquitoes (Diptera: Culicidae) from Mexico State, Mexico using morphology and COI DNA barcoding.

Mosquitoes are commonly identified to species level using morphological traits, but complementary methods for identification are often necessary when specimens are collected as immature stages, stored inadequately, or when delineation of species complexes is problematic. DNA-barcoding using the mitochondrial cytochrome c oxidase subunit 1 (COI) gene is one such tool used for the morphological identification of species. A comprehensive entomological survey of mosquito species in Mexico State identified by COI DNA barcoding and morphology is documented in this paper. Specimens were collected from all the physiographic provinces in Mexico State between 2017 and 2019. Overall, 2,218 specimens were collected from 157 localities representing both subfamilies Anophelinae and Culicinae. A species checklist that consists of 6 tribes, 10 genera, 20 subgenera, and 51 species, 35 of which are new records for Mexico State, is provided. Three hundred and forty-two COI sequences of 46 species were analysed. Mean intraspecific and interspecific distances ranged between 0% to 3.9% and from 1.2% to 25.3%, respectively. All species groups were supported by high bootstraps values in a Neighbour-Joining analysis, and new COI sequences were generated for eight species: Aedes chionotum Zavortink, Ae. vargasi Schick, Ae. gabriel Schick, Ae. guerrero Berlin, Ae. ramirezi Vargas and Downs, Haemagogus mesodentatus Komp and Kumm, Culex restrictor Dyar and Knab, and Uranotaenia geometrica Theobald. This study provides a detailed inventory of the Culicidae from Mexico State and discusses the utility of DNA barcoding as a complementary tool for accurate mosquito species identification in Mexico.

Is There a Minimum Number of Landmarks That Optimizes the Geometric Morphometric Analysis of Mosquito (Diptera, Culicidae) Wings?

Culicids are the most significant arthropods affecting human health. Thus, their correct identification is critical. The use of Geometric Morphometrics (GM) has been recently incorporated into mosquito taxonomy and has begun to complement classic diagnostic techniques. Since sampling size depends on the number of Landmarks (LMs) used, this study aimed to establish the minimum number of wing LMs needed to optimize GM analysis of mosquito species and/or genera from urban and peri-urban areas of Argentina. Female left wings were used for the optimization phase, in which 17 LMs were reduced to four by iterative LM exclusion. To verify its efficiency, Principal Component Analysis (PCA), Discriminant Analysis (DA), and Canonical Variate Analysis (CVA) were performed. Additionally, a phenogram was constructed to visualize the results. We observed that five LMs for the PCA, CVA, and phenogram and nine for the DA enabled discrimination and/or clustering of almost all species and genera. Therefore, we tested the LM selection by using nine LMs and adding new species. The resulting PCA showed little overlap between species and almost all species clustered as expected, which was also reflected in the phenogram. Significant differences were found between wing shape among all species, together with a low total error rate in the DA. In conclusion, the number of LMs can be reduced and still be used to effectively differentiate and cluster culicids. This is helpful for better exploitation of available material and optimization of data processing time when classic taxonomy methods are inadequate or the material is scarce.

Delimiting cryptic morphological variation among human malaria vector species using convolutional neural networks.

Deep learning is a powerful approach for distinguishing classes of images, and there is a growing interest in applying these methods to delimit species, particularly in the identification of mosquito vectors. Visual identification of mosquito species is the foundation of mosquito-borne disease surveillance and management, but can be hindered by cryptic morphological variation in mosquito vector species complexes such as the malaria-transmitting Anopheles gambiae complex. We sought to apply Convolutional Neural Networks (CNNs) to images of mosquitoes as a proof-of-concept to determine the feasibility of automatic classification of mosquito sex, genus, species, and strains using whole-body, 2D images of mosquitoes. We introduce a library of 1, 709 images of adult mosquitoes collected from 16 colonies of mosquito vector species and strains originating from five geographic regions, with 4 cryptic species not readily distinguishable morphologically even by trained medical entomologists. We present a methodology for image processing, data augmentation, and training and validation of a CNN. Our best CNN configuration achieved high prediction accuracies of 96.96% for species identification and 98.48% for sex. Our results demonstrate that CNNs can delimit species with cryptic morphological variation, 2 strains of a single species, and specimens from a single colony stored using two different methods. We present visualizations of the CNN feature space and predictions for interpretation of our results, and we further discuss applications of our findings for future applications in malaria mosquito surveillance.

Geometric morphometric wing analysis represents a robust tool to identify female mosquitoes (Diptera: Culicidae) in Germany.

Accurate species identification is the prerequisite to assess the relevance of mosquito specimens, but is often hindered by missing or damaged morphological features. The present study analyses the applicability of wing geometric morphometrics as a low-cost and practical alternative to identify native mosquitoes in Germany. Wing pictures were collected for 502 female mosquitoes of five genera and 19 species from 80 sampling sites. The reliable species identification based on interspecific wing geometry of 18 landmarks per specimen was tested. Leave-one-out cross validation revealed an overall accuracy of 99% for the genus and 90% for the species identification. Misidentifications were mainly due to three pairings of Aedes species: Aedes annulipes vs. Aedes cantans, Aedes cinereus vs. Aedes rossicus and Aedes communis vs. Aedes punctor. Cytochrome oxidase subunit I (COI) gene region was sequenced to validate the morphological and morphometric identification. Similar to the results of the morphometric analysis, the same problematic three Aedes-pairs clustered, but most other species could be well separated. Overall, our study underpins that morphometric wing analysis is a robust tool for reliable mosquito identification, which reach the accuracy of COI barcoding.

A framework based on deep neural networks to extract anatomy of mosquitoes from images.

We design a framework based on Mask Region-based Convolutional Neural Network to automatically detect and separately extract anatomical components of mosquitoes-thorax, wings, abdomen and legs from images. Our training dataset consisted of 1500 smartphone images of nine mosquito species trapped in Florida. In the proposed technique, the first step is to detect anatomical components within a mosquito image. Then, we localize and classify the extracted anatomical components, while simultaneously adding a branch in the neural network architecture to segment pixels containing only the anatomical components. Evaluation results are favorable. To evaluate generality, we test our architecture trained only with mosquito images on bumblebee images. We again reveal favorable results, particularly in extracting wings. Our techniques in this paper have practical applications in public health, taxonomy and citizen-science efforts.

Integrated Approaches in Support of Taxonomic Identification of Mosquitoes (Diptera: Culicidae) in Vector Surveillance in Spain.

The pandemic of Zika virus in 2016 and other arboviruses prompted La Rioja Government in Spain to implement an entomological surveillance program of mosquitoes (Diptera; Culicidae) in the region of La Rioja. The morphological identification was supported by genetic analysis using the COI (cytochrome c oxidase subunit I) and the ITS2 (internal transcribed spacer 2) genes. In total, we identified 24 species arranged in 6 genera: Aedes (7 species), Anopheles (4 species), Coquillettidia (1 species), Culex (7 species), Culiseta (4 species), and Uranotaenia (1 species). Aedes sticticus and Aedes geniculatus are newly reported for La Rioja region. In total, 465 COI sequences were analyzed for Culicinae and Anophelinae and 54 ITS2 sequences for Anophelinae; all individuals identified as the same species clustered together in the Neighbor Joining trees. The levels of sequence divergence based on COI ranged between 0% and 2.62%, while the interspecific genetic divergence ranged from 3.05% to 20.07%. Within the genus Culiseta, certain specimens of Culiseta annulata, Culiseta litorea, and Culiseta subochrea were morphologically misidentified due to variation in the main diagnostic characters. The interspecific genetic divergence based on the ITS2 ranged from 0% to 2.98%. An accurate identification of mosquito vectors is the first step to establish a vector surveillance program for preventing pathogen transmission.

Wing geometric morphometrics for identification of mosquito species (Diptera: Culicidae) of neglected epidemiological importance.

Culicidae comprises more than 3500 species, some of which are responsible for the spread of various human diseases, causing millions of deaths worldwide. Correct identification of these species is essential for the development of surveillance and control strategies. The most common method of mosquito identification is based on specific traits of the external morphology of species. However, identification of mosquitoes by morphological characters can be inaccurate or even unfeasible if the specimen is damaged or there is a lack of distinguishing features, as in the case of cryptic species complexes. Wing geometric morphometrics is a reliable, affordable tool for the identification of mosquito species, including sibling species. More importantly, it can be used in addition to both traditional morphologic identification methods as well as genetic approaches. Here, wing geometric morphometrics was used to identify sixteen mosquito species from eight genera: Aedes, Coquillettidia, Culex, Limatus, Mansonia, Psorophora, Runchomyia, and Wyeomyia. The 390 specimens used here were collected in São Paulo, Brazil using CDC traps, aspiration, and Shannon traps. Allometry was assessed by multivariate regression of the Procrustes coordinates on centroid size followed by canonical variate analysis and a pairwise cross-validated reclassification test. A Neighbor-Joining tree based on Mahalanobis distances was constructed with 1,000 bootstrap replicates using MorphoJ 1.02 and Past 2.17c. The canonical variate analysis of genera resulted in distinct clusters for Culex, Limatus, and Psorophora and partial overlapping between Aedes, Coquilettidia, and Mansonia, and between Runchomyia and Wyeomyia. Pairwise cross-validated reclassification tests indicated that genera were identified with an accuracy of at least 99% and subgenera with a mean accuracy of 96% and that in 160 of the 240 possible comparisons species were identified with an accuracy of 100%. Our results show that the eight genera in the study were correctly distinguished by wing shape, as were subgenera and most species, demonstrating that wing geometric morphometrics can be used for the identification of the mosquito species studied here.

First Record of Mansonia dyari in the State of Morelos, Mexico, Based on Morphology and COI DNA Barcoding.

Collections of mosquitoes were conducted for the surveillance of species of medical importance in the state of Morelos, Mexico, in June 2017. Species collected included Mansonia (Mansonia) dyari, which was identified using morphological characters and cytochrome c oxidase I DNA barcoding. Although 3 species of genus Mansonia have been previously reported in Mexico, this is the 1st confirmed record of Ma. dyari in Morelos State, where no Mansonia species had been recorded. Historical records of Ma. dyari and Ma. indubitans in Mexico were reviewed. Therefore, this record increases the number of mosquito species occurring in Morelos to 46. The specimens collected in this study were deposited in the Culicidae collection of the Universidad Autónoma Agraria Antonio Narro, Unidad Laguna.