Barcoding of Italian mosquitoes (BITMO): generation and validation of DNA barcoding reference libraries for native and alien species of Culicidae.

BACKGROUND: Mosquitoes (Culicidae), as disease vectors, represent a risk for human health worldwide. Repeated introductions of alien mosquito species and the spread of invasive species have been recorded in different countries. Traditionally, identification of mosquitoes relies on morphological observation. However, morphology-based identification is associated with a number of potential disadvantages, such as the high level of specialisation of the operator and its limited applicability to damaged samples. In these cases, species identification is achieved through molecular methods based on DNA amplification. Molecular-based taxonomy has also enabled the development of techniques for the study of environmental DNA (eDNA). Previous studies indicated the 16S mitochondrial ribosomal RNA (rRNA) gene as a promising target for this application; however, 16S rRNA sequences are available for only a limited number of mosquito species. In addition, although primers for the 16S rRNA gene were designed years ago, they are based on limited numbers of mosquito sequences. Thus, the aims of this study were to: (i) design pan-mosquito 16S rRNA gene primers; (ii) using these primers, generate a 16S rRNA gene mosquito reference library (with a focus on mosquitoes present in Italy); and (iii) compare the discriminatory power of the 16S rRNA gene with two widely used molecular markers, cytochrome c oxidase subunit 1 mitochondrial gene (COI) and internal transcribed spacer 2 (ITS2). METHODS: A total of six mosquito genera (28 mosquito species) were included in this study: Aedes (n = 16 species), Anopheles (5 species), Coquillettidia (1 species), Culex (3 species), Culiseta (2 species) and Uranotaenia (1 species). DNA was extracted from the whole mosquito body, and more than one specimen for each species was included in the analysis. Sanger sequencing was used to generate DNA sequences that were then analysed through the Barcode of Life Data Systems (BOLD). Phylogenetic analyses were also performed. RESULTS: Novel 16S rDNA gene, COI and ITS2 sequences were generated. The 16S rRNA gene was shown to possess sufficient informativeness for the identification of mosquito species, with a discriminatory power equivalent to that of COI. CONCLUSIONS: This study contributes to the generation of DNA barcode libraries, focussed on Italian mosquitoes, with a significant increase in the number of 16S rRNA gene sequences. We hope that these novel sequences will provide a resource for studies on the biodiversity, monitoring and metabarcoding of mosquitoes, including eDNA-based approaches.

Effects of Hurricane Irma on mosquito abundance and species composition in a metropolitan Gulf coastal city, 2016-2018.

Mosquitoes are the most common disease vectors worldwide. In coastal cities, the spread, activity, and longevity of vector mosquitoes are influenced by environmental factors such as temperature, humidity, and rainfall, which affect their geographic distribution, biting rates, and lifespan. We examined mosquito abundance and species composition before and after Hurricane Irma in Miami, Dade County, Florida, and identified which mosquito species predominated post-Hurricane Irma. Our results showed that mosquito populations increased post-Hurricane Irma: 7.3 and 8.0 times more mosquitoes were captured in 2017 than at baseline, 2016 and 2018 respectively. Warmer temperatures accelerated larval development, resulting in faster emergence of adult mosquitoes. In BG-Sentinel traps, primary species like Ae. tortills, Cx. nigripalpus, and Cx. quinquefasciatus dominated the post-Hurricane Irma period. Secondary vectors that dominated post-Hurricane Irma include An. atropos, An. crucians, An. quadrimaculatus, Cx. erraticus, and Ps. columbiae. After Hurricane Irma, the surge in mosquito populations in Miami, Florida heightened disease risk. To mitigate and prevent future risks, we must enhance surveillance, raise public awareness, and implement targeted vector control measures.

Analysis of diversity and an updated catalog of mosquito species (Diptera: Culicidae) in the Capivari-Monos Environmental Protection Area, São Paulo, Brazil.

The Capivari-Monos Environmental Protection Area (EPA) is located in the southern part of the São Paulo city Green Belt. Since the 1950s, this region has been affected by uncontrolled urban sprawl, resulting in a change in the ecological habits of some vector mosquitoes. Over the last two decades, cases of autochthonous bromeliad malaria associated with the presence of anopheline mosquitoes in the EPA have been recorded. Anopheles cruzii, the primary vector of plasmodia in the region, is abundant and found naturally infected with both Plasmodium vivax and Plasmodium malariae. In light of this, the present study sought to update the catalog of mosquito fauna in this EPA, analyze mosquito diversity among sites with different degrees of conservation and compare species using different collection techniques. Field collections were carried out from March, 2015 to April, 2017. A total of 20,755 specimens were collected, distributed in 106 different taxa representing 16 genera. Analysis of the diversity among the sites based on the Shannon and Simpson indices showed that the most preserved of them had the lowest indices because of the dominance of An. cruzii. The results highlight the increase in the number of different taxa collected as different mosquito collection techniques were included, confirming the importance of using several strategies to ensure adequate sampling of a local mosquito fauna when exploring a greater number of ecotopes. Furthermore, the survey produced the most recent and complete list of mosquito species in the Capivari-Monos EPA, a refuge and shelter for native and introduced mosquito species where new biocenoses, including pathogens, vertebrate hosts, and vectors can form, allowing zoonotic outbreaks in the local human population to occur.

iNaturalist community observations provide valuable data on human-mosquito encounters.

Mosquitoes (Diptera: Culicidae) and the pathogens they transmit represent a threat to human and animal health. Low-cost and effective surveillance methods are necessary to enable sustainable monitoring of mosquito distributions, diversity, and human interactions. This study examined the use of iNaturalist, an online, community-populated biodiversity recording database, for passive mosquito surveillance in the United Kingdom (UK) and Ireland, countries under threat from the introduction of invasive mosquitoes and emerging mosquito-borne diseases. The Mozzie Monitors UK & Ireland iNaturalist project was established to collate mosquito observations in these countries. Data were compared with existing long-term mosquito UK datasets to assess representativeness of seasonal and distribution trends in citizen scientist-recorded observations. The project collected 738 observations with the majority recorded 2020-2022. Records were primarily associated with urban areas, with the most common species Culex pipiens and Culiseta annulata significantly more likely to be observed in urban areas than other species. Analysis of images uploaded to the iNaturalist project also provided insights into human-biting behavior. Our analyses indicate that iNaturalist provides species composition, seasonal occurrence, and distribution figures consistent with existing datasets and is therefore a useful surveillance tool for recording information on human interactions with mosquitoes and monitoring species of concern.

Diversity of biting midges, mosquitoes and sand flies at four dog shelters in rural and peri-urban areas of Central Morocco.

Blood-feeding arthropods are involved in the transmission of several pathogens that have a major impact on public health. Entomological investigations highlighted the composition, abundance, and diversity of flying hematophagous arthropods at four dog shelters located in central Morocco during an eight-month study, with the aim of discussing their vectorial roles and assessing the risk of these shelters as foci for zoonotic diseases. Monitoring of the arthropod fauna for 64 catch nights resulted in the collection of 2,321 biting midges (Ceratopogonidae), 570 mosquitoes (Culicidae), and 475 sand flies (Psychodidae). Fourteen Culicoides species were recorded and dominant species were Culicoides imicola (55.96%), C. paolae (16.07%), C. circumscriptus (10.29%), and C. newsteadi (5.77%). Three mosquito species were collected, including Culex pipiens s.l. (96.84%), Culiseta longiareolata (2.80%), and Cx. perexiguus (0.36%). Ten sand fly species were collected, including seven Phlebotomus species (62.70%) and three Sergentomyia species (37.30%); Sergentomyia minuta was the most dominant species (34.31%), followed by Phlebotomus sergenti (32.42%), typical Ph. perniciosus (8.63%), Ph. alexandri (6.94%), and Ph. riouxi (6.52%). The coexistence of several vectors in these study areas indicates the potential circulation of a wide range of pathogens, including zoonotic ones, thus requiring the implementation of surveillance and control programs to prevent the emergence and spread of disease outbreaks.

Title: Diversité des culicoïdes, moustiques et phlébotomes collectés au niveau de quatre chenils du Maroc central. Abstract: Les arthropodes hématophages sont impliqués dans la transmission de plusieurs agents pathogènes ayant un impact majeur sur la santé publique. Des investigations entomologiques ont mis en évidence la composition, l'abondance et la diversité des arthropodes hématophages volants dans quatre chenils situés au centre du Maroc au cours d'une étude de huit mois, dans le but de discuter de leurs rôles vectoriels et d'évaluer le risque de ces chenils comme foyers de maladies zoonotiques. Le suivi de la faune des arthropodes au cours de 64 nuits de capture a permis la collecte de 2 321 culicoïdes (Ceratopogonidae), 570 moustiques (Culicidae) et 475 phlébotomes (Psychodidae). Quatorze espèces de Culicoides ont été recensées et les espèces dominantes étaient Culicoides imicola (55,96 %), C. paolae (16,07 %), C. circumscriptus (10,29 %) et C. newsteadi (5,77 %). Trois espèces de moustiques ont été collectées, dont Culex pipiens s.l. (96,84 %), Culiseta longiareolata (2,80 %) et Cx. perexiguus (0,36 %). Dix espèces de phlébotomes ont été collectées, dont sept espèces de Phlebotomus (62,70 %) et trois espèces de Sergentomyia (37,30 %); Sergentomyia minuta était l'espèce dominante (34,31 %), suivie de Phlebotomus sergenti (32,42 %), Ph. perniciosus typique (8,63 %), Ph. alexandri (6,94 %) et Ph. riouxi (6,52 %). La coexistence de plusieurs vecteurs dans ces zones d'étude indique la circulation potentielle d'un large éventail d'agents pathogènes, y compris zoonotiques, ce qui nécessite la mise en œuvre de programmes de surveillance et de contrôle pour prévenir l'émergence et la propagation d'épidémies.

Robust mosquito species identification from diverse body and wing images using deep learning.

Mosquito-borne diseases are a major global health threat. Traditional morphological or molecular methods for identifying mosquito species often require specialized expertise or expensive laboratory equipment. The use of convolutional neural networks (CNNs) to identify mosquito species based on images may offer a promising alternative, but their practical implementation often remains limited. This study explores the applicability of CNNs in classifying mosquito species. It compares the efficacy of body and wing depictions across three image collection methods: a smartphone, macro-lens attached to a smartphone and a professional stereomicroscope. The study included 796 specimens of four morphologically similar Aedes species, Aedes aegypti, Ae. albopictus, Ae. koreicus and Ae. japonicus japonicus. The findings of this study indicate that CNN models demonstrate superior performance in wing-based classification 87.6% (95% CI: 84.2-91.0) compared to body-based classification 78.9% (95% CI: 77.7-80.0). Nevertheless, there are notable limitations of CNNs as they perform reliably across multiple devices only when trained specifically on those devices, resulting in an average decline of mean accuracy by 14%, even with extensive image augmentation. Additionally, we also estimate the required training data volume for effective classification, noting a reduced requirement for wing-based classification compared to body-based methods. Our study underscores the viability of both body and wing classification methods for mosquito species identification while emphasizing the need to address practical constraints in developing accessible classification systems.

Mosquito dynamics and their drivers in peri-urban Antananarivo, Madagascar: insights from a longitudinal multi-host single-site survey.

BACKGROUND: Antananarivo, the capital city of Madagascar, is experiencing a steady increase in population growth. Due to the abundance of mosquito vectors in this locality, the population exposed to mosquito-borne diseases is therefore also increasing, as is the risk of epidemic episodes. The aim of the present study was to assess, in a resource-limited setting, the information on mosquito population dynamics and disease transmission risk that can be provided through a longitudinal entomological study carried out in a multi-host single site. METHODS: Mosquitoes were collected every 15 days over 16 months (from January 2017 to April 2018) using six CDC-light traps in a peri-urban area of Antananarivo. Multivariable generalised linear models were developed using indoor and outdoor densities of the predominant mosquito species as response variables and moon illumination, environmental data and climatic data as the explanatory variables. RESULTS: Overall, 46,737 mosquitoes belonging to at least 20 species were collected, of which Culex antennatus (68.9%), Culex quinquefasciatus (19.8%), Culex poicilipes (3.7%) and Anopheles gambiae sensu lato (2.3%) were the most abundant species. Mosquito densities were observed to be driven by moon illumination and climatic factors interacting at different lag periods. The outdoor models demonstrated biweekly and seasonal patterns of mosquito densities, while the indoor models demonstrated only a seasonal pattern. CONCLUSIONS: An important diversity of mosquitoes exists in the peri-urban area of Antananarivo. Some well-known vector species, such as Cx. antennatus, a major vector of West Nile virus (WNV) and Rift-Valley fever virus (RVFV), Cx. quinquefasciatus, a major vector of WNV, Cx. poicilipes, a candidate vector of RVFV and An. gambiae sensu lato, a major vector of Plasmodium spp., are abundant. Importantly, these four mosquito species are present all year round, even though their abundance declines during the cold dry season, with the exception of Cx. quinquefasciatus. The main drivers of their abundance were found to be temperature, relative humidity and precipitation, as well as-for outdoor abundance only-moon illumination. Identifying these drivers is a first step towards the development of pathogen transmission models (R0 models), which are key to inform public health stakeholders on the periods of most risk for vector-borne diseases.

Investigating the impact of climate and seasonality on mosquito (Diptera: Culicidae) vector populations in the connecting areas of the Tenasserim range forests in Thailand.

Mosquito-borne diseases pose a significant public health challenge globally. Our study focused on the seasonal diversity of mosquito species in the connecting areas of the Tenasserim (also known as Tanaosri) range forests in Thailand. Additionally, we employed the geometric morphometric technique to assess variations in wing size and shape among five predominant mosquito species. Throughout the study period, we collected a total of 9,522 mosquitoes, encompassing 42 species across eight genera. In these connecting areas of forests, the Simpson index and Shannon species diversity index were recorded at 0.86 and 2.36, respectively, indicating a high level of mosquito diversity. Our analysis using the Analysis of Similarities (ANOSIM) test showed significant seasonal differences in mosquito communities, with an R-value of 0.30 (p < 0.05) in the lower connecting areas and 0.37 (p < 0.05) in the upper connecting areas. Additionally, canonical correspondence analyses showed that the abundance of each mosquito species is influenced by various climate factors. Phenotypic analyses of wing size and shape have deepened our understanding of local adaptation and the seasonal pressures impacting these vectors. Notably, most species exhibited larger wing sizes in the dry season compared to other seasons. Additionally, seasonal assessments of wing shape in five predominant mosquito species revealed significant differences across seasonal populations (p < 0.05). Ongoing monitoring of these populations is crucial to enhancing our understanding of the seasonal effects on mosquito abundance and physiological adaptations. These insights are essential for developing more effective strategies to manage mosquito-borne diseases.

COI DNA barcoding to differentiate Haemagogus janthinomys and Haemagogus capricornii (Diptera: Culicidae) mosquitoes.

The genus Haemagogus (Diptera: Culicidae) includes species that are important vectors of pathogens such as the yellow fever virus. The accurate identification of these species is essential for the control of zoonoses. Females of Hg. capricornii and Hg. janthinomys are morphologically indistinguishable, which makes the use of alternative identification techniques desirable. This study aimed to obtain sequences of the mitochondrial cytochrome c oxidase I (COI) gene, in the region widely used for DNA barcoding, of Haemagogus specimens from the state of São Paulo, Brazil, to evaluate the effectiveness of these sequences in the molecular identification of the species. A total of 37 female and 2 male mosquitoes were collected in various locations in the state of São Paulo, using methods such as hand-nets, Shannon traps, CDC light traps with CO2 bait and Nasci aspirators. The sequences of a 710 bp fragment of the COI gene were amplified by PCR and sequenced. A phylogenetic tree reconstruction was conducted using the Bayesian approach implemented in MrBayes v3.2.2, providing support values for taxa where genetic clusters may indicate the presence of new or cryptic species. We obtained 39 COI sequences representing three species: Haemagogus capricornii, Haemagogus leucocelaenus, and Haemagogus janthinomys. Bayesian analysis of the sequences produced clades that corroborate the morphological identification of the species. The separation of Hg. capricornii and Hg. janthinomys received 100 % statistical support and the Hg. capricornii was very well supported (91 %). The two sequences from male specimens, morphologically identified as Hg. capricornii, were grouped in the same clade, a sister clade of Hg. janthinomys. It is important to highlight that the Hg. janthinomys were positioned in several subclades, showing a polymorphism of this species within the state, a situation not observed for Hg. capricornii. For the first time, sequences of the mtCOI gene from Hg. capricornii were obtained and related to morphologically identified specimens. COI sequences proved effective in the molecular identification of Haemagogus species. This study contributes to the expansion of the GenBank database, providing the first sequences of Hg. capricornii and new sequences for Hg. janthinomys and Hg. leucocelaenus.

Characterization of mosquito host-biting networks of potential Rift Valley fever virus vectors in north-eastern KwaZulu-Natal province, South Africa.

BACKGROUND: Rift Valley fever virus (RVFV) is a zoonotic mosquito-borne virus with serious implications for livestock health, human health, and the economy in Africa, and is suspected to be endemic in north-eastern KwaZulu-Natal (KZN), South Africa. The vectors of RVFV in this area are poorly known, although several species, such as Aedes (Neomelaniconion) mcintoshi, Aedes (Neomelaniconion) circumluteolus, Aedes (Aedimorphus) durbanensis, and Culex (Lasioconops) poicilipes may be involved. The aim of the study was to determine the vertebrate blood meal sources of potential RVFV mosquito vectors in north-eastern KZN and to characterize the host-biting network. METHODS: Blood-fed mosquitoes were collected monthly from November 2019 to February 2023 using a backpack aspirator, CO2-baited Centers for Disease Control and Prevention (CDC) miniature light traps and tent traps, in the vicinity of water bodies and livestock farming households. The mosquitoes were morphologically identified. DNA was extracted from individual mosquitoes and used as templates to amplify the vertebrate cytochrome c oxidase I (COI) and cytochrome b (cytb) genes using conventional polymerase chain reaction (PCR). Amplicons were sequenced and queried in GenBank and the Barcode of Life Data systems to identify the vertebrate blood meal sources and confirm mosquito identifications. All mosquitoes were screened for RVFV using real time reverse transcription (RT)-PCR. RESULTS: We identified the mammalian (88.8%) and avian (11.3%) blood meal sources from 409 blood-fed mosquitoes. Aedes circumluteolus (n = 128) made up the largest proportion of collected mosquitoes. Cattle (n = 195) and nyala (n = 61) were the most frequent domestic and wild hosts, respectively. Bipartite network analysis showed that the rural network consisted of more host-biting interactions than the reserve network. All mosquitoes tested negative for RVFV. CONCLUSIONS: Several mosquito species, including Ae. circumluteolus, and vertebrate host species, including cattle and nyala, could play a central role in RVFV transmission. Future research in this region should focus on these species to better understand RVFV amplification.

AI-driven convolutional neural networks for accurate identification of yellow fever vectors.

BACKGROUND: Identifying mosquito vectors is crucial for controlling diseases. Automated identification studies using the convolutional neural network (CNN) have been conducted for some urban mosquito vectors but not yet for sylvatic mosquito vectors that transmit the yellow fever. We evaluated the ability of the AlexNet CNN to identify four mosquito species: Aedes serratus, Aedes scapularis, Haemagogus leucocelaenus and Sabethes albiprivus and whether there is variation in AlexNet's ability to classify mosquitoes based on pictures of four different body regions. METHODS: The specimens were photographed using a cell phone connected to a stereoscope. Photographs were taken of the full-body, pronotum and lateral view of the thorax, which were pre-processed to train the AlexNet algorithm. The evaluation was based on the confusion matrix, the accuracy (ten pseudo-replicates) and the confidence interval for each experiment. RESULTS: Our study found that the AlexNet can accurately identify mosquito pictures of the genus Aedes, Sabethes and Haemagogus with over 90% accuracy. Furthermore, the algorithm performance did not change according to the body regions submitted. It is worth noting that the state of preservation of the mosquitoes, which were often damaged, may have affected the network's ability to differentiate between these species and thus accuracy rates could have been even higher. CONCLUSIONS: Our results support the idea of applying CNNs for artificial intelligence (AI)-driven identification of mosquito vectors of tropical diseases. This approach can potentially be used in the surveillance of yellow fever vectors by health services and the population as well.

A Handheld Tool for the Rapid Morphological Identification of Mosquito Species (VectorCam) for Community-Based Malaria Vector Surveillance: Summative Usability Study.

BACKGROUND: Malaria impacts nearly 250 million individuals annually. Specifically, Uganda has one of the highest burdens, with 13 million cases and nearly 20,000 deaths. Controlling the spread of malaria relies on vector surveillance, a system where collected mosquitos are analyzed for vector species' density in rural areas to plan interventions accordingly. However, this relies on trained entomologists known as vector control officers (VCOs) who identify species via microscopy. The global shortage of entomologists and this time-intensive process cause significant reporting delays. VectorCam is a low-cost artificial intelligence-based tool that identifies a mosquito's species, sex, and abdomen status with a picture and sends these results electronically from surveillance sites to decision makers, thereby deskilling the process to village health teams (VHTs). OBJECTIVE: This study evaluates the usability of the VectorCam system among VHTs by assessing its efficiency, effectiveness, and satisfaction. METHODS: The VectorCam system has imaging hardware and a phone app designed to identify mosquito species. Two users are needed: (1) an imager to capture images of mosquitos using the app and (2) a loader to load and unload mosquitos from the hardware. Critical success tasks for both roles were identified, which VCOs used to train and certify VHTs. In the first testing phase (phase 1), a VCO and a VHT were paired to assume the role of an imager or a loader. Afterward, they swapped. In phase 2, two VHTs were paired, mimicking real use. The time taken to image each mosquito, critical errors, and System Usability Scale (SUS) scores were recorded for each participant. RESULTS: Overall, 14 male and 6 female VHT members aged 20 to 70 years were recruited, of which 12 (60%) participants had smartphone use experience. The average throughput values for phases 1 and 2 for the imager were 70 (SD 30.3) seconds and 56.1 (SD 22.9) seconds per mosquito, respectively, indicating a decrease in the length of time for imaging a tray of mosquitos. The loader's average throughput values for phases 1 and 2 were 50.0 and 55.7 seconds per mosquito, respectively, indicating a slight increase in time. In terms of effectiveness, the imager had 8% (6/80) critical errors and the loader had 13% (10/80) critical errors in phase 1. In phase 2, the imager (for VHT pairs) had 14% (11/80) critical errors and the loader (for VHT pairs) had 12% (19/160) critical errors. The average SUS score of the system was 70.25, indicating positive usability. A Kruskal-Wallis analysis demonstrated no significant difference in SUS (H value) scores between genders or users with and without smartphone use experience. CONCLUSIONS: VectorCam is a usable system for deskilling the in-field identification of mosquito specimens in rural Uganda. Upcoming design updates will address the concerns of users and observers.

Smart technology for mosquito control: Recent developments, challenges, and future prospects.

Smart technology coupled with digital sensors and deep learning networks have emerging scopes in various fields, including surveillance of mosquitoes. Several studies have been conducted to examine the efficacy of such technologies in the differential identification of mosquitoes with high accuracy. Some smart trap uses computer vision technology and deep learning networks to identify live Aedes aegypti and Culex quinquefasciatus in real time. Implementing such tools integrated with a reliable capture mechanism can be beneficial in identifying live mosquitoes without destroying their morphological features. Such smart traps can correctly differentiates between Cx. quinquefasciatus and Ae. aegypti mosquitoes, and may also help control mosquito-borne diseases and predict their possible outbreak. Smart devices embedded with YOLO V4 Deep Neural Network algorithm has been designed with a differential drive mechanism and a mosquito trapping module to attract mosquitoes in the environment. The use of acoustic and optical sensors in combination with machine learning techniques have escalated the automatic classification of mosquitoes based on their flight characteristics, including wing-beat frequency. Thus, such Artificial Intelligence-based tools have promising scopes for surveillance of mosquitoes to control vector-borne diseases. However working efficiency of such technologies requires further evaluation for implementation on a global scale.

Survey of mosquito species in intensive pig farms in Hunan province, China.

Mosquitoes (Diptera: Culicidae) are one of the most studied groups of arthropods worldwide due to their high transmission capacity for pathogens, including viruses and parasites. During June to October 2022, the prevalence of mosquito species in 12 intensive pig farms from 12 representative administrative regions in Hunan province of China was investigated using traps with ultraviolet light. All collected mosquitoes were counted and identified to species according to morphological and molecular methods. A total of 4,443 mosquito specimens were collected in the pig farms, and they represented one family, four genera and nine species. Culex pipiens pipiens (24%) was the most common mosquito species, followed by Armigeres subalbatus (23.4%) and Culex tritaeniorhynchus (20.6%). Phylogenetic analyses based on mitochondrial cox1 sequences revealed all mosquito species from present study grouping into distinct monophyletic groups corresponding to nine known mosquito species with strongly supported. The results of the present investigation have implications for the ongoing control of mosquito infestation in pig farms in Hunan province, China. This is the first report of mosquito populations in intensive pig farms in Hunan province, China.

Application of circular statistics in temporal distribution of adult mosquitoes in Qingdao, Shandong Province, China, 2021-2023.

BACKGROUND: Analyses of the temporal distribution of mosquitoes are presented in statistical charts, but it is difficult to prove in statistics whether differences in peak periods exist among different years or habitats. This study aimed to investigate the application of circular statistics in determining the peak period and a comparison of differences. METHODS: Surveillance of adult mosquitoes was conducted twice a month by light traps in five different habitats from March to November for 3 years (2021-2023) in Qingdao, Shandong Province, China. The Kruskal-Wallis test was performed to determine the differences in mosquito density among different years and habitats. Circular statistics and line charts were employed to determine the peak period and a comparison of differences. RESULTS: Among a total of 14,834 adult mosquitoes comprising five mosquito species from four genera, Culex pipiens pallens was dominant and accounted for 89.6% of the specimens identified. Aedes albopictus, Armigeres subalbatus, and Anopheles sinensis made up 5.7%, 4.2%, and 0.5%, respectively. Culex tritaeniorhynchus accounted for less than 0.1%. The mean mosquito density (females/trap night) for the trapping period was 10.3 in 2021, 5.6 in 2022, and 3.6 in 2023. Among five habitats, the highest mosquito density was 8.9 in livestock sheds, followed by 6.8 in parks, 5.9 in rural dwellings, 5.5 in urban dwellings, and 5.4 in hospitals. No statistically significant differences were found among different years (H = 1.96, d.f. 2, P = 0.376) and habitats (H = 0.45, d.f. 4, P = 0.978). Overall, the peak period of mosquito activity fell in the months from June to September. The peak period among 3 years differed significantly (F(2,7022) = 119.17, P < 0.01), but there were no statistically significant differences in peak period among different habitats (F(4,7020) = -159.09, P > 0.05). CONCLUSION: Circular statistics could be effectively combined with statistical charts to elucidate the peak period of mosquitoes and determine the differences in statistics among different years and habitats. These findings will provide valuable information for mosquito control and public health management.

Resolution of cryptic mosquito species through wing morphometrics.

Mosquitoes are medically important insects, and accurate species identification is crucial to understanding vector biology, forming the cornerstone of successful vector control programs. Identification is difficult owing to morphologically similar species. Wing morphometrics can provide a simple, fast, and accurate way to classify species, and using it as a method to differentiate vector species among its cryptic congeners has been underexplored. Using a total of 227 mosquitoes and 20 landmarks per specimen, we demonstrated the utility of wing morphometrics in differentiating species two groups occurring in sympatry - Culex (Culex) vishnui group and Culex (Lophoceraomyia) subgenus, as well as explored population-level variation in the wing shape of Aedes albopictus across habitats. Cytochrome oxidase subunit I (COI) gene region was sequenced to validate the morphological and morphometric identification. Procrustes ANOVA regression and CVA based on wing shape reflected that the wing landmarks across all species differed significantly, and leave-one-out cross validation revealed an overall high accuracy of >97% for the two Culex groups. Wing morphometrics uncovered population-level variation within Aedes albopictus, but cross validation accuracy was low. Overall, we show that wing geomorphometric analysis is able to resolve cryptic Culex species (including vectors) occurring sympatrically, and is a robust tool for identifying mosquitoes reliably.

Mosquito (Diptera: Culicidae) diversity and arbovirus detection across an urban and agricultural landscape.

Fragmented landscapes in Mexico, characterized by a mix of agricultural, urban, and native vegetation cover, presents unique ecological characteristics that shape the mosquito community composition and mosquito-borne diseases. The extent to which landscape influences mosquito populations and mosquito-borne diseases is still poorly understood. This work assessed the effect of landscape metrics -agriculture, urban, and native vegetation cover- on mosquito diversity and arbovirus presence in fragmented tropical deciduous forests in Central Mexico during 2021. Among the 21 mosquito species across six genera we identified, Culex quinquefasciatus was the most prevalent species, followed by Aedes aegypti, Ae. albopictus, and Ae. epactius. Notably, areas with denser native vegetation cover displayed higher mosquito species richness, which could have an impact on phenomena such as the dilution effect. Zika and dengue virus were detected in 85% of captured species, with first reports of DENV in several Aedes species and ZIKV in multiple Aedes and Culex species. These findings underscore the necessity of expanding arbovirus surveillance beyond Ae. aegypti and advocate for a deeper understanding of vector ecology in fragmented landscapes to adequately address public health strategies.

Diversity and ecological niche model of malaria vector and non-vector mosquito species in Covè, Ouinhi, and Zangnanado, Southern Benin.

The present study aimed to assess mosquito species diversity, distribution, and ecological preferences in the Covè, Ouinhi, and Zangnanado communes, Southern Benin. Such information is critical to understand mosquito bio-ecology and to focus control efforts in high-risk areas for vector-borne diseases. Mosquito collections occurred quarterly in 60 clusters between June 2020 and April 2021, using human landing catches. In addition to the seasonal mosquito abundance, Shannon's diversity, Simpson, and Pielou's equitability indices were also evaluated to assess mosquito diversity. Ecological niche models were developed with MaxEnt using environmental variables to assess species distribution. Overall, mosquito density was higher in the wet season than in the dry season in all communes. A significantly higher Shannon's diversity index was also observed in the wet season than in the dry seasons in all communes (p < 0.05). Habitat suitability of An. gambiae s.s., An. coluzzii, Cx. quinquefasciatus and Ma. africana was highly influenced by slope, isothermality, site aspect, elevation, and precipitation seasonality in both wet and dry seasons. Overall, depending on the season, the ecological preferences of the four main mosquito species were variable across study communes. This emphasizes the impact of environmental conditions on mosquito species distribution. Moreover, mosquito populations were found to be more diverse in the wet season compared to the dry season.

Integrating wing morphometrics and mitochondrial DNA analysis to assess the filaria vector Mansonia uniformis (Diptera: Culicidae) populations in Thailand.

Mansonia uniformis (Diptera: Culicidae) is recognized as a vector of Brugia malayi and has been reported to transmit Wuchereria bancrofti, both causing lymphatic filariasis in humans. This study employed geometric morphometrics (GM) to investigate wing shape variation and analyzed genetic diversity through cytochrome c oxidase subunit 1 (COI) gene analyses in Ma. uniformis populations across Thailand. Wing GM analyses indicated significant differences in wing shape based on Mahalanobis distances among nearly all population pairs (p < 0.05), with no significant correlation between wing shape and geographic distance (r = 0.210, p > 0.05). Genetic analyses identified 63 haplotypes and 49 polymorphic sites, with the overall population exhibiting a nucleotide diversity of 0.006 (± 0.001) and a haplotype diversity of 0.912 (± 0.017). Deviations from neutrality, as indicated by Tajima's D and Fu's FS tests for the overall Ma. uniformis populations in Thailand, were statistically significant and negative, suggesting population expansion (both p < 0.05). Analysis of molecular variance revealed no significant genetic structure when all populations were categorized based on collection sites and geographic regions. However, significant differences in FST values were observed between some populations. These findings enhance our understanding of the geographical and genetic factors influencing Ma. uniformis populations, which are crucial for developing effective control strategies in Thailand.

Sequencing and Description of the Mitochondrial Genome of Orthopodomyia fascipes (Diptera: Culicidae).

The genus Orthopodomyia Theobald, 1904 (Diptera: Culicidae) comprises 36 wild mosquito species, with distribution largely restricted to tropical and temperate areas, most of which are not recognized as vectors of epidemiological importance due to the lack of information related to their bionomy and involvement in the cycle transmission of infectious agents. Furthermore, their evolutionary relationships are not completely understood, reflecting the scarcity of genetic information about the genus. Therefore, in this study, we report the first complete description of the mitochondrial genome of a Neotropical species representing the genus, Orthopodomyia fascipes Coquillet, 1906, collected in the Brazilian Amazon region. Using High Throughput Sequencing, we obtained a mitochondrial sequence of 15,598 bp, with an average coverage of 418.5×, comprising 37 functional subunits and a final portion rich in A + T, corresponding to the control region. The phylogenetic analysis, using Maximum Likelihood and Bayesian Inference based on the 13 protein-coding genes, corroborated the monophyly of Culicidae and its two subfamilies, supporting the proximity between the tribes Orthopodomyiini and Mansoniini, partially disagreeing with previous studies based on the use of molecular and morphological markers. The information generated in this study contributes to a better understanding of the taxonomy and evolutionary history of the genus and other groups of Culicidae.