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.

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.

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.

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.

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.

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.

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.

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.

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.

Double vision: 2D and 3D mosquito trajectories can be as valuable for behaviour analysis via machine learning.

BACKGROUND: Mosquitoes are carriers of tropical diseases, thus demanding a comprehensive understanding of their behaviour to devise effective disease control strategies. In this article we show that machine learning can provide a performance assessment of 2D and 3D machine vision techniques and thereby guide entomologists towards appropriate experimental approaches for behaviour assessment. Behaviours are best characterised via tracking-giving a full time series of information. However, tracking systems vary in complexity. Single-camera imaging yields two-component position data which generally are a function of all three orthogonal components due to perspective; however, a telecentric imaging setup gives constant magnification with respect to depth and thereby measures two orthogonal position components. Multi-camera or holographic techniques quantify all three components. METHODS: In this study a 3D mosquito mating swarm dataset was used to generate equivalent 2D data via telecentric imaging and a single camera at various imaging distances. The performance of the tracking systems was assessed through an established machine learning classifier that differentiates male and non-male mosquito tracks. SHAPs analysis has been used to explore the trajectory feature values for each model. RESULTS: The results reveal that both telecentric and single-camera models, when placed at large distances from the flying mosquitoes, can produce equivalent accuracy from a classifier as well as preserve characteristic features without resorting to more complex 3D tracking techniques. CONCLUSIONS: Caution should be exercised when employing a single camera at short distances as classifier balanced accuracy is reduced compared to that from 3D or telecentric imaging; the trajectory features also deviate compared to those from the other datasets. It is postulated that measurement of two orthogonal motion components is necessary to optimise the accuracy of machine learning classifiers based on trajectory data. The study increases the evidence base for using machine learning to determine behaviours from insect trajectory data.

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.

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.

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.

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.

Diversity and molecular characterization of insect-specific flaviviruses in mosquitoes (Diptera: Culicidae) collected in central and northern Argentina.

The genus Flavivirus comprises approximately 80 different viruses. Phylogenetic relationships among its members indicate a clear ecological separation between those viruses transmitted by mosquitoes, ticks, with no known vector, and insect-specific Flaviviruses. The diversity and phylogenetic relationships among insect-specific flaviviruses circulating in the central and northern regions of Argentina were studied by performing molecular detection and characterization of the NS5 protein gene in mosquitoes collected in Córdoba, Chaco and Tucumán provinces. Overall, 68 out of 1776 pools were positive. CxFV, KRV and CFAV circulate in the 3 studied provinces. Several mosquito species (Aedes aegypti, Culex bidens, Cx. dolosus, Cx. interfor, Cx. quinquefasciatus, Cx. saltanensis, Haemagogus spegazzini) were found infected. A wide circulation of CxFV was observed in the central-northern region of Argentina. CxFV strains detected in our study clustered with strains circulating in Santa Fe and Buenos Aires provinces (Argentina), and other countries such as Indonesia, Mexico, Uganda and Taiwan. The presence of these viruses in mosquitoes could play an important role from the public health perspective, because it has been shown that previous CxFV infection can increase or block the infection of the mosquito by other pathogenic flaviviruses.

Seasonal mosquito (Diptera: Culicidae) dynamics and the influence of environmental variables in a land use gradient from Yucatan, Mexico.

Mosquito-borne diseases constitute a significant global impact on public and animal health. Climatic variables are recognized as major drivers in the mosquitoes' life history, principally rainfall and temperature, which directly influence mosquito abundance. Likewise, urbanization changes environmental conditions, and understanding how environmental variables and urbanization influence mosquito dynamics is crucial for the integrated management of mosquito-borne diseases, especially in the context of climate change. In this study, our aim was to observe the effect of temperature, rainfall, and the percentage of impervious surface on the abundance of mosquito species over a temporal scale of one complete year of fortnightly samplings, spanning from June 2021 to June 2022 in Yucatan, Mexico. We selected nine localities along an urbanization gradient (three natural, three rural, and three urban) from Mérida City to Reserva de la Biosfera Ría Celestún. Using BG-traps, mosquitoes were collected biweekly at each locality. Additionally, we estimated the percentage of impervious surface. Daily data of the maximum, mean and minimum temperatures, diurnal temperature range and rainfall were accumulated weekly. We calculated the accumulated quantities of temperatures and rainfall and lagged from one to four weeks before sampling for each locality. Generalized linear mixed models were then performed to study the influence of environmental variables and percentage of impervious surfaces on each of the 15 most abundant species. A total of 131,525 mosquitoes belonging to 11 genera and 49 species were sampled with BG-Sentinel traps baited with BG-lure and dry ice. The most frequently significative variable is the accumulated precipitation four weeks before the sampling. We observed a positive relationship between Cx. quinquefasciatus and Cx. thriambus with the diurnal temperature range. For Ae. aegypti, we observed a positive relationship with minimum temperature. Conversely, the percentage of impervious surface serves as a proxy of anthropogenic influence and helped us to distinguishing species exhibiting habitat preference for urban and rural environments, versus those preferring natural habitats. Our results characterize the species-specific effects of environmental variables (temperature, rainfall and impervious surface) on mosquito abundance.

[The mosquitoes (Diptera: Culicidae) of Djibouti: bibliographical review (1970-2023)]. / Les moustiques (Diptera : Culicidae) de Djibouti : revue bibliographique (1970-2023).

Background and justification: The Republic of Djibouti is located in the Horn of Africa, on the Gulf of Aden and the Bab-el-Mandeb detroit, at the southern entrance to the Red Sea. Prior to its independence in 1977, the Republic of Djibouti was known by two names: "Côte française des Somalis" until 1967, then "Territoire Français de Afars et Issas". As part of our doctoral research on the ecology of mosquitoes in Djibouti, we noted a lack of information on the species encountered, and felt it essential to draw up a list of species before embarking on ecological monitoring. The aim of this work is to survey publications on mosquitoes in Djibouti and to synthesize data from this scientific literature in order to update the national inventory of Culicidae. Materials and methods: An exhaustive search of electronic bibliographic databases (PubMed, Scopus, HAL Open Archive, Science Direct and Google Scholar) was carried out. Reference lists were filtered to access additional articles in order to obtain more data. Two keywords were used: "Djibouti" and "French Territory of Afars and Issas". A selection of scientific publications on Djibouti mosquitoes and/or diseases transmitted by mosquito vectors was made. Researches were conducted in articles selected. The names of the species listed were checked and validated by referring to the site Mosquito Taxonomic Inventory. Results: A total of 13 studies, published between 1970 and 2023, were found. Over the years, the composition of the Culicidae fauna has become well known. In part, the movement of people traveling to and from neighboring countries has been linked to the detection of new species and the reappearance of mosquito species in Djibouti. Numerous studies have been carried out over the years, including purely taxonomic studies and others focusing on the incrimination of mosquito vectors and the characterization of the pathogens they transmit. A total of 37 species, belonging to two subfamilies (Anophelinae and Culicinae), of mosquitoes divided between 7 genera (Aedes, Anopheles, Culex, Culiseta, Lutzia, Mimomyia and Uranotaenia) have been mentioned across the country. The number of species per genus is distributed as follows: 5 species of Aedes including 1 subspecies, 14 species of Anopheles including two subspecies, 12 species of Culex including 1 subspecies, 1 species for each of the genera Culiseta and Lutzia and finally 2 species respectively for the genera Mimomiya and Uranotaenia. Five species have been incriminated as vectors of diseases such as malaria, dengue fever, yellow fever, West Nile virus and chikungunya. Others are known for their potential role in pathogen transmission, including Zika and Rift Valley virus. Discussion - Conclusion: The bibliographical research enabled us to summarize the research carried out over more than half a century in the history of Djibouti, and to update the inventory of the country's mosquitoes, which now includes 37 species. Species names were reviewed and updated, and the case of Anopheles gambiae was also addressed. Two species mentioned as part of the Culicidae fauna of Djibouti appeared to be doubtful and are up for discussion. These results provide a useful information base for defining vector control priorities in Djibouti. They will also inform, guide and facilitate future consultations of our database. In addition, this study will help to identify research ways on mosquitoes in Djibouti.

Mosquito Species Diversity and Abundance Patterns in Plots with Contrasting Land Use and Land Cover in Bloomington, Indiana.

Land use and land cover (LULC) gradients are associated with differences in mosquito species composition and the entomological risk of mosquito-borne disease. Here, we present results from a season-long study of mosquito species richness and abundance with samples collected at 9 locations from 2 plots with contrasting LULC, an urban farm and a forest preserve, in Bloomington, IN, a city in the midwestern USA. With a total sampling effort of 234 trap-nights, we collected 703 mosquitoes from 9 genera and 21 species. On the farm, we collected 15 species (285 mosquitoes). In the preserve, we collected 19 species (418 mosquitoes). Thirteen species were common in both study plots, 2 were exclusive to the farm, and 6 were exclusive to the forest preserve. In both plots, we collected Aedes albopictus and Ae. japonicus. In the farm, the most common mosquito species were Culex restuans/Cx. pipiens and Coquillettidia perturbans. In the preserve, Ae. japonicus and Ae. triseriatus were the 2 most common mosquito species. Time series analysis suggests that weather factors differentially affected mosquito species richness and mosquito abundance in the plots. Temperature, relative humidity (RH), and precipitation were positively associated with richness and abundance at the farm, while increases in the SD of RH decreased both richness and abundance at the preserve. Our results highlight the importance that LULC has for mosquito species diversity and abundance and confirm the presence of Ae. albopictus and Ae. japonicus in southwestern Indiana.

Island mosquitoes of Thailand: an update on species diversity and DNA barcoding.

Mosquitoes (Diptera: Culicidae) are among the most medically significant insects, with several species acting as vectors for human pathogens. Although there are frequent reports of mosquito-borne diseases in the border island areas of Thailand, comprehensive data on the diversity and DNA barcoding of these mosquito species remain limited. This study investigated mosquito diversity in two main archipelagos in Thailand-the Trat archipelago (comprising Chang Island and Kood Island) and the Ranong archipelago (comprising Chang Island and Phayam Island)-and generated DNA barcode data from the mosquitoes found there. The survey across these islands discovered a total of 41 species, highlighting the presence of several species known to be vectors for human diseases. Thirty-seven mosquito species from the island areas were documented to provide reference DNA barcode sequences for mosquitoes in Thailand's island regions. Two species, Aedes fumidus and Finlaya flavipennis, have been added as new COI sequence records in the database. DNA barcoding was highly effective in classifying almost all species by identifying barcoding gaps, except for Anopheles baimaii and Anopheles dirus, which could not be distinguished. Additionally, the study noted that geographical variations might influence certain mosquito species, such as Anopheles barbirostris A3 and Mansonia dives, causing them to be split into two distinct subgroups. The findings of this study are crucial, as they aid in classifying mosquito species using molecular techniques and expand our knowledge of disease vectors in these biodiverse regions.

Comparative evaluation of two lures to attract female mosquitoes in an urban natural reserve during daytime.

The most widely used attractant to capture adult female mosquitoes is CO2. However, there are also baits available on the market that emit a scent resembling human skin. These baits were specifically designed to attract highly anthropophilic species such as Aedes albopictus and Aedes aegypti. In this study, we compare the effectiveness of CDC traps baited either with CO2 or with a commercial blend simulating skin odor, BG-Sweetscent, for trapping female mosquitoes during daylight hours in an urban reserve in the City of Buenos Aires. We employed a hurdle generalized linear mixed model to analyze trap capture probability and the number of mosquitoes captured per hour, considering the effects of attractant, mosquito species, and their interaction. Traps baited with CO2 captured ten mosquito species, while those baited with BG-Sweetscent captured six in overall significantly lower abundance. The odds of capturing mosquitoes were 292% higher for the CO2-baited traps than for those baited with BG-Sweetscent. No evidence of a combined effect of attractant type and species on female mosquito captures per hour was found. Results indicated that CDC traps baited with CO2 were more effective than those baited with BG-Sweetscent in capturing more mosquito species and a higher number of mosquitoes within each species, even if the species captured with CO2 exhibited a certain level of anthropophilia. This result has practical implications for mosquito surveillance and control in urban natural reserves.