Update on the composition and distribution of the mosquito fauna (Diptera: Culicidae) in Cabo Verde, a country at risk for mosquito-borne diseases.

Mosquitoes play a critical role as vectors of pathogens affecting both humans and animals. Therefore, understanding their biodiversity and distribution is crucial to developing evidence-based vector control strategies. The current study updated the composition and distribution of mosquito species through a comprehensive survey of all municipalities of Cabo Verde. From October 2017 to September 2018, mosquito larvae and pupae were collected from 814 aquatic habitats. Anopheles gambiae (Giles, 1902) and Culex pipiens (Linnaeus, 1758) complexes were subjected to PCR-based techniques for sibling species identification. Ten mosquito species from 5 genera were identified: Aedes aegypti (Linnaeus, 1762), Aedes caspius (Pallas, 1771), Anopheles arabiensis (Patton, 1905), Anopheles pretoriensis (Theobald, 1903), Culex bitaeniorhynchus (Giles, 1901), Cx. pipiens, Culex quinquefasciatus (Say, 1823), Culex tritaeniorhynchus (Giles, 1901), Culiseta longiareolata (Macquart, 1838), and Lutzia tigripes (de Grandpre & de Charmoy, 1901). Santiago Island reported the highest number of species (n = 8). Ae. aegypti and Cx. quinquefasciatus were the most widely distributed species across the country. An. arabiensis was the sole species identified within the An. gambiae complex. The findings from our study will help guide health policy decisions to effectively control mosquito-borne diseases.

Field evaluation of an automated mosquito surveillance system which classifies Aedes and Culex mosquitoes by genus and sex.

BACKGROUND: Mosquito-borne diseases are a major concern for public and veterinary health authorities, highlighting the importance of effective vector surveillance and control programs. Traditional surveillance methods are labor-intensive and do not provide high temporal resolution, which may hinder a full assessment of the risk of mosquito-borne pathogen transmission. Emerging technologies for automated remote mosquito monitoring have the potential to address these limitations; however, few studies have tested the performance of such systems in the field. METHODS: In the present work, an optical sensor coupled to the entrance of a standard mosquito suction trap was used to record 14,067 mosquito flights of Aedes and Culex genera at four temperature regimes in the laboratory, and the resulting dataset was used to train a machine learning (ML) model. The trap, sensor, and ML model, which form the core of an automated mosquito surveillance system, were tested in the field for two classification purposes: to discriminate Aedes and Culex mosquitoes from other insects that enter the trap and to classify the target mosquitoes by genus and sex. The field performance of the system was assessed using balanced accuracy and regression metrics by comparing the classifications made by the system with those made by the manual inspection of the trap. RESULTS: The field system discriminated the target mosquitoes (Aedes and Culex genera) with a balanced accuracy of 95.5% and classified the genus and sex of those mosquitoes with a balanced accuracy of 88.8%. An analysis of the daily and seasonal temporal dynamics of Aedes and Culex mosquito populations was also performed using the time-stamped classifications from the system. CONCLUSIONS: This study reports results for automated mosquito genus and sex classification using an optical sensor coupled to a mosquito trap in the field with highly balanced accuracy. The compatibility of the sensor with commercial mosquito traps enables the sensor to be integrated into conventional mosquito surveillance methods to provide accurate automatic monitoring with high temporal resolution of Aedes and Culex mosquitoes, two of the most concerning genera in terms of arbovirus transmission.

A DNA barcode reference library of Portuguese mosquitoes.

Mosquitoes are important biological vectors of pathogens and species identification in all life stages is the first step for effective monitoring and control of mosquito-borne diseases. Molecular methods for species identification have been developed over the last years to overcome the limitations of the taxonomic identification based on morphology. DNA barcoding, using a fragment of the mitochondrial cytochrome oxidase I (COI) gene, can be used for species identification but a reliable and comprehensive reference database of verified sequences is required. In this study, we aimed to generate a DNA barcode reference library for the identification of mosquito species from Portuguese mosquito fauna, including most relevant vector species. Mosquitoes captured under the National Vector Surveillance Program (REVIVE) were processed for DNA extraction, COI gene fragment amplification and sequencing. Nighty-eight barcode sequences were obtained, representing 26 species and 6 genera. Sequences were submitted to GenBank and BOLD and were used for validation of phenetic classification. Barcode Index Number (BIN) assignment and Automatic Barcode Gap Discovery (ABGD) were used and clustered COI sequences into twenty-five molecular operational taxonomic units (MOTUs). This is the first comprehensive study that combines morphological and molecular identification of most mosquito species present in Portugal aiming to offer a reliable framework for mosquito species identification.