Identification of wild collected mosquito vectors of diseases using gas chromatography-mass spectrometry in Jazan Province, Saudi Arabia

An overview of the mosquitoes of Saudi Arabia (Diptera: Culicidae), with updated keys to the adult females

Despite the fact that mosquito-borne infections have considerable consequences for public health in Saudi Arabia, there is neither a thorough review of the species that occur in the country nor updated keys for the identification of the adult females. In this study, species accounts are given for 49 Saudi Arabian mosquito species, as well as Aedes albopictus (Skuse), which is not recorded in Saudi Arabia, but is medically important and is found in some countries of the Middle East and North Africa. Taxonomic notes provide additional information for certain taxa and/or aid their identification.

Prediction of mosquito species and population age structure using mid-infrared spectroscopy and supervised machine learning

Despite the global efforts made in the fight against malaria, the disease is resurging. One of the main causes is the resistance that Anopheles mosquitoes, vectors of the disease, have developed to insecticides. Anopheles must survive for at least 10 days to possibly transmit malaria. Therefore, to evaluate and improve malaria vector control interventions, it is imperative to monitor and accurately estimate the age distribution of mosquito populations as well as their population sizes. Here, we demonstrate a machine-learning based approach that uses mid-infrared spectra of mosquitoes to characterise simultaneously both age and species identity of females of the African malaria vector species Anopheles gambiae and An. arabiensis, using laboratory colonies. Mid-infrared spectroscopy-based prediction of mosquito age structures was statistically indistinguishable from true modelled distributions. The accuracy of classifying mosquitoes by species was 82.6%. The method has a negligible cost per mosquito, does not require highly trained personnel, is rapid, and so can be easily applied in both laboratory and field settings. Our results indicate this method is a promising alternative to current mosquito species and age-grading approaches, with further improvements to accuracy and expansion for use with wild mosquito vectors possible through collection of larger mid-infrared spectroscopy data sets.

Prediction of mosquito species and population age structure using mid-infrared spectroscopy and supervised machine learning

Despite the global efforts made in the fight against malaria, the disease is resurging. One of the main causes is the resistance that Anopheles mosquitoes, vectors of the disease, have developed to insecticides. Anopheles must survive for at least 10 days to possibly transmit malaria. Therefore, to evaluate and improve malaria vector control interventions, it is imperative to monitor and accurately estimate the age distribution of mosquito populations as well as their population sizes. Here, we demonstrate a machine-learning based approach that uses mid-infrared spectra of mosquitoes to characterise simultaneously both age and species identity of females of the African malaria vector species Anopheles gambiae and An. arabiensis, using laboratory colonies. Mid-infrared spectroscopy-based prediction of mosquito age structures was statistically indistinguishable from true modelled distributions. The accuracy of classifying mosquitoes by species was 82.6%. The method has a negligible cost per mosquito, does not require highly trained personnel, is rapid, and so can be easily applied in both laboratory and field settings. Our results indicate this method is a promising alternative to current mosquito species and age-grading approaches, with further improvements to accuracy and expansion for use with wild mosquito vectors possible through collection of larger mid-infrared spectroscopy data sets.

Assessment and an updated list of the mosquitoes of Saudi Arabia

BACKGROUND

Mosquito-borne pathogens are important causes of diseases in the Kingdom of Saudi Arabia. Knowledge of the mosquito fauna is needed for the appropriate control of the vectors that transmit the pathogens and prevent the diseases they cause. An important first step is to have an up-to-date list of the species known to be present in the country. Original occurrence records were obtained from published literature and critically scrutinized to compile a list of the mosquito species that occur within the borders of the Kingdom.

RESULTS

Fifty-one species have been recorded in the Kingdom; however, the occurrence of two of these species is unlikely. Thus, the mosquito fauna of the Kingdom comprises 49 species that include 18 anophelines and 31 culicines. Published records are provided for each species. Problematic records based on misidentifications and inappropriate sources are discussed and annotated for clarity.

CONCLUSION

Integrated morphological and molecular methods of identification are needed to refine the list of species and accurately document their distributions in the Kingdom.

Prediction of mosquito species and population age structure using mid-infrared spectroscopy and supervised machine learning

Despite the global efforts made in the fight against malaria, the disease is resurging. One of the main causes is the resistance that Anopheles mosquitoes, vectors of the disease, have developed to insecticides. Anopheles must survive for at least 10 days to possibly transmit malaria. Therefore, to evaluate and improve malaria vector control interventions, it is imperative to monitor and accurately estimate the age distribution of mosquito populations as well as their population sizes. Here, we demonstrate a machine-learning based approach that uses mid-infrared spectra of mosquitoes to characterise simultaneously both age and species identity of females of the African malaria vector species Anopheles gambiae and An. arabiensis. mid-infrared spectroscopy-based prediction of mosquito age structures was statistically indistinguishable from true modelled distributions. The accuracy of classifying mosquitoes by species was 82.6%. The method has a negligible cost per mosquito, does not require highly trained personnel, is rapid, and so can be easily applied in both laboratory and field settings. Our results indicate this method is a promising alternative to current mosquito species and age-grading approaches, with further improvements to accuracy and expansion for use with other mosquito vectors possible through collection of larger mid-infrared spectroscopy data sets.

Sexual chemoecology of mosquitoes (Diptera, Culicidae): Current knowledge and implications for vector control programs

Mosquitoes (Diptera: Culicidae) act as vectors of medical and veterinary importance, due to their ability to transmit many pathogens and parasites. Renewed interest has been recently devoted to the potential of sterile insect technique (SIT) for mosquito suppression. However, the success of the SIT is mostly dependent on the ability of sterile males to compete for mates with the wild ones in the field. Nevertheless, little is known on the sexual chemical ecology of mosquitoes, with special reference to the role of chemical signals in males. We reviewed the current knowledge on mosquito sexual chemical ecology and other key cues affecting courtship and mating behavior. The information available on the aggregation and sex pheromones in mosquito males is rather limited. To the best of our knowledge, the components of the aggregation pheromone stimulating swarming mechanisms have been fully characterized only for Aedes aegypti, while evidence for aggregation pheromones in other mosquito species remains elusive. Further research on this issue is needed, as well as to dissect the relative importance of visual (with special reference to swarming landmarks), vibrational, olfactory and tactile cues perceived during swarming and mate. On the other hand, more knowledge is available for cuticular hydrocarbons, which modulate mating behavior in several species of economic importance. These compounds, coupled with volatile aggregation components, have potential interest for the development of monitoring and trapping systems. In addition, the analyses of cuticular hydrocarbons are essential for discrimination between closely related mosquito species and/or populations.

Modeling the distribution of the West Nile and Rift Valley Fever vector Culex pipiens in arid and semi-arid regions of the Middle East and North Africa

BACKGROUND

The Middle East North Africa (MENA) region is under continuous threat of the re-emergence of West Nile virus (WNV) and Rift Valley Fever virus (RVF), two pathogens transmitted by the vector species Culex pipiens. Predicting areas at high risk for disease transmission requires an accurate model of vector distribution, however, most Cx. pipiens distribution modeling has been confined to temperate, forested habitats. Modeling species distributions across a heterogeneous landscape structure requires a flexible modeling method to capture variation in mosquito response to predictors as well as occurrence data points taken from a sufficient range of habitat types.

METHODS

We used presence-only data from Egypt and Lebanon to model the population distribution of Cx. pipiens across a portion of the MENA that also encompasses Jordan, Syria, and Israel. Models were created with a set of environmental predictors including bioclimatic data, human population density, hydrological data, and vegetation indices, and built using maximum entropy (Maxent) and boosted regression tree (BRT) methods. Models were created with and without the inclusion of human population density.

RESULTS

Predictions of Maxent and BRT models were strongly correlated in habitats with high probability of occurrence (Pearson's r=0.774, r=0.734), and more moderately correlated when predicting into regions that exceeded the range of the training data (r=0.666,r=0.558). All models agreed in predicting high probability of occupancy around major urban areas, along the banks of the Nile, the valleys of Israel, Lebanon, and Jordan, and southwestern Saudi Arabia. The most powerful predictors of Cx. pipiens habitat were human population density (60.6% Maxent models, 34.9% BRT models) and the seasonality of the enhanced vegetation index (EVI) (44.7% Maxent, 16.3% BRT). Maxent models tended to be dominated by a single predictor. Areas of high probability corresponded with sites of independent surveys or previous disease outbreaks.

CONCLUSIONS

Cx. pipiens occurrence was positively associated with areas of high human population density and consistent vegetation cover, but was not significantly driven by temperature and rainfall, suggesting human-induced habitat change such as irrigation and urban infrastructure has a greater influence on vector distribution in this region than in temperate zones.