Dataset Information

Geometric Morphometric Wing Analysis of Avian Malaria Vector, Culiseta longiareolata, from Two Locations in Algeria.

ABSTRACT: The application of geometric morphometry on mosquito wings (Culicidae) is considered a powerful tool for evaluating correlations between the phenotype (e.g., shape) and environmental or genetic variables. However, this has not been used to study the wings of the avian malaria vector, Culiseta longiareolata. Therefore, the goal of this study is to investigate the intra-specific wing variations between male and female Cs. longiareolata populations in different types of larval habitats and climatic conditions in Algeria. A total of 256 Cs. longiareolata mosquito samples were collected from January 2020 to July 2021 in three cities (Annaba, El-Tarf, and Guelma) of northeastern Algeria that have two distinct climatic condition levels (sub-humid and sub-arid) and different types of larval habitats (artificial and natural). Nineteen (19) wing landmarks (LMs) were digitized and analyzed based on geometric morphometry. Our results revealed differences in the wing shape of female and male mosquito populations, indicating sexual dimorphism. Moreover, canonical variance analysis (CVA) showed that factors, such as climatic conditions and type of larval habitats, also affect the wing shape of female and male Cs. longiareolata mosquito populations. Furthermore, the wing shape of male populations was more distinct compared with female populations.

SUBMITTER: Boumaza M

PROVIDER: S-EPMC9693553 | biostudies-literature | 2022 Nov

REPOSITORIES: biostudies-literature

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Geometric Morphometric Wing Analysis of Avian Malaria Vector, Culiseta longiareolata, from Two Locations in Algeria.

Boumaza Mounir M Merabti Brahim B Adjami Yasmine Y Ouakid Mohamed Laid ML Carvajal Thaddeus M TM

Insects 20221108 11

The application of geometric morphometry on mosquito wings (Culicidae) is considered a powerful tool for evaluating correlations between the phenotype (e.g., shape) and environmental or genetic variables. However, this has not been used to study the wings of the avian malaria vector, Culiseta longiareolata. Therefore, the goal of this study is to investigate the intra-specific wing variations between male and female Cs. longiareolata populations in different types of larval habitat ...[more]

PMID: 36354855

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

Project description:Mosquitoes are responsible for the transmission of important infectious diseases, causing millions of deaths every year and endangering approximately 3 billion people around the world. As such, precise identification of mosquito species is crucial for an understanding of epidemiological patterns of disease transmission. Currently, the most common method of mosquito identification relies on morphological taxonomic keys, which do not always distinguish cryptic species. However, wing geometric morphometrics is a promising tool for the identification of vector mosquitoes, sibling and cryptic species included. This study therefore sought to accurately identify mosquito species from the three most epidemiologically important mosquito genera using wing morphometrics. Twelve mosquito species from three epidemiologically important genera (Aedes, Anopheles and Culex) were collected and identified by taxonomic keys. Next, the right wing of each adult female mosquito was removed and photographed, and the coordinates of eighteen digitized landmarks at the intersections of wing veins were collected. The allometric influence was assessed, and canonical variate analysis and thin-plate splines were used for species identification. Cross-validated reclassification tests were performed for each individual, and a Neighbor Joining tree was constructed to illustrate species segregation patterns. The analyses were carried out and the graphs plotted with TpsUtil 1.29, TpsRelw 1.39, MorphoJ 1.02 and Past 2.17c. Canonical variate analysis for Aedes, Anopheles and Culex genera showed three clear clusters in morphospace, correctly distinguishing the three mosquito genera, and pairwise cross-validated reclassification resulted in at least 99% accuracy; subgenera were also identified correctly with a mean accuracy of 96%, and in 88 of the 132 possible comparisons, species were identified with 100% accuracy after the data was subjected to reclassification. Our results showed that Aedes, Culex and Anopheles were correctly distinguished by wing shape. For the lower hierarchical levels (subgenera and species), wing geometric morphometrics was also efficient, resulting in high reclassification scores.

Dataset Information

Geometric Morphometric Wing Analysis of Avian Malaria Vector, Culiseta longiareolata, from Two Locations in Algeria.

Publications

Geometric Morphometric Wing Analysis of Avian Malaria Vector, <i>Culiseta longiareolata</i>, from Two Locations in Algeria.

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