scholarly journals Short Communication: Landmark-based geometric morphometric analysis of wings to distinguish the sex of Aedes mosquito vectors in Thailand

2019 ◽  
Vol 20 (2) ◽  
pp. 419-424
Author(s):  
TANAWAT CHAIPHONGPACHARA ◽  
SEDTHAPONG LAOJUN

Chaiphongpachara T, Laojun S. 2019. Short Communication: Landmark-based geometric morphometric analysis of wings to distinguish the sex of Aedes mosquito vectors in Thailand. Biodiversitas 20: 419-424. Aedes mosquitoes (Diptera: Culicidae) are medically important insects which are vectors of yellow fever, dengue fever, chikungunya, West Nile, and the Zika virus, emerging problems worldwide. Typically, male (non-vector) and female (vector) Aedes mosquitoes can easily be separated, however, the samples in the field is often incomplete, making it difficult to separate male and female mosquitoes. The goal of this research is to study the effectiveness of the landmark-based geometric morphometric technique to distinguish the sex of male and female Aedes mosquito vectors, including Ae. aegypti, Ae. albopictus, and Ae. scutellaris, in Thailand. Evaluation of wing size by centroid size analysis found that males and females are distinctly different; females are larger than males in three species of Aedes mosquito. The wing centroid size of Ae. aegypti and Ae. albopictus were very similar, however, Ae. scutellaris was smaller than in both other species. The wing shape between sexes was different in all groups of Aedes mosquitoes. The accuracy of the sex’s classification of Aedes vectors was quite high (more than > 80% from the cross-validated reclassification test). The results of this study prove that landmark-based geometric morphometric can distinguish sexes in Aedes vectors which can be used to solve problems in the field when it is necessary to distinguish the sexes of Aedes mosquitoes with damaged samples.

2019 ◽  
Vol 20 (7) ◽  
Author(s):  
TANAWAT CHAIPHONGPACHARA

Abstract. Chaiphongpachara T. 2019. Outline-based geometric morphometric analysis to identify two Anopheles and three Culex mosquitoes in Thailand. Biodiversitas 20: 1866-1872. Geometric morphometric (GM) techniques have become popular for applications in entomology studies, especially mosquitoes. Outline-based methods (OTLs) are one such form of GM technique used to analyze pseudo-landmarks on contours or boundary outlines. This study investigated the efficacy of an OTL to distinguish two species of Anopheles mosquitoes including An. epiroticus and An. subpictus s.l. and three species of Culex mosquitoes, including Cx. quinquefasciatus, Cx. visnui and Cx. whitmorei in Thailand and compared outlines, including internal outline 1 (IOL1), internal outline 2 (IOL2) and external outline (EOL) within the mosquito wing to assess the optimal outline for analysis. The results indicated that OTLs were highly effective with certain species and each outline had the potential difference for identification. For size analysis, An. epiroticus had a mean perimeter length of IOL2 and EOL length larger than An. subpictus. Different sizes between species was found in IOL1, which was statistically significant after Bonferroni test (p < 0.05). Culex size analysis of IOL2 and EOL demonstrated a statistical difference for all species while size difference patterns between outlines found that IOL1 differed from other outlines. While shapes in two Anopheles of all cells were different between species, there was statistical significance based on Mahalanobis distances (p < 0.05). Almost all pairwise Mahalanobis distances between Culex species of IOL1, IOL2 and EOL established statistical differences, except for pairs of Cx. visnui and Cx. whitmorei via IOL2 analysis.  


2017 ◽  
Vol 37 (3) ◽  
pp. 194-201 ◽  
Author(s):  
Felix Vaux ◽  
James S. Crampton ◽  
Bruce A. Marshall ◽  
Steven A. Trewick ◽  
Mary Morgan-Richards

2020 ◽  
Vol 21 (7) ◽  
Author(s):  
Ishak Ariawan ◽  
YENI HERDIYENI ◽  
ISKANDAR ZULKARNAEN SIREGAR

Abstract. Ariawan I, Herdiyeni Y, Siregar IZ. 2020. Short Communication: Geometric morphometric analysis of leaves venation in Shorea spp. for identification using Digital Image Processing. Biodiversitas 21: 3303-3309. Shorea is one of the genera of the Dipterocarpaceae family which consists of more than 190 species. Massive exploitation of forests has threatened the sustainability of Shorea in nature. A total of 156 species has been listed on the IUCN (International Union for Conservation of Nature) red list. From the 156 species, 59.6% are in the critically endangered category, so urgent conservation is needed. However, during collection of Shorea at the seedling phase for conservation  purposes, it is often difficult to distinguish among them that can cause errors in their collection process. To avoid these errors, identification needs to be done, usually based on plant  leaf and flower  morphology. Leaves are easier because they have the main features that distinguish each plant species, one of which is the venation structure. Geometric morphometric techniques are a modern approach recognized as useful for the identification of species in many plants. Geometric morphometrics analyzes the position of the venation point using coordinate geometry values. This research was aimed to extract venation features of Shorea leaves using a geometric morphometric approach. The extraction process result in some features, such as straightness, different angle, length ratio, scale projection, and secondary nerves. On extracted features, an analysis was then performed to find out the best features in classifying species of Shorea spp. The results of this study indicated that the geometric morphometric approach could extract the value of the features of straightness, different angle, length ratio, scale projection, and secondary nerves. The secondary nerve feature is the best feature because it can distinguish between fourcommonly planted species of Shorea spp. (S. acuminata, S. leprosula, S. ovalis, and S. selanica). By using the support vector machine classification technique to identify species of Shorea spp., the classification results obtained an average accuracy of 84.46%.


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