Universidad de Buenos Aires, Facultad de Ciencias Exactas y Naturales, Intendente, Güiraldes (C1428), Departamento de Ecología, Genética y Evolución, Grupo de Estudio de Mosquitos, Buenos Aires, Argentina.
Universidad de Buenos Aires, Consejo Nacional de Investigaciones Científicas y Técnicas, Instituto de Ecología, Genética y Evolución de Buenos Aires (IEGEBA), Facultad de Ciencias Exactas y Naturales, Buenos Aires, Argentina.
J Med Entomol. 2021 Mar 12;58(2):576-587. doi: 10.1093/jme/tjaa187.
Culicids are the most significant arthropods affecting human health. Thus, their correct identification is critical. The use of Geometric Morphometrics (GM) has been recently incorporated into mosquito taxonomy and has begun to complement classic diagnostic techniques. Since sampling size depends on the number of Landmarks (LMs) used, this study aimed to establish the minimum number of wing LMs needed to optimize GM analysis of mosquito species and/or genera from urban and peri-urban areas of Argentina. Female left wings were used for the optimization phase, in which 17 LMs were reduced to four by iterative LM exclusion. To verify its efficiency, Principal Component Analysis (PCA), Discriminant Analysis (DA), and Canonical Variate Analysis (CVA) were performed. Additionally, a phenogram was constructed to visualize the results. We observed that five LMs for the PCA, CVA, and phenogram and nine for the DA enabled discrimination and/or clustering of almost all species and genera. Therefore, we tested the LM selection by using nine LMs and adding new species. The resulting PCA showed little overlap between species and almost all species clustered as expected, which was also reflected in the phenogram. Significant differences were found between wing shape among all species, together with a low total error rate in the DA. In conclusion, the number of LMs can be reduced and still be used to effectively differentiate and cluster culicids. This is helpful for better exploitation of available material and optimization of data processing time when classic taxonomy methods are inadequate or the material is scarce.
蚊类是影响人类健康的最重要节肢动物。因此，正确识别蚊类至关重要。几何形态测量学（GM）的应用最近已被纳入蚊虫分类学，并开始补充经典的诊断技术。由于采样大小取决于使用的地标（LM）数量，因此本研究旨在确定优化分析阿根廷城市和城郊地区蚊虫物种和/或属所需的最小翅膀 LM 数量。在优化阶段使用雌性左翅，通过迭代 LM 排除将 17 个 LM 减少到 4 个。为了验证其效率，进行了主成分分析（PCA）、判别分析（DA）和典范变量分析（CVA）。此外，还构建了 phenogram 以可视化结果。我们观察到，PCA、CVA 和 phenogram 的五个 LM 和 DA 的九个 LM 可以区分和/或聚类几乎所有的物种和属。因此，我们通过使用九个 LM 并添加新物种来测试 LM 选择。所得的 PCA 显示物种之间几乎没有重叠，几乎所有的物种都按预期聚类，这也反映在 phenogram 中。在所有物种中，翅膀形状存在显著差异，并且在 DA 中总误差率较低。总之，减少 LM 的数量仍然可以有效地区分和聚类蚊类。当经典分类方法不足或材料稀缺时，这有助于更好地利用现有材料和优化数据处理时间。
