Valor taxonómico de la forma del ala en seis especies de esfíngidos (Lepidoptera: Sphingidae).

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Bibliographic Details
Title:	Valor taxonómico de la forma del ala en seis especies de esfíngidos (Lepidoptera: Sphingidae).
Alternate Title:	Taxonomic value of wing shape in six species of hawkmoths (Lepidoptera: Sphingidae).
Authors:	Meriño, Beatriz Chávez¹, Flores, Daryl D. Cruz²
Source:	Cuban Journal of Biological Sciences / Revista Cubana de Ciencias Biológicas. oct2015-ene2016, Vol. 4 Issue 3, p99-104. 6p.
Abstract (English):	The geometric morphometric has become an useful tool to discriminate against species of different groups. Specifically in insects, the form of the wings can be used for the identification from these organisms to species level. In this work the form of the contour of the wing of six hawkmoths species was characterized using geometric morphometric and the capacity this way was evaluated to identify these species. We take 9, 8, 9, 8 and 17 pictures of the wings of Agrius cingulata, Cocytius duponchel, Manduca rustica, Protambulix strigilis, Pachylia ficus and Pseudosphinx tetrio respectively. The contour of the wings was described using from the Elliptic Coefficients of Fourier for 30 harmonic. The standardized matrix and the descriptors were reduced with an analysis of principal components and with the autovalues they were carried out analysis of discriminant function to analyze the possibility to use the form of the wing to differentiate species. Finally a net neural was designed starting from the autovalues. Four species can be identifying by means of the analysis discriminant function and the neural network. These results can be due, partly to that the sample size was limited. It is possible that an increase of sampling efford and re-training the neural network, will result in a better discrimination percentage. [ABSTRACT FROM AUTHOR]
Abstract (Spanish):	La morfometría geométrica se ha convertido en una herramienta útil para discriminar especies de diferentes grupos taxonómicos. En insectos, la forma de las alas ha sido utilizada para su identificación a nivel de especie. En este trabajo se caracterizó la forma del contorno del ala de cuatro especies de esfíngidos (Lepidoptera: Sphingidae) utilizando métodos de contornos y se evaluó la capacidad de esta forma para permitir la identificación de las especies. Se tomaron 64 fotografías digitales de las alas de Agrius cingulata, Cocytius duponchel, Manduca rustica, Protambulix strigilis, Pachylia ficus y Pseudosphinx tetrio respectivamente. El contorno de las alas se describió a partir de los coeficientes elípticos de Fourier para 30 armónicos, que fueron reducidos con un Análisis de Componentes Principales y sus puntajes fueron empleados como variables de forma. Para evaluar la posibilidad de emplear la forma del ala para identificar las especies se realizó un análisis de función discriminante lineal y se diseñó una red neural de tipo perceptrón multicapa. Mediante el análisis discriminante, se pudieron clasificar correctamente cuatro especies, al igual que al emplear la red neural. [ABSTRACT FROM AUTHOR]
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Database:	MedicLatina

Description
Abstract:	The geometric morphometric has become an useful tool to discriminate against species of different groups. Specifically in insects, the form of the wings can be used for the identification from these organisms to species level. In this work the form of the contour of the wing of six hawkmoths species was characterized using geometric morphometric and the capacity this way was evaluated to identify these species. We take 9, 8, 9, 8 and 17 pictures of the wings of Agrius cingulata, Cocytius duponchel, Manduca rustica, Protambulix strigilis, Pachylia ficus and Pseudosphinx tetrio respectively. The contour of the wings was described using from the Elliptic Coefficients of Fourier for 30 harmonic. The standardized matrix and the descriptors were reduced with an analysis of principal components and with the autovalues they were carried out analysis of discriminant function to analyze the possibility to use the form of the wing to differentiate species. Finally a net neural was designed starting from the autovalues. Four species can be identifying by means of the analysis discriminant function and the neural network. These results can be due, partly to that the sample size was limited. It is possible that an increase of sampling efford and re-training the neural network, will result in a better discrimination percentage. [ABSTRACT FROM AUTHOR]
ISSN:	2307695X