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Construction Of Identification Database Of Shanxi Cerambycidae Based On DNA Barcoding And Morphological Characteristics

Posted on:2022-08-03Degree:MasterType:Thesis
Country:ChinaCandidate:J Y QiFull Text:PDF
GTID:2543306560467564Subject:Forestry
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Cerambycoidea is one of the most important forest pests in Coleoptera.Many species of Cerambycoidea often damage trees and affect the health of forest ecosystem.Many species are quarantine or dangerous pests because of their harmful habit and strong concealment.Accurate and rapid identification of its species is the first step of pest control,and also laid a foundation for further biological and ecological researches.In this study,the DNA barcoding data and classic morphological data of Cerambycidae in Shanxi Province were systematically integrated,and the data were cataloged according to the classification system.The construction idea and framework of the identification database of Cerambycidae in Shanxi Province were put forward,which provided a reliable basis for the identification of Cerambycidae in forestry work and laid a foundation for the further development of pest identification APP.The main results are as follows: 1.Collect and summarize the specimens of Cerambycidae deposited in the laboratory and field collected recently.The classical morphological taxonomic study shows that there are 54 species of Cerambycidae specimens,belonging to 6 subfamilies and 43 genera.The DNA barcoding data of 28 species,21 genera,5 subfamilies were obtained by using the internationally used Co I gene fragment as molecular marker.A total of 96 species morphological characteristic images were obtained by image collection system including the whole body characteristics and diagnosis characteristics of each species.All images were cataloged according to the existing classification system,providing information for the construction of identification database in detail.2.Comparing the sequencing results of Co I gene of Cerambycidae with Gen Bank database and BOLD System 4 database,it was found that there were two species without clear identification results in morphology.The two species were identified as Menesia flavotecta and Acanthocinus sachalinensis by DNA barcoding.The 658 bp and 400 bp fragments of the 5 ’end of Co I gene sequence were analyzed respectively.The results showed that the genetic distance between Pachyta quadrimaculata and Pachyta bicuneata was less than 2%.The results of average base composition of mitochondrial Co I gene sequence showed that the A+T content was 66.6% and 63.27% respectively,which showed obvious bias of A+T base composition.Through the construction of phylogenetic tree,it was found that in the 400 bp fragment of the 5 ’end of the Co I gene sequence,the aggregation degree of species belonging to the same genus was relatively weak,and in the 658 bp fragment of the 5’ end of the Co I gene sequence,the species belonging to the same genus clustered into one branch in the phylogenetic relationship,and the confidence of the branches was high.Only Polyzonus fasciatus and Euttrapha cinnabarina have some deviation in polymerization,which needs further study.It shows that DNA barcoding is feasible and accurate for the classification and identification of Cerambycidae insects,and it also has a good identification effect for related species.3.Based on the needs of species identification of Cerambycidae pest control,through human-computer interaction mode,and based on the goal of developing mobile APP to provide scientific data source,the classical morphology and DNA barcode data were cataloged and integrated respectively,and the idea of constructing Cerambycidae identification database was proposed,and the framework of Shanxi Cerambycidae identification database was planned.While promoting the exchange and sharing of research achievements of Cerambycidae,it also laid a foundation for the research of Cerambycidae and forestry production services.
Keywords/Search Tags:Cerambycidae, identification characteristics, DNA barcode, database, cytochrome C oxidase subunitⅠ(CoⅠ), molecular detection
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