国际及国内DNA条形码的兴起极大地促进了昆虫多样性的研究，然而随着各种DNA条形码项目的启动与逐步推进，大量昆虫标本的重新鉴定成为迫切需要解决的问题，这主要是因为大量馆藏昆虫标本常无法保证能提取出可用的基因组DNA用于比对，通常情况下，所有新采集的标本均需检视鉴定，这很大程度上加重了分类学专家的负担，如何只对少量标本进行形态学鉴定，即有限的形态学介入，而无需检视所有标本，又能在一定统计学精度范围内进行昆虫身份识别，从而进一步用于昆虫多样性评价，是本项目要解决的关键科学问题。本项目将在申请人所取得的初步研究成果的基础上，通过开发相关贝叶斯算法，探讨昆虫多样性评估体系的构建，并以枯叶蛾类毛虫为例进行测试，从而为生产实践中昆虫多样性调查评估奠定理论与应用基础。

Studies on insects diversity have been greatly improved due to the technology of DNA barcoding worldwide. However, with the development of DNA barcoding, lots of insects specimens newly collected require re-identification of species membership due to the degrade of insects genomic DNA from museum. The general solution to this issue is to check all specimens under study so that experts in the field have been heavily burdened. The main goal of this research is to propose a new approach with which one can identify a small number of specimens in prior, then predict all other specimens under the framework of Bayesian theory with certain statistic significance,and further to assess species diversity of insects. An empirical case study of Lasiocampidae will be used to test the new method proposed in this study.