嗜尸性丽蝇发育历期数据对晚期PMI推断具有重要意义。但地理种群间发育相关数据缺乏参考性，将异地种群数据套用于同种本地种群可导致PMI结论与实际值出现显著偏差，是当前影响推断方法准确性和普及亟待解决的问题。我们前期研究发现巨尾阿丽蝇嗜尸行为典型，地理分布广泛且种群遗传多态性丰富。借助谱系地理方法分析其种群间遗传分化，结合发育历期差异，有望突破嗜尸性丽蝇的这一共同难题。本项目以该蝇为研究对象，在已完成其基因组测序及广域采样的基础上：1）通过重测序技术筛选遗传标记，以谱系地理方法分析地理种群间遗传多态性差异；2）依据种群聚类结果选取样本补采地，补采样本并以发育数据建立发育历期模型，比较种群间发育差异，进而分析种群间为适应不同生境而在遗传和表型水平发生的改变及其相关性；3）以遗传差异为变异系数，优化基于发育历期数据的晚期PMI推断模型。最终为嗜尸性丽蝇种群间发育历期数据的参考和利用提供理论依据。

Developmental duration data of necrophagous blow fly has great importance in the estimation of post-mortem interval (PMI). However, the developmental data among geographical populations have low reference value. Significant deviation between PMI estimation and actual value could be caused by directly applying developmental data of other geographical populations to a local population of the same species. It is urgent to tackle this barrier for its impact on the accuracy and popularity of inference method. Based on our previous study, Aldrichina grahami exhibits typical necrophagous behavior, broadly geographic distribution with rich genetic diversity. Using methodology of phylogeographic to analysis the genetic divergence between populations, then combing with difference of developmental duration data should make a breakthrough at this problem which shared by necrophagous blow flies. The present project takes A. grahami for research objects, based on the accomplishment of its whole genome sequencing and broad sampling. At first step, we screen out genetic markers using resequencing technology, then analysis difference of genetic diversity among geographical populations using phylogeographic methods. Secondly, we chose resampling regions based on populations clustering results, recapture samples and collect development data for developmental duration model construction, then make comparison on divergence of inter-populations development. We further investigate the variation and relevance at genetic and genotypes level among populations which resulted from adaptation into different environment. Thirdly, we set genetic divergence as coefficient of variation to optimize the developmental duration based late PMI estimation model. It will finally provide theoretical basis for the reference and utility of developmental duration data from different blow fly populations.