Computational Methods for Inference of Population Parameters

自然人口的成功管理和保护需要了解人口结构和迁移率。通过相对非侵入性方法获得的大量遗传数据日益增多，为在人口一级进行监测和管理提供了新的机会。这些标记也使档案材料的使用成为可能。然而，几乎没有可用的计算方法来充分利用人口数据。利用马尔可夫链蒙特卡罗(MCMC)的蒙特卡罗似然法在进化尺度上进行系统发育分析，在个体尺度上进行系谱分析已被证明是成功的。该项目开发了一种蒙特卡罗似然方法，用于从长期监测种群的遗传样本中进行推断。这将允许估计繁殖种群的有效规模与实际规模的比率，并将为估计混杂或迁徙结构提供可能性。目前正在制定关于鲑鱼种群的一般方法，对于这些种群，既有良好的数据，也有档案材料，该项目将产生普遍适用于种群研究的软件，而不考虑研究的生物体。

Successful management and conservation of natural populations requires knowledge of population structure and migration rates. The increasing availability of extensive genetic data obtained through relatively non-invasive methods, present new opportunities for monitoring and management at a population level. These markers also make possible the use of archive material. However, there are few computational methods available to fully exploit population data. Methods of Monte Carlo likelihood, using Markov chain Monte Carlo (MCMC) have proven successful on the evolutionary scale in phylogenetic analysis, and on the individual scale in pedigree analysis. This project develops a Monte Carlo likelihood approach to inferences from genetic samples taken in monitoring a population over time. This will permit estimation of the ratio of effective to actual sizes of breeding populations and will also provide likelihoods for the estimation of admixture or migration structure. The general approach is being developed on salmonid populations, for which there exist both good data and archive material, and the project will result in software generally applicable for population studies regardless of the study organism.