Computational Methods for Inference of Population Parameters

成功地管理和保护自然种群需要了解种群结构和迁移率。 通过相对非侵入性方法获得的广泛遗传数据越来越多，为在人口一级进行监测和管理提供了新的机会。这些标记还使档案材料的使用成为可能。 然而，有几个计算方法可充分利用人口数据。 蒙特卡罗似然法，使用马尔可夫链蒙特卡罗（MCMC）已被证明是成功的进化规模的系统发育分析，并在个人规模的系谱分析。 该项目开发了一种蒙特卡罗似然方法，用于根据在一段时间内监测种群时采集的遗传样本进行推断。 这将允许估计繁殖种群的有效大小与实际大小的比率，并且还将为估计混合物或迁移结构提供可能性。目前正在开发关于鲑鱼种群的一般方法，这方面既有良好的数据，也有档案材料，该项目将产生普遍适用于种群研究的软件，而不管研究生物体如何。

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.