Department of Forestry, Michigan State University, East Lansing, MI 48824, USA.
Mol Ecol. 2010 Sep;19(17):3549-64. doi: 10.1111/j.1365-294X.2010.04678.x. Epub 2010 Jul 7.
Population genetics theory is primarily based on mathematical models in which spatial complexity and temporal variability are largely ignored. In contrast, the field of landscape genetics expressly focuses on how population genetic processes are affected by complex spatial and temporal environmental heterogeneity. It is spatially explicit and relates patterns to processes by combining complex and realistic life histories, behaviours, landscape features and genetic data. Central to landscape genetics is the connection of spatial patterns of genetic variation to the usually highly stochastic space-time processes that create them over both historical and contemporary time periods. The field should benefit from a shift to computer simulation approaches, which enable incorporation of demographic and environmental stochasticity. A key role of simulations is to show how demographic processes such as dispersal or reproduction interact with landscape features to affect probability of site occupancy, population size, and gene flow, which in turn determine spatial genetic structure. Simulations could also be used to compare various statistical methods and determine which have correct type I error or the highest statistical power to correctly identify spatio-temporal and environmental effects. Simulations may also help in evaluating how specific spatial metrics may be used to project future genetic trends. This article summarizes some of the fundamental aspects of spatial-temporal population genetic processes. It discusses the potential use of simulations to determine how various spatial metrics can be rigorously employed to identify features of interest, including contrasting locus-specific spatial patterns due to micro-scale environmental selection.
群体遗传学理论主要基于数学模型，这些模型在很大程度上忽略了空间复杂性和时间可变性。相比之下，景观遗传学专门研究人口遗传过程如何受到复杂的空间和时间环境异质性的影响。它具有空间显式性，并通过结合复杂和现实的生活史、行为、景观特征和遗传数据，将模式与过程联系起来。景观遗传学的核心是将遗传变异的空间模式与通常高度随机的时空过程联系起来，这些过程在历史和当代时期都会产生这些模式。该领域应该受益于向计算机模拟方法的转变，这使得可以纳入人口和环境随机性。模拟的一个关键作用是展示扩散或繁殖等人口过程如何与景观特征相互作用，从而影响站点占据、种群大小和基因流的概率，而这些又决定了空间遗传结构。模拟也可用于比较各种统计方法，并确定哪些方法具有正确的 I 型错误或最高的统计能力来正确识别时空和环境效应。模拟还可以帮助评估特定的空间指标如何用于预测未来的遗传趋势。本文总结了时空种群遗传过程的一些基本方面。它讨论了使用模拟来确定各种空间指标如何可以严格地用于识别感兴趣的特征的潜在用途，包括由于微观尺度环境选择而导致的特定基因座的空间模式对比。
