ResistanceGA: An R package for the optimization of resistance surfaces using genetic algorithms
ResistanceGA: An R package for the optimization of resistance surfaces using genetic algorithms
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DOI：
10.1111/2041-210x.12984
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发表时间：
2014-07
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影响因子：
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通讯作者：
W. Peterman
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文献类型：
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作者：
W. Peterman
了解景观特征如何影响种群之间的功能连通性是景观遗传分析的基石。然而，最好地描述连通性的阻力表面的参数化在很大程度上是一个探索有限参数空间的主观过程。ResistanceGA是一种新的R包，它利用遗传算法根据成对遗传距离和沿着最低成本路径计算的CIRCUITSCAPE阻力距离或成本距离来优化阻力表面。该程序包中的功能允许优化分类和连续的阻力表面，以及同时优化多个阻力表面。关于使用Mantel试验准确地将成对遗传距离与抗性距离联系起来，存在着相当大的争议。最优化采用线性混合效应模型和最大似然群体效应参数确定AICC，这是遗传算法的适应度函数。电阻GA填补了景观遗传工具箱中的空白，允许对电阻表面进行无偏优化，并同时优化多个电阻表面，以创建新型复合电阻表面。
Understanding how landscape features affect functional connectivity among populations is a cornerstone of landscape genetic analyses. However, parameterization of resistance surfaces that best describe connectivity is largely a subjective process that explores a limited parameter space. ResistanceGA is a new R package that utilizes a genetic algorithm to optimize resistance surfaces based on pairwise genetic distances and either CIRCUITSCAPE resistance distances or cost distances calculated along least cost paths. Functions in this package allow for the optimization of both categorical and continuous resistance surfaces, as well as the simultaneous optimization of multiple resistance surfaces. There is considerable controversy concerning the use of Mantel tests to accurately relate pairwise genetic distances with resistance distances. Optimization in uses linear mixed effects models with the maximum likelihood population effects parameterization to determine AICc, which is the fitness function for the genetic algorithm. ResistanceGA fills a void in the landscape genetic toolbox, allowing for unbiased optimization of resistance surfaces and for the simultaneous optimization of multiple resistance surfaces to create a novel composite resistance surface.