Background: At any particular location, frequencies of alleles in organisms that are associated with adaptive traits are expected to change in future climates through local adaption and migration, including assisted migration (human-implemented when climate change is more rapid than natural migration rates). Making the assumption that the baseline frequencies of alleles across environmental gradients can act as a predictor of patterns in changed climates (typically future but possibly paleo-climates), a methodology is provided by AlleleShift of predicting changes in allele frequencies at populations locations. Methods: The prediction procedure involves a first calibration and prediction step through redundancy analysis (RDA), and a second calibration and prediction step through a generalized additive model (GAM) with a binomial family. As such, the procedure is fundamentally different to an alternative approach recently proposed to predict changes in allele frequencies from canonical correspondence analysis (CCA). My methodology of AlleleShift is also different in modelling and predicting allele counts through constrained ordination (not frequencies as in the CCA approach) and modelling both alleles for a locus (not solely the minor allele as in the CCA method; both methods were developed for diploid organisms where individuals are homozygous (AA or BB) or heterozygous (AB)). Whereas the GAM step ensures that allele frequencies are in the range of 0 to 1 (negative values are sometimes predicted by the RDA and CCA approaches), the RDA step is based on the Euclidean distance that is also the typical distance used in Analysis of Molecular Variance (AMOVA). The AlleleShift::amova.rda enables users to verify that the same 'mean-square' values are calculated by AMOVA and RDA, and gives the same final statistics with balanced data. Results: Besides data sets with predicted frequencies, AlleleShift provides several visualization methods to depict the predicted shifts in allele frequencies from baseline to changed climates. These include 'dot plot' graphics (function shift.dot.ggplot), pie diagrams (shift.pie.ggplot), moon diagrams (shift.moon.ggplot), 'waffle' diagrams (shift.waffle.ggplot) and smoothed surface diagrams of allele frequencies of baseline or future patterns in geographical space (shift.surf.ggplot). As these were generated through the ggplot2 package, methods of generating animations for a climate change time series are straightforward, as shown in the documentation of AlleleShift and in the supplementary materials. In addition, graphical methods are provided of showing shifts of populations in environmental space (population.shift) and to assess how well the predicted frequencies reflect the original frequencies for the baseline climate (freq.ggplot). Availability: AlleleShift is available as an open-source R package from https://github.com/RoelandKindt/AlleleShift . Genetic input data is expected to be in the adegenet::genpop format, which can be generated from the adegenet::genind format. Climate data is available from various resources such as WorldClim and Envirem.

中文翻译：

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AlleleShift：R程序包，用于预测和可视化响应气候变化的等位基因频率的总体水平变化

背景：在任何特定位置，与适应性状相关的有机体等位基因的频率预计将在未来的气候中通过局部适应和迁移而发生变化，包括辅助迁移（当气候变化比自然迁移速率快时，由人为实施）。假设跨环境梯度的等位基因的基线频率可以作为气候变化（通常是未来但可能是古气候）模式预测的指标，AlleleShift提供了一种方法来预测种群位置等位基因频率的变化。方法：预测过程包括通过冗余分析（RDA）进行的第一校准和预测步骤，以及通过具有二项式族的广义加性模型（GAM）进行的第二校准和预测步骤。因此，该程序从根本上不同于最近提出的通过规范对应分析（CCA）预测等位基因频率变化的替代方法。我的等位基因转移方法在通过约束排序（不像CCA方法那样频率）建模和预测等位基因计数以及对基因座的两个等位基因（不仅仅是CCA方法中的次要等位基因）建模和预测方面也有所不同；这两种方法都是针对二倍体开发的个体为纯合子（AA或BB）或杂合子（AB）的生物。GAM步骤确保等位基因频率在0到1的范围内（有时通过RDA和CCA方法预测负值），而RDA步骤基于欧几里得距离，这也是分子分析中使用的典型距离差异（AMOVA）。AlleleShift :: amova。rda使用户能够验证AMOVA和RDA是否计算出相同的“均方”值，并提供具有平衡数据的相同最终统计信息。结果：除具有预测频率的数据集外，AlleleShift还提供了几种可视化方法来描述等位基因频率从基线到气候变化的预测转变。这些包括“点图”图形（函数shift.dot.ggplot），饼图（shift.pie.ggplot），月亮图（shift.moon.ggplot），“华夫饼”图（shift.waffle.ggplot）和平滑表面地理空间中基线或未来模式的等位基因频率图（shift.surf.ggplot）。由于这些是通过ggplot2软件包生成的，因此为气候变化时间序列生成动画的方法非常简单，如AlleleShift文档和补充材料中所示。此外，提供了图形方法来显示环境空间中的人口迁移（人口迁移），并评估预测频率反映基线气候原始频率的频率（freq.ggplot）。可用性：AlleleShift可作为https://github.com/RoelandKindt/AlleleShift的开源R包提供。遗传输入数据应采用adegenet :: genpop格式，可以从adegenet :: genind格式生成。可从各种资源（例如WorldClim和Envirem）获得气候数据。偏移量），并评估预测频率对基线气候的原始频率的反映程度（freq.ggplot）。可用性：AlleleShift可作为https://github.com/RoelandKindt/AlleleShift的开源R包提供。遗传输入数据应采用adegenet :: genpop格式，可以从adegenet :: genind格式生成。可从各种资源（例如WorldClim和Envirem）获得气候数据。偏移量），并评估预测频率对基线气候的原始频率的反映程度（freq.ggplot）。可用性：AlleleShift可作为https://github.com/RoelandKindt/AlleleShift的开源R包提供。遗传输入数据应采用adegenet :: genpop格式，可以从adegenet :: genind格式生成。可从各种资源（例如WorldClim和Envirem）获得气候数据。