Kindt Roeland
World Agroforestry, CIFOR-ICRAF, Nairobi, Kenya.
PeerJ. 2021 Jun 15;9:e11534. doi: 10.7717/peerj.11534. eCollection 2021.
At any particular location, frequencies of alleles 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 of predicting changes in allele frequencies at the population level.
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). The RDA step is based on the Euclidean distance that is also the typical distance used in Analysis of Molecular Variance (AMOVA). Because the RDA step or CCA approach sometimes predict negative allele frequencies, the GAM step ensures that allele frequencies are in the range of 0 to 1.
provides data sets with predicted frequencies and several visualization methods to depict the predicted shifts in allele frequencies from baseline to changed climates. These visualizations include 'dot plot' graphics (function ), pie diagrams (), moon diagrams (), 'waffle' diagrams () and smoothed surface diagrams of allele frequencies of baseline or future patterns in geographical space (). As these visualizations were generated through the package, methods of generating animations for a climate change time series are straightforward, as shown in the documentation of and in the supplemental videos.
is available as an open-source R package from https://cran.r-project.org/package=AlleleShift and https://github.com/RoelandKindt/AlleleShift. Genetic input data is expected to be in the format, which can be generated from the format. Climate data is available from various resources such as and .
在任何特定地点，与适应性性状相关的等位基因频率预计会在未来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度更快时由人类实施）。假设跨环境梯度的等位基因基线频率可作为气候变化模式（通常是未来气候，但也可能是古气候）的预测指标，[作者]提供了一种在种群水平上预测等位基因频率变化的方法。
预测过程包括通过冗余分析（RDA）进行的第一步校准和预测步骤，以及通过具有二项分布族的广义相加模型（GAM）进行的第二步校准和预测步骤。因此，该过程与最近提出的一种从典范对应分析（CCA）预测等位基因频率变化的替代方法有根本不同。RDA步骤基于欧几里得距离，这也是分子方差分析（AMOVA）中使用的典型距离。由于RDA步骤或CCA方法有时会预测出负的等位基因频率，GAM步骤确保等位基因频率在0到1的范围内。
[作者]提供了具有预测频率的数据集以及几种可视化方法，以描绘从基线气候到变化后气候的等位基因频率预测变化。这些可视化包括“点图”图形（函数[具体函数名未给出]）、饼图（[具体函数名未给出]）、月亮图（[具体函数名未给出]）、“华夫饼”图（[具体函数名未给出]）以及地理空间中基线或未来模式的等位基因频率平滑表面图（[具体函数名未给出]）。由于这些可视化是通过[具体包名未给出]包生成的，为气候变化时间序列生成动画的方法很简单，如[具体包名未给出]的文档和补充视频所示。
