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.

Authors: Kindt, R.

Subjects: genetic variation, genetic sources, climate change

Publication type: Paper-UR, Publication

Year: 2021

DOI: https://doi.org/10.1101/2021.01.15.426775