Protocol for Projecting Allele Frequency Change under Future Climate Change at Adaptive-Associated Loci

Local adaptation of Dromiciops marsupials (Microbiotheriidae) from southern South America: Implications for species management facing climate change

The two species of the microbiotheriid marsupial genus Dromiciops (Dromiciops bozinovici: "Panchos's monito del monte" and Dromiciops gliroides: "monito del monte") exhibit a marked latitudinal genetic differentiation. Nevertheless, it is unclear whether this differentiation results from neutral processes or can be explained, to some extent, by local adaptation to different environmental conditions. Here, we used an SNP panel gathered by Rad-seq and searched for footprints of local adaptation (putative loci under selection) by exploring genetic associations with environmental variables in the two species of Dromiciops in Chilean and Argentinean populations. We applied three methods for detecting outlier SNPs and two genotype-environment associations approaches to quantify associations between allelic frequencies and environmental variables. Both species display strong genetic structure. D. bozinovici exhibited three distinct genetic groups, marking the first report of such structuring in this species using SNPs. In contrast, D. gliroides displayed four genetic clusters, consistent with previous studies. Both species exhibited an association of their genetic structure with environmental variables. D. bozinovici exhibited significant associations of allelic frequencies with elevation, precipitation during the warmest periods, and seasonality in the thermal regime. For D. gliroides, genetic variation appeared to be associated with more variables than D. bozinovici, including precipitation and temperature-related variables, isothermality, and elevation. All the outlier SNPs were mapped to the D. gliroides reference genome to explore if they fell within functionally known genes. These results represent a necessary first step toward identifying the genome regions that harbor genes associated with climate adaptations in Dromiciops. Notably, we identified genes involved in various functions, including carbohydrate synthesis (ALG8), muscle and neuronal regulation (MEF2D), and stress responses (PTGES3). Ultimately, this study contributes valuable insights that can inform targeted conservation strategies aimed at preserving the genetic diversity of Dromiciops in the face of environmental challenges.

AlleleShift: an R package to predict and visualize population-level changes in allele frequencies in response to climate change

Background

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 AlleleShift of predicting changes in allele frequencies at the population level.

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). 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.

Results

AlleleShift 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 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 visualizations 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 supplemental videos.

Availability

AlleleShift 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 adegenet::genpop format, which can be generated from the adegenet::genind format. Climate data is available from various resources such as WorldClim and Envirem.