slendr: a framework for spatio-temporal population genomic simulations on geographic landscapes

Area of Science:

• Population genetics
• Evolutionary biology
• Genomic data simulation

Background:

• Evolutionary forces shape genetic variation over time and space.
• Spatial components like isolation by distance are often neglected in population genetics.
• Lack of specialized tools hinders complex spatio-temporal population genetic modeling.

Purpose of the Study:

• Introduce a new framework for simulating spatially-explicit genomic data.
• Address the methodological gap in spatio-temporal population genetic modeling.
• Provide an R package (slendr) for building and evaluating such models.

Main Methods:

• Developed the slendr R package with a SLiM simulation back-end.
• Enabled programmatic and visual encoding of spatial population ranges and temporal dynamics.
• Integrated with tskit for tree-sequence processing and analysis within R.

Main Results:

• slendr allows simulation of spatially-explicit genomic data on real or custom landscapes.
• Users can define population dynamics, splits, and gene flow using a declarative language.
• The package supports both SLiM and msprime back ends for flexible model simulation.

Conclusions:

• slendr provides an efficient and reproducible solution for spatio-temporal genomic data simulation in R.
• The framework facilitates the integration of geospatial analysis, statistics, and visualization.
• Opens new possibilities for realistic modeling in population genetics and evolutionary studies.