slimr: An R package for integrating data and tailor-made population genomic simulations over space and time

Software for realistically simulating complex population genomic processes is revolutionizing our understanding of evolutionary processes, and providing novel opportunities for integrating empirical data with simulations. However, the integration between simulation software and software designed for working with empirical data is currently not well developed. In particular, SLiM 3.0, which is one of the most powerful population genomic simulation frameworks for linking evolutionary dynamics with ecological patterns and processes is a standalone scripting language with limited data manipulation abilities. Here we present slimr, an R package designed to create a available under aCC-BY-NC-ND 4.0 International license. (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made bioRxiv preprint doi: https://doi.org/10.1101/2021.08.05.455258; this version posted August 6, 2021. The copyright holder for this preprint seamless link between SLiM 3.0 and the R development environment, with its powerful data manipulation and analysis tools. ● We show how slimr, in combination with SliM, facilitates smooth integration between genetic data, ecological data and simulation in a single environment. The package enables pipelines that begin with data reading, cleaning, and manipulation, proceed to constructing empirically-based parameters and initial conditions for simulations, then to running numerical simulations, and finally to retrieving simulation results in a format suitable for comparisons with empirical data – aided by advanced analysis and visualization tools provided by R (such as ABC and deep learning). ● We demonstrate the use of slimr with an example from our own work on the landscape population genomics of desert mammals, highlighting the advantage of having a single integrated tool for both data analysis and simulation. ● slimr makes the powerful simulation ability of SliM 3.0 directly accessible to R users, allowing integrated simulation projects that incorporate empirical data without the need to switch between software environments. This should provide more opportunities for evolutionary biologists and ecologists to use realistic simulations to better understand the interplay between ecological and evolutionary processes. slimr is available at https://rdinnager.github.io/slimr/. Keywords: population genomics; simulation; landscape genomics; evolution; ecology; evolutionary ecology; application; software