@article {Malchow2020.11.17.384545, author = {Anne-Kathleen Malchow and Greta Bocedi and Stephen C. F. Palmer and Justin M. J. Travis and Damaris Zurell}, title = {RangeShiftR: an R package for individual-based simulation of spatial eco-evolutionary dynamics and species{\textquoteright} responses to environmental change}, elocation-id = {2020.11.17.384545}, year = {2020}, doi = {10.1101/2020.11.17.384545}, publisher = {Cold Spring Harbor Laboratory}, abstract = {Reliably modelling the demographic and distributional responses of a species to environmental changes can be crucial for successful conservation and management planning. Process-based models have the potential to achieve this goal, but so far they remain underused for predictions of species{\textquoteright} distributions. Individual-based models offer the additional capability to model inter-individual variation and evolutionary dynamics and thus capture adaptive responses.We present RangeShiftR, an R package that provides flexible and fast simulations of spatial eco-evolutionary dynamics and species{\textquoteright} responses to environmental changes. It implements the individual-based simulation software RangeShifter for the widely used statistical programming platform R. The package features additional auxiliary functions to support model specification and analysis of results. We provide an outline of the package{\textquoteright}s functionality, describe the underlying model structure with its main components and present a short example.RangeShiftR offers substantial model complexity, especially for the demographic and dispersal processes. It comes with comprehensive documentation and elaborate tutorials to provide a low entry level. Thanks to the implementation of the core code in C++, the computations are fast. The complete source code is published under a public licence, making adaptations and contributions feasible.The RangeShiftR package facilitates the application of individual-based and mechanistic modelling to eco-evolutionary questions by operating a flexible and powerful simulation model from R. It allows effortless interoperation with existing packages to create streamlined workflows that can include data preparation, integrated model specification, and results analysis. Moreover, the implementation in R strengthens the potential for coupling RangeShiftR with other models.Competing Interest StatementThe authors have declared no competing interest.}, URL = {https://www.biorxiv.org/content/early/2020/11/28/2020.11.17.384545}, eprint = {https://www.biorxiv.org/content/early/2020/11/28/2020.11.17.384545.full.pdf}, journal = {bioRxiv} }