RT Journal Article
SR Electronic
T1 RangeShiftR: an R package for individual-based simulation of spatial eco-evolutionary dynamics and species’ responses to environmental change
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 2020.11.17.384545
DO 10.1101/2020.11.17.384545
A1 Malchow, Anne-Kathleen
A1 Bocedi, Greta
A1 Palmer, Stephen C. F.
A1 Travis, Justin M. J.
A1 Zurell, Damaris
YR 2020
UL http://biorxiv.org/content/early/2020/11/28/2020.11.17.384545.abstract
AB 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’ 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’ 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’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.