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' distributions. Individual‐based models offer the additional capability to model inter‐individual variation and evolutionary dynamics and thus capture adaptive responses to environmental change.We present RangeShiftR, an R implementation of a flexible individual‐based modelling platform which simulates eco‐evolutionary dynamics in a spatially explicit way. The package provides flexible and fast simulations by making the software RangeShifter available 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 elaborate tutorials and comprehensive documentation to facilitate learning the software and provide help at all levels. As the core code is implemented 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.
Highlights
Under anthropogenic exploitation and rapid environmental changes, one of the most urgent challenges biologists face today is to understand and predict if and how species will persist, by adapting or undergoing changes in their geographic range (McGill et al 2015, Brondizio et al 2019)
The ambition for a more prominent representation of process-based models in ecological research led to the development of the standalone software RangeShifter (Bocedi et al 2014), a flexible individual-based model (IBM) that simulates spatial eco-evolutionary dynamics for a given species
Dispersal and evolution as interacting processes, organised within a modular structure in which each process has a number of modelling options
Summary
RangeShiftR: an R package for individual-based simulation of spatial eco-evolutionary dynamics and species’ responses to environmental changes. We present RangeShiftR, an R implementation of a flexible individual-based modelling platform which simulates eco-evolutionary dynamics in a spatially explicit way. RangeShiftR offers substantial model complexity, especially for the demographic and dispersal processes It comes with elaborate tutorials and comprehensive documentation to facilitate learning the software and provide help at all levels. 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.
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
