RangeShiftR: an R package for individual-based simulation of spatial eco-evolutionary dynamics and species' responses to environmental changes

Anne-Kathleen Malchow*, Greta Bocedi, Stephen C. F. Palmer, Justin M. J. Travis, Damaris Zurell

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

14 Citations (Scopus)
2 Downloads (Pure)

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.

Original languageEnglish
Pages (from-to)1443-1452
Number of pages10
JournalEcography
Volume44
Issue number10
Early online date29 Aug 2021
DOIs
Publication statusPublished - 1 Oct 2021

Bibliographical note

Acknowledgements
– We are grateful for valuable feedback from many users who tested previous versions of the package. The Figures 1 and 2 were created using the draw.io app. We acknowledge the support of the Open Access Publishing Fund of the Univ. of Potsdam.

Funding
– AM and DZ were supported by Deutsche Forschungsgemeinschaft (DFG) under grant agreement No. ZU 361/1-1. GB was supported by a Royal Society University Research Fellowship (UF160614).

Data Availability Statement

Data are available from GitHub: <https://github.com/ RangeShifter/RangeShiftR-package> (Malchow et al. 2021).

Keywords

  • connectivity
  • conservation
  • dispersal
  • evolution
  • population dynamics
  • range dynamics

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