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

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

doi:10.1101/2020.11.17.384545

Cited by 5 publications

(8 citation statements)

References 37 publications

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“…Simulation experiments combining habitat loss and climate change have already suggested potentially disastrous consequences for species survival in both hypothetical (Travis 2003) and real‐world (Sarmento Cabral et al 2013) systems. However, previous modelling studies have focused mostly on ecological processes and patterns, but eco‐evolutionary models may allow explicitly integrating genetic patterns (Urban et al 2016, Malchow et al 2020). Hence, quantifying genetic diversity and functional patterns of empirical communities and matching these empirical patterns with simulation results varying in environmental scenarios could indicate the most likely scenario that the empirical community is undergoing (Overcast et al 2019 for genetic and ecological patterns compared to biogeographical scenarios).…”

Section: Discussionmentioning

confidence: 99%

Temporal environmental variation may impose differential selection on both genomic and ecological traits

Leidinger

Vedder

Cabral

2021

Oikos

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The response of populations and species to changing conditions determines how community composition will change functionally, including via trait shifts. Selection from standing variation has been suggested to be more efficient than acquiring new mutations. Yet, studies on community trait composition and trait selection largely focus on phenotypic variation in ecological traits, whereas the underlying genomic traits remain understudied. Using a genome‐explicit, niche‐ and individual‐based model, we address the potential interactions between genomic and ecological traits shaping communities under an environmental selective forcing, namely temporal positively autocorrelated environmental fluctuation. In this model, all ecological traits are explicitly coded by the genome. For our experiments, we initialized 90 replicate communities, each with ca 350 initial species, characterized by random genomic and ecological trait combinations, on a 2D spatially explicit landscape with two orthogonal gradients (temperature and resource use). We exposed each community to two contrasting scenarios: without (i.e. static environments) and with temporal variation. We then analyzed emerging compositions of both genomic and ecological traits at the community, population and genomic levels. Communities in variable environments were species poorer than in static environments, and populations more abundant, whereas genomes had lower genetic linkage, mean genetic variation and a non‐significant tendency towards higher numbers of genes. The surviving genomes (i.e. those selected by variable environments) coded for enhanced environmental tolerance and smaller biomass, which resulted in faster life cycles and thus also in increased potential for evolutionary rescue. Under temporal environmental variation, larger, less linked genomes retained more variation in mean dispersal ability at the population level than at genomic level, whereas the opposite trend emerged for biomass. Our results provide clues to how sexually‐reproducing diploid plant communities might react to variable environments and highlights the importance of genomic traits and their interaction with ecological traits for eco‐evolutionary responses to changing climates.

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Section: Discussionmentioning

confidence: 99%

Temporal environmental variation may impose differential selection on both genomic and ecological traits

Leidinger

Vedder

Cabral

2021

Oikos

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“…Furthermore, RangeShifter 2.0 is also the core of the new package RangeShiftR (Malchow et al, 2020), which allows running RangeShifter from the R environment (R Core Team, 2020)…”

Section: Discussionmentioning

confidence: 99%

“…It is hence free for the wider community to use, modify and share. Furthermore, RangeShifter 2.0 is also the core of the new package RangeShiftR (Malchow et al, 2020), which allows running RangeShifter from the R environment (R Core Team, 2020) while maintaining the high performance of the C++ code, and includes functions assisting with the set-up of the simulations, the parameterisation and output analyses. RangeShiftR, in addition to improving and broadening RangeShifter accessibility, makes it easily available for multiple platforms, has access to R’s infrastructure for parallel and cluster computing and offers many opportunities for interoperation with other R packages.…”

Section: Discussionmentioning

confidence: 99%

RangeShifter 2.0: An extended and enhanced platform for modelling spatial eco-evolutionary dynamics and species’ responses to environmental changes

Bocedi

Palmer

Malchow

et al. 2020

Preprint

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Process-based models are becoming increasingly used tools for understanding how species are likely to respond to environmental changes and to potential management options. RangeShifter is one such modelling platform, which has been used to address a range of questions including identifying effective reintroduction strategies, understanding patterns of range expansion and assessing population viability of species across complex landscapes.Here we introduce a new version, RangeShifter 2.0, which incorporates important new functionality. It is now possible to simulate dynamics over user-specified, temporally changing landscapes. Additionally, the genetic and evolutionary capabilities have been strengthened, notably by introducing an explicit genetic modelling architecture, which allows for simulation of neutral and adaptive genetic processes. Furthermore, emigration, transfer and settlement rules can now all evolve, allowing for sophisticated simulation of the evolution of dispersal. We illustrate the potential application of RangeShifter 2.0’s new functionality by two examples. The first illustrates the range expansion of a virtual species across a dynamically changing UK landscape. The second demonstrates how the software can be used to explore the concept of evolving connectivity in response to land-use modification, by examining how movement rules come under selection over landscapes of different structure and composition.RangeShifter 2.0 is built using object-oriented C++ providing computationally efficient simulation of complex individual-based, eco-evolutionary models. The code has been redeveloped to enable use across operating systems, including on high performance computing clusters, and the Windows GUI has been enhanced. Furthermore, the recoding of the package has supported the development of a new version running under the R platform, RangeShiftR.RangeShifter 2.0 will facilitate the development of in-silico assessments of how species will respond to environmental changes and to potential management options for conserving or controlling them. By making the code available open source, we hope to inspire further collaborations and extensions by the ecological community.

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“…Malchow, A.‐K. et al 2021. RangeShiftR: an R package for individual‐based simulation of spatial eco‐evolutionary dynamics and species' responses to environmental changes.…”

Section: Discussionmentioning

confidence: 99%

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

et al. 2021

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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.

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

Cited by 5 publications

References 37 publications

Temporal environmental variation may impose differential selection on both genomic and ecological traits

Temporal environmental variation may impose differential selection on both genomic and ecological traits

RangeShifter 2.0: An extended and enhanced platform for modelling spatial eco-evolutionary dynamics and species’ responses to environmental changes

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

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