rs.bib

% Encoding: ISO-8859-1

@Article{Bocedi2014,
  author    = {G. Bocedi and S.C.F. Palmer and G. Pe'er and R.K. Heikkinen and Y.G. Matsinos and K. Watts and J.M.J. Travis},
  title     = {RangeShifter: a platform for modelling spatial eco-evolutionary dynamics and species' responses to environmental changes},
  journal   = {Methods in Ecology and Evolution},
  year      = {2014},
  volume    = {5},
  number    = {4},
  pages     = {388-396},
  abstract  = {Rapid environmental changes are threatening biodiversity and exposing species to novel ecological and evolutionary pressures. The scientific community increasingly recognises the need for dynamic models integrating sufficient complexity both to improve our understanding of species' responses to environmental changes and to inform effective management strategies.
 Using three illustrative examples, we introduce a novel modelling platform, RangeShifter, which integrates complex population dynamics and dispersal behaviour, includes plastic and evolutionary processes, and simulates scenarios on spatially-explicit landscapes. The software provides functionality for a wide variety of modelling applications ranging from applied questions, where it can be parameterised for real landscapes and species to compare alternative potential management interventions, to purely theoretical studies of species' eco-evolutionary dynamics and responses to different environmental pressures.
 RangeShifter provides an important tool for facilitating the advancement of ecological theory on species' spatial dynamics in response to environmental changes, and linking it directly to application in biodiversity conservation.},
  doi       = {10.1111/2041-210X.12162},
  file      = {:readme/papers/\Bocedi_etal_MEE_2014.pdf:PDF},
  groups    = {range shift},
  keywords  = {dynamic modelling; IBM ; environmental change; dispersal; population dynamics; connectivity; population viability},
  owner     = {DZ},
  timestamp = {2014.04.03},
}

@TechReport{Morton2011,
  author      = {Morton, D. and Rowland, C. and Wood, C. and Meek, L. and Marston, C. and Smith, G. and Wadsworth, R. and Simpson, I..},
  title       = {Final Report for LCM2007 - the new UK land cover map},
  institution = {NERC/Centre for Ecology \& Hydrology},
  year        = {2011},
  number      = {Countryside Survey Technical Report No 11/07},
  owner       = {DZ},
  timestamp   = {2019.08.27},
}

@Article{Dytham2009,
  author    = {Calvin Dytham},
  title     = {Evolved dispersal strategies at range margins},
  journal   = {Proceedings of the Royal Society B},
  year      = {2009},
  volume    = {276},
  number    = {1661},
  pages     = {1407-1413},
  abstract  = {Dispersal is a key component of a species's ecology and will be under different selection pressures in different parts of the range. For example, a long-distance dispersal strategy suitable for continuous habitat at the range core might not be favoured at the margin, where the habitat is sparse. Using a spatially explicit, individual-based, evolutionary simulation model, the dispersal strategies of an organism that has only one dispersal event in its lifetime, such as a plant or sessile animal, are considered. Within the model, removing habitat, increasing habitat turnover, increasing the cost of dispersal, reducing habitat quality or altering vital rates imposes range limits. In most cases, there is a clear change in the dispersal strategies across the range, although increasing death rate towards the margin has little impact on evolved dispersal strategy across the range. Habitat turnover, reduced birth rate and reduced habitat quality all increase evolved dispersal distances at the margin, while increased cost of dispersal and reduced habitat density lead to lower evolved dispersal distances at the margins. As climate change shifts suitable habitat poleward, species ranges will also start to shift, and it will be the dispersal capabilities of marginal populations, rather than core populations, that will influence the rate of range shifting.},
  doi       = {10.1098/rspb.2008.1535},
  file      = {:readme/papers/\Dytham_ProcRSocB_2009.pdf:PDF},
  keywords  = {* individual-based model * habitat quality * habitat turnover * population dynamics * spatial ecology * evolutionary ecology},
  owner     = {DZ},
  timestamp = {2009.03.12},
  url       = {http://rspb.royalsocietypublishing.org/content/276/1661/1407.full},
}

@Article{Henry2013,
  author    = {Roslyn C. Henry and Greta Bocedi and Justin M.J. Travis},
  title     = {Eco-evolutionary dynamics of range shifts: Elastic margins and critical thresholds},
  journal   = {Journal of Theoretical Biology},
  year      = {2013},
  volume    = {321},
  pages     = {1--7},
  doi       = {10.1016/j.jtbi.2012.12.004},
  file      = {:readme/papers/Henry_etal_JTheorBiol_2013.pdf:PDF},
  keywords  = {Climate change Environmental change Eco-evolutionary dynamics Evolutionary rescue Extinction thresholds},
  owner     = {DZ},
  timestamp = {2020.03.11},
}

@Article{Sciaini2018,
  author    = {Marco Sciaini and Matthias Fritsch and Cedric Scherer and Craig Eric Simpkins},
  journal   = {Methods in Ecology and Evolution},
  title     = {{NLMR} and landscapetools: An integrated environment for simulating and modifying neutral landscape models in R},
  year      = {2018},
  month     = {sep},
  number    = {11},
  pages     = {2240--2248},
  volume    = {9},
  doi       = {10.1111/2041-210x.13076},
  file      = {:readme/papers/Sciaini_etal_MEE_2018.pdf:PDF},
  keywords  = {artificial landscape landscape generator spatial patterns spatial visualisation virtual landscape},
  owner     = {DZ},
  timestamp = {2020.04.29},
}

@article{Travis2012,
  title={Modelling dispersal: an eco-evolutionary framework incorporating emigration, movement, settlement behaviour and the multiple costs involved},
  author={Travis, Justin MJ and Mustin, Karen and Barto{\'n}, Kamil A and Benton, Tim G and Clobert, Jean and Delgado, Maria M and Dytham, Calvin and Hovestadt, Thomas and Palmer, Stephen CF and Van Dyck, Hans and others},
  journal={Methods in Ecology and Evolution},
  volume={3},
  number={4},
  pages={628--641},
  year={2012},
  publisher={Wiley Online Library}
}

@Article{Malchow2020,
  author    = {Malchow, A.K. and Bocedi, G. and Palmer, S. C. F. and Travis, J. M. J. and Zurell, D.},
  journal   = {bioRxiv},
  title     = {RangeShiftR: an R package for individual-based simulation of spatial eco-evolutionary dynamics and species{\textquoteright} responses to environmental change},
  year      = {2020},
  pages     = {2020.11.17.384545},
  abstract  = {1. 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. 2. 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. 3. 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. 4. 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.},
  doi       = {10.1101/2020.11.17.384545},
  file      = {:readme/papers/Malchow_etal_bioRxiv_2020.pdf:PDF},
  owner     = {DZ},
  timestamp = {2020.11.20},
}

@Article{Bocedi2020,
  author    = {Greta Bocedi and Stephen C. F. Palmer and Anne-Kathleen Malchow and Damaris Zurell and Kevin Watts and Justin M. J. Travis},
  journal   = {bioRxiv},
  title     = {{RangeShifter} 2.0: An extended and enhanced platform for modelling spatial eco-evolutionary dynamics and species' responses to environmental changes},
  year      = {2020},
  pages     = {2020.11.26.400119},
  abstract  = {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.},
  doi       = {10.1101/2020.11.26.400119},
  file      = {:readme/papers/Bocedi_etal_bioRxiv_2020.pdf:PDF},
  owner     = {DZ},
  timestamp = {2020.11.30},
}

@Article{Bocedi2021,
  author    = {Bocedi, Greta and Palmer, Stephen C. F. and Malchow, Anne-Kathleen and Zurell, Damaris and Watts, Kevin and Travis, Justin M. J.},
  journal   = {Ecography},
  title     = {RangeShifter 2.0: an extended and enhanced platform for modelling spatial eco-evolutionary dynamics and species' responses to environmental changes},
  year      = {2021},
  number    = {10},
  pages     = {1453-1462},
  volume    = {44},
  abstract  = {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, we integrated a new genetic module, notably introducing an explicit genetic modelling architecture, which allows for simulation of neutral and adaptive genetic processes. Furthermore, emigration, transfer and settlement traits 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 graphical user interface has been enhanced. 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.},
  doi       = {https://doi.org/10.1111/ecog.05687},
  eprint    = {https://onlinelibrary.wiley.com/doi/pdf/10.1111/ecog.05687},
  file      = {:readme/papers/Bocedi_etal_Ecography_2021.pdf:PDF},
  keywords  = {animal movement, connectivity, distribution modelling, dynamic landscapes, individual-based modelling, population viability, process-based modelling},
  timestamp = {2021.08.30},
  url       = {https://onlinelibrary.wiley.com/doi/abs/10.1111/ecog.05687},
}

@Article{Malchow2021,
  author    = {Malchow, Anne-Kathleen and Bocedi, Greta and Palmer, Stephen C. F. and Travis, Justin M. J. and Zurell, Damaris},
  journal   = {Ecography},
  title     = {RangeShiftR: an R package for individual-based simulation of spatial eco-evolutionary dynamics and species' responses to environmental changes},
  year      = {2021},
  number    = {10},
  pages     = {1443-1452},
  volume    = {44},
  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.},
  doi       = {https://doi.org/10.1111/ecog.05689},
  eprint    = {https://onlinelibrary.wiley.com/doi/pdf/10.1111/ecog.05689},
  file      = {:readme/papers/Malchow_etal_Ecography_2021.pdf:PDF},
  keywords  = {connectivity, conservation, dispersal, evolution, population dynamics, range dynamics},
  owner     = {DZ},
  timestamp = {2021.08.30},
  url       = {https://onlinelibrary.wiley.com/doi/abs/10.1111/ecog.05689},
}

@Comment{jabref-meta: databaseType:bibtex;}