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.
Highlights
Faced with an accelerating global biodiversity crisis caused by multiple interacting and often anthropogenic environmental changes (Ceballos et al 2015, Urban 2015, IPBES 2019), biologists are striving to understand and predict how species will respond, in both ecological and evolutionary terms, to these threats and to management interventions (Urban et al 2016, Urban 2019)
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
Since its release, RangeShifter has been used in studies addressing a range of issues, including testing the effectiveness of alternative management interventions to improve connectivity and population persistence (Aben et al 2016, Henry et al 2017, Bleyhl et al 2021), facilitating range expansion (Synes et al 2015, 2020), improving reintroduction success (Heikkinen et al 2015, Ovenden et al 2019), investigating range dynamics of invasive (Fraser et al 2015, Dominguez Almela et al 2020) and recovering species (Sun et al 2016) and theoretically investigating how different traits and processes affect rate of range expansion (Bocedi et al 2014a, Henry et al 2014, Barros et al 2016, Santini et al 2016)
Summary
Faced with an accelerating global biodiversity crisis caused by multiple interacting and often anthropogenic environmental changes (Ceballos et al 2015, Urban 2015, IPBES 2019), biologists are striving to understand and predict how species will respond, in both ecological and evolutionary terms, to these threats and to management interventions (Urban et al 2016, Urban 2019). The main objective was to provide an individualbased, spatially explicit modelling platform that integrated population dynamics with sophisticated dispersal behaviour, and that could be used for a variety of applications, from theory development to in-silico testing of management interventions. We present the new RangeShifter 2.0, which, among various additions and improvements, includes two major novelties compared to the original version (RangeShifter 1.0): the option for implementing temporally dynamic landscapes and a module for the explicit modelling of neutral and adaptive genetics (controlling dispersal traits). We summarise the main RangeShifter features and briefly describe, and illustrate with examples, the two major additions of dynamic landscapes and explicit genetics, while we refer to the RangeShifter 2.0 User Manual () for an overview of the full functionality, including the description of smaller changes and new features in ver. 2.0
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