Abstract
AbstractAimMechanistic general ecosystem models are used to explore fundamental ecological dynamics and to assess possible consequences of anthropogenic and natural disturbances on ecosystems. The Madingley model is a mechanistic general ecosystem model (GEM) that simulates a coherent global ecosystem, consisting of photo‐autotrophic and heterotrophic life, based on fundamental ecological processes. The C++ implementation of the Madingley model delivers fast computational performance, but it (a) limits the userbase to researchers that are familiar with the intricacies of C++ programming, (b) has limited possibility to change model settings and provide model outputs required to address specific research questions, and (c) has limited reproducibility of simulation experiments. The aim of this paper is to present an R package of the Madingley model to aid with increasing the accessibility and flexibility of the model.InnovationThe MadingleyR R package streamlines the installation procedure and supports all major operating systems. MadingleyR enables users to combine multiple consecutive simulations, making case study specific modifications to MadingleyR objects along the way. Default input files are available from the package and study‐specific inputs can be easily loaded from the R environment. MadingleyR also provides functions to plot and summarize MadingleyR outputs. We provide a comprehensive description of the MadingleyR functions and workflow. We also demonstrate the applicability of the MadingleyR package using three case studies: (a) simulating the cascading effects of the loss of mega‐herbivores on food‐web structure, (b) simulating the impacts of increased land‐use intensity on the total biomass of different feeding guilds by restricting the total vegetation biomass available for feeding and (c) simulating the impacts of an intensive land‐use scenario on a continental scale.Main conclusionsThe MadingleyR package provides direct accessibility to simulations with the mechanistic ecosystem model Madingley and is flexible in its application without a loss in performance.
Highlights
IntroductionThe field of ecology focusing on patterns and processes over large temporal and spatial scales (broadly termed ‘macroecology’; McGill, 2019) has traditionally embraced a strong observational and correlative approach (Cabral et al, 2017; Connolly et al, 2017)
The field of ecology focusing on patterns and processes over large temporal and spatial scales has traditionally embraced a strong observational and correlative approach (Cabral et al, 2017; Connolly et al, 2017)
Dynamic global vegetation models (DGVMs) can be used to simulate plant communities globally and allow for the study of the interaction between vegetation and climate with greater detail than possible in the Madingley (Krinner et al, 2005; Kucharik et al, 2000), but do not simulate processes related to heterotrophs as done in the Madingley model
Summary
The field of ecology focusing on patterns and processes over large temporal and spatial scales (broadly termed ‘macroecology’; McGill, 2019) has traditionally embraced a strong observational and correlative approach (Cabral et al, 2017; Connolly et al, 2017). Methods to reduce ecosystem complexity and simultaneous improvements in computing power have increased the practical applicability of such approaches on a macroecological scale (Connolly et al, 2017; Grimm et al, 2017; Harfoot et al, 2014). The Madingley model is one of the few mechanistic general ecosystem models (GEMs) that can simulate a coherent global ecosystem consisting of both photo-autotrophic and heterotrophic life (Harfoot et al, 2014; Purves et al, 2013). Dynamic global vegetation models (DGVMs) can be used to simulate plant communities globally and allow for the study of the interaction between vegetation and climate with greater detail than possible in the Madingley (Krinner et al, 2005; Kucharik et al, 2000), but do not simulate processes related to heterotrophs as done in the Madingley model
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