<scp>MetaRange</scp>.jl: A Dynamic and Metabolic Species Range Model for Plant Species

Process-based models for range dynamics are urgently needed due to increasing intensity of human-induced biodiversity change. Despite a few existing models that focus on demographic processes, their use remains limited compared to the widespread application of correlative approaches. This slow adoption is largely due to the challenges in calibrating biological parameters and the high computational demands for large-scale applications. Moreover, increasing the number of simulated processes (i.e., mechanistic complexity) may further exacerbate those reasons of delay. Therefore, balancing mechanistic complexity and computational effectiveness of process-based models is a key area for improvement. A promising research direction is to expand demographically explicit metapopulation models by integrating metabolic constraints. We translated and expanded a previously developed R metapopulation model to Julia language and published it as a Julia module. The model integrates species-specific parameters such as preferred environmental conditions, biomass and dispersal ability with demographic rates (e.g., reproductive and mortality rates) derived from local temperature and biomass via the metabolic theory of ecology. We provide a simple application example for the model in which we illustrate a typical use case by predicting the future occurrence of <i>Orchis militaris</i> in Bavaria under different climate change scenarios. Our results show that climate change reduces habitat suitability overall, but some regions like the Franconian Forest and the Alps see increased suitability and abundance, confirming their role as refugia. Simulating metapopulation dynamics reveals that local population dynamics and dispersal are crucial for accurate predictions. For instance, increasing dispersal distance reduces overall abundance loss but also lessens population growth in refugia. This highlights the importance of measuring traits like dispersal ability to improve climate change forecasts.