Abstract
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
Orchis militaris 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.