Panta rhei – everything is in motion. This is especially true when we think of the complex dynamics of range shifting species and communities. Still, we heavily rely on statistical species distribution models (SDM) to predict species response to environmental change, although these ignore demography and dispersal mediated transient dynamics. Several approaches of varying model complexity have been suggested to overcome these shortcomings, for example by coupling SDM output with dispersal kernels or with dynamic population models. Others completely avoid using SDMs and suggest jointly estimating the environmental response of demographic rates, population dynamics and dispersal within a Bayesian framework.
Here, we used an individual-based plant community model as virtual reality, in which we knew all relevant processes and could monitor species’ response to climate change, we could sample different kinds of high-quality data and use these for parameterizing and testing different approaches for predicting range dynamics. Specifically, we compared five range (dynamic) models of varying complexity including classical SDMs, SDMs coupled with simple dispersal or more complex population dynamic models (SDM hybrids), and a hierarchical Bayesian process-based dynamic range model (DRM). For all models, we tested the effects of different demographic and community processes on predictive performance under climate change.
Results/Conclusions:
We found that all approaches improved range projections considerably compared to purely correlative SDMs. Under current climate, DRMs performed best, although only marginally. Under climate change, predictive performance varied considerably, with no clear winners, due to differences in the number of processes considered and the kind of data used for calibration. Still, all range dynamic models improved predictions under climate change substantially compared to purely correlative SDMs, and the population dynamic models also predicted reasonable extinction risks for most scenarios. When benchmarking data were simulated with more complex demographic and community processes, simple SDM hybrids including only dispersal often proved most reliable. This underlines that transient population dynamics are difficult to predict if not all range-shaping processes including interspecific interactions are well understood.
Our results reassure the clear merit in using dynamic approaches for modeling species’ response to climate change but also emphasize several needs for further model and data improvement. We present some perspectives for improving range projections through combination of multiple models and modeling philosophies, and through improved integration of diverse data sources, which should help making these approaches operational for larger numbers of species.