sModelProBio

Modelling the way forward for protecting biodiversity during climate change

First meeting: 09.03. -13.03.2026

iDiv member:
Henrique Pereira
Joseph Settele

Project summary:
Time is running out to prevent devastating biodiversity losses from climate change and safeguard human well being. Addressing this crisis requires accurate projections about which species and ecosystems are most at risk to ensure efficient use of limited management resources. We cannot efficiently protect what we cannot predict. Unlike climate scientists, biologists have not yet built a solid mechanistic basis for projecting future biodiversity change. Most biodiversity projection models ignore underlying biological processes, while extrapolating correlations between current species’ ranges and climate. However, as correlations between current species distributions and climate become uncoupled, we cannot rely solely on tools based on statistical descriptions of the past. Model inter comparison projects (MIPs) have been pivotal in climate science, but are only just emerging in biodiversity science. Currently, no biodiversity MIP exists that considers process-based models at the regional scale and population level, the relevant scales for conservation and national action planning under the Kunming-Montreal Global Biodiversity Framework.
The sModelProBio synthesis group will address these gaps by performing the first rigorous and reproducible regional biodiversity MIP and impact attribution using models, ranging from correlative to mechanistic. We will validate models on historical data, project future biodiversity under climate and land use change scenarios, and develop a conceptual framework to scale between existing global biodiversity projections and regional species-specific projections. The framework and results will create a step change in predictive biodiversity science under climate change, and support decision makers and the community of practice through robust tools and scientific evidence.

In person participants: tba

Remote participants: tba