New approach for predicting ecological dynamics during environmental change

Anthropogenic climate change is driving changes in ecosystems and biological communities across the World. However our understanding of the mechanisms involved and, therefore, our ability to predict future dynamics is very limited. We will develop predictive theory for understanding ecological dynamics, test this theory at large scaling using datasets for the last 21,000 years, and at fine scale using experimental data from plant communities in the Norwegian and American alpine zones. The central hypothesis of this work is that communities' functional composition and climate niches cannot change rapidly enough to track ongoing climate change and disturbance, creating ecological disequilibrium and lagged dynamics that are not captured by state-of-the-art global-change ecosystem models. This work will enable better prediction of ecological dynamics in real systems, a key basis for landscape and conservation management in a changing world.

Disequilibrium is a common feature of all ecosystems, and is becoming ever more important under global change. However, current macroecological theory has limited ability to predict the dynamics of non-steady-state systems. I propose to 1) develop a theory for predicting community dynamics from species' functional hypervolumes, 2) test this theory at broad scales using ecoinformatics approaches coupled to global datasets across the present-day and the Holocene, and 3) collect novel data in Norway and North America to test theory at fine-scale. This work will enable better prediction and management of near-future ecological dynamics. I will bring my expertise with community climate and hypervolume analysis to a Norwegian host environment doing world-class research on species response to climate change. Our combined backgrounds will enable novel insights into how communities' niches match changing climates, which have not been possible until now due to theoretical limitations in macroecology.