A predictive scaling framework of forest structure and functional diversity in a non-equilibrial world.

A key challenge to understanding the state of ecosystem services provided by the Northern Forest region (e.g., carbon stocks, habitat) lies in estimating forest dynamics across landscapes. With increasing disturbances (e.g., severe storms, drought) linked to climatic change, northeasterners need better predictive frameworks of forest dynamics that connect remotely sensed and in-situ data. Without theoretical grounding, relationships between in-situ and remotely sensed data may be limited to correlational analyses. Most existing theories relating tree sizes and abundances assume an equilibrial state, limiting insights into a world that largely exists in a non-equilibrial state due to disturbances. Our proposed work develops a framework for non-equilibrial scaling theory that links in-situ and remotely sensed data for predicting tree size distributions to better understand the distribution of carbon stocks and wildlife habitat connectivity across northeastern U.S. forests. The proposed research will aid in understanding carbon stocks at a landscape extent as it will provide a model for predicting whole forest functional type abundances by size if only remotely sensed canopy information is known. Research output will also provide landscape extent information on tree size distributions by functional type from the ground to the canopy that will inform biodiversity and connectivity studies by providing estimates of vegetation structure. By working with partner organizations that need information on carbon stocks and biodiversity connectivity between conserved and working lands in the Northern Forest region throughout the project, we will ensure that the research is relevant to societal needs.