Forecasting mid-century forest productivity by assimilating regional observations from plot networks and ecosystem experiments into a process-based model

Forecasting how forest ecosystems will be altered by a changing environment can help society anticipate changes to the ecosystem services upon which it depends. However, producing a forecast requires quantifying and propagating different components of uncertainty in ecosystem model predictions. Here we present an ecological forecast of forest productivity for loblolly pine plantations across the Southeastern U.S. through the mid-21^st century. To quantify uncertainty in ecosystem model parameters and processes, we used a hierarchical Bayesian approach (DAPPER; Data Assimilation to Predict Productivity for Ecosystems and Regions) to assimilate 35 years of global change research across the region into a process-based ecosystem model. The data assimilated included biometric, ecophysiological, and flux observations from regional plot networks as part of industrial forest research cooperatives, CO₂ fertilization experiments, throughfall exclusion experiments, nutrient addition experiments, and water addition experiments. By combining the parameter and process uncertainty with uncertainty in climate projections, we found that 1) productivity is forecasted to increase across the region between 2010 and 2055; 2) there is considerable uncertainty in the forecast that overlaps zero (i.e., no change or negative change) in the southern and western extents of the region; and 3) the ecosystem process model was the single largest source of uncertainty. Overall, beyond providing information for resource managers in the region, our study demonstrates how diverse time-series observations from regional plot networks and ecosystem experiments can be combined to advance ecosystem forecasting under global change.