LandRCBM: Bridging forest dynamics and carbon accounting to advance forest carbon predictions

Forests are central to the global carbon cycle, acting as the largest terrestrial carbon sinks. To estimate forest carbon stocks and predict their response to management interventions and environmental change, predictive models need to account for interacting processes such as tree growth, disturbances, succession, and dispersal, all of which are spatially constrained across landscapes. Understanding how these processes jointly influence carbon stocks therefore requires integrating forest landscape and carbon budget modelling approaches. Here, we introduce LandRCBM, a model integrating the forest landscape model LandR with the carbon budget model spadesCBM. LandRCBM tracks the carbon pools and fluxes required for IPCC forest carbon reporting by predicting carbon trajectories from ecological processes including tree growth, competition, regeneration, and dispersal. We demonstrate the model’s capabilities by simulating carbon dynamics under historical fires between 2000 and 2024 and under simulated fire regimes over 500 years in managed and unmanaged forests across a 50-million-hectare region of the northwestern Canadian boreal forest. Our simulations highlight the flexibility of the framework and demonstrate how species and disturbance dynamics shape short- and long-term carbon trajectories. We also identify key future research directions, including reducing uncertainty associated with model initialization and stand age estimates. Built following modular, open, and extensible principles, LandRCBM facilitates the integration of new data products and ecological processes. By moving beyond yield-curve–driven approaches and representing forest carbon as an emergent property of ecological dynamics, LandRCBM contributes to the ongoing transition toward spatially explicit forest carbon modelling based on ecological processes.