Toward improved uncertainty quantification in predictions of forest dynamics: A dynamical model of forest change

Models of forest dynamics are an important tool to understand and predict forest responses to global change. Despite recent model development, predictions of forest dynamics under global change remain highly variable reflecting uncertainty in future conditions, forest demographic processes, and the data used to parameterize and validate models. Quantifying this uncertainty and accounting for it when making adaptive management decisions is critical to our ability to conserve forest ecosystems in the face of rapidly changing conditions. Dynamical spatiotemporal models (DSTMs) are a particularly powerful tool in this setting given they quantify and partition uncertainty in demographic models and noisy forest observations, propagate uncertainty to predictions of forest dynamics, and support refinement of predictions based on new data and improved ecological understanding. A major challenge to the application of DSTMs in applied forest ecology has been the lack of a scalable, theoretical model of forest dynamics that generates predictions at the stand level—the scale at which management decisions are made. We address this challenge by integrating a matrix projection model motivated by the well-known McKendrick-von Foerster partial differential equation for size-structured population dynamics within a Bayesian hierarchical DSTM informed by continuous forest inventory data. The model provides probabilistic predictions of species-specific demographic rates and changes in the size-species distribution over time. The model is applied to predict long-term dynamics (60+ years) within the Penobscot Experimental Forest in Maine, USA, quantifying and partitioning uncertainty in inventory observations, process-based predictions, and model parameters for nine Acadian Forest species. We find that uncertainty in inventory observations drives variability in predictions for most species and limits the inclusion of ecological detail within the DSTM. We conclude with a discussion of how DSTMs can be used to reduce uncertainty in predictions of forest dynamics under global change through informed model refinement and the assimilation of multiple forest data sources.