Quantification and Incorporation of Uncertainty in Forest Growth and Yield Projections Using A Bayesian Probabilistic Framework: A Demonstration for Plantation Coastal Douglas-fir in the Pacific Northwest, USA

Duncan Willson, Vicente Monleon, Aaron Weiskittel


A Bayesian probabilistic modeling platform was used and evaluated for application in a relatively complex individual-tree growth and yield model for coastal Douglas-fir (Pseudotsuga menziesii var. menziesii (Mirb.) Franco), which was expressed as a mixed discrete and continuous Bayesian Network for annual projections. The modeling platform used a common and open-source Bayesian analysis program (JAGS v3.3.0), and was sufficiently flexible to handle a relatively complex model structure; namely, a differential form, highly dynamic, recursive, hierarchical, non-linear system of equations with a rather complex error structure. This novel probabilistic modeling platform met certain desirable criteria, including (1) accurate and tractable projections that included full error propagation; (2) flexible and comprehensive analytic capabilities; (3) full consideration of hierarchical and multi-level model structures; (4) capacity for random effects calibration; (5) allowance of hypothesis testing and updating knowledge across different system components, simultaneously with different sources of information (i.e., new data); (6) computational efficiency; and (7) relatively simple implementation as demonstrated in a complied scripting language. Probabilistic projections of forest growth and yield included all sources of errors and uncertainty (e.g., estimated parameters, ...


forest growth and yield, error propagation, model uncertainty, error budgets, individual tree growth models,

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