Skip to Main Content
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, USAAuthor(s): Duncan Wilson; Vicente Monleon; Aaron Weiskittel
Source: Mathematical and Computational Forestry & Natural-Resource Sciences. 11(2): 264-285.
Publication Series: Scientific Journal (JRNL)
Station: Pacific Northwest Research Station
PDF: Download Publication (599.0 KB)DOI: https://doi.org/11.3
DescriptionA 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 rather complex error structures. 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 varying sources of information (i.e., new data); (6) computational efficiency; and (7) relatively simple implementation as demonstrated in a compiled scripting language. Probabilistic projections of forest growth and yield included all sources of errors and uncertainty (e.g., estimated parameters, state variables, random effects, and residual errors). Cumulative error projections over a 40-year period for three sample Douglas-fir stands were determined. Projection errors for key metrics summed across all trees, such as total basal area and stem density, had coefficient of variations between 4-6% and 7-8%, respectively. Probabilistic projections were markedly different from deterministic projections made with the same model structure. Overall, this novel probabilistic platform showed strong promise as a general platform for ecological modeling, particularly when tractable and analytically correct error projections are required. In particular, the Bayesian probabilistic modeling approach used provided a natural platform for cross-disciplinary research, particularly between social and ecological research domains.
- Visit PNW's Publication Request Page to request a hard copy of this publication.
- We recommend that you also print this page and attach it to the printout of the article, to retain the full citation information.
- This article was written and prepared by U.S. Government employees on official time, and is therefore in the public domain.
CitationWilson, Duncan; Monleon, Vicente; Weiskittel, Aaron. 2019. 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. Mathematical and Computational Forestry & Natural-Resource Sciences. 11(2): 264-285.
KeywordsForest growth and yield, error propagation, model uncertainty, error budgets, individual tree growth models, coastal Douglas-fir, Oregon, Washington.
- Forest cover dynamics in the Pacific Northwest west side: regional trends and predictions.
- Modeling crown structural responses to competing vegetation control, thinning, fertilization, and Swiss needle cast in coastal Douglas-fir of the Pacific Northwest, USA.
- WestProPlus: a stochastic spreadsheet program for the management of all-aged Douglas-fir–hemlock forests in the Pacific Northwest.
XML: View XML