Bayesian calibration, comparison and averaging of six forest models, using data from Scots pine stands across Europe

• Published in: Forest ecology and management Vol. 289; pp. 255 - 268
• Main Authors: van Oijen, M., Reyer, C., Bohn, F.J., Cameron, D.R., Deckmyn, G., Flechsig, M., Härkönen, S., Hartig, F., Huth, A., Kiviste, A., Lasch, P., Mäkelä, A., Mette, T., Minunno, F., Rammer, W.
• Format: Journal Article
• Language: English
• Published: Kidlington Elsevier B.V 01.02.2013; Elsevier
• Subjects: Animal and plant ecology; Animal, plant and microbial ecology; Bayesian analysis; Biological and medical sciences; Bridging; Bulk molding compounds; Calibration; Dendrometry. Forest inventory; Dynamic modelling; Forest management; Forest management models; Forest management. Stand types and stand dynamics. Silvicultural treatments. Tending of stands. Natural regeneration; Forestry; Forests; Fundamental and applied biological sciences. Psychology; Growth prediction; Mathematical models; National forest inventories; Permanent sample plots; Pine; Pinus sylvestris; Stand types and stand dynamics. Silvicultural treatments. Tending of stands. Natural regeneration; Synecology; Terrestrial ecosystems; Uncertainty; Europe; Austria; Belgium; ANE, Europe; Finland; Estonia; Forest management models; Permanent sample plots; Uncertainty; Dynamic modelling; Growth prediction; National forest inventories; Forest management; Pinus sylvestris; Modeling; Softwood forest tree; Gymnospermae; Dynamic model; Forest inventory; Bayes estimation; Forest stand; Plot; Prediction; Forest ecology; Statistical estimation; Data; Calibration; Statistical method; Coniferales; Spermatophyta; Continuous inventory
• ISSN: 0378-1127; 1872-7042
• DOI: 10.1016/j.foreco.2012.09.043

► Six dynamic models for Scots pine were tested on 12 sites in four European countries. ► Bayesian calibration reduced uncertainties in parameters and predictions of five models. ► Country-specific and generic calibration led to similar predictive capacity. ► Bayesian model comparison slightly favoured the simpler models. ► Bayesian model averaging afforded good out-of-sample forecasts of forest growth. Forest management requires prediction of forest growth, but there is no general agreement about which models best predict growth, how to quantify model parameters, and how to assess the uncertainty of model predictions. In this paper, we show how Bayesian calibration (BC), Bayesian model comparison (BMC) and Bayesian model averaging (BMA) can help address these issues. We used six models, ranging from simple parameter-sparse models to complex process-based models: 3PG, 4C, ANAFORE, BASFOR, BRIDGING and FORMIND. For each model, the initial degree of uncertainty about parameter values was expressed in a prior probability distribution. Inventory data for Scots pine on tree height and diameter, with estimates of measurement uncertainty, were assembled for twelve sites, from four countries: Austria, Belgium, Estonia and Finland. From each country, we used data from two sites of the National Forest Inventories (NFIs), and one Permanent Sample Plot (PSP). The models were calibrated using the NFI-data and tested against the PSP-data. Calibration was done both per country and for all countries simultaneously, thus yielding country-specific and generic parameter distributions. We assessed model performance by sampling from prior and posterior distributions and comparing the growth predictions of these samples to the observations at the PSPs. We found that BC reduced uncertainties strongly in all but the most complex model. Surprisingly, country-specific BC did not lead to clearly better within-country predictions than generic BC. BMC identified the BRIDGING model, which is of intermediate complexity, as the most plausible model before calibration, with 4C taking its place after calibration. In this BMC, model plausibility was quantified as the relative probability of a model being correct given the information in the PSP-data. We discuss how the method of model initialisation affects model performance. Finally, we show how BMA affords a robust way of predicting forest growth that accounts for both parametric and model structural uncertainty.