Zusammenfassung
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.
