The form and magnitude of storm damage and stand disclosure patterns were assessed in 332 randomly chosen pure and regular stands of spruce (Picea abies L.) and beech (Fagus sylvatica L.) after storm LOTHAR, within a region of the Swiss Midlands. This data was analysed in relation to maximal wind speed, measured with Doppler radar techniques and other influential factors such as relief, allometric characteristics, silvicultural history, and neighbourhood. In addition, storm damage, assessed from aerial photographs over an extended perimeter (about 70,000 ha) was considered. A storm of the magnitude of LOTHAR (December 26 1999), with an average maximal wind speed of 45 m s À1 (160 km h À1 ) appears to have a highly chaotic wind field structure, with great spatial and temporal variation of wind gusts. Wind speeds were not a significant predictor for damage in spruce stands and only weakly influential for beech. The consequences of this high randomness were analysed to estimate the return time of such a storm at the stand level. It lies between 86 and 113 years for spruce, 357 and 408 for beech. Only a few independent variables were significant and the overall explanatory strength of the model was unexpectedly low (R 2 =0.07 for spruce and 0.30 for beech). Among the more reliable predisposing factors were mixture and aspect combined with gradient. An admixture of 10% or more broadleaved tree species or wind-firm conifers like Douglas fir [Pseudotsuga menziesii (Mirb.) Franco] significantly reduced the vulnerability of spruce stands (by a factor of more than three). On wind-exposed aspects, damage was more than twice the average. Steeper slopes caused a significant reduction in susceptibility (by a factor of six for slopes over 50%, in comparison to gentle slopes <20%). Other factors such as height to diameter ratio of trees or time since last thinning did not appear to be significant predictors.

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If the regulatory requirements are symmetrical, the use of symmetrical confidence intervals as a decision rule for bioequivalence assessment leads, as shown by simulations, to better level properties and an inferior power compared to a rule based on shortest confidence intervals. A choice between these two approaches will have to depend on a loss function. For asymmetric regulatory requirements, symmetrical confidence intervals should not be used; however, a decision can still be based on posterior probabilities, pr (theta epsilon [r1, r2]/x), or shortest confidence intervals. For purposes of inference, presentation and interpretation of results, we think that the use of symmetrical confidence intervals alone can be misleading and we therefore recommend that the posterior probabilities and densities, or at least the shortest confidence intervals, be given.

This study introduces five facets that can improve inference in small area estimation (SAE) problems: (1) model groups, (2) test of area effects, (3) conditional EBLUPs, (4) model selection, and (5) model averaging. Two contrasting case studies with data from the Swiss and Norwegian national forest inventories demonstrate the five facets. The target variable of interest was mean stem volume per hectare on forested land in 108 Swiss forest districts (FD) and in 14 Norwegian municipalities (KOM) in the County of Vestfold. Auxiliary variables from airborne laser scanning (Switzerland) and photogrammetric point clouds (Vestfold) with full coverage and a resolution of 25 m × 25 m (Switzerland) and 16 m × 16 m (Vestfold) were available. Only the data metric mean canopy height was statistically significant. Ten linear fixed-effects models and three mixed linear models were assessed. Area effects were statistically significant in the Swiss case but not in Vestfold case. A model selection based on AIC favored separate linear regression models for each FD and a single common regression model in Vestfold. Model averaging increased, on average, an estimated variance by 15%. Reported estimates of uncertainty were consistently larger than corresponding unconditional EBLUPs.

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