Search

Copyright of Forestry Chronicle is the property of Canadian Institute of Forestry and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Estimating prediction uncertainty for a single tree-based model is hindered by the complex structure of these models. In this paper, we addressed this issue with a case study applied to northern hardwood stands in Québec, Canada. SaMARE is a stochastic single tree-based model that was designed for these types of stands. Using a Monte Carlo approach, the model can provide a mean predicted value and its confidence limits for some plot-level attributes. The mean predicted values were compared to observed values in terms of bias and accuracy. In addition to these common statistics, we compared nominal coverage of Monte Carlo-simulated confidence intervals with real (observed) coverage to verify the adequacy of the simulated uncertainty. A comparison was made using several plot-level attributes, which exhibited an increasing discriminative complexity. This complexity ranges from coarse attributes, such as all-species basal area, up to more complex ones, such as basal area for stems of a particular species and with sawlog potential. The results showed that in terms of absolute value, biases were small, but could be relatively high with respect to the average observed value when the discriminative complexity of the attribute increased. The comparison between nominal and real coverage of confidence intervals gave satisfactory results for all-species plot-level attributes. However, for some species-specific attributes, the Monte Carlo-simulated confidence intervals overestimated the real coverage. [Copyright &y& Elsevier]

Modelling volumes by log grades in standing hardwood trees is often hindered by the nature of the response variable. In this paper, we used a two-part conditional model to account for the excess of zero responses for some log grades. Moreover, this approach was used as a framework to compare three different tree classifications in their ability to predict volumes by log grades in standing yellow birch (Betula alleghaniensis Britton) and sugar maple (Acer saccharum Marsh.) trees. A tree grade classification was compared with two preharvest tree classifications based on mortality risk assessment. A cross-validation was also carried out to evaluate the two parts of the model. The results showed that the two-part conditional approach was efficient in this case study. Compared with a general model, the three classifications improved the maximum likelihood. According to the Akaike and Bayesian information criteria, the tree grade classification was the “best” one. All three classifications proved to be better able to distinguish log grade occurrence than log grade volume. Although it implies additional cost, the implementation of the tree grade classification into the preharvest inventories would improve the prediction of volumes by log grades for yellow birch and sugar maple trees. [ABSTRACT FROM AUTHOR]

Copyright of Forestry Chronicle is the property of Canadian Institute of Forestry and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In this study, a diameter increment model was calibrated for individual trees in hardwood stands in Quebec, Canada. Two random effects, a first one for the plot and a second one for the time interval nested in the plot, and a covariance structure were included in the model to account for spatial and serial correlations. The diameter at breast height, species group, vigor and product classes, and basal area were the explanatory variables that were tested in this analysis. The adequacy of the covariance features (random effects and covariance structure) were tested through empirical correlations calculated from normalized residuals. A cross-validation was also carried out to evaluate the model. The normalized residuals showed no departure from the assumption of independently and normally distributed error terms with homogeneous variances. Consequently, the statistical inferences could be considered as valid. The results showed that the average diameter increment pattern differs among the species. Although tree product and vigor were significant explanatory variables, their effects were relatively small. On the other hand, basal area had a large and significant negative effect on diameter increment. Our study demonstrates that empirical correlations calculated from normalized residuals can be used as an additional tool to test the adequacy of the covariance features in a mixed-effects model. [ABSTRACT FROM AUTHOR]