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Four variable-exponent taper equations and their modified forms were evaluated for lodgepole pine (Pinus contorta var. latifolia Engelm.) trees in Alberta, Canada. A nonlinear mixed-effects modeling approach was applied to account for within-and between-tree variations in stem form. Even though a direct modeling of within-tree autocorrelation by a variance-covariance structure failed to achieve convergence, most of the autocorrelation was accounted for when random-effects parameters were included in the models. Using an independent data set, the best taper equation with two random-effects parameters was chosen based on its ability to predict diameter inside bark, whole tree volume, and sectioned log volume. Diameter measurements from various stem locations were evaluated for tree-specific calibrations by predicting random-effects parameters using an approximate Bayesian estimator. It was found that an upper stem diameter at 5.3 m above ground was best suited for calibrating treespecific predictions of diameter inside bark, whole tree volume, and sectioned log volume.

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Model validation is an important part of model development. It is performed to increase the credibility and gain sufficient confidence about a model. This paper evaluated the usefulness of 10 statistical tests, five parametric and five nonparametric, in validating forest biometric models. The five parametric tests are the paired t test, the Χ2 test, the separate t test, the simultaneous F test, and the novel test. The five nonparametric tests are the Brown-Mood test, the Kolmogorov–Smirnov test, the modified Kolmogorov–Smirnov test, the sign test, and the Wilcoxon signed-rank test. Nine benchmark data sets were selected to evaluate the behavior of these tests in model validation; three were collected from Alberta and six were published elsewhere. It was shown that the usefulness of statistical tests in model validation is very limited. None of the tests seems to be generic enough to work well across a wide range of models and data. Each model passed one or more tests, but not all of them. Because of this, caution should be exercised when choosing a statistical test or several tests together to try to validate a model. It is important to reduce and remove any potential personal bias in selecting a favorite test, which can influence the outcome of the results.

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We estimated the impact of global climate change on lodgepole pine (Pinus contorta Dougl. ex Loud. var. latifolia Engelm.) site productivity in Alberta based on the Alberta Climate Model and the A2 SRES climate change scenario projections from three global circulation models (CGCM2, HADCM3, and ECHAM4). Considerable warming is apparent in all three models. On average, the increases in mean GDD5 (growing degree-days >5 8C) are 18%, 38%, and 65% by the 2020s, 2050s, and 2080s, respectively. Change in precipitation is essentially nil. This results in proportional increases in dryness index. We used the dryness index to predict the potential future range and GDD5 to predict its potential productivity. Generally, lodgepole pine site index is predicted to increase steadily by 3 m for each 30-year period. Offsetting this increase is a large reduction in suitable area as drying increases. At first, the warming increases the potential range up to 67% by the 2020s but then shrinks from 34% to 58% of its current area by 2080. Such major changes will need to be considered when setting long-term forest management plans. The increased risk of both wildfire and insect outbreaks further compounds this planning problem, especially because these disturbance events can interact and further increase risk.Résumé : Nous avons estimé l'impact des changements climatiques sur la productivité de stations dominées par le pin tordu latifolié (Pinus contorta Dougl. ex Loud. var. latifolia Engelm.), en Alberta, en se basant sur le modèle climatique de l'Alberta et les projections du scénario de changements climatiques A2 SRES de trois modèles de circulation globale (CGCM2, HADCM3 et ECHAM4). Un réchauffement considérable est apparent dans les trois modèles. En moyenne, l'augmentation du nombre moyen de degrés-jours de croissance au dessus de 5 8C (DJC 5 ) est de respectivement de 18 %, 38 % et 65 % pour les années 2020, 2050 et 2080. Les changements de précipitation sont essentiellement nuls. Cette situation engendre une augmentation proportionnelle de l'indice d'aridité. Nous avons utilisé l'indice d'aridité pour prédire l'étendue potentielle future et le DJC 5 pour prédire la productivité potentielle. Généralement, l'indice de qualité de station du pin tordu devrait augmenter régulièrement de 3 m pour chaque période de 30 ans. Cette augmentation est compensée par une forte réduction de la superficie productive à mesure que l'aridité augmente. Dans un premier temps, le réchauffement augmente l'étendue potentielle jusqu'à 67 % vers les années 2020, mais la réduit par la suite pour atteindre 34 % à 58 % de la superficie actuelle vers 2080. Des changements aussi importants devront être considérés lors de l'élaboration de plans d'aménagement forestier à long terme. L'augmentation des risques de feu et d'épidémie d'insecte complique davantage ce problème de planification surtout parce que ces perturbations peuvent interagir entre elles et augmenter encore davantage les risques.[Traduit par la Rédaction]