Shongming Huang

Development of ecoregion-based height-diameter models for white spruce in boreal forests.

Abstract A large number of height–diameter functions were evaluated using felled tree data for white spruce ( Picea glauca (Moench) Voss) grown in Albertas boreal forests. The logistic-type function was found to produce the most satisfactory fits. Residual analyses were conducted to identify the error structure of the model. A weighting factor of w i =1/ H 3/2 i was found appropriate for satisfying the equal error variance assumption. Regional differences in the height–diameter models were examined and tested using the nonlinear extra sum of squares method. It was found that height–diameter models were different among different ecoregions. Incorrectly applying a height–diameter model fitted from one ecoregion to different ecoregions resulted in overestimations between 1.10% and 29.05%, or underestimations between 1.92% and 21.92%.

Modeling individual tree mortality for white spruce in Alberta

Abstract An individual tree mortality model, based on the provincial PSP data, was developed for white spruce ( Picea glauca (Moench) Voss), an important tree species in Alberta, Canada. Annual probability of survival was modeled by a generalized logistic function with the measurement interval length as a predictor variable to overcome the problem of unequal measurement intervals. Potential predictor variables were selected based on their ecological importance to tree mortality and unknown parameters were estimated using the maximum likelihood method. A U-shaped mortality trend was captured by diameter and diameter squared. Annual diameter increment was used to indicate tree vigor. Basal area of larger trees and a ratio of basal area of larger broadleaf trees to stand total basal area were both used to capture competition from neighboring trees. The newly developed mortality function outperformed the old one previously used in the Mixedwood Growth Model (MGM) based on both goodness-of-fit and prediction statistics.

Accounting for serial correlation and its impact on forecasting ability of a fixed- and mixed-effects basal area model: a case study

Successfully accounting for serial correlations has always been a vital part of growth and yield modeling when using repeated measurement data. In this case study, 16 alternative functions addressing the serial correlations of errors from a basal area model of black spruce (Picea mariana (Mill.) B.S.P.) were examined and compared. Results from this study showed that functions incorporated into the fixed and mixed models to account for the serial correlations improved model fit. The serial correlation of the residuals from the fixed model with directly modeled error structure was significantly lower than that from the fixed model without a modeled error structure. For the mixed model, modeling error structure resulted in only a moderate reduction in serial correlation of residuals. The comparison of the fixed and mixed models with and without directly modeling the error structure showed that for fixed model, a substantial improvement in forecasting ability was achieved when the error structure was directly modeled to account for serial correlation, and when the forecasts were adjusted based on the estimated correlation. But for the mixed model, further modeling of the error structure to account for more serial correlation resulted in worsened or comparative forecasting ability of the fitted model.

A STEM PROFILE MODEL CALIBRATED BY NONLINEAR MIXED-EFFECTS MODELING

A stem profile model was developed for black spruce (Picea mariana (Mill.) B.S.P.) trees in Alberta, Canada using a nonlinear mixed model approach. The model included two random parameters to capture between-subject variation and a general covariance structure to model within-subject residual autocorrelation. After evaluating various covariance structures, the 4-banded toeplitz and the spatial power structures were chosen for further evaluation. The 4banded toeplitz structure provided a better fit. The model was further evaluated using an independent data set to examine its validation accuracy. Model validation results showed that the model was able to accurately predict stem diameters at the population and subject-specific levels. Both covariance structures produced reliable model predictions, but the spatial power structure was superior to the 4-banded toeplitz structure. One to four stem diameters were used to predict random parameters and to subsequently generate subject-specific predictions. At least three stem diameters were needed to achieve better subject-specific predictions than population-average predictions.