An individual tree height increment model for mixed white spruce–aspen stands in Alberta, Canada

Abstract

Individual tree-height increment models were developed for white spruce ( Picea glauca (Moench) Voss) and aspen ( Populus tremuloides Michx.) growing in the boreal mixed-species in Alberta. The models were formulated based on a selected base function (the Box–Lucas function), and the method of parameter prediction. Height increment was modeled as a nonlinear function of tree height, tree diameter, diameter increment, stand density, relative competitiveness of the tree in the stand, site productivity, and species composition. Since the data from permanent sample plots used in this study were time-dependent and cross-sectional, diagnostic techniques were applied to identify the models' error structure. Appropriate fits based on the identified error structure were accomplished using the nonlinear least squares procedures with a first-order autoregressive process. The models were also validated on independent testing data sets representing the population on which the models are to be used. Results showed that the average prediction biases were not significantly different from zero at α = 0.05, suggesting that the fitted models appropriately described the data and performed well when predictions were made. Biological implications of the variables that affect height increment in mixed-species stands were discussed.