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- W2108298562 abstract "Summary Individual-tree basal area increment (BAI) models were developed for major tree species in the boreal forest of Ontario, Canada. A composite distance-independent individual-tree BAI model was structured based on the log-linearized gamma base function using a dataset derived from a network of ~1800 permanent growth plots. A suite of fixed effects covariates that included basal area of trees larger than the subject tree, diameter at breast height, stand basal area and latitude, along with ecological land classification ecosites used as a random effect parameter, were found to be significant covariates in the model. A model fitted with the intercept designated as a mixed-effects parameter had a smaller Akaike’s information criterion compared with other competing models. The mixed-effects modelling approach allowed us to select the best suite of covariates by correctly specifying the covariance structure according to the hierarchy of the fitting data. Furthermore, inclusion of ecosite as a random effect parameter increased ecological resolution in the BAI model by localizing its prediction and improves compatibility between growth and yield modelling and other important management tools such as the forest resources inventory." @default.
- W2108298562 created "2016-06-24" @default.
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- W2108298562 date "2012-01-19" @default.
- W2108298562 modified "2023-10-01" @default.
- W2108298562 title "Mixed-effects basal area increment models for tree species in the boreal forest of Ontario, Canada using an ecological land classification approach to incorporate site effects" @default.
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- W2108298562 doi "https://doi.org/10.1093/forestry/cpr070" @default.
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