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Engelmann spruce site index models: a comparison of model functions and parameterizations.

Nigh G - PLoS ONE (2015)

Bottom Line: Engelmann spruce (Picea engelmannii Parry ex Engelm.) is a high-elevation species found in western Canada and western USA.The model parameterizations that were tested are indicator variables, mixed-effects, GADA, and g-GADA.Model parameterization had more of an influence on the fit than did model formulation, with the indicator variable method providing the best fit, followed by the mixed-effects modelling (9% increase in the variance for the Chapman-Richards and Schumacher formulations over the indicator variable parameterization), g-GADA (optimal approach) (335% increase in the variance), and the GADA/g-GADA (with the GADA parameterization) (346% increase in the variance).

View Article: PubMed Central - PubMed

Affiliation: Forest Analysis and Inventory Branch, British Columbia Ministry of Forests, Lands and Natural Resource Operations, Victoria, British Columbia, Canada.

ABSTRACT
Engelmann spruce (Picea engelmannii Parry ex Engelm.) is a high-elevation species found in western Canada and western USA. As this species becomes increasingly targeted for harvesting, better height growth information is required for good management of this species. This project was initiated to fill this need. The objective of the project was threefold: develop a site index model for Engelmann spruce; compare the fits and modelling and application issues between three model formulations and four parameterizations; and more closely examine the grounded-Generalized Algebraic Difference Approach (g-GADA) model parameterization. The model fitting data consisted of 84 stem analyzed Engelmann spruce site trees sampled across the Engelmann Spruce - Subalpine Fir biogeoclimatic zone. The fitted models were based on the Chapman-Richards function, a modified Hossfeld IV function, and the Schumacher function. The model parameterizations that were tested are indicator variables, mixed-effects, GADA, and g-GADA. Model evaluation was based on the finite-sample corrected version of Akaike's Information Criteria and the estimated variance. Model parameterization had more of an influence on the fit than did model formulation, with the indicator variable method providing the best fit, followed by the mixed-effects modelling (9% increase in the variance for the Chapman-Richards and Schumacher formulations over the indicator variable parameterization), g-GADA (optimal approach) (335% increase in the variance), and the GADA/g-GADA (with the GADA parameterization) (346% increase in the variance). Factors related to the application of the model must be considered when selecting the model for use as the best fitting methods have the most barriers in their application in terms of data and software requirements.

No MeSH data available.


Related in: MedlinePlus

Mean height prediction error (part a) and standard deviation of the height prediction errors (part b) versus breast height age for the three model functions and four model parameterizations.The indicator variable and mixed-effects parameterization lines are nearly identical and are indistinguishable on the graphs for the CR and SCH models. The results for the HIV model with the mixed-effects parameterization is not shown because it produced unreliable height estimates for some trees.
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pone.0124079.g001: Mean height prediction error (part a) and standard deviation of the height prediction errors (part b) versus breast height age for the three model functions and four model parameterizations.The indicator variable and mixed-effects parameterization lines are nearly identical and are indistinguishable on the graphs for the CR and SCH models. The results for the HIV model with the mixed-effects parameterization is not shown because it produced unreliable height estimates for some trees.

Mentions: Fig 1 shows the mean error (part a) and the standard deviation in the errors (part b) in the predicted heights versus breast height age for 11 model/parameterization combinations (the HIV/mixed-effects combination is not presented as explained in the Discussion) to give an indication of the predictive ability of the models. The means and standard deviations were calculated at 5 year intervals but are presented as connected lines to improve readability. The variation in the means and the standard deviations increases as age increases because there are fewer data points to support these statistics.


Engelmann spruce site index models: a comparison of model functions and parameterizations.

Nigh G - PLoS ONE (2015)

Mean height prediction error (part a) and standard deviation of the height prediction errors (part b) versus breast height age for the three model functions and four model parameterizations.The indicator variable and mixed-effects parameterization lines are nearly identical and are indistinguishable on the graphs for the CR and SCH models. The results for the HIV model with the mixed-effects parameterization is not shown because it produced unreliable height estimates for some trees.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4390286&req=5

pone.0124079.g001: Mean height prediction error (part a) and standard deviation of the height prediction errors (part b) versus breast height age for the three model functions and four model parameterizations.The indicator variable and mixed-effects parameterization lines are nearly identical and are indistinguishable on the graphs for the CR and SCH models. The results for the HIV model with the mixed-effects parameterization is not shown because it produced unreliable height estimates for some trees.
Mentions: Fig 1 shows the mean error (part a) and the standard deviation in the errors (part b) in the predicted heights versus breast height age for 11 model/parameterization combinations (the HIV/mixed-effects combination is not presented as explained in the Discussion) to give an indication of the predictive ability of the models. The means and standard deviations were calculated at 5 year intervals but are presented as connected lines to improve readability. The variation in the means and the standard deviations increases as age increases because there are fewer data points to support these statistics.

Bottom Line: Engelmann spruce (Picea engelmannii Parry ex Engelm.) is a high-elevation species found in western Canada and western USA.The model parameterizations that were tested are indicator variables, mixed-effects, GADA, and g-GADA.Model parameterization had more of an influence on the fit than did model formulation, with the indicator variable method providing the best fit, followed by the mixed-effects modelling (9% increase in the variance for the Chapman-Richards and Schumacher formulations over the indicator variable parameterization), g-GADA (optimal approach) (335% increase in the variance), and the GADA/g-GADA (with the GADA parameterization) (346% increase in the variance).

View Article: PubMed Central - PubMed

Affiliation: Forest Analysis and Inventory Branch, British Columbia Ministry of Forests, Lands and Natural Resource Operations, Victoria, British Columbia, Canada.

ABSTRACT
Engelmann spruce (Picea engelmannii Parry ex Engelm.) is a high-elevation species found in western Canada and western USA. As this species becomes increasingly targeted for harvesting, better height growth information is required for good management of this species. This project was initiated to fill this need. The objective of the project was threefold: develop a site index model for Engelmann spruce; compare the fits and modelling and application issues between three model formulations and four parameterizations; and more closely examine the grounded-Generalized Algebraic Difference Approach (g-GADA) model parameterization. The model fitting data consisted of 84 stem analyzed Engelmann spruce site trees sampled across the Engelmann Spruce - Subalpine Fir biogeoclimatic zone. The fitted models were based on the Chapman-Richards function, a modified Hossfeld IV function, and the Schumacher function. The model parameterizations that were tested are indicator variables, mixed-effects, GADA, and g-GADA. Model evaluation was based on the finite-sample corrected version of Akaike's Information Criteria and the estimated variance. Model parameterization had more of an influence on the fit than did model formulation, with the indicator variable method providing the best fit, followed by the mixed-effects modelling (9% increase in the variance for the Chapman-Richards and Schumacher formulations over the indicator variable parameterization), g-GADA (optimal approach) (335% increase in the variance), and the GADA/g-GADA (with the GADA parameterization) (346% increase in the variance). Factors related to the application of the model must be considered when selecting the model for use as the best fitting methods have the most barriers in their application in terms of data and software requirements.

No MeSH data available.


Related in: MedlinePlus