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.
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机译:英格曼云杉(Picea engelmannii Parry ex Engelm。)是在加拿大西部和美国西部发现的一种高海拔物种。由于该物种越来越成为收获的目标,因此需要更好的身高生长信息以更好地管理该物种。启动该项目来满足这一需求。该项目的目标是三方面的:为恩格尔曼云杉开发场地指数模型;比较三个模型公式和四个参数化之间的拟合,建模和应用问题;并更仔细地研究扎根的通用代数差分法(g-GADA)模型参数化。模型拟合数据由在整个恩格尔曼云杉–亚高山冷杉生物地理气候带采样的84棵经分析的恩格尔曼云杉站点树组成。拟合模型基于Chapman-Richards函数,改良的Hossfeld IV函数和Schumacher函数。测试的模型参数是指标变量,混合效果,GADA和g-GADA。模型评估基于Akaike信息准则的有限样本校正版本和估计的方差。模型参数化对拟合的影响比模型公式更大,指标变量方法提供了最佳拟合,其次是混合效应建模(Chapman-Richards和Schumacher公式的方差比指标增加了9%变量参数化),g-GADA(最佳方法)(方差增加335%)和GADA / g-GADA(使用GADA参数化)(方差增加346%)。选择要使用的模型时,必须考虑与模型应用有关的因素,因为就数据和软件要求而言,最佳拟合方法在应用中会遇到最大障碍。
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