Nigh Gordon
Forest Analysis and Inventory Branch, British Columbia Ministry of Forests, Lands and Natural Resource Operations, Victoria, British Columbia, Canada.
PLoS One. 2015 Apr 8;10(4):e0124079. doi: 10.1371/journal.pone.0124079. eCollection 2015.
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.
恩氏云杉（Picea engelmannii Parry ex Engelm.）是一种生长在加拿大西部和美国西部高海拔地区的树种。随着该树种越来越多地成为采伐目标，为了更好地管理该树种，需要更完善的树高生长信息。启动这个项目就是为了满足这一需求。该项目的目标有三个：开发恩氏云杉的地位指数模型；比较三种模型公式和四种参数化方法在拟合、建模及应用方面的问题；更深入地研究地面广义代数差分法（g-GADA）模型参数化。模型拟合数据包括在恩氏云杉-亚高山冷杉生物地理气候区采集的84株经过树干解析的恩氏云杉标准木。拟合模型基于查普曼-理查兹函数、修正的霍斯费尔德IV函数和舒马赫函数。所测试的模型参数化方法有指示变量法、混合效应法、GADA和g-GADA。模型评估基于赤池信息准则的有限样本校正版本和估计方差。模型参数化对拟合的影响比模型公式更大，指示变量法拟合效果最佳，其次是混合效应建模（查普曼-理查兹函数和舒马赫函数公式的方差比指示变量参数化增加9%）、g-GADA（最优方法）（方差增加335%）以及GADA/g-GADA（采用GADA参数化）（方差增加346%）。在选择模型时，必须考虑与模型应用相关的因素，因为最佳拟合方法在数据和软件要求方面的应用障碍最多。
