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首页> 外文期刊>Mathematical and Computational Forestry & Natural-Resource Sciences >Quantification and Incorporation of Uncertainty in Forest Growth and Yield Projections Using A Bayesian Probabilistic Framework: A Demonstration for Plantation Coastal Douglas-fir in the Pacific Northwest, USA
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Quantification and Incorporation of Uncertainty in Forest Growth and Yield Projections Using A Bayesian Probabilistic Framework: A Demonstration for Plantation Coastal Douglas-fir in the Pacific Northwest, USA

机译：使用贝叶斯概率框架的森林生长和产量预测中不确定性的量化与融合作用：美国太平洋西北地区沿海道格拉斯 - 杉木的演示

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A Bayesian probabilistic modeling platform was used and evaluated for application in arelatively complex individual-tree growth and yield model for coastal Douglas-fir (Pseudotsuga menziesiivar. menziesii (Mirb.) Franco), which was expressed as a mixed discrete and continuous Bayesian Networkfor annual projections. The modeling platform used a common and open-source Bayesian analysis program(JAGS v3.3.0), and was sufficiently flexible to handle a relatively complex model structure; namely, a differential form, highly dynamic, recursive, hierarchical, non-linear system of equations with rather complexerror structures. This novel probabilistic modeling platform met certain desirable criteria, including: (1)accurate and tractable projections that included full error propagation; (2) flexible and comprehensiveanalytic capabilities; (3) full consideration of hierarchical and multi-level model structures; (4) capacityfor random effects calibration; (5) allowance of hypothesis testing and updating knowledge across differentsystem components, simultaneously with varying sources of information (i.e., new data); (6) computationalefficiency; and (7) relatively simple implementation as demonstrated in a compiled scripting language.Probabilistic projections of forest growth and yield included all sources of errors and uncertainty (e.g.,estimated parameters, state variables, random effects, and residual errors). Cumulative error projectionsover a 40-year period for three sample Douglas-fir stands were determined. Projection errors for keymetrics summed across all trees, such as total basal area and stem density, had coefficient of variationsbetween 4-6% and 7-8%, respectively. Probabilistic projections were markedly different from deterministicprojections made with the same model structure. Overall, this novel probabilistic platform showed strongpromise as a general platform for ecological modeling, particularly when tractable and analytically correcterror projections are required. In particular, the Bayesian probabilistic modeling approach used provided anatural platform for cross-disciplinary research, particularly between social and ecological research domains.
机译：使用贝叶斯概率造型平台，并评估沿海道格拉斯 - 冷杉的阶级性复杂的单独树生长和产量模型（Pseudotsuga menziesiivar。Menziesii（MiRB。）Franco），其表达为一年一度的混合离散和连续的贝叶斯网络投影。建模平台使用普通和开源贝叶斯分析程序（JAGS V3.3.0），并且足够灵活地处理相对复杂的模型结构;即，具有相当复杂的布兰结构的差分形式，高度动态，递归，分层，非线性方程式的非线性系统。这种新颖的概率建模平台符合某些理想的标准，包括：（1）包括完全误差传播的准确和易易触发的投影; （2）灵活和综合敏感功能; （3）完全考虑分层和多级模型结构; （4）随机效应校准的能力; （5）对不同系统组件的假设测试和更新知识的允许，同时具有不同的信息来源（即，新数据）; （6）计算效率; （7）相对简单的实施方式，如编译的脚本语言所示。森林增长的服务器投影和产量包括所有错误和不确定性的来源（例如，估计参数，状态变量，随机效应和残余错误）。确定了三个样本道格拉斯冷杉立场的40年期间的累积误差投影。在所有树上汇总的菌的投影误差，例如总基部和茎密度，分别具有4-6％和7-8％的变异系数。概率突起与用相同模型结构制造的确定性分化显着不同。总体而言，这一新颖的概率平台表明，作为生态建模的一般平台，特别是当需要进行贸易和分析的正确误差投影时。特别是，贝叶斯概率建模方法使用了跨学科研究的横跨学科研究，特别是在社会和生态研究领域之间。

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