Models based on national forest inventory (NFI) data intend to project forests under management and policy scenarios. This study aimed at quantifying the influence of NFI sampling uncertainty on parameters and simulations of the demographic model MARGOT. Parameter variance–covariance structure was estimated from bootstrap sampling of NFI field plots. Parameter variances and distributions were further modeled to serve as a plug-in option to any inventory-based initial condition. Forty-year time series of observed forest growing stock were compared with model simulations to balance model uncertainty and bias. Variance models showed high accuracies. The Gamma distribution best fitted the distributions of transition, mortality and felling rates, while the Gaussian distribution best fitted tree recruitment fluxes. Simulation uncertainty amounted to 12% of the model bias at the country scale. Parameter covariance structure increased simulation uncertainty by 5.5% in this 12%. This uncertainty appraisal allows targeting model bias as a modeling priority.

中文翻译：

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在大规模森林人口模型“MARGOT”中建模和传播基于清单的抽样不确定性

基于国家森林清单 (NFI) 数据的模型旨在预测管理和政策情景下的森林。本研究旨在量化 NFI 抽样不确定性对人口统计模型 MARGOT 的参数和模拟的影响。从 NFI 场图的引导抽样估计参数方差 - 协方差结构。参数方差和分布被进一步建模，以作为任何基于库存的初始条件的插件选项。将观察到的森林蓄积量的 40 年时间序列与模型模拟进行比较，以平衡模型的不确定性和偏差。方差模型显示出很高的准确性。Gamma 分布最适合过渡、死亡率和砍伐率的分布，而高斯分布最适合树木补充通量。在国家范围内，模拟不确定性占模型偏差的 12%。参数协方差结构在这 12% 中增加了 5.5% 的模拟不确定性。这种不确定性评估允许将模型偏差作为建模优先级。