• Key message

We successfully transformed Pinus sylvestris yield tables into diameter distribution models. The best results were obtained with the parameter recovery method based on both mean and quadratic mean diameter, which explained 70% of the variability of frequencies by diameter classes and provided better results in the analysis of errors. On the other hand, the method based on stand density, dominant diameter and quadratic mean diameter explained less variability of frequencies by diameter classes (64.4%).

• Context

Old datasets used to develop yield table models can be recovered to transform those yield tables into diameter distribution models that provide a more detailed description of size variability and forest structure.

• Aims

We used archived measurements collected to develop yield table models for Pinus sylvestris L in central Spain, to transform those yield tables into a diameter distribution model by using parameter recovery methods.

• Methods

We compared two different parameter recovery methods, one based on both mean and quadratic mean diameter and another one based on dominant diameter, stand density and quadratic mean diameter and used a set of 104 even aged plots to analyze the performance of the said methods for the transformation of Pinus sylvestris L yield tables in central Spain into a diameter distribution model.

• Results

The parameter recovery method based on both mean and quadratic mean diameter explained 70% of the variability of frequencies by diameter classes and provided better results than the method based on stand density, dominant diameter and quadratic mean diameter that explained 64.4% of the variability of frequencies by diameter classes. However, more important than the method itself were the errors that propagated from the models predicting the different variables used in the parameter recovery.

• Conclusion

Based on the results from the analysis of errors by diameter classes, the method using both mean and quadratic mean diameter outperformed the method using dominant diameter, stand density and quadratic mean diameter and is the best option to transform P. sylvestris yield tables into diameter distribution models.

中文翻译：

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樟子松产量表转换为直径分布模型的两种参数恢复方法的比较

• 关键信息

我们成功地将樟子松产量表转换为直径分布模型。使用基于均值和二次均值的参数恢复方法可获得最佳结果，该方法可解释按直径类别划分的频率变化的70％，并在误差分析中提供更好的结果。另一方面，基于林分密度，优势直径和二次平均直径的方法解释了按直径类别划分的频率变化较小（64.4％）。

•上下文

可以恢复用于开发产量表模型的旧数据集，以将这些产量表转换为直径分布模型，从而更详细地描述大小可变性和森林结构。

•目的

我们使用收集的存档测量结果为西班牙中部的樟子松（Pinus sylvestris L）开发产量表模型，通过使用参数恢复方法将这些产量表转换为直径分布模型。

• 方法

我们比较了两种不同的参数恢复方法，一种基于均值和二次平均直径，另一种基于主直径，林分密度和二次平均直径，并使用一组104个甚至老化的图来分析所述方法在处理数据时的性能。将西班牙中部的樟子松产量表转换为直径分布模型。

•结果

基于均值和二次平均直径的参数恢复方法解释了按直径类别划分的频率变化的70％，并且比基于林分密度，主导直径和二次平均直径的解释频率变化64.4％的方法提供了更好的结果按直径分类。但是，比方法本身更重要的是从模型传播的误差，这些误差预测了参数恢复中使用的不同变量。

•结论

根据按直径类别进行的误差分析的结果，使用均值和二次平均直径的方法优于使用主直径，林分密度和二次平均直径的方法，并且是将樟子松产量表转换为直径分布的最佳选择楷模。