• Key message

Parametric indirect models derived from stem analysis of dominant trees were more robust than rule-based machine learning techniques for predicting Site Index of Scots pine stands as a function of climate.

• Context

The uncertainties derived from climate change make it necessary to develop new methods for representing the relationships between site conditions and forest growth.

• Aims

To compare parametric vs nonparametric approaches for modeling site index (SI) of Scots pine stands using bioclimatic variables.

• Methods

We used Random Forest, Boosted Trees, and Cubist techniques for directly predicting the SI of 41 research plots of Scots pine stands, and six parametric models for indirectly predicting SI using stem analysis data. As predictors, we used raster maps of 19 bioclimatic variables.

• Results

The fitted models explained up to \(\sim\)80% of the SI variability, using from five to nine bioclimatic predictors. Though the apparent performance of the parametric models was lower than the rule-based, their bootstrap validation statistics were noticeably higher.

• Conclusion

Parametric indirect models seemed to be the most robust modeling alternative.

中文翻译：

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建立基于气候的站点指数模型的基于规则与参数的方法：西班牙西北部苏格兰松林的案例研究

• 关键信息

从优势树的茎分析中得出的参数间接模型比基于规则的机器学习技术更健壮，可以预测苏格兰松树的立地指数随气候变化。

• 语境

气候变化带来的不确定性使得有必要开发新的方法来表示工地条件与森林生长之间的关系。

•目的

为了比较使用生物气候变量对苏格兰松树的立地指数（SI）进行建模的参数方法和非参数方法。

• 方法

我们使用随机森林，助推树和立体派技术直接预测41个苏格兰松树林研究区的SI，并使用六个参数模型使用茎分析数据间接预测SI。作为预测指标，我们使用了19个生物气候变量的栅格图。

• 结果

拟合的模型使用五到九种生物气候预测因子解释了高达（（sim））的SI变异的80％。尽管参数模型的表观性能低于基于规则的模型，但其自举验证统计数据明显更高。

• 结论

参数间接模型似乎是最可靠的建模替代方案。