Technical University in Zvolen, T.G. Masaryka 24, 96001 Zvolen, Slovakia; National Forest Centre, T.G. Masaryka 22, 96001 Zvolen, Slovakia.
Technical University in Zvolen, T.G. Masaryka 24, 96001 Zvolen, Slovakia; Departamento de Sistemas y Recursos Naturales, Escuela Técnica Superior de Ingeniería de Montes, Forestal y del Medio Natural, Universidad Politécnica de Madrid, Ciudad Universitaria s/n, 28040 Madrid, Spain.
Sci Total Environ. 2023 Aug 25;888:164123. doi: 10.1016/j.scitotenv.2023.164123. Epub 2023 May 13.
Process-based models and empirical modelling techniques are frequently used to (i) explore the sensitivity of tree growth to environmental variables, and (ii) predict the future growth of trees and forest stands under climate change scenarios. However, modelling approaches substantially influence predictions of the sensitivity of trees to environmental factors. Here, we used tree-ring width (TRW) data from 1630 beech trees from a network of 70 plots established across European mountains to build empirical predictive growth models using various modelling approaches. In addition, we used 3-PG and Biome-BGCMuSo process-based models to compare growth predictions with derived empirical models. Results revealed similar prediction errors (RMSE) across models ranging between 3.71 and 7.54 cm of basal area increment (BAI). The models explained most of the variability in BAI ranging from 54 % to 87 %. Selected explanatory variables (despite being statistically highly significant) and the pattern of the growth sensitivity differed between models substantially. We identified only five factors with the same effect and the same sensitivity pattern in all empirical models: tree DBH, competition index, elevation, Gini index of DBH, and soil silt content. However, the sensitivity to most of the climate variables was low and inconsistent among the empirical models. Both empirical and process-based models suggest that beech in European mountains will, on average, likely experience better growth conditions under both 4.5 and 8.5 RCP scenarios. The process-based models indicated that beech may grow better across European mountains by 1.05 to 1.4 times in warmer conditions. The empirical models identified several drivers of tree growth that are not included in the current process-based models (e.g., different nutrients) but may have a substantial effect on final results, particularly if they are limiting factors. Hence, future development of process-based models may build upon our findings to increase their ability to correctly capture ecosystem dynamics.
(i) 探索树木生长对环境变量的敏感性，(ii) 预测树木和森林在气候变化情景下的未来生长情况。然而，建模方法会极大地影响树木对环境因素敏感性的预测。在这里，我们使用了来自欧洲山区 70 个样地网络中的 1630 棵山毛榉的树木年轮宽度 (TRW) 数据，使用各种建模方法构建了经验预测生长模型。此外，我们还使用了 3-PG 和 Biome-BGCMuSo 基于过程的模型，将生长预测与衍生的经验模型进行比较。结果表明，模型之间的预测误差 (RMSE) 相似，范围在 3.71 到 7.54 cm2 的基面积增量 (BAI) 之间。模型解释了 BAI 大部分可变性，范围从 54%到 87%。选定的解释变量(尽管在统计学上具有高度显著性)和生长敏感性模式在模型之间有很大的差异。我们只在所有经验模型中确定了五个具有相同影响和相同敏感性模式的因素：树木胸径、竞争指数、海拔、胸径基尼指数和土壤粉砂含量。然而，对大多数气候变量的敏感性较低，并且在经验模型之间不一致。经验模型和基于过程的模型都表明，欧洲山区的山毛榉在 4.5 和 8.5 RCP 情景下，平均来说可能会有更好的生长条件。基于过程的模型表明，在温暖的条件下，山毛榉在整个欧洲山区的生长可能会更好，增长幅度在 1.05 到 1.4 倍之间。经验模型确定了一些树木生长的驱动因素，这些因素不包括在当前基于过程的模型中(例如，不同的养分)，但可能对最终结果产生实质性影响，特别是如果它们是限制因素。因此，未来基于过程的模型的发展可能会基于我们的研究结果，以提高其正确捕捉生态系统动态的能力。
