Department of Environmental Systems Science, Forest Ecology, Swiss Federal Institute of Technology (ETH Zurich), Universitätstrasse 16, 8092, Zürich, Switzerland.
Forest Resources and Management, Sustainable Forestry, Swiss Federal Research Institute (WSL), Zürcherstrasse 111, 8903, Birmensdorf, Switzerland.
Sci Rep. 2021 Oct 6;11(1):19845. doi: 10.1038/s41598-021-98880-2.
Tree mortality is key for projecting forest dynamics, but difficult to portray in dynamic vegetation models (DVMs). Empirical mortality algorithms (MAs) are often considered promising, but little is known about DVM robustness when employing MAs of various structures and origins for multiple species. We analysed empirical MAs for a suite of European tree species within a consistent DVM framework under present and future climates in two climatically different study areas in Switzerland and evaluated their performance using empirical data from old-growth forests across Europe. DVM projections under present climate showed substantial variations when using alternative empirical MAs for the same species. Under climate change, DVM projections showed partly contrasting mortality responses for the same species. These opposing patterns were associated with MA structures (i.e. explanatory variables) and occurred independent of species ecological characteristics. When comparing simulated forest structure with data from old-growth forests, we found frequent overestimations of basal area, which can lead to flawed projections of carbon sequestration and other ecosystem services. While using empirical MAs in DVMs may appear promising, our results emphasize the importance of selecting them cautiously. We therefore synthesize our insights into a guideline for the appropriate use of empirical MAs in DVM applications.
树木死亡率是预测森林动态的关键，但在动态植被模型（DVM）中很难描述。经验死亡率算法（MA）通常被认为很有前途，但对于在不同结构和起源的 MA 下，为多种物种使用 DVM 时的稳健性知之甚少。我们在瑞士两个气候差异较大的研究区域内，使用一致的 DVM 框架，分析了一套欧洲树种的经验死亡率算法，并使用欧洲古老森林的经验数据评估了它们在当前和未来气候下的性能。当使用不同的 MA 为同一物种建模时，DVM 预测在当前气候下会出现很大的差异。在气候变化下，对于同一物种，DVM 预测的死亡率响应部分相反。这些相反的模式与 MA 结构（即解释变量）有关，与物种的生态特征无关。当将模拟的森林结构与古老森林的数据进行比较时，我们发现基础面积经常被高估，这可能导致碳封存和其他生态系统服务的错误预测。虽然在 DVM 中使用经验死亡率算法似乎很有前途，但我们的结果强调了谨慎选择的重要性。因此，我们将我们的见解综合为在 DVM 应用中使用经验死亡率算法的适当指南。
