Dynamic vegetation models (DVMs) are important tools to understand and predict the functioning and dynamics of terrestrial ecosystems under changing environmental conditions. In these models, uncertainty in the description of demographic processes, in particular tree mortality, is a persistent problem. Current mortality formulations lack realism and are insufficiently constrained by empirical evidence. It has been suggested that empirically estimated mortality submodels would enhance DVM performance, but due to the many processes and interactions within a DVM, the claim has rarely been tested. Here, we compare the performance of three alternative growth-dependent tree mortality submodels in the DVM ForClim: (1) a mortality function with theoretical foundation (ForClim v3.3); (2) a mortality function with parameters directly estimated based on forest inventory data; and (3) the same function, but with parameters estimated using an inverse approach through Bayesian calibration (BC). Time series of inventory data from 30 ecologically distinct Swiss natural forest reserves collected over 35+ yr, including the main tree species of Central Europe, were used for the calibration and subsequent validation of the mortality functions and the DVM. The recalibration resulted in mortality parameters that differed from the direct empirical estimates, particularly for the relationship between tree size and mortality. The calibrated parameters outperformed the direct estimates, and to a lesser extent the original mortality function, for predicting decadal-scale forest dynamics at both calibration and validation sites. The same pattern was observed regarding the plausibility of their long-term projections under contrasting environmental conditions. Our results demonstrate that inverse calibration may be useful even when direct empirical estimates of DVM parameters are available, as structural model deficiencies or data problems can result in discrepancies between direct and inverse estimates. Thus, we interpret the good performance of the inversely calibrated model for long-term projections (which were not a calibration target) as evidence that the calibration did not compensate for model errors. Rather, we surmise that the discrepancy was mainly caused by a lack of representativeness of the mortality data. Our results underline the potential for learning more about elusive processes, such as tree mortality or recruitment, through data integration in DVMs.

中文翻译：

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贝叶斯校准的依赖于生长的树木死亡率模型，用于模拟欧洲温带森林的动态。

动态植被模型（DVM）是在变化的环境条件下了解和预测陆地生态系统功能和动态的重要工具。在这些模型中，人口统计过程描述的不确定性，特别是树木的死亡率，是一个持续存在的问题。当前的死亡率公式缺乏现实性，并且没有足够的经验证据约束。有人提出，凭经验估算的死亡率子模型将提高DVM的性能，但是由于DVM内的许多过程和相互作用，因此对该索赔的检验很少。在这里，我们比较了DVM ForClim中三个替代的依赖于生长的树木死亡率子模型的性能：（1）具有理论基础的死亡率函数（ForClim v3.3）；（2）具有直接基于森林清单数据估算的参数的死亡率函数；（3）相同的功能，但参数通过贝叶斯校准（BC）使用逆方法估算。来自超过35年的30种生态上独特的瑞士天然林保护区（包括中欧的主要树种）收集的清单数据的时间序列用于校准和随后验证死亡率函数和DVM。重新校准导致的死亡率参数不同于直接的经验估计值，尤其是树木大小与死亡率之间的关系。在预测标定和验证地点的十年尺度森林动态时，标定参数的性能优于直接估计值，并且在较小程度上优于原始死亡率函数。在不同的环境条件下，就其长期预测的合理性观察到了相同的模式。我们的结果表明，即使可以使用DVM参数的直接经验估算，逆校正也可能有用，因为结构模型缺陷或数据问题可能会导致直接估算和逆估算之间出现差异。因此，我们将反向校准模型的长期预测（不是校准目标）的良好性能解释为校准不能补偿模型误差的证据。相反，我们推测差异主要是由于死亡率数据缺乏代表性所致。我们的结果强调了通过DVM中的数据集成来了解有关树木死亡或募集等难以捉摸的过程的更多潜力。