Tree mortality plays a fundamental role in the dynamics of forest ecosystems, yet it is one of the most difficult phenomena to accurately predict. Various modeling strategies have been developed to improve individual tree mortality predictions. One less explored strategy is the use of a multistage modeling approach. Potential improvements from this approach have remained largely unknown. In this study, we developed a novel multistage approach and compared its performance in individual tree mortality predictions with a more conventional approach using an identical individual tree mortality model formulation. Extensive permanent plot data (n ​= ​9442) covering the Acadian Region of North America and over multiple decades (1965–2014) were used in this study. Our results indicated that the model behavior with the multistage approach better depicted the observed mortality and showed a notable improvement over the conventional approach. The difference between the observed and predicted numbers of dead trees using the multistage approach was much smaller when compared with the conventional approach. In addition, tree survival probabilities predicted by the multistage approach generally were not significantly different from the observations, whereas the conventional approach consistently underestimated mortality across species and overestimated tree survival probabilities over the large range of DBH in the data. The new multistage approach also predictions of zero mortality in individual plots, a result not possible in conventional models. Finally, the new approach was more tolerant of modeling errors because it based estimates on ranked tree mortality rather than error-prone predicted values. Overall, this new multistage approach deserves to be considered and tested in future studies.

树木死亡率在森林生态系统的动态变化中起着重要作用，但它是最难准确预测的现象之一。已经开发了各种建模策略来改进个体树木死亡率预测。一种较少探索的策略是使用多阶段建模方法。这种方法的潜在改进在很大程度上仍然未知。在这项研究中，我们开发了一种新颖的多阶段方法，并将其在个体树木死亡率预测中的性能与使用相同个体树木死亡率模型公式的更传统方法进行了比较。广泛的永久地块数据（n = 9442）覆盖北美阿卡迪亚地区，并在本研究中使用了数十年（1965-2014）。我们的结果表明，采用多阶段方法的模型行为更好地描述了观察到的死亡率，并且显示出比传统方法有显着改进。与传统方法相比，使用多阶段方法观察到的和预测的死树数量之间的差异要小得多。此外，多阶段方法预测的树木存活概率通常与观察结果没有显着差异，而传统方法始终低估了物种间的死亡率，并高估了数据中大范围 DBH 的树木存活概率。新的多阶段方法还预测单个地块的死亡率为零，这是传统模型无法实现的结果。最后，新方法对建模错误的容忍度更高，因为它基于对排名靠前的树木死亡率的估计，而不是容易出错的预测值。总的来说，这种新的多阶段方法值得在未来的研究中加以考虑和测试。