Secondary forest regrowth shapes community succession and biogeochemistry for decades, including in the Upper Great Lakes region. Vegetation models encapsulate our understanding of forest function, and whether models can reproduce multi‐decadal succession patterns is an indication of our ability to predict forest responses to future change. We test the ability of a vegetation model to simulate C cycling and community composition during 100 years of forest regrowth following stand‐replacing disturbance, asking (a) Which processes and parameters are most important to accurately model Upper Midwest forest succession? (b) What is the relative importance of model structure versus parameter values to these predictions? We ran ensembles of the Ecosystem Demography model v2.2 with different representations of processes important to competition for light. We compared the magnitude of structural and parameter uncertainty and assessed which sub‐model–parameter combinations best reproduced observed C fluxes and community composition. On average, our simulations underestimated observed net primary productivity (NPP) and leaf area index (LAI) after 100 years and predicted complete dominance by a single plant functional type (PFT). Out of 4,000 simulations, only nine fell within the observed range of both NPP and LAI, but these predicted unrealistically complete dominance by either early hardwood or pine PFTs. A different set of seven simulations were ecologically plausible but under‐predicted observed NPP and LAI. Parameter uncertainty was large; NPP and LAI ranged from ~0% to >200% of their mean value, and any PFT could become dominant. The two parameters that contributed most to uncertainty in predicted NPP were plant–soil water conductance and growth respiration, both unobservable empirical coefficients. We conclude that (a) parameter uncertainty is more important than structural uncertainty, at least for ED‐2.2 in Upper Midwest forests and (b) simulating both productivity and plant community composition accurately without physically unrealistic parameters remains challenging for demographic vegetation models.

中文翻译：

---

森林群落结构和碳循环百年预测的结构和参数不确定性。

几十年来，次生森林的再生长影响着社区的演替和生物地球化学，包括上大湖地区。植被模型封装了我们对森林功能的理解，而模型是否可以重现数十年的演替模式则表明了我们预测森林对未来变化的反应的能力。我们测试了植被模型在林分替换干扰后的100年森林再生长中模拟碳循环和群落组成的能力，并询问（a）哪些过程和参数对于准确模拟中西部上演森林演替最重要？（b）对于这些预测，模型结构相对于参数值的相对重要性是什么？我们以生态竞争人口模型v2.2的集合形式运行，这些竞争过程对光竞争很重要。我们比较了结构和参数不确定性的大小，并评估了哪种子模型参数组合最能再现观察到的C通量和群落组成。平均而言，我们的模拟低估了100年后的净初级生产力（NPP）和叶面积指数（LAI），并预测了单一植物功能类型（PFT）的完全优势地位。在4,000个模拟中，只有9个处于NPP和LAI的观测范围内，但是这些预测都预测早期硬木或松木PFT会完全不占优势。从生态学角度看，一组不同的七个模拟是合理的，但观测到的NPP和LAI却被低估。参数不确定性大；NPP和LAI介于平均值的〜0％到> 200％之间，任何PFT都可能成为主导。导致预测NPP不确定性最大的两个参数是植物-土壤水的电导率和生长呼吸，这两个都是不可观察到的经验系数。我们得出的结论是：（a）参数不确定性比结构不确定性更重要，至少对于中西部中部森林的ED-2.2而言更是如此；（b）在没有物理不现实参数的情况下准确地模拟生产力和植物群落组成对于人口植被模型仍然具有挑战性。