Forest growth models are increasingly being used in forestry and ecology research as predictive tools to help developing practical guidelines and to improve understanding of the drivers of forest ecosystem functioning. Models are usually calibrated using parameters directly obtained or estimated from empirical field observation, and hence are subject to uncertainty. Thus, output accuracy depends on input parameters precision and on how influential is each parameter on model behaviour. Hence, it is important to analyse parameter-related uncertainty and its effects on model outputs. This can be done by performing sensitivity analyses, which allow to explore the influence of one or several calibration parameters on model outputs. As studies on tree root parameters are particularly scarce, the aim of the present work was to evaluate the influence of parameters related to fine roots on estimations of long-term forest growth patterns in pure and mixed forests, using FORECAST (a hybrid forest growth model) as a virtual lab. The fine root parameters assessed were biomass, turnover rate, and nitrogen content. The analysis was performed by simulating monospecific stands of two contrasting species ( L. and L.), and mixed stands formed by both species. In all cases, FORECAST showed good capability to contain uncertainty propagation during the first and middle stages of stand development (<40 years). After that moment, model output uncertainty steadily increased, but it reached different maximum uncertainty levels depending on stand type. Simulations of the less nutrient demanding manifested very little sensitivity when growing in monospecific stands. However, monospecific stands showed intermediate sensitivity, but significant species interactions occurred in mixed stands that determined the biggest impact detected of uncertainty related to fine root parameters over model outputs. All things considered, FORECAST displayed an interesting capability to capture some of the interspecific interactions that are key in mixed forests functioning. Our results suggest an acceptable model performance under uncertain parameterization but also caution against expecting accurate quantitative estimations of forest growth, especially when considering long-term scenarios in complex mixed stands.

中文翻译：

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混交林长期生产力估算对细根相关不确定参数的敏感性

森林生长模型越来越多地被用作林业和生态学研究的预测工具，以帮助制定实用指南并提高对森林生态系统功能驱动因素的理解。模型通常使用直接获得或从经验现场观察估计的参数进行校准，因此存在不确定性。因此，输出精度取决于输入参数精度以及每个参数对模型行为的影响程度。因此，分析与参数相关的不确定性及其对模型输出的影响非常重要。这可以通过执行灵敏度分析来完成，灵敏度分析可以探索一个或多个校准参数对模型输出的影响。由于对树根参数的研究特别缺乏，本工作的目的是使用 FORECAST（一种混合森林生长模型）评估与细根相关的参数对纯林和混交林长期森林生长模式估计的影响）作为虚拟实验室。评估的细根参数是生物量、周转率和氮含量。该分析是通过模拟两个对比物种（L.和L.）的单特异性林分以及这两个物种形成的混合林分来进行的。在所有情况下，FORECAST 在林分发展的第一阶段和中期阶段（<40 年）都表现出了良好的遏制不确定性传播的能力。此后，模型输出不确定性稳步增加，但根据林分类型达到不同的最大不确定性水平。当在单一特异性林中生长时，对营养需求较低的模拟表现出非常小的敏感性。然而，单一林分表现出中等敏感性，但混合林分中发生了显着的物种相互作用，这决定了与模型输出的细根参数相关的不确定性检测到的最大影响。考虑到所有因素，预测显示了一种有趣的功能，可以捕获一些对于混合森林功能至关重要的种间相互作用。我们的结果表明，在不确定的参数化下，模型性能是可以接受的，但也警告不要期望对森林生长进行准确的定量估计，特别是在考虑复杂混合林分的长期情景时。