Continuous cover forestry (CCF) is forest management based on ecological principles and this management type is currently re-visited in many countries. CCF woodlands are known for their structural diversity in terms of tree size and species and forest planning in CCF needs to make room for multiple forest development pathways as opposed to only one management target. As forest management diversifies and management types such as CCF become more common, models used for projecting forest development need to have a generic and flexible bottom-up design. They also need to be able to handle uncertainty to a larger extent and more comprehensively than is necessary with single, traditional forest management types. In this study, a spatial tree model was designed for analyzing a data set involving 18 plots from CCF stands in Southern Finland. The tree model has specific ingrowth, growth and mortality model components, each including a spatially explicit competition effect involving neighboring trees. Approximations were presented that allow inference of the model components operating in annual steps based on time-series measurements from several years. We employed Bayesian methodology and posterior predictive distributions to simulate forest development for short- and long-term projections. The Bayesian approach allowed us to incorporate uncertainties related to model parameters in the projections, and we analyzed these uncertainties based on three scenarios: (1) known plot and tree level random effects, (2) known plot level random effects but unknown tree level random effects, and (3) unknown random effects. Our simulations revealed that uncertainties related to plot effects can be rather high, particularly when accumulated across many years whilst the length of the simulation step only had a minor effect. As the plot and tree effects are not known when tree models are applied in practice, in such cases, it may be possible to significantly improve model projections for a single plot by taking one-off individual-tree growth measurements from the plot and using them for calibrating the model. Random plot effects as used in our tree model are also a way of describing environmental conditions in CCF stands where other traditional descriptors based on stand height and stand age fail to be suitable any more.

中文翻译：

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预测森林动态和相关不确定性的贝叶斯方法：在连续覆盖森林中的应用

连续覆盖林业（CCF）是一种基于生态原则的森林管理，目前许多国家都重新审视这种管理类型。 CCF 林地以其树木大小和物种的结构多样性而闻名，CCF 的森林规划需要为多种森林发展路径腾出空间，而不是只有一种管理目标。随着森林管理的多样化和CCF等管理类型的日益普遍，用于预测森林发展的模型需要具有通用且灵活的自下而上的设计。与单一的传统森林管理类型相比，它们还需要能够更大程度、更全面地处理不确定性。在本研究中，设计了一个空间树模型来分析涉及芬兰南部 CCF 林地 18 个地块的数据集。树木模型具有特定的向内生长、生长和死亡模型组件，每个组件都包括涉及邻近树木的空间明确竞争效应。提出的近似值允许根据几年来的时间序列测量来推断每年运行的模型组件。我们采用贝叶斯方法和后验预测分布来模拟短期和长期预测的森林发展。贝叶斯方法使我们能够在预测中纳入与模型参数相关的不确定性，并且我们基于三种场景分析了这些不确定性：（1）已知的地块和树级随机效应，（2）已知的地块级随机效应但未知的树级随机效应效应，以及（3）未知的随机效应。我们的模拟显示，与情节效应相关的不确定性可能相当高，特别是当多年累积时，而模拟步骤的长度仅产生很小的影响。由于在实践中应用树木模型时，地块和树木的影响是未知的，在这种情况下，通过从地块中进行一次性单棵树生长测量并使用它们，可以显着改进单个地块的模型预测用于校准模型。我们的树木模型中使用的随机图效应也是描述 CCF 林分环境条件的一种方式，而基于林分高度和林龄的其他传统描述符不再适用。