Plant Ecology and Ecosystems Research, University of Goettingen, Untere Karspüle 2, 37073, Goettingen, Germany.
Silviculture and Forest Ecology of the Temperate Zones, University of Goettingen, Büsgenweg 1, 37077, Goettingen, Germany.
BMC Ecol. 2018 Nov 20;18(1):47. doi: 10.1186/s12898-018-0203-y.
Old-growth and primeval forests are passing through a natural development cycle with recurring stages of forest development. Several methods for assigning patches of different structure and size to forest development stages or phases do exist. All currently existing classification methods have in common that a priori assumptions about the characteristics of certain stand structural attributes such as deadwood amount are made. We tested the hypothesis that multivariate datasets of primeval beech forest stand structure possess an inherent, aggregated configuration of data points with individual clusters representing forest development stages. From two completely mapped primeval beech forests in Albania, seven ecologically important stand structural attributes characterizing stand density, regeneration, stem diameter variation and amount of deadwood are derived at 8216 and 9666 virtual sampling points (moving window, focal filtering). K-means clustering is used to detect clusters in the datasets (number of clusters (k) between 2 and 5). The quality of the single clustering solutions is analyzed with average silhouette width as a measure for clustering quality. In a sensitivity analysis, clustering is done with datasets of four different spatial scales of observation (200, 500, 1000 and 1500 m, circular virtual plot area around sampling points) and with two different kernels (equal weighting of all objects within a plot vs. weighting by distance to the virtual plot center).
The clustering solutions succeeded in detecting and mapping areas with homogeneous stand structure. The areas had extensions of more than 200 m, but differences between clusters were very small with average silhouette widths of less than 0.28. The obtained datasets had a homogeneous configuration with only very weak trends for clustering.
Our results imply that forest development takes place on a continuous scale and that discrimination between development stages in primeval beech forests is splitting continuous datasets at selected thresholds. For the analysis of the forest development cycle, direct quantification of relevant structural features or processes might be more appropriate than classification. If, however, the study design demands classification, our results can justify the application of conventional forest development stage classification schemes rather than clustering.
古老的和原始的森林正在经历一个自然的发展周期，其中有反复出现的森林发展阶段。确实存在几种将不同结构和大小的斑块分配给森林发展阶段或相的方法。目前所有现有的分类方法都有一个共同点，即对某些林分结构属性（如枯木量）的特征做出先验假设。我们测试了这样一个假设，即原始山毛榉林分结构的多元数据集具有数据点的固有聚合配置，每个聚类代表森林发展阶段。从阿尔巴尼亚的两个完全测绘的原始山毛榉林中，我们得出了七个生态上重要的林分结构属性，这些属性描述了林分密度、更新、茎干直径变化和枯木量，它们是在 8216 和 9666 个虚拟采样点（移动窗口，焦点滤波）上得出的。使用 K-均值聚类法（k 值为 2 到 5）来检测数据集的聚类。使用平均轮廓宽度作为聚类质量的度量标准来分析单个聚类解决方案的质量。在敏感性分析中，在四个不同的观测空间尺度（200、500、1000 和 1500 米，以采样点为中心的圆形虚拟样地）和两种不同的核（样地内所有对象的权重相等与到虚拟样地中心的距离加权）的数据集上进行聚类。
聚类解决方案成功地检测和绘制了具有同质林分结构的区域。这些区域的扩展超过 200 米，但聚类之间的差异非常小，平均轮廓宽度小于 0.28。获得的数据集具有同质的配置，只有非常弱的聚类趋势。
我们的结果表明，森林的发展是在一个连续的尺度上进行的，在原始山毛榉林中，发展阶段的区分是在选定的阈值处对连续数据集进行分割。对于森林发展周期的分析，直接量化相关结构特征或过程可能比分类更合适。然而，如果研究设计需要分类，我们的结果可以证明应用传统的森林发展阶段分类方案而不是聚类是合理的。
