How does silviculture affect storm damage in forests of south-western Germany? Results from empirical modeling based on long-term observations
How does silviculture affect storm damage in forests of south-western Germany? Results from empirical modeling based on long-term observations
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DOI：
10.1007/s10342-010-0432-x
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发表时间：
2012-01-01
影响因子：
2.8
通讯作者：
Kohnle, Ulrich
中科院分区：
文献类型：
--
作者：
Albrecht, Axel;Hanewinkel, Marc;Kohnle, Ulrich
风暴是中欧森林最重要的干扰因素。利用巴登-符腾堡州（德国西南部）的长期生长和产量实验的数据，可以将风暴损害与单棵树木的其他死亡原因分开，我们研究了土壤、地点、林分和树木参数对风暴损害的影响，特别关注造林干预措施的影响。为此，采用四步建模方法来提取以下主要风险因素：(1) 林分层面风暴损害的一般发生；(2) 林分总体损害的发生；(3) 林分内部分风暴损害的发生。然后抵消步骤 3 中获得的估计林分水平风暴损坏概率，以描述每个部分受损林分内单棵树木的损坏潜力 (4)。应用广义线性混合模型。我们的结果表明，树种和林分高度是最重要的风暴风险因素，也是表征长期风暴风险的因素。此外，过去木材砍伐和选择性间伐的数据对于解释风暴损害倾向似乎比林分密度、土壤和场地条件或地形变量等数据更为重要。当使用加权方法（总结单个预测变量或预测变量组的相对权重）进行量化时，清除可以解释高达 20% 的风暴风险。逐步建模方法证明了该分析的一个重要方法论特征，因为它能够以统计上正确的方式考虑大量观察结果而不会造成损坏（“零膨胀”）。这些结果为量化森林管理对风暴损害风险的直接影响奠定了可靠的基础。
Storms represent the most important disturbance factor in forests of Central Europe. Using data from long-term growth and yield experiments in Baden-Wuerttemberg (south-western Germany), which permit separation of storm damage from other causes of mortality for individual trees, we investigated the influence of soil, site, forest stand, and tree parameters on storm damage, especially focusing on the influence of silvicultural interventions. For this purpose, a four-step modeling approach was applied in order to extract the main risk factors for (1) the general stand-level occurrence of storm damage, (2) the occurrence of total stand damage, and (3) partial storm damage within stands. The estimated stand-level probability of storm damage obtained in step 3 was then offset in order to describe the damage potential for the individual trees within each partially damaged stand (4). Generalized linear mixed models were applied. Our results indicate that tree species and stand height are the most important storm risk factors, also for characterizing the long-term storm risk. Additionally, data on past timber removals and selective thinnings appear more important for explaining storm damage predisposition than for example stand density, soil and site conditions or topographic variables. When quantified with a weighting method (summarizing the relative weight of single predictors or groups of predictors), removals could explain up to 20% of storm risk. The stepwise modeling approach proved an important methodological feature of the analysis, since it enabled consideration of the large number of observations without damage ("zero inflation") in a statistically correct way. These results form a reliable basis for quantifying forest management's direct impact on the risk of storm damage.