4.4 Article

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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EUROPEAN JOURNAL OF FOREST RESEARCH
卷 131, 期 1, 页码 229-247

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SPRINGER
DOI: 10.1007/s10342-010-0432-x

关键词

Long-term storm damage; Windthrow; Generalized linear mixed models; Empirical modeling; Silviculture; Thinning

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  1. German Federal Ministry of Education and Research [0330622]

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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.

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