Forest diversity-productivity relationships have been intensively investigated in recent decades. However, few studies have considered the interplay between species and structural diversity in driving productivity. We analyzed these factors using data from 52 permanent plots in southwestern Germany with more than 53,000 repeated tree measurements. We used basal area increment as a proxy for productivity and hypothesized that: (1) structural diversity would increase tree and stand productivity, (2) diversity-productivity relationships would be weaker for species diversity than for structural diversity, and (3) species diversity would also indirectly impact stand productivity via changes in size structure. We measured diversity using distance-independent indices. We fitted separate linear mixed-effects models for fir, spruce and beech at the tree level, whereas at the stand level we pooled all available data. We tested our third hypothesis using structural equation modeling. Structural and species diversity acted as direct and independent drivers of stand productivity, with structural diversity being a slightly better predictor. Structural diversity, but not species diversity, had a significant, albeit asymmetric, effect on tree productivity. The functioning of structurally diverse, mixed forests is influenced by both structural and species diversity. These sources of trait diversity contribute to increased vertical stratification and crown plasticity, which in turn diminish competitive interferences and lead to more densely packed canopies per unit area. Our research highlights the positive effects of species diversity and structural diversity on forest productivity and ecosystem dynamics.

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We address the problem of how to integrate risk assessment into forest management and therefore provide a comprehensive review of recent and past literature on risk analysis and modeling and, moreover, an evaluation and summary on these papers. We provide a general scheme on how to integrate concepts of risk into forest management decisions. After an overview of the risk management process and the main hazards in forests (storm, snow, insects, fire), the paper focuses on the principal methods used to assess risks from these hazards for commercial forestry. We review mechanistic models, empirical models, and expert systems and consider the needs for different spatial scales of risk assessment, from the regional to the single-tree level. In addition to natural hazards and their secondary effects, we deal with economic aspects of risk analysis. Monte Carlo simulations to deal with volatile timber prices and ways to include risk in classical Faustmann approaches are briefly discussed along with the integration of portfolio theory into forest management decision making and attitude toward risk. Special attention is paid to the implications for risk modeling under climate change.

Shifts in tree species distributions caused by climatic change are expected to cause severe losses in the economic value of European forestland. However, this projection disregards potential adaptation options such as tree species conversion, shorter production periods, or establishment of mixed species forests. The effect of tree species mixture has, as yet, not been quantitatively investigated for its potential to mitigate future increases in production risks. For the first time, we use survival time analysis to assess the effects of climate, species mixture and soil condition on survival probabilities for Norway spruce and European beech. Accelerated Failure Time (AFT) models based on an extensive dataset of almost 65,000 trees from the European Forest Damage Survey (FDS)--part of the European-wide Level I monitoring network--predicted a 24% decrease in survival probability for Norway spruce in pure stands at age 120 when unfavorable changes in climate conditions were assumed. Increasing species admixture greatly reduced the negative effects of unfavorable climate conditions, resulting in a decline in survival probabilities of only 7%. We conclude that future studies of forest management under climate change as well as forest policy measures need to take this, as yet unconsidered, strongly advantageous effect of tree species mixture into account.

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.