The rapid development of portable terrestrial laser scanning (TLS) devices in recent years has led to increased attention to their applicability for forest inventories, especially where direct measurements are very expensive or nearly impossible. However, in terms of precision and reproducibility, there are still some pending questions. In this study, we investigate the influence of stand parameters on the TLS-related visibility in forest plots. We derived 2740 stand parameters from Swiss national forest inventory sample plots. Based on these parameters, we defined virtual scenes of the forest plots with the software “Blender”. Using Blender’s ray-tracing features, we assessed the 3D coverage in a cubic space and 2D visibility properties for each of the virtual plots with different scanner placement schemes. We provide a formula to calculate the maximum number of possible hits for any object size at any distance from a scanner with any resolution. Additionally, we show that the Weibull scale parameter describing a stand, in addition to the number of trees and the mean diameter of the dominant 100 trees per hectare, has a significant and relevant influence on the visibility of the sample plot. Furthermore, we show the effectiveness and the efficiency of 40 scanner location patterns. These experiments demonstrate that intuitively distributing scanner locations evenly within the sample plot, with similar distances between locations and from the edge of the sample plot, provides the best overall visibility of the stand.

Climate change severely affects mountain forests and their ecosystem services, e.g., by altering disturbance regimes. Increasing timber harvest (INC) via a close-to-nature forestry may offer a mitigation strategy to reduce disturbance predisposition. However, little is known about the efficiency of this strategy at the scale of forest enterprises and potential trade-offs with biodiversity and ecosystem services (BES). We applied a decision support system which accounts for disturbance predisposition and BES indicators to evaluate the effect of different harvest intensities and climate change scenarios on windthrow and bark beetle predisposition in a mountain forest enterprise in Switzerland. Simulations were carried out from 2010 to 2100 under historic climate and climate change scenarios (RCP4.5, RCP8.5). In terms of BES, biodiversity (structural and tree species diversity, deadwood amount) as well as timber production, recreation (visual attractiveness), carbon sequestration, and protection against gravitational hazards (rockfall, avalanche and landslides) were assessed. The INC strategy reduced disturbance predisposition to windthrow and bark beetles. However, the mitigation potential for bark beetle disturbance was relatively small (− 2.4%) compared to the opposite effect of climate change (+ 14% for RCP8.5). Besides, the INC strategy increased the share of broadleaved species and resulted in a synergy with recreation and timber production, and a trade-off with carbon sequestration and protection function. Our approach emphasized the disproportionally higher disturbance predisposition under the RCP8.5 climate change scenario, which may threaten currently unaffected mountain forests. Decision support systems accounting for climate change, disturbance predisposition, and BES can help coping with such complex planning situations.

To understand the state and trends in biodiversity beyond the scope of monitoring programs, biodiversity indicators must be comparable across inventories. Species richness (SR) is one of the most widely used biodiversity indicators. However, as SR increases with the size of the area sampled, inventories using different plot sizes are hardly comparable. This study aims at producing a methodological framework that enables SR comparisons across plot‐based inventories with differing plot sizes. We used National Forest Inventory (NFI) data from Norway, Slovakia, Spain, and Switzerland to build sample‐based rarefaction curves by randomly incrementally aggregating plots, representing the relationship between SR and sampled area. As aggregated plots can be far apart and subject to different environmental conditions, we estimated the amount of environmental heterogeneity (EH) introduced in the aggregation process. By correcting for this EH, we produced adjusted rarefaction curves mimicking the sampling of environmentally homogeneous forest stands, thus reducing the effect of plot size and enabling reliable SR comparisons between inventories. Models were built using the Conway–Maxell–Poisson distribution to account for the underdispersed SR data. Our method successfully corrected for the EH introduced during the aggregation process in all countries, with better performances in Norway and Switzerland. We further found that SR comparisons across countries based on the country‐specific NFI plot sizes are misleading, and that our approach offers an opportunity to harmonize pan‐European SR monitoring. Our method provides reliable and comparable SR estimates for inventories that use different plot sizes. Our approach can be applied to any plot‐based inventory and count data other than SR, thus allowing a more comprehensive assessment of biodiversity across various scales and ecosystems.

The aim of this study is to develop climate-sensitive single-tree growth models, to be used in stand based prediction systems of managed forest in Switzerland. Long-term observations from experimental forest management trials were used, together with retrospective climate information from 1904 up to 2012. A special focus is given to the role of transformation of modelling basal area increment, helping to normalize the random error distribution. A nonlinear model formulation was used to describe the basic relation between basal area increment and diameter at breast height. This formulation was widely expanded by groups of explanatory variables, describing competition, stand development, site, stand density, thinning, mixture, and climate. The models are species-specific and contain different explanatory variables per group, being able to explain a high amount of variance (on the original scale, up to 80% in the case of Quercus spec.). Different transformations of the nonlinear relation where tested and based on the mean squared error, the square root transformation performed best. Although the residuals were homoscedastic, they were still long-tailed and not normal distributed, making robust statistics the preferred method for statistical inference. Climate is included as a nonlinear and interacting effect of temperature, precipitation and moisture, with a biological meaningful interpretation per tree species, e.g., showing better growth for Abies alba in warm and wet climates and good growing conditions for Picea abies in colder and dryer climates, being less sensitive on temperature. Furthermore, a linear increase in growth was found to be present since the 1940s. Potentially this is an effect of the increased atmospheric CO₂ concentration or changed management in terms of reduced nutrient subtractions from forest ground, since industrialization lowered the demand of residue and slash uptake.