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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. [ABSTRACT FROM AUTHOR]

Soil properties influence plant physiology and growth, playing a fundamental role in shaping species niches in temperate forest ecosystems. Here, we investigated the impact of soil data quality on the performance of species distribution models (SDMs) of 41 woody plant species in Swiss forests. We compared models based on measured soil properties with those based on digitally mapped soil properties on regional (Swiss Forest Soil Maps) and global scales (SoilGrids). We first calibrated topo-climatic SDMs with measured soil data and plant species presences and absences from mature temperate forest stand plots. We developed further models using the same soil predictors, but with values extracted from digital soil maps at the nearest neighbouring plots of the Swiss National Forestry Inventory. The predictive power of SDMs without soil information compared to those with soil information, as well as measured soil information vs digitally mapped, was evaluated with metrics of model performance and variable contribution. On average, models with measured and digitally mapped soil properties performed significantly better than those without soil information. SDMs based on measured and Swiss Forest Soil Maps showed higher performance, especially for species with an 'extreme' niche position (e.g., preference for high or low pH), compared to those using SoilGrids. Nevertheless, if no regional soil maps are available, SoilGrids should be tested for their potential to improve SDMs. Moreover, among the tested soil predictors, pH, and clay content of the topsoil layers most improved the predictive power of SDMs for forest woody plants. In conclusion, we demonstrate the value of regional soil maps for predicting the distribution of woody species across strong environmental gradients in temperate forests. The improved accuracy of SDMs and insights into drivers of distribution may support forest managers in strategies supporting e.g. biodiversity conservation, or climate adaptation planning., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Authors. Published by Elsevier B.V. All rights reserved.)

• Five vegetation structure metrics are derived from airborne laser scanning data. • The metrics describe the 3D forest edge structure, particularly the shelterbelt. • Our approach allows to retrieve area-wide, continuous edge structure information. • The information is used to assess the structural complexity of forest edges. Forest edges represent the transition zone (ecotone) between the forest interior and the surrounding open land. Due to their great ecological importance, the value of assessing the structure of forest edges has been recognized. In Switzerland, for example, forest edge structure is assessed during field surveys of the Swiss National Forest Inventory (NFI). However, these assessments are time consuming and limited to sample plots. Publicly available countrywide airborne laser scanning (ALS) data, in contrast, offers possibilities to retrieve forest edge structure information over large spatial extents. In this study, we derived five metrics from ALS point clouds, namely the canopy height variability; ratios of the areas of the three edge components, i.e. shrub belt, shelterbelt and forest layer; the sky-view fraction; the shelterbelt slope; and the front density of the forest edge. These metrics describe the three-dimensional edge structure and therefore could enhance existing NFI edge metrics, which focus on two-dimensional structure characteristics. An expert assessment of the ALS edge metrics demonstrated the ability of the defined set of edge metrics to capture ecologically relevant and indicative characteristics of forest edges. Understanding this relationship between edge metrics and ecological functions is a prerequisite if ALS metrics are to be integrated into NFIs. We subsequently used the ALS edge metrics to group 284 forest edge into three classes with respect to their structural complexity using k-means clustering. The results indicated that the structual complexity was low for 173, medium for 46 and high for 65 forest edges, respectively. Applied to countrywide ALS data sets, our approach allows to retrieve area-wide, spatially continuous information on the forest edge conditions, and, if multitemporal ALS data is available, to monitor the development of the forest edges. [ABSTRACT FROM AUTHOR]

Efficient forest operations are required for the provision of biodiversity and numerous ecosystem services, such as wood production, carbon sequestration, protection against natural hazards and recreation. In numerous countries, under difficult terrain conditions, the costs of forest management and harvesting are not covered by timber revenue. One possible option to increase the cost-effectiveness of the forestry sector is the application of state-of-the-art harvesting and extraction techniques, so-called best suitable harvesting methods. We present a case study from Switzerland, where a lack of competitiveness in the forestry sector is of particular interest, with the aim of quantifying the efficiency gains if estimated best suitable harvesting methods were to be rigorously applied instead of the currently applied harvesting methods. For this purpose, we developed a spatial decision support system to allocate estimated best suitable harvesting methods to plots, while concurrently considering hauling route limitations, extraction route properties and stand characteristics. Our approach was based on productivity models and supported with expert-defined decision trees. The evaluation of the estimated best suitable harvesting methods and the comparison with the currently applied harvesting methods were completed for all 6500 National Forest Inventory (NFI) plots in Switzerland. We draw the following three major conclusions from our study: First, our modeling approach is an effective method to allocate estimated best suitable harvesting methods to NFI plots. Second, applying estimated best suitable harvesting methods would lead to cost reductions, in particular in the regions that include steep terrain and where harvesting mainly relies on cable- and air based extraction methods. Third, assuming an average timber price of 75 CHF m −3, 64 % instead of 52 % of the forest area could be harvested economically over the whole country if estimated best suitable methods were applied. This advantage would mainly be caused by a shift towards more mechanized harvesting methods. Improving the cost-effectiveness of the forestry sector is of high global relevance, as the increased use of domestic timber resources is a cost-efficient way to reduce atmospheric carbon emissions. The methodological framework described here was developed for Switzerland in particular, but it could be applied to Central Europe and other parts of Europe with a large amount of mountain forests. • An effective method to allocate the best suitable harvesting methods to NFI plots. • Integration of productivity models and expert-defined decision trees. • A larger proportion of the forest can be managed to cover costs or generate profits. • In Switzerland, 64 instead of 52 % of all forest area could be accessed economically. • This gain is mainly caused by a shift towards more mechanized harvesting methods. [ABSTRACT FROM AUTHOR]

• Regeneration in small gaps is exclusively direct ingrowth of dominant canopy species. • Representative sampling underestimates post-disturbance development potential. • Targeted gap sampling is needed to monitor forest adaptation to climate change. Tree regeneration (TR) in canopy gaps is a key process to understand how forest ecosystems might adapt to future environmental changes. Since successful TR is the result of a complex interplay of several stochastic events such as gap formation, seed production, ungulate pressure and diseases, some of the processes have been neglected or strongly reduced for model applications. Most empirical data on TR originate from (national) forest inventories based on statistically optimized designs for timber resource estimations and use geographically fixed permanent plots. Consequently, these representative samples record TR both in gaps as well as under closed canopy conditions. In this study, we compared TR in 63 representative plots of the Swiss National Forest Inventory ('NFI sampling'), located in beech and silver fir-beech forests along an elevation gradient in the Jura Mts., with an opportunistic 'gap sampling' strategy targeting TR-clusters. We analyzed quantitative (e.g., number of individuals) and qualitative (e.g., species diversity, similarity to surrounding canopy) differences between the 'NFI sampling' and 'gap sampling,' as well as the environmental factors (e.g., gap size, cluster age, elevation) influencing the composition of TR in small canopy gaps. The 'gap sampling' recorded higher median TR densities (+62%, stems ha−1) and species richness (4.0 ± 2.1, mean ± sd) than the 'NFI sampling'. More importantly, the 'gap sampling' provided much more consistent results among sites (i.e., 4x lower variance). Consequently, the 'gap sampling' revealed not only general but also species-specific patterns across TR size classes and along elevational gradients in contrast to the lack of patterns in the 'NFI sampling'. Species richness decreased as TR grew taller, and the tallest TR size class (>130 cm) was almost solely comprised of the dominant species in the surrounding canopy (direct ingrowth). The high amount of direct ingrowth in TR indicates that small canopy gaps might offer only limited management potential to alter forests and that extensive disturbances or management at early TR stages (sapling) would be necessary to promote species better suited to the expected future site conditions. We raise the question of whether a forest monitoring that is statistically optimized for timber resource estimation is best suited to detect adaptation effects to quickly changing environments best visible at the regeneration stage, or whether forest inventory tools should be extended by methods that focus on disturbed parts of forests where changes and adaptations take place. We propose the implementation of an opportunistic gap-based monitoring of TR that essentially serves to identify the best forest management strategies (e.g. gap size, intervention intervals) in forests adapting to environmental change. [ABSTRACT FROM AUTHOR]