Klimawandel und Störungsereignisse stellen eine grosse Herausforderung für die Bewirtschaftung von Gebirgswäldern dar. Im Rahmen der naturnahen Waldbewirtschaftung könnte eine Erhöhung der Bewirtschaftungsintensität (INC-Strategie) die Störungsanfälligkeit verringern, jedoch ist wenig zu den Zielkonflikten mit der Biodiversitätsförderung und der Bereitstellung von Ökosystemleistungen auf Ebene des Forstbetriebes bekannt. In dieser Studie wurden die Auswirkungen verschiedener Bewirtschaftungsintensitäten auf die Störungsanfälligkeit gegenüber Windwurf und Borkenkäfer mit einem Entscheidungsunterstützungssystem (Decision Support System, DSS) untersucht. Dies erfolgte im Dischmatal bei Davos (1350 ha, Teil des Forstbetriebes Davos), für das Simulationen der Waldentwicklung über einen Zeitraum den 2010 bis 2100 unter historischem Klima und Klimawandelszenarien (RCP4.5, RCP8.5) durchgeführt wurden. Zudem wurden Indikatoren für die Biodiversität sowie für Ökosystemleistungen einbezogen. Die Ergebnisse der Fallstudie zeigten, dass die INC-Strategie die Störungsanfälligkeit gegenüber Windwurf und Borkenkäfer verringerte. Der Effekt auf die Borkenkäferanfälligkeit war jedoch relativ gering verglichen mit dem verstärkenden Effekt des Klimawandels unter dem RCP8.5-Szenario. Die INC-Strategie führte zu einer Erhöhung der Erholungsfunktion und der Holzproduktion und zu Zielkonflikten mit der Kohlenstoffspeicherung und der Schutzwirkung. Ausserdem zeigte sich ein positiver Zusammenhang zwischen der Verbesserung der Biodiversitätsindikatoren und dem Rückgang der Störungsanfälligkeit. Unsere Ergebnisse weisen auf eine starke Zunahme der Störungsanfälligkeit unter einem RCP8.5-Klimawandelszenario hin, welche die forstliche Planung vor grosse Herausforderungen stellt. Entscheidungsunterstützungssysteme können helfen, in solch komplexen Planungssituationen die Anpassung an den Klimawandel und die Störungsanfälligkeit mit der Biodiversitätsförderung und der Bereitstellung von Ökosystemleistungen in Einklang zu bringen.

Most strategic and operational forest management decisions are taken based on stand-level information, and quantitative models of forest dynamics are key for developing sustainable management strategies. However, data on forest stands for the initialisation of such models that are representative at large spatial scales, e.g., countries or ecoregions, are often lacking. National Forest Inventories (NFIs) provide forest data from small sample plots at large spatial scales, yet deriving full stand information based on such data is challenging. Here, we evaluate seven methods of varying complexity for deriving quantitative stand descriptions based on sample data as provided by the Swiss NFI. We selected 271 extensively measured Swiss forests stands with unimodal diameter distributions, classified them as beech-vs. spruce-dominated in five development stages and randomly placed a small sized sample plot in each stand using the Swiss NFI sampling design (i.e., a circular plot of 500 m2). Seven modelling approaches were used to derive diameter distributions and species-specific stem numbers (i.e., tree species composition) from the sample data that are representative for a particular stand (local scale) and for stand types in general (generalised scale). The prediction performance of the modelling approaches was evaluated using 100 random samples per stand to calculate prediction errors. Generalised even-aged diameter distributions were best predicted by the simultaneous parameter prediction method (PPM), i.e. a combined three-step regression approach, with on average 1.3 to 2.5 times lower prediction errors compared to the simple pooling of diameter samples. However, uneven-aged diameter distributions were best predicted by pooling. At the local scale, the simultaneous PPM performed best for data from sample plots with fewer than 17 to 19 trees across all devel-opment stages. Prediction performance of the PPMs increased for structurally and spatially diverse local stands with positively skewed diameter distributions. A Random Forest approach was most suitable for predicting species composition at both the generalised and the local scale. Our study evaluates the strengths and weaknesses of methods to model stands based on data from small sample plots. We emphasise terminological pitfalls by consequently distinguishing local accuracy and generalised representativity of the stand descriptions. We demonstrate the feasibility of deriving locally accurate stands using data from small forest sample plots and evaluate the derivation of generalised stands representative at large regions. At both scales, our developments contribute to an improved initialisation of forest models and thus to a more realistic modelling of forest development under future boundary conditions.

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

Abstract 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.

Climate-adaptive forest management aims to sustain the provision of multiple forest ecosystem services and biodiversity (ESB). However, it remains largely unknown how changes in adaptive silvicultural interventions affect trade-offs and synergies among ESB in the long term. We used a simulation-based sensitivity analysis to evaluate popular adaptive forest management interventions in representative Swiss low- to mid-elevation beech- and spruce-dominated forest stands. We predicted stand development across the twenty-first century using a novel empirical and temperature-sensitive single-tree forest stand simulator in a fully crossed experimental design to analyse the effects of (1) planting mixtures of Douglas-fir, oak and silver fir, (2) thinning intensity, and (3) harvesting intensity on timber production, carbon storage and biodiversity under three climate scenarios. Simulation results were evaluated in terms of multiple ESB provision, trade-offs and synergies, and individual effects of the adaptive interventions. Timber production increased on average by 45% in scenarios that included tree planting. Tree planting led to pronounced synergies among all ESBs towards the end of the twenty-first century. Increasing the thinning and harvesting intensity affected ESB provision negatively. Our simulations indicated a temperature-driven increase in growth in beech- (+ 12.5%) and spruce-dominated stands (+ 3.7%), but could not account for drought effects on forest dynamics. Our study demonstrates the advantages of multi-scenario sensitivity analysis that enables quantifying effect sizes and directions of management impacts. We showed that admixing new tree species is promising to enhance future ESB provision and synergies among them. These results support strategic decision making in forestry.

Eine nachhaltige Waldbewirtschaftung ist für die Bereitstellung von Waldleistungen von zentraler Bedeutung. Für die Forstbetriebe ist dies jedoch eine komplexe Planungsaufgabe, da eine Vielzahl an Waldleistungen und die Biodiversität berücksichtigt werden müssen. Zudem stellt der wachsende Einfluss des Klimawandels eine besondere Herausforderung dar. Im Projekt SessFor wurde der Prototyp eines Entscheidungs-Unterstützungssystems (Decision Support System, DSS) für die strategische und langfristige Planung von Forstbetrieben entwickelt. Das DSS simuliert die Waldentwicklung unter verschiedenen Bewirtschaftungs- und Klimaszenarien und bewertet die Bereitstellung von Waldleistungen mittels einer multikriteriellen Entscheidungsanalyse. Das DSS wurde in drei Fallstudien im Mittelland und den Voralpen über einen Zeitraum von 50 Jahren angewandt. Dabei wurden die Auswirkungen von unterschiedlichen Bewirtschaftungsstrategien auf die Waldleistungen Holzproduktion, Erholung (visuelle Attraktivität), Schutzfunktion und Kohlenstoffspeicherung sowie auf die Biodiversität (v.a. Strukturvielfalt und Baumartendiversität) bewertet. Der höchste Gesamtnutzen für die betrachteten Waldleistungen unter gegenwärtigem und zukünftigem Klima ergab sich in fast allen Fällen unter der aktuellen Bewirtschaftungspraxis. Eine Ausnahme bildete eine Fallstudie im Mittelland, bei der der höchste Gesamtnutzen unter einer reduzierten Holznutzung resultierte. Die untersuchten Klimaszenarien hatten eine relativ geringe Auswirkung auf den Gesamtnutzen. Einzelne Waldleistungsindikatoren zeigten jedoch negative Auswirkungen des Klimawandels für die Betriebe im Mittelland und positive Auswirkungen für den höher gelegenen Forstbetrieb in den Voralpen. Insgesamt zeigte die Studie auf, wie mithilfe des DSS die langfristige Entwicklung von Waldleistungen und Biodiversitätsindikatoren unter verschiedenen Bewirtschaftungsstrategien und Klimabedingungen untersucht werden kann. Die Stärken des DSS liegen insbesondere in der grossen Anzahl berücksichtigter Waldleistungen und in der breiten empirischen Datengrundlage. Die multikriterielle Entscheidungsanalyse unterstützt zudem eine wissenschaftlich fundierte und transparente Entscheidungsfindung für die langfristige Planung.

Forests provide multiple services, and in the face of global change adaptive management strategies are needed, which inevitably must be based on models. However, most locally accurate forest models are tied to the stand scale and cannot readily be applied across large areas. Empirical data for model initialisation are often not available at large spatial scales. National Forest Inventories (NFIs) provide spatially representative tree and stand samples, but their samples are typically small, that is, only a few trees are measured per plot, and they are truncated, that is, not each tree has the same probability of being observed. To overcome these issues, we develop and apply a methodology to derive stand descriptions from small sample data, taking the Swiss NFI as a case study. We extended the traditional Weibull function to (multi‐)truncated unimodal and bimodal forms that are suitable for the representation of samples from survey designs with multiple callipering thresholds. Subsequently, we applied these functions in an extended parameter prediction method to derive stand diameter distributions from representative samples. Additionally, we predicted species compositions using a multinomial logistic regression model and assigned them to the diameter distributions of the stands. The diameter distribution of 9.1% of the Swiss NFI samples was better described by a bimodal than a unimodal Weibull function. The uni‐ and bimodal diameter model in combination with the model to determine species composition can be used to predict stand descriptions from single small samples or entire forest types in the target area. Thereby, the bimodal form is suitable for capturing stand structures with distinct under‐ and overstorey. In Switzerland, the diameter distributions of stands are typically positively skewed. Our method can be applied to any large‐scale dataset (e.g. NFI) and allows to generate initial conditions in terms of spatially representative stands. These, in turn, are suitable for forest stand simulators, which allows for developing adaptive forest management strategies at large scales, by simulating realistic and site‐specific stand development while still reflecting detailed management measures. Furthermore, stand descriptions can be used to assess tree species diversity, regeneration and harvest potentials.

Self-thinning dynamics are often considered when managing stand density in forests and are used to constrain forest growth models. However, self-thinning relationships are often quantified using only data at a conceptualised self-thinning line, even though self-thinning can begin before the stand actually reaches a self thinning line. Also, few self-thinning relationships account for the effects of species composition in mixed species forests, and stand structure such as relative height of species (in mixtures), and/or size or age cohorts in uneven-aged forests. Such considerations may be important given the effects of global climate change and interest in mixed-species and uneven-aged forests. The objective of this study was to develop self-thinning relationships based on changes in the tree density relative to mean tree diameter, instead of focusing only on data for state variables (e.g. tree density) at the self thinning line. This was done while also considering how the change in tree density is influenced by site quality and stand structure (species composition and relative height). The relationships were modelled using data from temperate Australian Eucalyptus plantations (436 plots), subtropical forests in China (88 plots), and temperate forests in Switzerland (1055 plots). Zero-inflated and hurdle generalized linear models with Poisson and negative binomial distributions were fit for several species, as well as for all-species equations. The intercepts and slopes of the self-thinning lines were higher than many published studies which may have resulted from both the less restrictive equation form and data selection. The rates of self-thinning often decreased as the proportion of the object species increased, as relative height increased (species or size cohort became more dominant), and as site (quality) index increased. The effects of aridity varied between species, with self-thinning increasing with aridity index for Abies alba, Pinus sylvestris, Quercus petraea and Quercus robur, but decreasing with aridity index for Eucalyptus nitens, Fagus sylvatica and Picea abies as sites became wetter and cooler. Self thinning model parameters were not correlated with species traits, including specific leaf area, wood basic density or crown diameter - stem diameter allometry. All-species self-thinning relationships based on all data could be adjusted using a correction factor for rarer species where there were insufficient data to develop species specific equations. The approach and equations developed could be used in forest growth models to calculate how the tree density declines as mean tree size increases, as height changes relative to other cohorts or species, as species proportions change, and as climatic and edaphic conditions change.

In a Europe shaped by centuries of forest management, the task of today's scientists in characterising, understanding and modelling natural forests is highly challenging. Although numerous forest reserves exist, most remain hardly comparable case studies. Contrarily, National Forest Inventories (NFIs) consist of systematically distributed sample plots with varying time since last intervention and provide representative data. These characteristics make NFIs a unique opportunity to investigate hidden natural forests. Here we propose using NFI plots free of human influence for >40 to >70 years ('latent reserves') to conduct large-scale studies on near-natural forests. We tested this original concept in Swiss forests. We characterised compositional and structural attributes of 'latent reserves' and compared them with those of managed forests to assess whether the former demonstrated more signs of naturalness than the latter. As an example of an application, we analysed the tree- and stand-level factors affecting natural tree mortality in 'latent reserves'. Up to 15.3% of Swiss NFI plots fulfilled the criteria of 'latent reserves', and most of these plots were distributed at mid- to high elevations where accessibility and management opportunities are limited. 'Latent reserves' showed more signs of naturalness than managed forests-a higher proportion of broadleaves, higher mortality rates, higher stand density and more deadwood. However, their size structure and basal area did not differ from those of managed forests, most likely because of a lower site productivity. Although 'latent reserves' were transitioning towards a natural state, more time without management might be required for these forests to become fully detached from the effects of past management, especially at high elevations. Mortality analyses in 'latent reserves' showed that species-specific tree mortality had a U-shaped response to tree size, was negatively related to tree growth and was higher when competition was stronger. Synthesis. Our findings demonstrate the potential of 'latent reserves' to study near-natural forests at the country level, and point to further opportunities for larger-scale collaborations. Investigating 'latent reserves' represents a first step towards a deeper understanding of such forests using existing long-term data and shows promise for further research in Europe.

Abstract Tree regeneration is a key process for long‐term forest dynamics, determining changes in species composition and shaping successional trajectories. While tree regeneration is a highly stochastic process, tree regeneration studies often cover narrow environmental gradients only, focusing on specific forest types or species in distinct regions. Thus, the larger‐scale effects of temperature, water availability, and stand structure on tree regeneration are poorly understood. We investigated these effects in respect of tree recruitment (in‐growth) along wide environmental gradients using forest inventory data from Flanders (Belgium), northwestern Germany, and Switzerland covering more than 40 tree species. We employed generalized linear mixed models to capture the abundance of tree recruitment in response to basal area, stem density, shade casting ability of a forest stand as well as site‐specific degree‐day sum (temperature), water balance, and plant‐available water holding capacity. We grouped tree species to facilitate comparisons between species with different levels of tolerance to shade and drought. Basal area and shade casting ability of the overstory had generally a negative impact on tree recruitment, but the effects differed between levels of shade tolerance of tree recruitment in all study regions. Recruitment rates of very shade‐tolerant species were positively affected by shade casting ability. Stem density and summer warmth (degree‐day sum) had similar effects on all tree species and successional strategies. Water‐related variables revealed a high degree of uncertainty and did not allow for general conclusions. All variables had similar effects independent of the varying diameter thresholds for tree recruitment in the different data sets. Synthesis: Shade tolerance and stand structure are the main drivers of tree recruitment along wide environmental gradients in temperate forests. Higher temperature generally increases tree recruitment rates, but the role of water relations and drought tolerance remains uncertain for tree recruitment on cross‐regional scales.

Sustainable forest management plays a key role for forest biodiversity and the provisioning of ecosystem services (BES), including the important service of carbon sequestration for climate change mitigation. Forest managers, however, find themselves in the increasingly complex planning situation to balance the often conflicting demands in BES. To cope with this situation, a prototype of a decision support system (DSS) for strategic (long-term) planning at the forest enterprise level was developed in the present project. The DSS was applied at three case study enterprises (CSEs) in Northern Switzerland, two lowland and one higher-elevation enterprise, for a 50-year time horizon (2010 to 2060) under present climate and three climate change scenarios (‘wet’, ‘medium’, ‘dry’). BES provisioning (for biodiversity, timber production, recreation, protection against gravitational hazards and carbon sequestration) was evaluated for four management scenarios (no management, current (BAU), lower and higher management intensity) using a utility-based multi-criteria decision analysis. Additionally, four alternative preference scenarios for BES provisioning were investigated to evaluate the robustness of the results to shifting BES preferences. At all CSEs, synergies between carbon sequestration, biodiversity and protection function as well as trade-offs between carbon sequestration and timber production occurred. The BAU management resulted in the highest overall utility in 2060 for different climate and BES preference scenarios, with the exception of one lowland CSE under current BES preference, where a lower intensity management performed best. Although climate change had a relatively small effect on overall utility, individual BES indicators showed a negative climate change impact for the lowland CSEs and a positive effect for the higher elevation CSE. The patterns of overall utility were relatively stable to shifts in BES preferences, with exception of a shift toward a preference for carbon sequestration. Overall, the study demonstrates the potential of the DSS to investigate the development of multiple BES as well as their synergies and trade-offs for a set of lowland and mountainous forest enterprises. The new system incorporates a wide set of BES indicators, a strong empirical foundation and a flexible multi-criteria decision analysis, enabling stakeholders to take scientifically well-founded decisions under changing climatic conditions and political goals.

SwissStandSim: ein klimasensitives, einzelbaumbasiertes Waldwachstumsmodell Die Entwicklung unserer Waldbestände unter dem Klimawandel und die Beeinflussung der zukünftigen Waldleistungen durch heutiges Handeln sind zu wichtigen waldbaulichen Fragen geworden. Daher braucht es modellgestützte Hilfsmittel für die forstliche Planung und die Optimierung von Behandlungen. In diesem Artikel wird das neue, wissenschaftlich abgestützte Waldwachstumsmodell SwissStandSim vorgestellt, das Teil eines zukünftigen Entscheidungsunterstützungssystems für die schweizerische Forstwirtschaft werden soll. SwissStand-Sim basiert auf den Daten der langfristigen ertragskundlichen Versuchsflächen der Schweiz. Diese werden verwendet, um ein klimasensitives, einzelbaumbasiertes Waldwachstumsmodell zu erstellen. Die Daten wurden mit möglichst vielen Umweltvariablen kombiniert, um die demografischen Prozesse Bewirtschaftung, Wachstum, Mortalität und Einwuchs zu modellieren. Die Klimavariablen zeigten ökologisch interessante Wechselwirkungen, so besonders zwischen Niederschlag und Temperatur, und zwar für das Wachstum wie auch die Mortalität. Eine weitere Wechselwirkung zeigte sich zwischen der Stärke der Bewirtschaftung und der Mortalität. Je stärker die Bewirtschaftung, desto höher war die Mortalität in den darauffolgenden Jahren. Dies lässt Interpretationen zu bezüglich Vulnerabilität von Beständen nach starken Eingriffen. Beispielhafte Simulationen mit SwissStandSim für drei ertragskundliche Versuchsflächen zeigen eine gute Übereinstimmung zwischen Daten und Modell, wobei der Häufigkeit und der Intensität von Störungsereignissen eine Schlüsselrolle zukommt. Die Herleitung von SwissStandSim beruhte auf einem mittleren Störungsregime, was je nach lokaler Situation zu einer Über- oder einer Unterschätzung der Mortalität führen kann. Mit SwissStandSim kann die Bewirtschaftung von Beständen flexibel simuliert werden. Damit ist SwissStandSim dazu prädestiniert, im Rahmen eines an der WSL laufenden Projektes (SessFor, NFP73) Teil eines praxistauglichen Werkzeugs für die Entscheidungsunterstützung für waldbauliche Fragen auf der Bestandesebene zu werden.

Forest management is becoming increasingly complex due to increasing demands in ecosystem service provisioning and future climate change impacts. For a sustainable forest management, scientifically well-founded decision support is therefore urgently required. Within the project SessFor, a decision support system for strategic planning at the forest enterprise level is being developed, based on the climate sensitive forest model SwissStandSim and initialized from forest inventory data. The system is currently applied to the forest enterprise Wagenrain (440 ha), located in the Swiss Plateau region. Indicators for biodiversity and ecosystem service provisioning (timber production, recreation value and carbon sequestration) are calculated for different management strategies and evaluated using a multi-criteria decision analysis. Preliminary results demonstrate the suitability of the system to evaluate ecosystem service provisioning under different management strategies and to identify the best management strategy, based on criteria defined by the forest manager. Furthermore, results show how the system can be used to assess developments for time-scales of 50–100 years under different climate change scenarios. In the ongoing project, the system will be applied to other case study regions, including mountain forests, which are of key importance in Switzerland and other alpine areas.

While the Swiss NFI (NFI) delivers detailed information on the state of forest resources at the time of the field visit, data on the annual carbon (C) balance in dead organic matter (DOM) and soil are beyond the scope of the NFI. The annual C balance of DOM and soil on NFI sample plots is thus estimated with the C cycling model YASSO07.

Knowledge regarding tree species dynamics is essential to understand forest responses to the environment, and to evaluate management options in adapting forest ecosystems to future climates. As maintaining tree species diversity and promoting structural stand heterogeneity are among the strategic elements in adapting forest management to climate change, the monitoring tree diversity is an ongoing challenge. Large-scale forest inventories have been proposed as a suitable basis for forest diversity analysis on large spatial and temporal scales. We used Swiss forest inventory data (NFI) to analyse temporal changes in tree species richness on small plots from 1983 to 2006. For two size groups of trees (`small' trees with dbh from 12 to 35 cm from plots with 200 m(2) area, and 'large' trees with dbh >= 36 cm from plots with 500 m(2) area), we identified the number and the tree species appearing (`gains') or disappearing (`losses') from each plot during the study period, and related these changes to site, stand and management characteristics. We found that species richness change was size-dependent and varied largely due to regional differences in the past land-use history of the Swiss forests. 'Gains' of 'small' trees were higher in stands with diverse vertical structure, with less competitive pressure as well as in warm environments, whereas 'gains' of 'large' trees were mostly related to climate and were highest in warm and moderately moist habitats. 'Losses' in both tree-size groups were mainly promoted by management. Our analysis suggests high vulnerability of Picea abies and high competitiveness of Fagus sylvatica, and underlines the potential of Abies alba in forming future Swiss forests. Despite of the silvicultural paradigm to create more species rich forests, most silvicultural interventions decreased small-scale species richness. This calls for further studies on the effect of management on tree species diversity.

Key message Volume predictions of sample trees are basic inputs for essential National Forest Inventory (NFI) estimates. The predicted volumes are rarely comparable among European NFIs because of country-specific dbh-thresholds and differences regarding the inclusion of the tree parts stump, stem top, and branches. Twenty-one European NFIs implemented harmonisation measures to provide consistent stem volume predictions for comparable forest resource estimates. Context The harmonisation of forest information has become increasingly important. International programs and interest groups from the wood industry, energy, and environmental sectors require comparable information. European NFIs as primary source of forest information are well-placed to support policies and decision-making processes with harmonised estimates. Aims The main objectives were to present the implementation of stem volume harmonisation by European NFIs, to obtain comparable growing stocks according to five reference definitions, and to compare the different results. Methods The applied harmonisation approach identifies the deviations between country-level and common reference definitions. The deviations are minimised through country-specific bridging functions. Growing stocks were calculated from the un-harmonised, and harmonised stem volume estimates and comparisons were made. Results The country-level growing stock results differ from the Cost Action E43 reference definition between − 8 and + 32%. Stumps and stem tops together account for 4 to 13% of stem volume, and large branches constitute 3 to 21% of broadleaved growing stock. Up to 6% of stem volume is allocated below the dbh-threshold. Conclusion Comparable volume figures are available for the first time on a large-scale in Europe. The results indicate the importance of harmonisation for international forest statistics. The presented work contributes to the NFI harmonisation process in Europe in several ways regarding comparable NFI reporting and scenario modelling.

In the Swiss National Forest Inventory (NFI), wood volume and changes in wood volume are estimated based on the stem volume of individual trees using various models: stem volume models and tariff models, models estimating volumes of large and small branches and growth models. Many of the models applied in the fourth NFI were described in previous publications, but some of them have subsequently been completed or adjusted based on methodological developments. This chapter mainly updates descriptions published previously.

Accurate and representative prediction of ingrowth is essential for modeling forest development. Besides the number of ingrowth trees, the basic tree attributes diameter and species are also important. In this study, these three characteristics were modeled based on data from the Swiss National Forest Inventory (NFI). The study covered large gradients of stand conditions and climate variables, making the models suitable to predict ingrowth under climate change. As the number of ingrowth trees per plot included more zeros than is expected for a Poisson distribution, we used three alternative probability distributions: zero-inflated Poisson distribution (ZIP), negative binomial distribution (NB) and zero-inflated negative binomial distribution (ZINB). Models with each of the three variants were fitted with and without random effects, resulting in six different model types. Model selection was performed backward using the BIC criterion. Of the final models, ZIP showed the best predictions of independently observed number of ingrowth trees. Our results indicate that the number of ingrowth trees strongly depended on the development stage of forests and on stand basal area, while temperature and precipitation, nitrogen deposition and water holding capacity each had a lower but still significant and plausible effect. The Weibull function was used to describe the probability distribution of the diameter of ingrowth trees and parameters were estimated using the Likelihood approach. The diameter of ingrowth trees was larger where there was a better site index and decreased with increasing stand density. Further, twelve species groups of ingrowth trees were fitted with a multinomial regression approach and showed clear dependence on climate: the probability of spruce and larch ingrowth clearly decreased with increasing temperature, whilst all other tree species profited from warmer conditions. The probability of fir, beech and ash ingrowth increased with increasing basal area, demonstrating the relevance of shade tolerance. The most important variable for predicting the species of ingrowth was the leading tree species group in a plot.

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.

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.