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Climate-smart forestry (CSF) has gained increasing attention in recent years. Yet, a framework to assess the three pillars of CSF, in terms of mitigation and adaptation to climate change and ecosystem service (ES) provision based on national forest inventory (NFI) data is currently lacking. In this study, we present an assessment framework of CSF based on forest indicators derived from NFI. To quantify the three pillars of CSF we defined 1) mitigation based on the forest carbon stock, 2) adaptation using biodiversity and susceptibility to disturbances caused by storm and bark beetles and 3) ES provision with timber production and protection against avalanches and rockfall. We demonstrate the application of this framework using Swiss NFI data and projections of future forest development under four different management scenarios. Our results show that, in general, the carbon stock is increasing at the national scale when current management practices are maintained. Increases are especially pronounced in the Jura and Southern Alps regions. Trade-offs occur between enhancing mitigation and adaption, and the sustainable provision of ES such as timber production. Scenarios with increased harvesting intensities lead to a higher timber supply in the short-term, while decreasing the carbon stock, biodiversity-related old-growth indicators and the protective function of forests. The indicator framework presented in this study provides a first methodological approach that can support policy making to balance the different forest services in the context of CSF., Ecological Indicators, 133, ISSN:1470-160X, ISSN:1872-7034

Stand-level process-based models have rarely been applied to uneven-aged forests that contain many size classes and negative exponential shaped size distributions. However, the relative simplicity of such models, in terms of parameterisation, use and interpretation, could make them valuable tools for studying and managing such forests. In particular, the effects of climate change on the stand-level growth of forests with negative exponential shaped size distributions has received very little attention compared with even-aged forests. The first objective of this study was to validate 3-PG, a stand-level process-based model, for five types of uneven-aged forests in Switzerland; (1) Fagus sylvatica dominated, (2) Picea abies dominated, (3) mixtures of Picea abies and Abies alba, (4) mixtures of Picea abies, Abies alba and Fagus sylvatica, and (5) mixtures of Larix decidua, Pinus cembra and P. abies. The second objective was to use 3-PG to examine how climate change has influenced the growth of these forests since the 1930s. 3-PG predictions of biomass, biomass partitioning in above- and belowground components, and light absorption were validated using inventory data from 23 plots, which had been monitored for an average of 81 years (15 to 112 years). For all species and size classes (2–3 per species), 3-PG produced accurate predictions of root biomass, stem biomass and outputs derived from it such as mean diameter, basal area and height, which were all highly correlated with the observed values (R2 > 0.86). The slope of predicted versus observed values was often not significantly different to 1 (averaged 1.13) and the bias averaged −1.2%. 3-PG simulations to examine the effects of climate change without the confounding effects of stand structure and management, showed that the growth of the five forests types has, on average, increased by 17% since the 1930s. The growth was mainly influenced by temperature, while in the case of A. alba, growth was largely influenced by vapour pressure deficit. The accelerated growth rates imply that thinning intensities also need to increase to prevent high stand densities from inhibiting regeneration in these uneven-aged forests. This study shows that 3-PG can be used to predict the growth dynamics of uneven-aged mixed-species forests, and to our knowledge, this is the first time a stand-level process-based forest growth model has been used and validated for such forests., Forest Ecology and Management, 494, ISSN:0378-1127, ISSN:1872-7042