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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

Additional file 1: Supplementary material.

The files refer to the data used in Portier et al. "‘Latent reserves’: a hidden treasure in National Forest Inventories" (2020) *Journal of Ecology*. **'Latent reserves'** are defined as plots in National Forest Inventories (NFI) that have been free of human influence for -40 to -70 years. They can be used to investigate and acquire a deeper understanding of attributes and processes of near-natural forests using existing long-term data. To determine which NFI sample plots could be considered ‘latent reserves’, criteria were defined based on the information available in the Swiss NFI database: * Shrub forests were excluded. * Plots must have been free of any kind of management, including salvage logging or sanitary cuts, for a minimum amount of time. Thresholds of 40, 50, 60 and 70 years without intervention were tested. * To ensure that species composition was not influenced by past management, plots where potential vegetation was classified as deciduous by Ellenberg & Klötzli (1972) had to have an observed proportion of deciduous trees matching the theoretical proportion expected in a natural deciduous forest, as defined by Kienast, Brzeziecki, & Wildi (1994). * Plots had to originate from natural regeneration. * Intensive livestock grazing must never have occurred on the plots. The tables stored here were derived from the first, second and third campaigns of the Swiss NFI. The raw data from the Swiss NFI can be provided free of charge within the scope of a contractual agreement (http://www.lfi.ch/dienstleist/daten-en.php). **** The files 'Data figure 2' to 'Data figure 8' are publicly available and contain the data used to produce the figures published in the paper. The files 'Plot-level data for characterisation of 'latent reserves' and 'Tree-level data for characterisation of 'latent reserves' contain all the data required to reproduce the section of the article concerning the characterisation of 'latent reserves' and the comparison to managed forests. The file 'Data for mortality analyses' contains the data required to reproduce the section of the article concerning tree mortality in 'latent reserves'. The access to these three files is restricted as they contain some raw data from the Swiss NFI, submitted to the Swiss law and only accessible upon contractual agreement.

The files refer to the data and R code used in Mey et al. "From small forest samples to generalised uni- and bimodal stand descriptions" (2021) _Methods in Ecology and Evolution_. Generalised stand descriptions are coming from the simultaneous examination of samples that are representative for a specific target area (here, Switzerland) and link available information about forest stand attributes. They combine the modelling of uni- or bimodal diameter distributions and species compositions, i.e. the shares of stems of individual species. Generalised stand descriptions may be used to interpret tree species diversity, regeneration and harvest potentials on a plot-level basis, and to initialise forest models with representative stand data. The data stored here were derived from the fourth campaigns of the Swiss National Forest Inventory (NFI). The raw data from the Swiss NFI can be provided free of charge within the scope of a contractual agreement (http://www.lfi.ch/dienstleist/daten-en.php). --------------------------------------- The file 'Data Figures 2 and 4' is publicly available and contains the data used to produce the Figures 2 and 4 published in the paper. The files 'Data diameter modelling' and 'Data species modelling' contain all the data required to reproduce the diameter and species model building. The access to these two files is restricted as they contain raw data from the fourth Swiss NFI, submitted to the Swiss law and only accessible upon contractual agreement. The files 'Script diameter and species modelling' and 'Functions diameter modelling' are publicly available and provide the R code used to derive the generalised stand descriptions from the Swiss NFI data.