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The forest-based sector plays a significant role in supporting Europe on its pathway towards a more integrated and bio-based circular economy. Beyond the supply of timber, forest ecosystems offer a wide range of products and services beneficial to human wellbeing. Non-wood forest products (NWFPs) play an integral role in provisioning forest ecosystem services and constitute a huge portfolio of species from various taxonomic kingdoms. As diverse as the resources themselves is the list of end-products that may be derived from raw non-wood materials. Multiple value-chains of NWFPs provide benefits to actors across all stages of the supply chain. Forest management has not yet directed full attention towards NWFPs, since timber production remains the main management objective, although multi-purpose management is recognised as a key principle of the sector’s sustainability paradigm. Lack of knowledge of the socio-economic relevance of NWFPs for European societies and diverse property rights frameworks increase the complexity in forest-based decision making additionally. In this study, the future potential of 38 NWFPs for diversifying the forest bioeconomy is investigated by means of multi-criteria analysis, including stakeholder interaction and expert involvement. The results for six case studies in different biogeographical zones in Europe indicate the latent opportunities NWFPs provide to forest owners who are willing to focus their management on the joint production of wood and non-wood resources as well as their value networks. This study intends to unravel perspectives for forest owners in particular, as they often represent principal decision makers in forest ecosystem management, act as main suppliers of NWFP raw materials, and thus can be understood as key stakeholders in a forest bioeconomy. Even though regional perspectives differ, due to varying socio-economic and ecological environments, there is huge potential to strengthen the economic viability of rural ar

This study investigates the impact of location uncertainty on the predictive performance of Bayesian Logistic Regression (BLR) for forest fire ignition prediction in Austria. Historical forest fire ignitions are used to create a dataset for training models with the capability to assess the general forest fire ignition susceptibility. Each recorded fire ignition contains a timestamp, the estimated location of the ignition and a radius defining the area within which the unknown true location of the ignition point is located. As the values of the predictive features are calculated based on the assumed location, and not the unknown true location, the training data is biased due to input uncertainties. This study is set to assess the impact of input data uncertainty on the predictive performance of the model. For this we use a data binning approach that splits the input data into groups based on their location uncertainty and use them later for training multiple BLR models. The predictive performance of the models is then compared based on their accuracy, area under the receiver operating characteristic curve (AUC) scores and brier scores. The study revealed that higher location uncertainty leads to decreased accuracy and AUC score, accompanied by an increase in the brier score, while demonstrating that the BLR model trained on a smaller high-quality dataset outperforms the model trained on the full dataset, despite its smaller size. The study’s contribution is to provide insights into the practical implications of location uncertainty on the quality of forest fire susceptibility predictions, with potential implications for forest risk management and forest fire documentation.

Fire indices are used to describe the weather conditions that influence fire ignition and fire behavior. Although many studies analyzed their performance on fire occurrence at daily resolution, few focused on their ability to capture the burned area, which is usually analyzed at the weekly or monthly scale. Cumulative Logarithmic Area Ranking Efficiency (CLARE) is a newly developed metric that takes burned area into account when assessing daily fire danger. The use of CLARE in addition to the Area Under the receiver operating characteristic Curve (AUC) in the selection process of fire indices or fire occurrence models provides a complementary metric that allows for the evaluation of a model's ability to assess burned area. We evaluated the CLARE performance in 11 regions ranging from the European Alps to the Mediterranean basin. We also assessed the impact of (i) different groups of input variables (meteorological variables vs. fire indices), (ii) model complexity in terms of number of variables, and (iii) the modeling approach (Generalized Linear Models vs. Maxent) on the performance of CLARE. We found that models that achieve a high AUC for predicting fire occurrence may fail to show a high performance when predicting burned area. Using a multi-variable modeling approach is likely to provide higher CLARE performance than using single-variable fire index models, especially among models that have high AUC. Moreover, using this approach led to better multi-variable meteorological model performance than single-variable fire index models for some regions. This may be particularly valuable for regions where the calculation of fire indices is not possible. Finally, the differences between the modeling approaches were mainly related to the region or input variable groups. Overall, our results highlight that including burned area in the fire danger assessment process is feasible across a wide range of environmental conditions and provides valuable insights.

Europe's forest provide multiple ecosystem services for societies, ranging from provisioning (e.g. wood) and regulating (e.g. climate mitigation and biodiversity) to cultural (e.g. recreation) services. In this paper, we assess the state and prospects of forest ecosystem services provision in Europe, introducing new data from the European collaborative research projects SINCERE, NOBEL and CLEARING HOUSE, and combining it with findings from the literature. We identify six challenges (1 an insufficient alignment of FES supply and demand, 2 lacking policy integration, 3 ambiguous and conflicting regulatory frameworks, 4 a lack of precise information on FES demand and provision, and innovations to align both, 5 an increasing pressure to adapt to climate change, and 6 a striking diversity constraining European level policy solutions) and three opportunities (1 increasingly heterogenous forest owner objectives potentially matching pluralistic societal demands, 2 diversifying forest enterprises levering innovations in regulating and cultural ecosystem services provision, and 3 the potential of forests to mitigate climate change). Subsequently, we introduce four distinct but complimentary policy pathways for European forest policy to better align forest ecosystem services provision and demand: 1 Better monitoring of FES supply and demand, 2 Enhanced policy integration, 3 Payments for ecosystem services, and 4 Bottom-up participation and learning among ecosystem services innovators. We conclude by emphasizing the momentum that the EU Green Deal unfolds for a future European forest policy to incentivise the provision of multiple forest ecosystem services.