The goal of B4EST is to increase forest survival, health, resilience and productivity under climate change and natural disturbances, while maintaining genetic diversity and key ecological functions, and fostering a competitive EU bio-based economy. B4EST will provide forest tree breeders, forest managers and owners, and policy makers with: 1) better scientific knowledge on adaptation profiles and sustainable productivity, and added value of raw materials in important European tree species for forestry, 2) new and flexible adaptive tree breeding strategies, 3) tree genotypes of highly adaptive and economical value, 4) decision-support tools for the choice and use of Forest Reproductive Material (FRM) while balancing production, resilience and genetic diversity, including case studies developed with industrial partners, 5) integrative performance models to guide FRM deployment at stand and landscape level, 6) economic analyses of risks/benefits/costs, and 6) policy recommendations. B4EST will capitalise on the resources developed by past and current EU projects to produce -together with tree breeders, forest managers and owners, and the industry- operational solutions to better adapt forests to climate change and reinforce the competitiveness of the EU forest-based sector. To cover the geographical, economic and societal needs of forestry in the EU, B4EST will work with 8 (six native, two non-native) conifers and broadleaves with advanced breeding programmes (Norway spruce, Scots pine, maritime pine, poplars, Douglas-fir, eucalypts) or that are case studies of pest-threatened forests (ash) or valuable non-wood products (stone pine). Our approach will result in a high degree of data and knowledge integration, involving multiple and new target traits and their trade-offs; genomic information; temporal and spatial assessments in a wide range of environments; stakeholder demands; and forest owner and manager risk perception and acceptability of new breeding strategies.

SUPERB pursues the overall goal to create a lasting enabling environment for transformative change towards large-scale forest and forest landscape restoration, which empowers decision makers to take just and informed decisions for restoration of biodiversity, ecosystem services and carbon sequestration in a manner that minimises region specific trade-offs and maximises synergies between ecosystem services. SUPERB develops and synthesises a multidisciplinary, practical, and scientific restoration knowledge basis and makes it publicly available. In 12 large-scale demonstrators across Europe, we will showcase best practices responding to key forest restoration and adaptation challenges on some hundreds of hectares per demo and with the potential for immediate upscaling to over one million hectares in 10-15 years. For large scale restoration to be successful, many actors from different sectors and disciplines must behave synergistically and in a mutually reinforcing way. We will speed up transformative change and further upscaling through innovative stakeholder involvement across scales to ensure the favorability and uptake of the proposed approaches. A comprehensive multi-language online Forest Ecosystem Restoration Gateway will guide stakeholders to find answers to their restoration questions, advise them on how to deal with barriers and enablers and provide access to easily applicable and comprehensible tools and materials that support restoration, e.g., best practices for forest restoration or the development of scalability plans, a tree species selection application, an innovative funding guide, and much more. The Gateway will also host a restoration Marketplace, where market agents, e.g. potential funders and landowners, can agree on bids for restoration projects. SUPERB will boost and measure its impact through its extensive and systematically enlarged stakeholder communities and networks, to ensure the relevance of the project outputs and their positive uptake.

Many current or projected future land-based renewable energy schemes are highly dependent on very localised climatic conditions, especially in regions of complex terrain. For example, mean wind speed, which is the determining factor in assessing the viability of wind farms, varies considerably over distances no greater than the size of a typical farm. Variations in the productivity of bio-energy crops also occur on similar spatial scales. This localised climatic variation will lead to significant differences in response of the landscape in hosting land-based renewables (LBR) and without better understanding could compromise our ability to deploy LBR to maximise environmental and energy gains. Currently climate prediction models operate at much coarser scales than are required for renewable energy applications. The required downscaling of climate data is achieved using a variety of empirical techniques, the reliability of which decreases as the complexity of the terrain increases. In this project, we will use newly emerging techniques of very high resolution nested numerical modelling, taken from the field of numerical weather prediction, to develop a micro-climate model, which will be able to make climate predictions locally down to scales of less than one kilometre. We will conduct validation experiments for the new model at wind farm and bio-energy crop sites. The model will be applied to the problems of (i) predicting the effect of a wind farm on soil carbon sequestration on an upland site, thus addressing the question of carbon payback time for wind farm schemes and (ii) for predicting local yield variations of bio-energy crops. Extremely high resolution numerical modelling of the effect of wind turbines on each other and on the air-land exchanges will be undertaken using a computational fluid dynamics model (CFD). The project will provide a new tool for climate impact prediction at the local scale and will provide new insight into the detailed physical, bio-physical and geochemical processes affecting the resilience and adaptation of sensitive (often upland) environments when hosting LBR.