In eco2adapt we will develop the Ecosystem-based Adaptation (EbA) framework, derived from Nature-Based Solutions (NBS), that harnesses biodiversity and ecosystem services to reduce vulnerability and build social-ecological resilience to climate change. We will work in Living Labs in Europe and China, located in climate hotspots, and adopt a cutting-edge approach to investigate how forest managers integrate disturbance and vulnerability into decision-making. Scenarios of how disturbance affects forest dynamics and ecosystem services at a landscape scale will be derived through modelling, and in Living Labs, stakeholders will learn how their choices affect ecosystem services in neighbouring forests. We will combine interdisciplinary knowledge from scientists and stakeholders in Europe and China to understand perception and provide incentivization for adopting EbA solutions, through local capacity-building and national policy plans. Through Scenario Workshops and Stakeholders Working Groups, we will use a capacity-building approach to create and promote innovative technical, economic and governance mechanisms at a regional level. Semantic technology will be applied to create a knowledge base for hosting FAIR data and creating a SmartPhone Application (named the OneForest ToolBox) that allows users to access and add data concerning climate-resilient species, provenances, mixtures, management techniques and ecosystem services, whilst taking into account future uncertainties about climate and societal changes. We also provide a suite of cutting-edge tools to monitor vulnerability and resilience (including invasive species and above-and below- ground biodiversity), at all levels of society – from the citizen to the policy-maker. By including tailored communication to all levels of society, we will reach out to a broad audience that has the capacity to cause positive change.

Our general goal in this project is to further our understanding of the necessity, effectiveness, risks and ethical implications of assisted gene flow (AGF) to improve adaptation to climate change. To reach these goals, our plan is to: (i) Document the consequences of actual manipulations of gene flow in natural populations, by measuring in detail the genetic, demographic and ecological consequences of AGF. (ii) Build eco-evolutionary models predicting the quantitative impact of AGF on demography and genetic diversity, and optimizing the AGF protocols taking into account various management constraints. (iii) Analyze the moral dilemmas around AGF, and how these dilemmas differ between exploited, keystone and rare endangered species. We will particularly focus on the case of perennial plants, which are particularly threatened by climate change. We will consider the contrasted situations of rare endangered perennial herbs versus very long-lived common forest trees.

EFFORTE draws a red line through critical, cost/benefit driving processes, and environmentally concerns of today´s forestry. Starting with efficient fulfilment of various customer demands the red line goes along efficient utilization of Big Data sources, present knowledge and critical new knowledge foreseen as outcomes from this project. Technical development and mechanization has been a winning concept for high productivity now emphasizing more gentle methods and just in time deliveries to different industry customers. This is possible to reach if new knowledge, improved methods and technical development are combined with better transfer of information and data from different sources (e.g processes, geo data from LiDAR scanning, other conditions such as weather data etc).These Big Data sources have been available for some years, but it is not until recently that hardware, data communication and merging possibilities enable full potential for a revolution of new applications. In the EFFORTE proposal we have identified three main subjects that have specific importance for efficiency, productivity and environmental concern in forest practice. Two of these implies to increasing crucial knowledge and the third, Big Data applications, combines the new knowledge with high resolution information sources into practice increasing efficiency in forest management and the connected value chains. The main objectives of EFFORTE are: i) To develop scientifically firm and techno-economically feasible methodology to predict trafficability prior to forest operations. ii) To increase forest growth and productivity of tree planting and young stand management iii) To develop, customize and pilot modern ‘Big data’ solutions that will increase productivity and decrease negative environmental impact (e.g. soil, water and reduced fuel consumption). By EFFORTE we expect to make difference in efficiency, productivity and sustainability for a growing Bio-based economy in Europe.

Trees are an important component of the biosphere. They dominate many regions of the world as natural forests or plantations. They are not only important economically through the multiple uses of wood, but also for climate change mitigation through their function as carbon dioxide sinks, as well as for their role in the regulation of the hydrological cycle. Recent studies on tree mortality and climate change show that some of the world's forest ecosystems may already be responding with increasing tree mortality rates, in response to climatic warming and drought, even in environments that are not normally considered water-limited. In the long term, growth and survival of trees largely depend on their ability to adapt to the predicted rapid climatic changes. Among the possible strategies for adapting forests to climate change, the optimal use of natural genetic variation certainly represents an important component, along with genetic improvement, forest management and facilitation of migration. A sustainable management of forest ecosystems will require an understanding of the processes underlying adaptation to climate change. Knowledge of processes, and finally genes, involved in the variation of traits affecting fitness, the assessment of their variability in natural tree populations and their use as an indicator of adaptability should improve management practices facing climate change. An important management goal, in the context of climate change, is to optimize biomass growth versus water used through transpiration, even in forests that are presently only exposed to moderate soil water deficits. The ratio between these two traits is called the water use efficiency (WUE). Recent modelling studies, based on plastic responses of ecophysiological traits, have shown that predicted climatic changes will impact the WUE of forest ecosystems. The H2Oak project focuses on the diversity of WUE in two major French oak species : Quercus robur (pedunculate oak) and Q. petraea (sessile oak). In France they represent 11% of the total timber harvested and one fourth of the total forested area (~4Mha). These two species are largely sympatric, but have different ecological requirements: Q. petraea is more frequently found on well drained soils and is more tolerant to drought, whereas Q. robur is able to survive and grow on poorly drained sites and displays a higher tolerance to water-logging. Q. petraea has a higher WUE compared to Q. robur. Such an eco-hydrological niche segregation has been suggested to have implications for conservation in habitats that face changing water constraints caused by climate change. The main objective of the H2Oak project is to determine whether WUE and underlying traits play an adaptive role in oaks in terms of (i) impact on tree fitness, (ii) past selection and adaptation of provenances to their specific environments, and (iii) sufficient standing genetic diversity for future adaptation related to climate change, especially increased soil water deficit. The ultimate goal is to define genetic factors that can be used to evaluate the impact of silviculture and forest management practices on the adaptive potential of efficient use of water in oak forests. The proposed project will take a large step towards the discovery of genes underlying the ecological divergence within these oak species using forward genetics approaches (including QTL and association mapping), and ecological genomics. The H2Oak project will follow a progression of tasks advancing from detailing the genomic regions related to WUE in known progenies, using extreme phenotypes to refine diversity of physiological responses, screening candidate genes in diversity panels based on geographic distribution of populations, and applying the discovered adaptive molecular markers to study adaptation in natural regenerations of oak seedlings under silvicultural management.