Until the early 2000s, detection of infectious disease emergence (in animals and humans) relied on classical reporting of cases for known pathogens (called indicator-based surveillance [IBS]). Despite having standardized procedures for verification and confirmation of cases by field practitioners, laboratories and health officials, the IBS lacks sensitivity, mainly due to non-reporting and delayed reporting of cases. In a context of a continuously changing environment due to climate change, animal and human mobility, population growth and urbanization, there is an increased risk of emergence of new and exotic pathogens, which may pass undetected with the IBS. Hence, the need to detect signals of infectious disease emergence using informal sources (event-based surveillance [EBS]). The current EBS mainly relies on data from one type of source (e.g., electronic media, laboratory data or health records); thus, decision makers are confounded with the interpretation of data from multiple sources and systems. To overcome this, the project MOOD (MOnitoring Outbreak events for Disease surveillance in a data science context) aims to harness the state-of-the-art data mining and analytical techniques to big data originating from multiple sources to improve monitoring of the (re-)emergence of zoonotic infectious diseases in Europe, including antimicrobial resistance (AMR). Indeed, zoonotic diseases present the additional difficulty of needing a common framework to address the surveillance issues both in animals and humans. To this purpose, MOOD will establish a “one serves all” framework and visualisation platform that will allow real-time analysis and interpretation of epidemiological and gene sequence data in combination with climate, environmental and socio-economic covariates in an integrated and interdisciplinary “One health” approach. The MOOD framework will link research, national and international animal and public health organizations in Europe and beyond, to develop: 1) Data mining methods for collecting and combining heterogeneous and multi-source big data, 2) A network of disease experts to interpret the (possibly) weak signals and identify the drivers of infectious disease emergence, 3) Data analysis methods applied to Big data, including, but not limited to, spatial-temporal analysis, social network analysis, and fuzzy logic, to model infectious disease (re-)emergence and spread, 4) Ready-to-use online platform destined to a community of animal and public health users, including the public, tailored to their needs, and including capacity building and a network of disease experts to facilitate risk assessment of detected signals. The outcomes from MOOD will be designed in collaboration with national and regional European stakeholders, to assure their routine use during and beyond the project duration. These users will be implicated in the project to identify and adapt the project according to their needs. MOOD will complement and link to existing surveillance systems and other related projects or initiatives global and European level. The functionalities of MOOD will be tested and adapted through continuous assessment and evaluation using case studies on air-borne, vector-borne, water-borne and food-borne diseases, as well as AMR. Throughout the project, extensive consultations with potential users, studies into the barriers to open data sharing, dissemination and training activities, and studies on the cost-effectiveness of the project will support future sustainable user uptake.

Finding pathways for humans to coexist with biodiversity in Europe requires significant levels of up-to-date knowledge on species status, distribution, relative abundance, and their interactions with humans and each other. This is because of the human-dominatednature of the landscape with its associated pressures and drivers, the manifold forms of direct and indirect interactions betweenhumans and biodiversity, and the resulting need for adaptive management. Effective conservation requires continental scalecoordination, which requires continental scale data. This can only be achieved if we avail of methods that (1) can target many speciesat the same time, and (2) can make use of data collected for many different purposes by a diversity of professional and citizenscientists. Digital camera traps are one such tool, the use of which has exploded in recent years. There are literally tens of millions ofimages of wildlife being produced each year across Europe. However, the state of data processing tools and data sharing proceduresare not yet developed to allow an efficient classification, storage, and sharing of this data. Camera traps are also used in a myriaddifferent ways, with different field sampling protocols used in different areas and dependent on the primary motivations of the usersand their target species. It is unclear to what extent data collected under different regimes can be compared. In order to make thisvast data resource more available for scientists and biodiversity managers this project proposes a set of four interlinkedworkpackages that will; (1) Explore legal, institutional, and social contraints on data sharing with a view to identify pathways thatfaciliate making data as open and available as possible. (2) Develop efficient and AI-enabled database structures that facilitate theefficient processing of raw data, the safe storage of the data, and export formats that conform to emerging data standards tofacilitate data sharing and comparative analysis. (3) An exploration of statistical analysis tools and procedures that find ways tomaximise the integration of data collected under different protocols into common analysis, essentially determining which data, onwhich species, can be used to determine which inferences. (4) A set of demonstration analyses that reveal the possibility and added-value that can be obtained when data is pooled across projects and countries. These illustrative analyses will cover a range ofbiodiversity policy areas, including One Health, Climate Change, Invasive Species, Natura 2000 site management, and conservationof Habitat Directive listed species.

Average global temperatures are projected to rise by 3–5 ¬°C by 2100. A changing climate leads to changes in the frequency; intensity, spatial extent, duration, and can result in unprecedented extreme weather and climate events. Climate change (CC) will affect the ecosystem processes such as primary production, and the distribution and abundance of plant species. Climate change will also alter the plant diseases since the rate at which pathogens evolve and overcome host resistance may increase. Because abiotic factors such as temperature affect host susceptibility to pathogens and pathogen aggressiveness, interactions between plant resistance traits and abiotic stress tolerance may represent the most substantial impact of climate change on plant productivity. VitiSmart is a 3 years project with interdisciplinary research activities, structured with a well-balanced division between research and innovation, basic and applied research, field data collection and demonstration, socio-economic, technology transfer and dissemination actions, as well as management. It will contribute to the achievement of sustainable development goals by integrating economic, social and environmental dimensions and addressing mutually food security and climate challenges. The project is composed of 3 main themes: 1) Sustainably increasing agricultural quality, productivity and incomes; 2) Adapting and building resilience to climate change; and 3) Reducing chemical inputs while maintaining quality using biocontrol methods. The project aims to produce, at the end of the project, a resilient viticultural system able to speedily recover from biotic and abiotic stresses. This will be achieved by combining resilient cultivars with beneficial microorganisms to acquire a natural-cross-tolerance while maintaining yield. The main objectives of the project are to: • Strengthen climate change models on grapevine crop systems by understanding how climate change will affect cropping systems (as opposed to crop productivity); • Improve both preventive and curative strategies for more grapevine cultivars tolerant to pathogen under a CC context; • Understanding of the molecular and physiological pathways underlying the interaction between grape cultivars/beneficial microbial agents/pathogens/climate change; • Facilitate interdisciplinary research integrating innovative adaptive strategies with socio-economical aspects of grapevine production; • Support the European grapevine growers by matching consumer demands for top quality grapes and food safety; The project aims to produce, at the end of the project, a resilient viticultural system able to speedily recover from biotic and abiotic stresses. This will be achieved by combining resilient cultivars with beneficial microorganisms to acquire a natural-cross-tolerance while maintaining yield.

The ongoing biodiversity crisis imperils Nature’s Contributions to People and is being exacerbated by climate change. Genetic diversity within species is key to maintaining adaptive potential and ecosystem resilience, and is one of the three pillars of biodiversity, but is widely ignored in both policy and management, due to knowledge and implementation gaps. In GINAMO, we follow a co-creation process to provide clear scientific guidelines and ready-to-use workflows to estimate genetic indicators that are understood and embraced by end users. Two indicators in the Kunming-Montreal Global Biodiversity Framework are appropriate for monitoring and reporting on genetic diversity. These indicators relate a) to a minimum effective population size, Ne, of 500, with Ne being an essential biodiversity variable enabling the quantification of genetic diversity loss, and b) to maintain genetically distinct populations within species. In GINAMO we first will determine best practices to obtain accurate and robust Ne estimates for species with reference DNA-based data (WP2). Genetic data will help designing realistic evolutionary scenarios for simulations, to understand how spatial distributions, life history traits, data quantity and types, sampling strategies and statistical methods affect Ne estimates. For species without DNA-based data available, in WP3 and WP4 we will develop best practices to estimate Ne from proxies with publicly available data sources (e.g population size counts, occurrence data in observation portals, and relevant terrestrial habitat properties generated by earth observation data). A key component in GINAMO is to partner and co-decide from the outset with the stakeholder community for an optimal integration of all resources produced from WP2 to WP4 activities (i.e. databases, scripts, and guidelines) to meet their concerns, reporting duties and monitoring needs. Standardised and automated workflows will be co-created for assessing genetic indicators on various transboundary geographical scales, following FAIR (findable, accessible, interoperable and reusable) principles (WP5). The impact of the co-creation processes on participants’ knowledge, perceived usefulness of genetic indicators and willingness to implement, will be evaluated through interviews, focus groups and surveys (WP6). This co-creation process will strongly benefit from the multidisciplinary research team, including both natural and social scientists with expertise in policy and implementation. GINAMO effectively fits all three themes of the call as it will integrate various sources of available data in existing biodiversity databases (Theme 3) to address knowledge gaps (Theme 2) and provide outreach materials and Open Science tools for genetic indicators applicable in international (e.g. EU Biodiversity Strategy for 2030) and national policies, to improve new biodiversity data collection and inform specific conservation management actions (Theme 1).