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).

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.

Fungi constitute one of the largest groups of organisms on earth with central importance for ecosystem functioning. Despite their obvious relevance for understanding nature and ecosystem change, they have traditionally been neglected in conservation and monitoring, implying a wide-ranging knowledge gap. This project application has an overarching goal of closing this gap, by bringing fungi firmly on the biodiversity map. It will use existing citizen science data to explore spatiotemporal changes in fungal communities and analyse how well the Habitats Directive captures fungal biodiversity. Further, it will develop and test new tools and methods for fungal biodiversity mapping and monitoring, combining citizen science and standardized sampling of DNA from environmental sampling (eDNA). Finally, an important objective is to consolidate open data resources underlying collaboration on fungal biodiversity, by substantially improving taxonomic identification and data linked to DNA-based fungal occurrences. Overall, the project will hence address all three themes of the open Biodiversa call. The project is structured into clearly delegated, yet interlinked thematic work packages (WPs): 1. Improving identification of and unambiguous communication on fungal species 2. Applying and Improving AI tools for fungal monitoring 3. Involving citizen scientists in biodiversity discovery and monitoring 4. Sampling fungal communities by eDNA and 5. Analysing fungal biodiversity patterns in time and space. The project involves computer scientists, bioinformaticians, ecologists, taxonomists and citizen scientists collaborating to solve questions of societal interest. It is novel and seeking maximal applied impact by combining well-established, but so far isolated, tools in innovative ways. The consortium behind the project has a strong track record of previous collaborations and bridges research traditions in Northern, Central and Southern Europe, securing transfer of knowledge across regions, and a wide geographical scope on the ground for those WPs where this is central, i.e., WPs 1, 3 and 4. The project will not only provide a much-needed insight into the conservation status of fungi in Europe. Due to the critical roles fungi play in ecosystems, and their sensitivity to ecosystem change, improved insights into the fungal dimension of biodiversity will be of huge importance for understanding, more broadly, how global change affect ecosystems and associated ecosystem services mediated by fungi. Finally, we believe that the project will have impact on conservation and monitoring in other organism groups, by showcasing how molecular and AI methods in combination with unambiguous communication on species can be combined to increase credibility and impact of biodiversity data