GINAMO: Genetic Indicators for Nature Monitoring

Context: Genetic diversity is the foundation of biodiversity and essential for long-term survival, adaptation, and resilience for populations, species, and ecosystems. While genetic diversity has long been neglected in biodiversity policy and management, the current Convention on Biological Diversity (CBD) Kunming-Montreal Global Biodiversity Framework (GBF) now includes genetic diversity monitoring, also for wild species. Tools and indicators to monitor genetic diversity are available but are rarely used due to the gap in knowledge transfer between conservation science and application. GINAMO aims to fill this gap by developing best practices and guidelines for applying genetic diversity indicators. This will enable routine integration of genetic criteria into biodiversity assessments, from policy on regional, national and EU levels, to global conventions and obligations. Main objectives: GINAMO aims to determine the best ways to accurately estimate the two genetic diversity indicators of the GBF: 1) the proportion of populations within species with an effective population size greater than 500, and 2) the proportion of populations of a species maintained. To help to implement these indicators, GINAMO designs, facilitates and evaluates processes in which scientists and stakeholders together develop methodologies and workflows that are scientifically sound, effective, and in line with nature management and policy requirements. Main activities: GINAMO focuses on generating best practices for assessing genetic indicators from both genetic and non-genetic data. GINAMO also evaluates how satellite earth observation data can be used to generate proxies for genetic diversity. Workflows will be standardised to provide easily accessible data for researchers, nature managers and policy makers.

The biodiversity crisis imperils nature’s contributions to people. Genetic diversity is key to maintain adaptive potential and ecosystem resilience. It is one of the three pillars of biodiversity, but is widely ignored in policy and management, due to knowledge and implementation gaps. GINAMO follows a co-creation process to provide scientific guidelines and ready-to-use workflows to estimate genetic indicators. Two indicators in the Kunming-Montreal Global Biodiversity Framework are appropriate for monitoring and reporting on genetic diversity. These indicators relate to a minimum effective population size (Ne) of 500, and to maintain genetically distinct populations within species. GINAMO determines best practices to obtain accurate Ne estimates for species with DNA-based data (WP2). Genetic data helps designing realistic evolutionary scenarios 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, GINAMO develops best practices to estimate Ne from proxies (WP3, WP4). GINAMO will partner with the stakeholder community for an optimal integration of all resources to meet their concerns, reporting duties and monitoring needs. Standardised, automated workflows are co-created for assessing these indicators on various transboundary geographical scales, following FAIR principles (WP5). The impact of the co-creation processes on participants’ knowledge, perceived usefulness and willingness to implement, is evaluated through surveys and interviews (WP6). GINAMO effectively fits all three themes of the call: to integrate various sources of available data in existing biodiversity databases (Theme 3), to address knowledge gaps related to biodiversity loss (Theme 2), and to improve biodiversity data collection and inform specific conservation management actions (Theme 1).