Genes from Space: Monitoring genetic diversity indicators with the help of EO data

Genetic diversity is essential for the adaptive potential and resilience of species and ecosystems. Recognizing its importance, target 4 of the Kunming-Montreal Global Biodiversity Framework (GBF) aims to monitor and conserve genetic diversity and includes two species-specific genetic diversity indicators. The headline indicator “Ne> 500” (ratio of populations with an effective population size over 500, a conventional threshold to safeguard adaptive potential) and the supplementary indicator “populations maintained” (PM) (proportion of populations that are still present) are traditionally assessed by conducting field studies and collecting and processing lots of genetic samples. These indicators should be monitored for a taxonomically diverse set of ca. 100 species per country. Because the established DNA-based approach to estimate effective population sizes requires substantial investment of time, expertise and money per sample, it is not practical to obtain sufficient  data for reporting on the GBF genetic diversity indicators based solely on DNA data. The ISSI “Genes From Space” project developed a new method to calculate these indicators based on existing data (if any) and/or local knowledge complemented with Earth Observation (EO) data. The approach uses EO data combined with species occurrence data to estimate habitat size and structure, and density information to estimate whether the effective population size is above or below the Ne=500 threshold. Making use of already available and processed satellite data products, this new approach is time- and cost-effective and only requires basic computing power and internet access. By using data from 30 terrestrial plant and animal species on five different continents, a sensitivity analysis was conducted to evaluate the method’s sensitivity to different input parameters. The results showed that the calculation of indicator values was most robust with well-dispersed populations and species with a large range. The method is, however, most sensitive for species with low dispersal capacities and very low population densities.