Lessons from Implementing the Genetic Diversity Indicator Across Multiple Taxa; From Conceptual Guidance to Taxon-Specific and Technical Workflow Recommendations

The Kunming–Montreal GBF emphasizes the need for robust and scalable measures of genetic diversity (Headline Indicator 4.2). Recent work highlights that population estimates, via population delineation combined with density estimates, or effective population size (Ne) can be used for measuring genetic diversity indicators. While streamlined R workflows have been proposed in earlier studies, and an online tool (BON in a Box) is currently being beta-tested by Genes from Space, there remains a critical need to understand how such methods are applied across diverse taxa and what analytical considerations are required for consistent implementation.

To address this need, we measured the Genetic Diversity Indicator for South Korea across eight major taxonomic groups (plants, insects, birds, fish, mammals, non-insect invertebrates, amphibians, and algae). We conducted two full-day workshops per taxon (16 total workshops), each with three subject-matter experts. These workshops generated key insights into species-selection criteria, essential data requirements, and methodological challenges unique to each group. We also performed sensitivity analyses examining within and among user variation, the influence of prior exposure to genetic diversity literature on population estimate, and the effect of adding environmental layers (e.g., temperature, elevation) on increasing the accuracy of population estimates. Given abstract length limits, we focus here only on the resulting analytical protocol.

We developed a unified QGIS-based workflow that allows users to flexibly incorporate both the quality (e.g., high-resolution raster) and quantity (e.g., elevation, bathymetry) of available spatial data. Analysts generate environmental envelopes and draw polygons informed by multiple, taxon-appropriate layers, increasing the ecological accuracy of population delineation and subsequent population estimates. To support repeatability and future monitoring, we embedded self-evaluation metadata within each species’ geopackage, including user confidence scores, perceived data sufficiency, and recommendations for expert reassessment. Furthermore, a quantitative method was implemented to detect species distribution change between two time points.

By synthesizing lessons learned across eight taxonomic groups and formalizing a transparent, adaptable, and repeatable analytical protocol, this work provides practical guidance for countries preparing to implement Genetic Diversity Indicator 4.2 and contributes to emerging global efforts to standardize genetic diversity monitoring.

This study was funded by the National Institute of Biological Resources, South Korea(NIBR202405203,NIBR202505103,NIBR202505203.