The Ne500 genetic indicator in species with diverse life history strategies: best estimation practices from DNA data and operationalisation

Population genetic diversity (GD) is essential for long-term adaptation of wild species to changing environments. Humans have eroded ~10% of global GD, a silent crisis undermining food security, health and climate resilience.  Effective population size Ne is a genetic parameter crucial for monitoring GD, understanding evolutionary processes and designing conservation strategies as it impacts the rate of random genetic drift, inbreeding, and adaptive potential. The “proportion of populations within species with Ne above 500” (Ne500) is an indicator endorsed by the Kunming-Montreal Global Biodiversity Framework that can be calculated from DNA-based or proxy data. Our aims are (1) to develop best practices for estimating Ne when genetic data is available, and (2) to operationalize this indicator via FAIR workflows to facilitate its reporting to the CBD. 

We intend to improve the estimation of Ne by accounting for life-history traits (LHT) such as  longevity and reproductive strategies. We implemented an existing LHT-based categorization framework based on demographic matrices across a wide range of taxa. This allowed us to select DNA datasets from a subset of 18 European species across the main categories of the LHT framework, thus covering a large range of evolutionary histories and biological characteristics. 

We estimated genetic Ne and other population genetic metrics for 195 populations across the 18 species, and explored their variation across the LHT framework. We observed a large variation of Ne estimates across LHT categories, including populations with Ne below and above 500. We showed a negative relationship between Ne and population-specific divergence across a large proportion of LHT categories. We discuss situations where genetic or genomic data offer clear advantages over proxy data for reporting the Ne500 indicator, and we highlight the practical challenges of reliably estimating Ne in natural populations, particularly when spatial and age structures interact.

We are currently implementing operational workflows for end-users to compute Ne500 from a representative set of populations on the Galaxy-Ecology platform. These workflows infer genetic clusters to help with delimiting populations and control the effect of genomic data quality on Ne estimates.