What can genetic diversity monitoring tell us about adaptive potential? Insights from a Swiss pilot study

Genetic diversity is considered essential for populations’ adaptive potential and long-term persistence, yet it has historically received less attention in conservation policy than species and ecosystem diversity. The Kunming–Montreal Global Biodiversity Framework (GBF) explicitly addresses the need to maintain genetic diversity in order to preserve the adaptive potential of populations. This calls for a systematic monitoring of genetic diversity in natural populations, raising a central question: to what extent can genetic data capture populations’ adaptive potential at scales relevant for conservation planning? Traditional genetic diversity metrics cannot directly quantify adaptive potential, and the required extensive phenotypic or fitness data for a quantitative genetic prediction of adaptive potential are rarely available for wild populations of conservation concern.  Genomic approaches, such as genetic offsets, which estimate the genetic changes required for populations to remain adapted under projected future climatic conditions, offer a promising avenue to infer climate adaptation status using genome-wide data.  Here, we evaluate the suitability of genetic offsets as an informative genetic variable for climate adaptation aligned with GBF objectives. Using Gradient Forest models applied to whole-genome re-sequencing data from five species included in Switzerland’s national pilot study for genetic diversity monitoring, we mapped spatial patterns of adaptive genetic composition and predicted genomic offsets under multiple climate change scenarios. To contextualize these predictions, we integrated them with landscape connectivity and analyses of recent gene-flow, to assess whether populations facing high offsets are isolated or well-connected. Our findings underscore both the potential and limitations of genetic offsets to contribute to GBF goals and targets. While offsets can provide valuable insights into expected temporal shifts in climate adaptation, they do not directly assess adaptive potential and are strongly influenced by species-specific traits and demographic histories. We therefore propose to combine genetic offsets with complementary metrics, such as landscape connectivity and gene-flow, to capture climate adaptation lag in a monitoring context, supporting GBF implementation at national and global scales.