Santiago Gonzalez Martinez au congrès international ESEB2025

European conifers represent a diverse group of long-lived forest species occupying a broad range of climatic environments across the continent. In this talk, I will present insights from our research on past, present, and future climate adaptation in four conifer species with contrasting demographic histories: stone pine (Pinus pinea), maritime pine (P. pinaster), Scots pine (P. sylvestris), and English yew (Taxus baccata). The divergent demographic backgrounds have shaped distinct levels of genetic diversity and population structure—from exceptionally low diversity in stone pine to extensive standing variation observed in Scots and maritime pines; the two latter species having also contrasted capacity for adaptive evolution (higher in maritime pine than in Scots pine). Our analyses consistently reveal a polygenic basis for adaptive traits, with, for example, around 6% of SNPs showing non-zero effects on key phenotypes in maritime pine. This complex architecture presents challenges for pinpointing climate-associated candidate genes. Moreover, despite the prevalence of phenotypic clines in European conifers—particularly in traits such as growth phenology—rigorous tests for local adaptation often reveal adaptation lags and cases of population maladaptation. In English yew, such patterns appear to be exacerbated by strong population isolation. Furthermore, demographic history influences the dynamics of genetic load: from an excess of fixed deleterious alleles in stone pine to evidence of purging in marginal maritime pine populations, with no clear effects in Scots pine. Finally, I will discuss the application of predictive frameworks, such as the calculation of genomic offsets, across these species. While these approaches offer promising tools for forecasting climate vulnerability, I will argue that their interpretation must be contextualised and approached with caution. Moreover, using complementary statistics together with genomic offsets has the potential to provide more meaningful predictions.