Data and code from: Evaluating genomic offset predictions in a forest tree with high population genetic structure

Genomic offset models are increasingly popular tools for identifying populations at risk of maladaptation under climate change. These models estimate the extent of genetic change required for populations to remain adapted under future climate change scenarios, but face strong limitations and still lack broad empirical testing. Using 9,817 single nucleotide polymorphisms (SNPs) genotyped in 454 trees from 34 populations of maritime pine, a species with a marked population genetic structure, we found substantial variability across genomic offset predictions from different methods, SNP sets, and general circulation models. Using five common gardens, we mostly found positive associations between genomic offset predictions and mortality, as expected. However, contrary to our expectations, we observed very few negative monotonic associations between genomic offset predictions and height. Higher mortality rates were also observed in national forest inventory plots with high genomic offset, but..., , , # Data and code for the paper: Evaluating genomic offset predictions in a forest tree with high population genetic structure

Juliette Archambeau^1,2, Marta Benito-Garzón¹, Marina de-Miguel^1,3, Alexandre Changenet¹, Francesca Bagnoli⁴, Frédéric Barraquand⁵, Maurizio Marchi⁴, Giovanni G. Vendramin⁴, Stephen Cavers², Annika Perry² and Santiago C. González-Martínez¹

**1** INRAE, Univ. Bordeaux, BIOGECO, F-33610 Cestas, France

**2** UK Centre for Ecology & Hydrology, Bush Estate, Penicuik, United Kingdom

**3** EGFV, Univ. Bordeaux, Bordeaux Sciences Agro, INRAE, ISVV, F-33882, Villenave d'Ornon, France

**4** Institute of Biosciences and BioResources, National Research Council, 50019 Sesto Fiorentino, Italy

**5** CNRS, Institute of Mathematics of Bordeaux, F-33400 Talence, France

**Corresponding author:** Juliette Archambeau, [[e-mail]](mailto:juli.ar...,