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

Predicting how tree populations will respond to climate change is an urgent societal concern. An increasingly popular way to make such predictions is the genomic offset (GO) approach, which aims to use genomic and climate data to identify populations that may experience climate maladaptation in the near future. More precisely, GO tries to represent the change in allele frequencies required to maintain the current gene-climate relationships under climate change. However, the GO approach has major limitations and, despite promising validation of its predictions using height data from common gardens, it still lacks broad empirical testing. In the present study, we evaluated the consistency and empirical validity of GO predictions in maritime pine (Pinus pinaster Ait.), a tree species from southwestern Europe and North Africa with a marked population genetic structure. First, gene-climate relationships were estimated using 9,817 SNPs genotyped in 454 trees from 34 populations; and candidate...,
See paper for methods.
, , # 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-Garzón [1], Marina de-Miguel [1,3], Alexandre Changenet [1], Francesca Bagnoli [4], Frédéric Barraquand [5], Maurizio Marchi [4], Giovanni G. Vendramin [4], Stephen Cavers [2], Annika Perry [2] and Santiago C. González-Martínez[1]

**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:[e-mail])

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## Data

### Genomic data

#### Population genetic struct...