Testing the impact of environmental variables on the predictive performance of genomic offset statistics: crop-specific metrics computed from the EWEMBI v.1 climate dataset.

The ability of species and populations to adapt to their environment faces increasing challenges as climate change accelerates. Recent methods based on genomic offset (GO) statistics aim to quantify the risk of non-adaptation of populations to future climates. While several studies have evaluated the ability of different offset statistics to predict population (mal)adaptation, the impact of the chosen climate data —which could vary in relevance and quality— remains unexplored. To explore this question, we analyzed the case of 157 pearl millet landraces cultivated in West Africa, for which fitness proxies were measured during field trials conducted over two years. We calculated geometric and gradient forest genomic offset statistics using three different historical climate datasets: bioclimatic variables from WorldClim (v.2.1), from CHELSA (v.2.1), and a dataset specifically designed for this study derived from the EWEMBI (v.1) climate database. We provide here the climate data computed from the EWEMBI v.1 dataset, along with the code used to extract the climate crop-specific metrics.