Genomic Scan of Endangered Moroccan Endemic Trees (argania Spinosa L. Skeels) along a Latitudinal Gradient: Exploring Gene-Environment Correlations

Understanding the adaptive mechanism of trees to climate change and effective management of genetic resources necessitate a comprehensive knowledge of the relationship between environmental factors and their adaptive power. This study focuses on the endangered Moroccan endemic tree, Argania spinosa L., known for its remarkable adaptive power in the Mediterranean climate and heavily impacted by climate change. Through genomic scans along a latitudinal gradient, we aimed to identify genomic regions associated with convergent or divergent selection by searching the genome for outlier loci demonstrating high population differentiation. Variation of dominant markers (Amplified Fragment Length Polymorphism) was correlated with variation of environmental factors to discern signals of natural selection in 13 natural populations of Argania spinosa L. along a latitudinal transect.Genotyping of 130 individuals with 477 AFLP markers revealed six outlier loci exhibiting elevated population differentiation values within argan forests. The BayeScan v2.1 software was used to detect outlier loci, and the (MLM) method strongly associated these loci with at least five environmental factors, particularly bioclimatic variables. Notably, all outlier loci exhibited significant correlations with Soil Moisture, with additional associations with Temperature, Precipitation, Landforms, and Soil Type, influencing the local adaptation of Argania spinosa L.The outlier AFLP loci may not be the direct targets of natural selection, but locate closely to genes that are potential involved in local adaptation. Consequently, further investigations are imperative to validate these candidate loci. This research underscores the significance of considering bioclimatic variables, especially Soil Moisture, in understanding the local adaptation of Argania spinosa L. amid climate change in Morocco.