Genomic and functional adaptation of Aristotelia chilensis across the Atacama–Patagonia aridity gradient

The dataset supermatrix4.csv compiles comprehensive individual-level data for Aristotelia chilensis, a native Chilean tree species, sampled along a pronounced latitudinal aridity gradient. It comprises 225 rows (individual plants) and 51 columns (variables), integrating ecological, morphological, physiological, and environmental information. Each row represents a unique individual annotated with population and genetic cluster identifiers, spatial coordinates (latitude, longitude, altitude), and an array of functional and structural traits. Functional traits include critical photo-inactivation water content (a drought tolerance proxy), specific leaf area (SLA), and root-to-shoot biomass ratio, all of which are central to plant water-use strategies and growth efficiency. Morphometric traits—such as stem diameter, plant height, number of stems, canopy width (maximum and minimum), and canopy exposure—describe above-ground architectural variation. Physiological attributes include anthocyanin and phenolic concentrations, antioxidant capacity estimated via ABTS and DPPH radical scavenging assays, and germination percentage. These measures provide insight into chemical defense mechanisms and reproductive performance. Environmental predictors were extracted using point-based values corresponding to the coordinates of each sampled population, sourced from globally recognized high-resolution datasets due to their relevance to plant eco-physiology. The Aridity Index (AI), a key proxy for water availability, was calculated as the ratio of annual precipitation to potential evapotranspiration (AI = PPT / PET), following Fisher et al. (2011). Climatic variables include mean annual precipitation (PPT) and a suite of bioclimatic variables (BIO1–BIO19) from the CHELSA dataset, which provides fine-resolution climatologies suitable for ecological modeling (Karger et al., 2017). Potential evapotranspiration (PET), a metric for atmospheric water demand, was also derived from the methods of Fisher et al. (2011). In addition, UV-B radiation values were extracted from the glUV dataset, which offers spatially explicit estimates of biologically effective ultraviolet exposure (Beckmann et al., 2014). Soil (edaphic) variables were obtained from the SoilGrids 2.0 global database (Poggio et al., 2021), including soil texture fractions (sand, silt, clay), water retention capacity, and a set of nutrient and structural properties such as nitrogen content, soil organic carbon (SOC), cation exchange capacity (CEC), and the proportion of coarse fragments. These variables support fine-scale trait–environment analyses and help evaluate potential adaptive responses across heterogeneous climatic and soil gradients. The dataset is well-suited for integrative ecological and evolutionary research, enabling analyses of the interaction between genomic variation, phenotypic traits, and abiotic selective pressures. The accompanying maqui.vcf file contains genomic variant data in VCF v4.2 format, generated using BBMap v38.69. It includes 2,356 high-quality single nucleotide polymorphisms (SNPs) detected across 188 diploid individuals of A. chilensis, aligned to the Aristotelia chilensis v1.0 reference genome. Variant calling was based on 415,851,949 reads, with an average read length of 138.41 bp, an average total base quality of 39.99, and a mean mapping quality (MAPQ) of 41.03. SNPs are distributed across at least 37 reference contigs, ranging from approximately 63,940 to 113,184 base pairs in length. Genotypes are encoded in standard VCF format and include quality metrics per sample. This dataset enables population genomics analyses, including assessments of genetic structure, differentiation, and genotype–environment associations. Funding Statement This work was supported by Fundacion para la Innovación Agraria (grant PYT-2018-0138). References Beckmann, M. et al. (2014). glUV: a global UV-B radiation data set for macroecological studies. Methods in Ecology and Evolution 5, 372–383. Fisher, J.B., Whittaker, R.J. & Malhi, Y. (2011). ET come home: potential evapotranspiration in geographical ecology. Global Ecology and Biogeography 20, 1–18.Karger, D.N. et al. (2017). Climatologies at high resolution for the Earth's land surface areas. Scientific Data 4, 170122.Poggio, L. et al. (2021). SoilGrids 2.0: producing soil information for the globe with quantified spatial uncertainty. Soil 7, 217–240.