Replication data for \"Foliar spectral signatures reveal adaptive divergence in live oaks (Quercus section Virentes) across species and environmental niches\"

This dataset contains raw and processed spectral data from dried leaf samples collected from individuals of the seven species in Quercus section Virentes. Samples were obtained from both wild populations and common garden experiments. The data were generated to investigate patterns of phenotypic differentiation, local adaptation, and the relationship between phenotype, environment, and evolutionary history. These files are associated with the article: “Spectral signatures reveal adaptive divergence in live oaks (Quercus section Virentes) across species and environmental niches.” (Submitted to New Phytologist) Contents Dry_leafs_spectral_data.tar.gz Folder containing raw spectral data (.sig files) and associated metadata. • Wild_individuals_spectra/ (.*.sig) Contains raw reflectance spectra from dried leaves of individuals sampled from wild populations. Each .sig file represents a scan from a single leaf surface and is labeled by individual ID. These data are suitable for spectral trait extraction and classification modeling. • Greenhouse_individuals_spectra/ (.*.sig) Contains raw reflectance spectra from dried leaves of individuals grown under controlled conditions in common gardens (greenhouses). These samples represent genetically characterized lineages, enabling analyses that separate genetic from environmental influences on spectral traits. • ID_spectra_files.csv A metadata key linking individual .sig file names to specimen-level information, including species identity, population origin, geographic coordinates, and sample source (wild or common garden). This file is essential for interpreting the spectral data. Results_New_phytologist.tar.gz Contains processed analytical results used in the corresponding publication. • pRDA_Results.xlsx Results from partial redundancy analyses (pRDA), assessing the relationship between phenotypic (spectral and trait-based) variation and environmental variables. • PST_results.xlsx Results of phenotypic differentiation (PST) across traits, including comparisons to neutral genetic differentiation (FST), where available. Processed_data.tar.gz Contains compiled, cleaned datasets ready for analysis. • Wild_CTW_spectra_427.csv Spreadsheet containing specimen metadata and continuum-removed (CTW-transformed) leaf reflectance spectra across the full wavelength range (400–2500 nm). Each row represents a scan from a single dried leaf surface. • Wild_Raw_spectra_427.csv Spreadsheet containing specimen metadata and raw reflectance spectra (non-transformed) for the same wavelength range (400–2500 nm). Each row represents a scan from a single dried leaf surface. • nSSR_data.csv Microsatellite genotyping data for individuals of Quercus section Virentes, sampled from natural populations. Each row includes allelic data from a set of nuclear microsatellite loci. These data were used to assess neutral genetic diversity and structure, and to compare with phenotypic divergence metrics. • Geneland_Phenotypic_group_K6.csv Results of Bayesian clustering using the Geneland algorithm applied to morphological and/or spectral trait data. This dataset corresponds to a model with K = 6 phenotypic groups, including individual assignments and posterior probabilities. • Structure_Genetic_K7.csv Results of STRUCTURE Bayesian clustering based on microsatellite data, assuming K = 7 genetic clusters. Each row provides admixture proportions for an individual across inferred genetic groups. Useful for assessing population structure and its correspondence with phenotype and environment. Potential Research Applications These data are suitable for a wide range of research applications, including: • Studies of local adaptation and functional trait divergence • Spectral modeling of leaf structural and chemical traits • Analysis of interspecific or population-level variation in long-lived tree species • Validation of non-destructive methods in functional ecology and biodiversity science