This R code repository focuses on Quantitative Trait Loci (QTL) mapping and Species Distribution Modeling (SDM) specifically applied to Gasterosteus aculeatus, commonly known as the three-spined stickleback. The stickleback is a prominent model organism in evolutionary biology, particularly for studying rapid adaptation and speciation in diverse environments. This resource provides computational tools to identify genomic regions associated with complex traits and to predict the geographical distribution of species based on environmental variables. The scripts are developed in R, a versatile environment for statistical analysis and data visualization. QTL analysis involves statistical methods to link phenotypic traits (e.g., morphology, behavior, physiology) to specific genomic regions, often utilizing genetic markers from crosses or natural populations. SDM, also known as ecological niche modeling, uses algorithms to correlate species occurrence data with environmental predictors (e.g., climate, topography) to map potential habitats . The technical capabilities of these scripts would include handling large genomic datasets, performing statistical associations for QTL, and implementing various SDM algorithms (e.g., MaxEnt, GLM, Random Forest) to generate distribution maps and assess environmental influences. Performance metrics for SDM often include AUC (Area Under the Curve) and TSS (True Skill Statistic) . This digital good is highly relevant for researchers in evolutionary genomics, ecological genetics, and conservation biology. It facilitates the investigation of the genetic architecture of adaptive traits in sticklebacks and the prediction of their distribution under current or future environmental conditions. Applications include understanding the genetic basis of freshwater adaptation, studying the impact of climate change on species ranges, and informing conservation strategies for vulnerable populations. The benefits include robust statistical analysis for complex genetic and ecological data, and the ability to generate predictive models for species occurrence. The code is compatible with standard R installations and can be used with various types of genetic marker data (e.g., SNPs) and environmental datasets (e.g., WorldClim).