Global change is impacting biodiversity across all habitats on earth. New selection pressures from changing climatic conditions and other anthropogenic activities are creating heterogeneous ecological and evolutionary responses across many species’ geographic ranges. Yet we currently lack standardised and reproducible tools to effectively predict the resulting patterns in species vulnerability to declines or range changes. We developed an informatic toolbox that integrates ecological, environmental and genomic data and analyses (environmental dissimilarity, species distribution models, landscape connectivity, neutral and adaptive genetic diversity, genotype-environment associations and genomic offset) to estimate population vulnerability. In our toolbox, functions and data structures are coded in a standardised way so that it is applicable to any species or geographic region where appropriate data are available, for example individual or population sampling and genomic datasets (e.g. RAD-seq, ddRAD-seq, whole genome sequencing data) representing environmental variation across the species geographic range. To demonstrate multi-species applicability, we apply our toolbox to three georeferenced genomic datasets for co-occurring East African spiny reed frogs (Afrixalus fornasini, A. delicatus and A. sylvaticus) to predict their population vulnerability, as well as demonstrating that range loss projections based on adaptive variation can be accurately reproduced from a previous study using data for two European bat species (Myotis escalerai, and M. crypticus). Our framework sets the stage for large scale, multi-species genomic datasets to be leveraged in a novel climate change vulnerability framework to quantify intraspecific differences in genetic diversity, local adaptation, range shifts and population vulnerability based on exposure, sensitivity, and landscape barriers. Methods Raw sequence data is available at the European Nucleotide Archive (ENA): Myotis escalerai and M. crypticus (PRJEB29086), and the NCBI Short Read Archive (SRA): Afrixalus fornasini – (SRP150605). Input data (processed genomic data and spatial-environmental data prior to running the toolbox) available as part of this repository. Methods: see methods text of manuscript and tutorials: Setup and running the LotE toolbox - https://cd-barratt.github.io/Life_on_the_edge.github.io/Vignette