bioRxivEvolutionary Biology
Life on the edge: a new toolbox for population-level climate change vulnerability assessments
C. D. Barratt, R. E. Onstein, M. L. Pinsky, S. Steinfartz, H. S. Kuehl, B. R. Forester, O. Razgour
Abstract
O_LIGlobal 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. C_LIO_LIWe 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 and genotype-environment associations) 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. C_LIO_LIWe apply our toolbox to a georeferenced genomic dataset for the East African spiny reed frog (Afrixalus fornasini) to predict population vulnerability, as well as demonstrating that range loss projections based on adaptive variation can be accurately reproduced using data for two European bat species (Myotis escalerai, and M. crypticus). C_LIO_LIOur 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 range shift potential. C_LI
- assay: DNA methylation
- assay: whole-genome sequencing
- research focus: benchmarking
- approach: statistical model
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