Multiscale Landscape Genomics: Unveiling Adaptive Evolution in Dynamic Environments

This research topic explores the integration of multiscale landscape genomics to uncover the intricacies of adaptive evolution, particularly in response to changing environmental conditions. Building on recent advances in genotype-environment association (GEA) studies, this research will examine the sensitivity of genomic models to spatial resolution variations. By analyzing high-resolution environmental proxies, such as topographic variables derived from digital elevation models, the study aims to identify optimal grain sizes that enhance the detection of candidate genes under selection pressures. The research is poised to provide insights into applying multiscale variables in GEA models, advancing our understanding of species' local adaptations amidst environmental heterogeneity. Additionally, this topic will delve into the practical implications for conservation management, offering a pathway to more accurately predict and manage the genetic diversity of natural populations. By aligning with the specialty of evolutionary and population genetics, this research will contribute to the broader mission of understanding genetic dynamics in fluctuating ecosystems, supporting sustainable development goals related to biodiversity and life on land.