Environmental Drivers of Genomic Adaptation

Contact person: Inger Skrede
Keywords: machine learning, landscape and conservation genomics, climate data, statistical modelling, microhabitat
Research group: Section for Genetics and Evolutionary Biology (Evogene) and Centre for Ecological and Evolutionary Synthesis (CEES) Department of Biosciences, Geo-Ecology Research Group (GEco/NHM)
Researchers: Inger Skrede (Evogene/IBV), H?vard Kauserud (Evogene/IBV), Mark Ravinet (CEES/Evogene/IBV), Anne Brysting (CEES/Evogene/IBV), Rune Halvorsen (GEco/NHM), Olav Skarpaas (GEco/NHM)
Department of Biosciences (IBV), Natural History Museum (NHM)

This research topic explores how fine-scale environmental factors impact genomic adaptation using advanced data science approaches. Small organisms such as fungi with compact genomes, are ideal for analyzing local adaptation with high precision. By employing machine learning to extensive datasets such as genomes and high-throughput phenotyping, we can model organismal responses to specific abiotic and biotic factors - such as variations in temperature, moisture, and soil factors. Integrating diverse data types, including genomic data, bioclimatic variables, meteorological records, remote sensing images and photos, allows for the construction of detailed predictive models that reveal the environmental drivers of adaptation. These approaches also allow us to estimate organismal evolvability based on their genomic diversity. This interdisciplinary approach spans computer science, biology, and climatology, leveraging “big data on small scales” to illuminate the complex interactions between small organisms and their habitats.

Examples of relevant topics from methodological research:

  • Machine learning driven climate modeling
  • Machine learning image analyses for forecasting nature types and climate on microscale
  • Genomic data analyses (bioinformatics and statistics)
  • Genome-Wide Association Studies (GWAS)
  • Big data modelling of data on small geographical scales
  • Advancement in microclimate modelling
  • High throughput phenotyping

Examples of relevant topics from natural sciences or technology:

  • Local adaptation to climate change
  • Climate change effects on organisms in complex habitats 
  • Forecasting anthropogenic influence on biodiversity
  • Development in landscape genomics

External partners:

  • Collaboration, mentoring and internship will be offered by relevant external partners for the specific projects.