Geonomics

Geonomics performs forward-time, individual-based, spatially explicit landscape genomic simulations to investigate drivers of spatial patterns of genomic diversity.

Key Features:

• Forward-time, individual-based simulations: Implements forward-time, individual-based simulation frameworks for modeling evolutionary dynamics.
• Spatially explicit modeling with full pedigrees: Represents full spatial pedigrees to capture spatially explicit ancestry and dispersal effects on genomic variation.
• Python implementation: Implemented in Python, enabling customization and extensibility through code-level interfaces.
• Validation against population genetics models: Validated against classic models in population genetics to ensure expected behavior.
• Complex scenario simulation: Supports simulations of polygenic selection, multiple trait selection, complex landscapes, and nonstationary environmental changes.
• Performance scaling: Runtime scales primarily with landscape raster size, and memory usage scales with maximum population size and recombination rate.
• Model approximations: Approximates recombination and movement processes within its simulation models.

Scientific Applications:

• Landscape-genetic inference: Assess the impact of landscape features on genetic diversity and spatial genetic structure.
• Environmental change response: Explore evolutionary responses to nonstationary environmental changes over time.
• Population genetic processes: Investigate the roles of genetic drift, gene flow, and selection pressures within spatially structured populations.

Methodology:

Uses forward-time, individual-based, spatially explicit simulations with full spatial pedigrees implemented in Python; validated against classic population genetics models; includes approximations of recombination and movement, with performance influenced by landscape raster size, maximum population size, and recombination rate.