CDMetaPOP
CDMetaPOP models multilocus selection in a spatially-explicit, individual-based landscape genetics framework to simulate genotype–environment associations and evolutionary dynamics across complex landscapes.
Key Features:
- Integration with CDPOP/CDMetaPOP: Integrates with the landscape genetics programs CDPOP and CDMetaPOP to extend landscape-level individual-based simulation capabilities.
- Multivariate Environmental Gradients: Implements multivariate environmental gradients using a linear additive model to evaluate genotype–environment associations across loci.
- Simulation of Multilocus Selection: Simulates selection across any number of loci influenced by multiple environmental variables within individuals in space.
- Validation and Evaluation: Validated using individual-based selection simulations under Wright–Fisher assumptions and evaluated across simple to complex selection scenarios.
- Complex Landscape Simulations: Simulates multilocus selection across complex landscapes including linked loci and spatially varying environmental variables.
- Gene Flow and Selection Interactions: Quantifies contributions and interactions between gene flow and selection-driven processes across multivariate landscapes.
Scientific Applications:
- Landscape genetics: Models how spatially varying environmental factors drive genetic variation and structure across landscapes.
- Population viability: Assesses how multilocus selection and landscape processes influence population persistence and evolutionary trajectories.
- Adaptation to environmental change: Evaluates genotype–environment associations to study adaptive responses to changing conditions.
- Habitat fragmentation effects: Investigates impacts of habitat fragmentation on gene flow, selection, and multilocus evolutionary dynamics.
Methodology:
Uses an individual-based eco-evolutionary model in a spatially-explicit framework that simulates multilocus selection and genotype–environment associations via linear additive models, with validation under Wright–Fisher assumptions.
Topics
Details
- Programming Languages:
- Python
- Added:
- 1/14/2020
- Last Updated:
- 12/10/2020
Operations
Publications
Landguth EL, Forester BR, Eckert AJ, Shirk AJ, Menon M, Whipple A, Day CC, Cushman SA. Modelling multilocus selection in an individual‐based, spatially‐explicit landscape genetics framework. Molecular Ecology Resources. 2019;20(2):605-615. doi:10.1111/1755-0998.13121. PMID:31769930.