Population genetics meets ecology: a guide to individual-based simulations in continuous landscapes

Area of Science:

• Population Biology
• Ecological Modeling
• Computational Biology

Background:

• Individual-based simulations are vital in population biology.
• Implementing these simulations in continuous geographic space presents significant challenges.
• Realistic spatial modeling requires careful consideration of various ecological processes.

Purpose of the Study:

• To provide a practical guide for researchers on implementing individual-based spatial simulations.
• To address common pitfalls in modeling continuous geography.
• To demonstrate effective parameterization and application of spatial models.

Main Methods:

• Detailed discussion of mating, reproduction, density-dependence, and dispersal mechanisms in spatial contexts.
• Explanation of how to parameterize simulations for specific population dynamics, such as achieving target densities.
• Demonstration of model implementation using the SLiM individual-based simulator.

Main Results:

• Exploration of how landscape variation in ecological processes influences population dynamics.
• Integration of natural selection and genetic variation effects on demographic processes.
• Illustrative vignettes showcasing diverse applications, including climate change adaptation, habitat dynamics, invasive species spread, and resource-limited population regulation.

Conclusions:

• Individual-based spatial simulations are powerful tools for understanding complex ecological dynamics.
• Practical guidance and simulator demonstrations facilitate more robust and realistic population modeling.
• The presented methods and examples offer a framework for diverse ecological research questions.