When wildlife populations shrink or isolate from other populations, they often lose genetic diversity — variation in DNA — that allows for trait variation and adaptation to shifting environments over time. Thus, wildlife conservation and management increasingly considers genetic information to plan, understand, and evaluate projects, including actions that increase or decrease wildlife population sizes. However, changes in genetic variation in response to management actions are not intuitive due to the influence of multiple interacting drivers of change, like environmental variation or life history traits. 

Simulation is an excellent strategy to understand the predicted consequences of different proposed or implemented actions. However, few genetic simulators that are robust to a wide variety of life history traits are capable of easily incorporating common management actions. 

To fill this gap, researchers developed cgsim, an R package for simulating the genetic consequences of management interventions for populations of wildlife species. Specifically, they developed a set of functions to understand the effects of four main aspects of managing small, declining, or isolated populations: 

• Loss of genetic diversity to drift (that is, random change),
• Augmenting existing populations (for example, through translocation)
• Population reduction through targeted removals, and 
• Population catastrophes driven by stochastic extrinsic forces. 

This single population simulation model is individual-based, and flexible to a range of life history traits, making it suitable for many wildlife species of management interest.