Cgsim: An R Package for Simulation of Population Genetics for Conservation and Management Applications.

Area of Science:

• Population genetics
• Wildlife management
• Conservation biology

Background:

• Genetic information is crucial for wildlife conservation and management interventions.
• Predicting genetic changes from management actions (translocations, removals) is complex due to interacting factors like drift and life history traits.
• Existing genetic simulators can be difficult to parameterize for common management actions.

Purpose of the Study:

• To develop cgsim, an R package for simulating the genetic consequences of wildlife management interventions.
• To provide a user-friendly tool for understanding the effects of population augmentation, reduction, and genetic diversity loss.
• To aid in planning and evaluating conservation strategies for small, declining, or isolated populations.

Main Methods:

• Developed cgsim, an individual-based, single-population simulation model in R.
• Incorporated functions to simulate genetic drift, population augmentation, targeted removals, and population catastrophes.
• Validated the model by comparing simulations to theoretical expectations and an empirical case study (Greater Sage-Grouse).

Main Results:

• The cgsim package offers a flexible tool to simulate genetic changes under various management scenarios.
• The model effectively predicts genetic diversity loss due to drift and the impacts of population interventions.
• Validation confirmed cgsim's accuracy against theoretical predictions and its practical applicability.

Conclusions:

• cgsim simplifies the simulation of genetic consequences for wildlife management, addressing a gap in current tools.
• The R package facilitates better planning and evaluation of conservation actions by predicting genetic outcomes.
• cgsim is a valuable resource for researchers and practitioners in wildlife conservation and population genetics.