cgsim: An R package for simulating conservation relevant outcomes for management actions

Wildlife conservation and management increasingly considers genetic information to plan, understand, and evaluate implemented population interventions. These actions commonly include conservation translocation and population reductions through removals. Change in genetic variation in response to management actions can be unintuitive due to the influence of multiple interacting drivers of change (e.g., genetic drift, life history traits, environmental stochasticity). 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 parameterizing these common management actions. To fill this gap, we developed cgsim, an R package for simulating the genetic consequences of management interventions for populations of wildlife species. We developed a set of functions to specifically understand the effects of four main aspects of managing small, declining, or isolated populations: loss of genetic diversity to drift, augmenting existing populations (e.g., translocation), population reduction through targeted removals, and population catastrophes driven by stochastic extrinsic forces. Our single population simulation model is individual-based, and flexible to a range of life history traits.