demetR: a Bayesian population simulation web-application for harvest management

Abstract

Abstract: Management of large carnivore populations represents an important challenge in conservation, requiring balancing their cultural, economic, and ecological value with potential risks of human–wildlife conflicts. Harvest can provide an effective tool for managing populations, but it can be difficult to define appropriate harvest quotas or assess the consequences of other conservation measures. We introduce the web-application ‘demetR’ (“Dynamic Environment for Modeling and Estimating Trajectories in R,” available at https://pop-eco.shinyapps.io/demetR/) to evaluate the effects of harvest scenarios and other conservation policies on brown bear (Ursus arctos) and American black bear (U. americanus) populations. We developed a Bayesian population trajectory model to simulate brown bear and black bear populations in response to user-defined demographic parameters and harvest. Model simulations are performed using fixed or stochastic demographic parameters, allowing for informative and non-informative priors. We provide an overview of the general layout, along with descriptions of model inputs and outputs. We then provide examples of bear populations simulated using deterministic and stochastic approaches with varying levels of harvest. Performing computer simulations of different management scenarios offers an economical and efficient way to test practices before their application, and can be valuable for decision-making. This model can also be applied to other species with similar life-history traits. Future developments will provide users with greater input flexibility and adaptations to specific population structures of other large carnivores. Management decisions can be costly, with long-lasting ecological and economic consequences. Models such as the one we present here, in the context of structured decision-making and adaptive management, can improve the quality and quantity of information needed to make these decisions.