Computer simulation of wolf-removal strategies for animal-damage control

Because of the sustained growth of the gray wolf (Canis lupus) population in the western Great Lakes region of the United States, management agencies are anticipating gray wolf removal from the federal endangered species list and are proposing strategies for wolf management. Strategies are needed that would balance public demand for wolf conservation with demand for protection against wolf depredation on livestock, poultry, and pets. We used a stochastic, spatially structured, individually based simulation model of a hypothetical wolf population, representing a small subset of the western Great Lakes wolves, to predict the relative performance of 3 wolf-removal strategies. Those strategies included reactive management (wolf removal occurred in summer after depredation), preventive management (wolves removed in winter from territories with occasional depredation), and population-size management (wolves removed annually in winter from all territories near farms). Performance measures included number of depredating packs and wolves removed, cost, and population size after 20 years. We evaluated various scenarios about immigration, trapping success, and likelihood of packs engaging in depredation. Four robust results emerged from the simulations: 1) each strategy reduced depredation by at least 40% compared with no action, 2) preventive and population-size management removed fewer wolves than reactive management because wolves were removed in winter before pups were born, 3)population-size management was least expensive because repeated annual removal kept most territories near farms free of wolves, and 4) none of the strategies threatened wolf populations unless they were isolated because wolf removal took place near farms and not in wild areas. For isolated populations, reactive management alone ensured conservation and reduced depredation. Such results can assist decision makers in managing gray wolves in the western Great Lakes states.