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Abstract:  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, arid 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.

Key words:  animal damage, Canis lupus, control, endangered species, model, recovery, simulation, wolf

Table of Contents

• Introduction
• Methods
• Results
• Discussion
• Literature Cited

Figures and Tables

• Figure 1 -- Annual sequence of wolf-removal and demographic events in the wolf-removal model used to evaluate 4 hypothetical removal strategies.

• Table 1 -- Mean performance in year 20 of hypothetical wolf-removal strategies with the following base-case assumptions: 5 immigrants per year, 60% capture probability for pups and yearlings and 30% for adults, and 20% annual probability of wolves in a farm territory becoming prone to depredation.
• Table 2 -- Mean performance in year 20 of hypothetical wolf-removal strategies under 2 scenarios of the immigration rate.
• Table 3 -- Mean performance in year 20 of hypothetical wolf-removal strategies under 80% capture probability for pups and yearlings and 50% for adults.
• Table 4 -- Mean performance in year 20 of hypothetical wolf-removal strategies under a 1% annual probability of wolves in a farm territory becoming prone to depredation.

Robert G. Haight (photo) is a research forester for the North Central Research Station of the United States Department of Agriculture Forest Service, and an adjunct professor at the University of Minnesota. He obtained his B.S. in forestry in 1978 from University of California, Berkeley and his Ph.D. in forest management in 1985 from Oregon State University. His research interests are in the application of computer modeling to problems of forest and wildlife management.