PARENT SESSION
Symposium #14: Spatial control of natural and managed systems: theory and applications.
Sponsored by ESA Theoretical Ecology Section
Organized by: L. Gross.
Wednesday, August 8, 2001. 8:00 AM to 12:00 PM. Madison Ballroom A

Spatial control and individual-based modeling: hunting and bears in the Southern Appalachians.

Salinas, Rene'¹, Gross, Louis¹, ¹

ABSTRACT- The black bear habitat of the Smoky Mountain region consists of the Great Smoky Mountains National Park, Cherokee and Pisgah National Forests, and a variety of other nearby public lands. The area has one of the largest populations of black bears in the North America, but increasing development in areas bordering the public lands provides for greater opportunities for bear-human interactions which are detrimental to both. Hunting pressure on bears interacts with environmental factors since years with low mast (mainly acorn) production lead to greater bear movement and generally higher harvests. Years with low mast production lead to very low fecundity, affecting demographics of the bear population for many years. Stronger restrictions on hunting could lead to higher bear population densities and associated increases in harmful bear-human interactions. Hunting restrictions are set in different spatial regions by different wildlife regulations. Without such restrictions, overharvesting could lead to a greatly reduced bear population particularly if there are consecutive mast failure years. Although these environmental factors cannot be controlled, the harvest season and locations can be. We will describe a methodology to apply spatial control, using an individual-based model for the black bear that accounts for differences in gender, age, and size and how these affect movement, mating and foraging rules. The model can be applied to compare the effects of alternative harvesting strategies on the black bear population. The model allows simulation of periodic and stochastic fluctuations in mast failure and offers the possibility to determine optimal harvesting strategies using a variety of different optimization criteria.

KEY WORDS: mathematical model, computational ecology, theoretical ecology, population management