AN INTEGER PROGRAMMING APPROACH FOR SPATIALLY AND TEMPORALLY OPTIMIZING WILDLIFE POPULATIONS

This paper presents mixed integer linear programming formulations that optimize the spatial layout of management actions for providing wildl ife habitat, over time. The formulations focus on wildlife growth and dispersal as a dynamic, probabilistic process. Habitat fragmentation/c onnectivity is thus modeled indirectly. Multiple timber age classes ar e accounted for as different wildlife habitat types, which define carr ying capacity limitations that are tracked spatially. A variety of obj ective functions are specified, including ones based on piecewise-appr oximated nonlinear functions that relate wildlife populations to the p robability of species viability. All of the formulations and objective functions are demonstrated with a case example.