An integer programming approach for spatially and temporally optimizing wildlife populations

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