A. Ross Kiester

A COMPARISON OF RESERVE SELECTION ALGORITHMS USING DATA ON TERRESTRIAL VERTEBRATES IN OREGON

We compare the number of species represented and the spatial pattern of reserve networks derived using five types of reserve selection algorithms on a set of vertebrate distribution data for the State of Oregon (USA). The algorithms compared are: richness-based heuristic algorithms (four variations), weighted rarity-based heuristic algorithms (two variations), progressive rarity-based heuristic algorithms (11 variations), simulated annealing, and a linear programming-based branch-and-bound algorithm. The linear programming algorithm provided optimal solutions to the reserve selection problem, finding either the maximum number of species for a given number of sites or the minimum number of sites needed to represent all species. Where practical, we recommend the use of linear programming algorithms for reserve network selection. However, several simple heuristic algorithms provided near-optimal solutions for these data. The near-optimality, speed and simplicity of heuristic algorithms suggests that they are acceptable alternatives for many reserve selection problems, especially when dealing with large data sets or complicated analyses.

A Hierarchical Framework for Conserving Biodiversity

Society recognizes a large variety of values associated with biodiversity including aesthetic, economic, conservation, and educational (McNeely et al. 1990; Heywood and Watson 1995). These values are all ultimately related to the definition of biodiversity as a manifestation of genetic diversity, the primary raw material that is filtered by natural selection, resulting in evolutionary and ecological adaptation of biota to environmental conditions. Minimizing additional loss of biodiversity will provide the best assurance that biota will adapt to the increasing rate and spatial extent of environmental change (Pratt and Cairns 1992), and that societal values will be sustained.

Planar and spherical hierarchical, multi-resolution cellular automata

We present a new generalized definition of spatial hierarchy and use it to create a data structure for spatial hierarchies on the plane and the sphere. The data structure is then used as the basis for hierarchical, multi-resolution cellular automata which are topology-independent so that many topologies may be studied. In these systems the dynamics of a focal cell is dependent on its neighbors and also the cell above and below it in the next coarser or finer resolution. Results from a multi-resolution version of the Game of Life show complex and unexpected behavior which is dependent on the topology chosen and initial conditions. These results and the software which produced them provide a proof of concept for the new data structure and algorithms. These may be especially useful for data analysis and simulation at the global scale.

Introduction to discrete global grids

This part presents 3 papers presented at the second International Conference on Discrete Global Grids.