Blair Csuti

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.

Choosing reserve networks with incomplete species information

Existing methods for selecting reserve networks require data on the presence or absence of species at various sites. This information, however, is virtually always incomplete. In this paper, we analyze methods for choosing priority conservation areas when there is incomplete information about species distributions. We formulate a probabilistic model and find the reserve network that represents the greatest expected number of species. We compare the reserve network chosen using this approach with reserve networks chosen when the data is treated as if presence/absence information is known and traditional approaches are used. We find that the selection of sites differs when using probabilistic data to maximize the expected number of species represented versus using the traditional approaches. The broad geographic pattern of which sites are chosen remains similar across these different methods but some significant differences in site selection emerge when probabilities of species occurrences are not near 0 or 1.

A note on optimal algorithms for reserve site selection

A recent note by Underhill (Biol. Conserv., 70, 85-7, 1994) points out the need for the use of optimization models and a closer working relationship with mathematicians for the solution of biological management problems such as the reserve site selection problem. In this note we give the mathematical formulation of what he terms ‘the more realistic’ version of the reserve selection problem, namely, the problem of maximizing the number of species preserved given a fixed budget for reserve sites. We also discuss some straight-forward data reduction schemes which may reduce the solution time for these problems when they are solved using general off-the-shelf optimization code as mentioned by Underhill.

A comparison of taxonomic distinctness versus richness as criteria for setting conservation priorities for North American birds

In choosing sites for a conservation reserve network, representation of the greatest number of species in the sites selected is a common objective. This approach implicitly assumes that all species have equal conservation value. An alternative objective is to represent the greatest genetic diversity in selected sites. This approach gives greater weight to species that are more genetically distinct. Such species tend to contain more unique genetic material, which would be lost if such species became extinct. In this paper, we calculate a diversity measure for a given set of species based on the branch length of the phylogenetic tree for the set. We use genetic distances between bird species in 147 genera based on the results of DNA hybridization research. Distribution information for bird species in the US comes from the Breeding Bird Survey. We compare resulting conservation reserve networks when the objective is the number of genera represented versus the diversity of genera represented. We find that the different objectives produce notably similar results.