Effects of data characteristics on the results of reserve selection algorithms

We tested the effects of four data characteristics on the results of reserv e selection algorithms. The data characteristics were nestedness of feature s (land types in this case), rarity of features, size variation of sites (p otential reserves) and size of data sets (numbers of sites and features). W e manipulated data sets to produce three levels, with replication, of each of these data characteristics while holding the other three characteristics constant. We then used an optimizing algorithm and three heuristic algorit hms to select sites to solve several reservation problems. We measured effi ciency as the number or total area of selected sites, indicating the relati ve cost of a reserve system. Higher nestedness increased the efficiency of all algorithms (reduced the total cost of new reserves). Higher rarity redu ced the efficiency of all algorithms (increased the total cost of new reser ves). More variation in site size increased the efficiency of all algorithm s expressed in terms of total area of selected sites. We measured the subop timality of heuristic algorithms as the percentage increase of their result s over optimal (minimum possible) results. Suboptimality is a measure of th e reliability of heuristics as indicative costing analyses. Higher rarity r educed the suboptimality of heuristics (increased their reliability) and th ere is some evidence that more size variation did the same for the total ar ea of selected sites. We discuss the implications of these results for the use of reserve selection algorithms as indicative and real-world planning t ools.