We analyse optimal and heuristic place prioritization algorithms for biodiversity conservation area network design which can use probabilistic data on the distribution of surrogates for biodiversity. We show how an Expected Surrogate Set Covering Problem (ESSCP) and a Maximal Expected Surrogate Covering Problem (MESCP) can be linearized for computationally efficient solution. For the ESSCP, we study the performance of two optimization software packages (XPRESS and CPLEX) and five heuristic algorithms based on traditional measures of complementarity and rarity as well as the Shannon and Simpson indices of α‐diversity which are being used in this context for the first time. On small artificial data sets the optimal place prioritization algorithms often produced more economical solutions than the heuristic algorithms, though not always ones guaranteed to be optimal. However, with large data sets, the optimal algorithms often required long computation times and produced no better results than heuristic ones. Thus there is generally little reason to prefer optimal to heuristic algorithms with probabilistic data sets.

Surrogacy analysis consists of determining a set of biotic or environmental parameters which can be rapidly assessed in the field and reliably used to prioritize places for biodiversity conservation. Whether adequate surrogate sets exist remains an open and relatively unexplored question though its solution is central to the aims of conservation biology. This paper analyses the surrogacy problem by prioritizing places using surrogate lists and comparing these results with those obtained by using more comprehensive species lists. More specifically, it explores (i) the possibility of using bird distributions, which are often easily available, as surrogates for species at risk (endangered and threatened species), which are presumed to be an important component of biodiversity; and (ii) the methodological question of how spatial scale influences surrogate success. The data set analysed, from southern Québec, is one of the most complete biotic data sets available at the regional scale. Contrary to some previous analyses, the results obtained suggest that the surrogacy problem is potentially solvable.

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The place prioritization problem in conservation biology is that of establishing a sequentially prioritized list of places on the basis of biodiversity content. Such a list can then be used to select reserve networks that are designed to be fully representative of the biodiversity of an area as efficiently as possible (for instance, with minimum area or cost). The usual goal is the representation of all chosen biodiversity surrogates up to or beyond a required target, or to the greatest available extent. The purpose of this paper is to compare the respective performances of two place prioritization software packages, SITES and ResNet, on four datasets (distributions of termite genera in Namibia, breeding bird species in the Falkland Islands/Islas Malvinas, vertebrate species in Texas and flora and fauna species that are at risk in Québec), to determine their respective merits. The two software packages implement radically different algorithms: SITES is based on a simulated annealing procedure for finding (local) optima; ResNet uses an algorithm based on rarity and complementarity. This analysis indicates that the rarity-complementarity based algorithm of ResNet surpasses the simulated annealing approach of SITES with respect to time and completeness. SITES, however, contains other features that are useful in conservation planning. Ways in which the two packages can be used together effectively are suggested.

The prioritization of places on the basis of biodiversity content is part of any systematic biodiversity conservation planning process. The place prioritization procedure implemented in the ResNet software package is described. This procedure is primarily based on the principles of rarity and complementarity. Application of the procedure is demonstrated with two analyses, one data set consisting of the distributions of termite genera in Namibia, and the other consisting of the distributions of bird species in the Islas Malvinas/Falkland Islands. The attributes that data sets should have for the effective and reliable application of such procedures are discussed. The procedure used here is compared to some others that are also currently in use.