Rapid biodiversity assessment and conservation planning require the use of easily quantified and estimated surrogates for biodiversity. Using data sets from Québec and Queensland, we applied four methods to assess the extent to which environmental surrogates can represent biodiversity components: (1) surrogacy graphs; (2) marginal representation plots; (3) Hamming distance function; and (4) Syrjala statistical test for spatial congruence. For Québec we used 719 faunal and floral species as biodiversity components, and for Queensland we used 2348 plant species. We used four climatic parameter types (annual mean temperature, minimum temperature during the coldest quarter, maximum temperature during the hottest quarter, and annual precipitation), along with slope, elevation, aspect, and soil types, as environmental surrogates. To study the effect of scale, we analyzed the data at seven spatial scales ranging from 0.01 • to 0.10 • longitude and latitude. At targeted representations of 10% for environmental surrogates and biodiversity components, all four methods indicated that using a full set of environmental surrogates systematically provided better results than selecting areas at random, usually ensuring that ≥90% of the biodiversity components achieved the 10% targets at scales coarser than 0.02 • . The performance of surrogates improved with coarser spatial resolutions. Thus, environmental surrogate sets are useful tools for biodiversity conservation planning. A recommended protocol for the use of such surrogates consists of randomly selecting a set of areas for which distributional data are available, identifying an optimal surrogate set based on these areas, and subsequently prioritizing places for conservation based on the optimal surrogate set. Efectividad de Sustitutos Ambientales para la Selección de Redes deÁreas de ConservaciónResumen: La evaluación rápida y la planificación de la conservación de biodiversidad requiere del uso de sustitutos de la biodiversidad fácilmente estimados y cuantificados. Utilizando datos de Québec y Queensland, aplicamos cuatro métodos para evaluar el grado en que los sustitutos ambientales pueden representar a los componentes de la biodiversidad: (i) gráficos de subrogación; (ii) parcelas de representación marginal; (iii) función de distancia Hamming; y (iv) prueba estadística de Svrjala para congruencia espacial. Para Québec, utilizamos como componentes de biodiversidad a 719 especies de fauna y flora, y para Queensland utilizamos 2348 especies de plantas. Consideramos como sustitutos ambientales a cuatro tipos de parámetros climáticos (temperatura media anual, temperatura mínima durante el trimestre más frío, temperatura máxima durante el trimestre más cálido y precipitación anual), la pendiente, la altitud, el aspecto y los tipos de suelo. Para estudiar el efecto de la escala, analizamos los datos en siete escalas espaciales que variaron de 0.01 • a 0.10 • de longitud y de latitud. En representaciones dirigidas a 10% de los sustitutos ambientales y los componentes de la bio...

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.