Reserve Selection Algorithms Research Articles

Assessing Potential Conservation and Restoration Areas of FreshwaterFish Fauna in the Indian River Basins.

Conservation efforts globally are skewed toward terrestrial ecosystems. To date, conservation of aquatic ecosystems, in particular fish fauna, is largely neglected. We provide a country-wide assessment of Indian river ecosystems in order to identify and prioritize areas for protection and restoration of freshwater fish fauna. Using various biodiversity and anthropogenic attributes, coupled with tools of ecological modeling, we delineated areas for fish fauna conservation and restoration in the 20 major river basins of India. To do this, we used prioritization analyses and reserve selection algorithms to derive conservation value index (CVI) and vulnerability index (VI) of the river basins. CVI was estimated using endemicity, rarity, conservation value, and taxonomic singularity, while VI was estimated using a disturbance index derived from percent geographic area of the basin under human settlements, human population density, predominant land use, and total number of exotic fish species in each basin. The two indices, CVI and VI, were converted into geo-referenced maps, and each map was super-imposed onto species richness and forest cover maps, respectively. After superimposition, areas with high CVI and low VI shade intensities were delineated for conservation, while areas with high CVI and high VI shade intensities were demarcated for restoration. In view of the importance of freshwater fish for human livelihoods and consumption, and ecosystems of India's rivers, we call for urgent attention to the conservation of their fish fauna along with restoration of their degraded habitats.

Conservation Biogeography of the Sahara‐Sahel: additional protected areas are needed to secure unique biodiversity

AbstractAimIdentification of priority conservation areas and evaluation of coverage of the current protected areas are urgently needed to halt the biodiversity loss. Identifying regions combining similar environmental traits (climate regions) and species assemblages (biogroups) is needed for conserving the biodiversity patterns and processes. We identify climate regions and biogroups and map species diversity across the Sahara‐Sahel, a large geographical area that exhibits wide environmental heterogeneity and multiple species groups with distinct biogeographical affinities, and evaluate the coverage level of current network of protected areas for biodiversity conservation.LocationSahara‐Sahel, Africa.MethodsWe use spatially explicit climate data with the principal component analysis and model‐based clustering techniques to identify climate regions. We use distributions of 1147 terrestrial vertebrates (and of 125 Sahara‐Sahel endemics) and apply distance clustering methods to identify biogroups for both species groups. We apply reserve selection algorithms targeting 17% of species distribution, climate regions and biogroups to identify priority areas and gap analysis to assess their representation within the current protected areas.ResultsSeven climate regions were identified, mostly arranged as latitudinal belts. Concentrations of high species richness were found in the Sahel, but the central Sahara gathers most endemic and threatened species. Ten biogroups (five for endemics) were identified. A wide range of biogroups tend to overlap in specific climate regions. Identified priority areas are inadequately represented in protected areas, and six new top conservation areas are needed to achieve conservation targets.Main conclusionsBiodiversity distribution in Sahara‐Sahel is spatially structured and apparently related to environmental variation. Although the majority of priority conservation areas are located outside the areas of intense human activities, many cross multiple political borders and require internationally coordinated efforts for implementation and management. Optimized biodiversity conservation solutions at regional scale are needed. Our work contradicts the general idea that deserts are uniform areas and provide options for the conservation of endangered species.

Identifying priority areas for island endemics using genetic versus specific diversity – The case of terrestrial reptiles of the Cape Verde Islands

Genetic diversity is critical for conservation of endemic populations. It enhances adaptation to rapid environmental changes and persistence over evolutionary time-scales. In small and isolated populations, such as on islands, this is even more relevant. Nevertheless, few studies regarding the establishment of protected areas (PAs) on islands have taken genetic diversity into account. The Cape Verde Islands are in a biodiversity hotspot and present to resource planners unique problems and possibilities, hence are a good case study. This work primarily aims to compare targeting evolutionary significant units (ESUs) versus species in reserve selection algorithms for the conservation of the endemic Cape Verdean reptile diversity by assessing the PAs network adequacy, identifying its gaps, and optimizing it based on ‘realistic’ (considering areas inside PAs with lower cost) and ‘ideal’ (considering all non-humanized areas with higher potential for conservation) cost scenarios. Results clearly indicate that analyses targeting ESUs are more effective in the protection of genetic diversity and less costly in terms of selected area, in total and inside PAs. Results also indicate that most ESUs and species are insufficiently protected and that extra PAs are needed on most islands to reach conservation targets. Surprisingly, the total area selected in ‘ideal’ and ‘realistic’ prioritization scenarios are identical on most islands both for analyses targeting ESUs or species. Therefore the ‘realistic’ scenario should be largely followed. The work provides an innovative methodological framework for supporting the use of genetic diversity in reserve design and its results should assist in local-scale conservation planning.

Methodological considerations in reserve system selection: A case study of Malagasy lemurs

Although data quality and weighting decisions impact the outputs of reserve selection algorithms, these factors have not been closely studied. We examine these methodological issues in the use of reserve selection algorithms by comparing: (1) quality of input data and (2) use of different weighting methods for prioritizing among species. In 2003, the government of Madagascar, a global biodiversity hotspot, committed to tripling the size of its protected area network to protect 10% of the country’s total land area. We apply the Zonation reserve selection algorithm to distribution data for 52 lemur species to identify priority areas for the expansion of Madagascar’s reserve network. We assess the similarity of the areas selected, as well as the proportions of lemur ranges protected in the resulting areas when different forms of input data were used: extent of occurrence versus refined extent of occurrence. Low overlap between the areas selected suggests that refined extent of occurrence data are highly desirable, and to best protect lemur species, we recommend refining extent of occurrence ranges using habitat and altitude limitations. Reserve areas were also selected for protection based on three different species weighting schemes, resulting in marked variation in proportional representation of species among the IUCN Red List of Threatened Species extinction risk categories. This result demonstrates that assignment of species weights influences whether a reserve network prioritizes maximizing overall species protection or maximizing protection of the most threatened species.

Simulating the effects of using different types of species distribution data in reserve selection

In a perfect world, systematic conservation planning would use complete information on the distribution of biodiversity. However, information on most species is grossly incomplete. Two main types of distribution data are frequently used in conservation planning: observed and predicted distribution data. A fundamental question that planners face is – which kind of data is better under what circumstances? We used simulation procedures to analyse the effects of using different types of distribution data on the performance of reserve selection algorithms in scenarios using different reserve selection problems, amounts of species distribution known, conservation targets and costs. To compare these scenarios we used occurrence data from 25 amphibian and 41 reptile species of the Iberian Peninsula and assumed the available data represented the whole truth. We then sampled fractions of these data and either used them as they were, or converted them to modelled predicted distributions. This enabled us to build three other types of species distribution data sets commonly used in conservation planning: “predicted”, “transformed predicted” and “mixed”. Our results suggest that reserve selection performance is sensitive to the type of species distribution data used and that the most cost-efficient decision depends most on the reserve selection problem and on how much we have of the species distribution data. Choosing the most appropriate type of distribution data should start by evaluating the scenario circumstances. While there is no one best approach for every scenario, we discovered that using a mixed approach usually provides an acceptable compromise between species representation and cost.

Incorporating ontogenetic dispersal, ecological processes and conservation zoning into reserve design

Computational methods for marine reserve design are frequently used as decision-support tools for the identification of conservation areas. Most reserve-selection algorithms minimise the cost of the reserve system whilst aiming to meet specified biodiversity objectives. Here, we extend a widely-used selection algorithm, Marxan, to incorporate several important considerations related to biodiversity processes and management. First we relax the scorched earth assumption to allow conservation features in non-reserve zones to contribute explicitly to conservation objectives. To achieve this, we generate conservation targets at landscape scales rather than focusing purely on the representation of features within reserves. Second, we develop the notion of spatial dependencies further to incorporate spatial heterogeneity in the value of individual conservation features such as habitat types. We use the example of ontogenetic migrations of fish from mangroves to coral reefs because it nicely demonstrates how spatial ecological processes generate predictable heterogeneity in habitat value that should be considered in the reserve design process. Lastly, we show how habitat value can be disaggregated into ecosystem processes and services. Using a case study for the Belize Barrier Reef we compare reserve networks generated using our new approach with the results of traditional analyses. Consideration of the contribution of different protection zones, connectivity among habitats and more complex management goals resulted in up to a 52% increase in the mean biomass of commercially and ecologically-important fish species represented in the landscape. Our approach strengthens the ecological basis of reserve-design algorithms and might facilitate the uptake of ecosystem-based management into reserve design.

Efficiency and effectiveness in representative reserve design in Canada: The contribution of existing protected areas

To be effective, reserve networks should represent all target species in protected areas that are large enough to ensure species persistence. Given limited resources to set aside protected areas for biodiversity conservation, and competing land uses, a prime consideration for the design of reserve networks is efficiency (the maximum biodiversity represented in a minimum number of sites). However, to be effective, networks may sacrifice efficiency. We used reserve selection algorithms to determine whether collections of existing individual protected areas in Canada were efficient and/or effective in terms of representing the diversity of disturbance-sensitive mammals in Canada in comparison to (1) an optimal network of reserves, and (2) sites selected at random. Unlike previous studies, we restricted our analysis to individual protected areas that met a criterion for minimum reserve size, to address issues of representation and persistence simultaneously. We also tested for effectiveness and efficiency using historical and present-day data to see whether protected area efficiency and/or effectiveness varied over time. In general, existing protected areas did not effectively capture the full suite of mammalian species diversity, nor are most existing protected areas part of a near-optimal solution set. To be effective, Canada’s network of reserves will require at minimum 22 additional areas of >2700 km 2. This study shows that even when only those reserves large enough to be effective are considered, protected areas systems may not be representative, nor were they representative at the time of establishment.

Connectivity, biodiversity conservation and the design of marine reserve networks for coral reefs

Networks of no-take reserves are important for protecting coral reef biodiversity from climate change and other human impacts. Ensuring that reserve populations are connected to each other and non-reserve populations by larval dispersal allows for recovery from disturbance and is a key aspect of resilience. In general, connectivity between reserves should increase as the distance between them decreases. However, enhancing connectivity may often tradeoff against a network’s ability to representatively sample the system’s natural variability. This “representation” objective is typically measured in terms of species richness or diversity of habitats, but has other important elements (e.g., minimizing the risk that multiple reserves will be impacted by catastrophic events). Such representation objectives tend to be better achieved as reserves become more widely spaced. Thus, optimizing the location, size and spacing of reserves requires both an understanding of larval dispersal and explicit consideration of how well the network represents the broader system; indeed the lack of an integrated theory for optimizing tradeoffs between connectivity and representation objectives has inhibited the incorporation of connectivity into reserve selection algorithms. This article addresses these issues by (1) updating general recommendations for the location, size and spacing of reserves based on emerging data on larval dispersal in corals and reef fishes, and on considerations for maintaining genetic diversity; (2) using a spatial analysis of the Great Barrier Reef Marine Park to examine potential tradeoffs between connectivity and representation of biodiversity and (3) describing a framework for incorporating environmental fluctuations into the conceptualization of the tradeoff between connectivity and representation, and that expresses both in a common, demographically meaningful currency, thus making optimization possible.

Biased data reduce efficiency and effectiveness of conservation reserve networks

Complementarity-based reserve selection algorithms efficiently prioritize sites for biodiversity conservation, but they are data-intensive and most regions lack accurate distribution maps for the majority of species. We explored implications of basing conservation planning decisions on incomplete and biased data using occurrence records of the plant family Proteaceae in South Africa. Treating this high-quality database as 'complete', we introduced three realistic sampling biases characteristic of biodiversity databases: a detectability sampling bias and two forms of roads sampling bias. We then compared reserve networks constructed using complete, biased, and randomly sampled data. All forms of biased sampling performed worse than both the complete data set and equal-effort random sampling. Biased sampling failed to detect a median of 1-5% of species, and resulted in reserve networks that were 9-17% larger than those designed with complete data. Spatial congruence and the correlation of irreplaceability scores between reserve networks selected with biased and complete data were low. Thus, reserve networks based on biased data require more area to protect fewer species and identify different locations than those selected with randomly sampled or complete data.

The Boundary‐Quality Penalty: a Quantitative Method for Approximating Species Responses to Fragmentation in Reserve Selection

Aggregation of reserve networks is generally considered desirable for biological and economic reasons: aggregation reduces negative edge effects and facilitates metapopulation dynamics, which plausibly leads to improved persistence of species. Economically, aggregated networks are less expensive to manage than fragmented ones. Therefore, many reserve-design methods use qualitative heuristics, such as distance-based criteria or boundary-length penalties to induce reserve aggregation. We devised a quantitative method that introduces aggregation into reserve networks. We call the method the boundary-quality penalty (BQP) because the biological value of a land unit (grid cell) is penalized when the unit occurs close enough to the edge of a reserve such that a fragmentation or edge effect would reduce population densities in the reserved cell. The BQP can be estimated for any habitat model that includes neighborhood (connectivity) effects, and it can be introduced into reserve selection software in a standardized manner. We used the BQP in a reserve-design case study of the Hunter Valley of southeastern Australia. The BQP resulted in a more highly aggregated reserve network structure. The degree of aggregation required was specified by observed (albeit modeled) biological responses to fragmentation. Estimating the effects of fragmentation on individual species and incorporating estimated effects in the objective function of reserve-selection algorithms is a coherent and defensible way to select aggregated reserves. We implemented the BQP in the context of the Zonation method, but it could as well be implemented into any other spatially explicit reserve-planning framework.

Area‐Based Refinement for Selection of Reserve Sites with the Benefit‐Function Approach

Optimization of resource use is necessary for efficient conservation planning. Many reserve-selection algorithms aim to identify representative but inexpensive networks, which may lead to selecting small sites due to their lower costs and collectively higher species richness. Nevertheless, larger sites would be preferable regarding species' long-term persistence. An area-based refinement can be used to overcome this problem. We used a reserve-planning framework in which continuous benefit functions valued representation (numbers of populations), and differential species weights were based on a species' local rarity and threatened status. We introduced a refinement based on the species-area relationship that provides relatively higher values for larger sites. We applied the proposed method to rich fen vegetation in southern Finland. The species-area refinement resulted in a network of significantly larger sites with minor trade-offs with representation (numbers of populations). Giving endangered species higher weights ensured that the trade-off occurred mostly between site size and representation of low-priority species. We recommend using a species-area refinement for practical, maximum-coverage conservation planning.

Potential impacts of climate change on Sub‐Saharan African plant priority area selection

ABSTRACTThe Global Strategy for Plant Conservation (GSPC) aims to protect 50% of the most important areas for plant diversity by 2010. This study selects sets of 1‐degree grid cells for 37 sub‐Saharan African countries on the basis of a large database of plant species distributions. We use two reserve selection algorithms that attempt to satisfy two of the criteria set by the GSPC. The grid cells selected as important plant cells (IPCs) are compared between algorithms and in terms of country and continental rankings between cells. The conservation value of the selected grid cells are then considered in relation to their future species complement given the predicted climate change in three future periods (2025, 2055, and 2085). This analysis uses predicted climate suitability for individual species from a previous modelling exercise.We find that a country‐by‐country conservation approach is suitable for capturing most, but not all, continentally IPCs. The complementarity‐based reserve selection algorithms suggest conservation of a similar set of grid cells, suggesting that areas of high plant diversity and rarity may be well protected by a single pattern of conservation activity.Although climatic conditions are predicted to deteriorate for many species under predicted climate change, the cells selected by the algorithms are less affected by climate change predictions than non‐selected cells. For the plant species that maintain areas of climatic suitability in the future, the selected set will include cells with climate that is highly suitable for the species in the future. The selected cells are also predicted to conserve a large proportion of the species richness remaining across the continent under climate change, despite the network of cells being less optimal in terms of future predicted distributions.Limitations to the modelling are discussed in relation to the policy implications for those implementing the GSPC.

Uncertainty Analysis for Regional‐Scale Reserve Selection

Methods for reserve selection and conservation planning often ignore uncertainty. For example, presence-absence observations and predictions of habitat models are used as inputs but commonly assumed to be without error. We applied information-gap decision theory to develop uncertainty analysis methods for reserve selection. Our proposed method seeks a solution that is robust in achieving a given conservation target, despite uncertainty in the data. We maximized robustness in reserve selection through a novel method, "distribution discounting," in which the site- and species-specific measure of conservation value (related to species-specific occupancy probabilities) was penalized by an error measure (in our study, related to accuracy of statistical prediction). Because distribution discounting can be implemented as a modification of input files, it is a computationally efficient solution for implementing uncertainty analysis into reserve selection. Thus, the method is particularly useful for high-dimensional decision problems characteristic of regional conservation assessment. We implemented distribution discounting in the zonation reserve-selection algorithm that produces a hierarchy of conservation priorities throughout the landscape. We applied it to reserve selection for seven priority fauna in a landscape in New South Wales, Australia. The distribution discounting method can be easily adapted for use with different kinds of data (e.g., probability of occurrence or abundance) and different landscape descriptions (grid or patch based) and incorporated into other reserve-selection algorithms and software.

Planning for robust reserve networks using uncertainty analysis

Planning land-use for biodiversity conservation frequently involves computer-assisted reserve selection algorithms. Typically such algorithms operate on matrices of species presence–absence in sites, or on species-specific distributions of model predicted probabilities of occurrence in grid cells. There are practically always errors in input data—erroneous species presence–absence data, structural and parametric uncertainty in predictive habitat models, and lack of correspondence between temporal presence and long-run persistence. Despite these uncertainties, typical reserve selection methods proceed as if there is no uncertainty in the data or models. Having two conservation options of apparently equal biological value, one would prefer the option whose value is relatively insensitive to errors in planning inputs. In this work we show how uncertainty analysis for reserve planning can be implemented within a framework of information-gap decision theory, generating reserve designs that are robust to uncertainty. Consideration of uncertainty involves modifications to the typical objective functions used in reserve selection. Search for robust-optimal reserve structures can still be implemented via typical reserve selection optimization techniques, including stepwise heuristics, integer-programming and stochastic global search.

Conservation Targets for Viable Species Assemblages in Canada: Are Percentage Targets Appropriate?

Percentage targets for conservation have become a popular tool (advocated in both the scientific literature and the conservation community) for setting minimum goals for the amount of land to be set aside as protected areas. However, there is little literature to support a consistent percentage target that might be widely applied. Moreover, most percentage targets have not taken into account issues of species persistence. A recent study of herbivores in Kruger National Park took into account issues of representation and persistence in setting conservation targets and found that results were consistently about 50% and were unaffected by different permutations of the reserve selection process. Here, we carry out a similar analysis for representation of mammals within sites that are predicted to allow for their persistence, across eight ecologically defined regions in Canada to test whether we see similar consistent patterns emerging. We found that percentage targets varied with the different permutations of the reserve selection algorithms, both within and between the study regions. Thus, we conclude that the use of percentage targets is not an appropriate conservation strategy.

Replacement cost: A practical measure of site value for cost-effective reserve planning

Conservation needs are often in direct competition with other forms of land-use, and therefore protection of biodiversity must be cost-efficient. While common reserve selection algorithms address this problem, quantitative planning tools often suggest an optimal set of sites that is not necessarily convenient for practical conservation. Besides cost-effective solutions we require flexibility if land-use conflicts are to be effectively resolved. We introduce a novel concept for site value in quantitative reserve planning. Replacement cost refers to the loss in solution value given that the optimal cost-efficient solution cannot be protected and alternative solutions, with particular sites forcibly included or excluded, are needed. This cost can be defined either in terms of loss of biological value or in terms of extra economic cost, and it has clear mathematical definitions in the context of benefit-function-based reserve planning. A main difference with the much-used concept of irreplaceability is that the latter tells about the likelihood of needing a site for achieving a particular conservation target. Instead, replacement cost tells us at what cost (biological or economic) can we exclude (or include) a site from the reserve network. Here, we illustrate the concept with hypothetical examples and show that replacement-cost analysis should prove useful in an interactive planning process, improving our understanding of the importance of a site for cost-efficient conservation.

How well do Important Bird Areas represent species and minimize conservation conflict in the tropical Andes?

ABSTRACTWhere high species richness and high human population density coincide, potential exists for conflict between the imperatives of species conservation and human development. We examine the coincidence of at‐risk bird species richness and human population in the countries of the tropical Andes. We then compare the performance of the expert‐driven Important Bird Areas (IBA) scheme against a hypothetical protected‐areas network identified with a systematic reserve selection algorithm seeking to maximize at‐risk bird species representation. Our aim is to assess the degree to which: IBAs contain a higher richness of at‐risk species than would be expected by chance, IBAs contain more people than would be expected by chance, and IBAs are congruent with complementary areas that maximize species representation with an equivalent number of sites. While the correlation of richness and population was low for the region as a whole, representation of all at‐risk bird species required many sites to be located in areas of high human population density. IBA sites contained higher human population densities than expected by chance (P < 0.05) and were markedly less efficient in representing at‐risk bird species of the region than sites selected using the reserve selection algorithm. Moreover, overlap between IBAs and these latter sites was very limited. Expert‐driven selection procedures may better reflect existing sociopolitical forces, including land ownership and management regimes, but are limited in their ability to develop an efficient, integrated network of sites to represent priority species. Reserve selection algorithms may serve this end by optimizing complementarity in species representation among selected sites, whether these sites are adopted independently or as a supplement to the existing reserve network. As tools of site selection, they may be particularly useful in areas such as the tropical Andes where complex patterns of species disjunction and co‐occurrence make the development of representative reserve networks particularly difficult. Furthermore, they facilitate making spatially explicit choices about how reserve sites are located in relation to human populations. We advocate their use not in replacement of approaches such as the IBA initiative but as an additional, complementary tool in ensuring that such reserve networks are developed as efficiently as practically possible.

Differences in the Umbrella Effects of African Amphibians and Mammals Based on Two Estimators of the Area of Occupancy

Conservation organizations are collecting large-scale data regarding distribution and threats to vertebrate taxa. These data sets will enable planners to systematically identify large-scale conservation priorities; however, they will cover only a tiny proportion of living organisms. Therefore, it is essential to investigate to what extent the areas selected for conservation actions can provide protection for other species. We analyzed the umbrella effect between amphibians and mammals across mainland Africa. We built habitat suitability models within the geographic ranges of 1654 species, based on data collected in the framework of the World Conservation Union (IUCN) Global Amphibian Assessment and IUCN Global Mammal Assessment. We applied systematic reserve selection algorithms to two sets of estimators of the area of occupancy of amphibians and mammals (geographic ranges and estimated suitable areas) and thus selected four reserve systems. We then quantified the protection that each of the four systems provided for amphibians and mammals. Reserves selected for amphibians and mammals were comparable in area, with the former concentrated in the Afrotropical region and the latter more evenly dispersed. Mammal reserves left fewer gaps in species coverage among amphibians than the reverse, but amphibian reserves included a larger proportion of each mammal's area of occupancy than the reverse. For both taxa, setting reserves to include estimated suitable areas instead of ranges resulted in the clustering of reserves in the tropics. Furthermore, it efficiently protected hidden gaps (species with unsuitable portions of their range inside protected areas) in the other taxon and included a higher proportion of the area of occupancy of the other taxon. Overall, amphibians and mammals in Africa acted as an umbrella for a high proportion of species in the other taxon. Focusing on estimated suitable areas instead of ranges improved the umbrella effect of both taxa.

Connectivity of reef fish between mangroves and coral reefs: Algorithms for the design of marine reserves at seascape scales

Many species of coral reef fish undertake ontogenetic migrations between seagrass beds, mangroves, and coral reefs. A recent study from the Caribbean found that the availability of mangrove nursery habitat had a striking impact on the community structure and biomass of reef fish in their adult, coral reef habitat. The biomass of several species more than doubled when the reefs were connected to rich mangrove resources (defined as having at least 70 km of fringing Rhizophora mangle within a region of 200 km 2). Here, the results of this large-scale empirical study are translated into a series of algorithms for use in natural resource management planning. Four algorithms are described that identify (i) the relative importance of mangrove nursery sites, (ii) the connectivity of individual reefs to mangrove nurseries, (iii) areas of nursery habitat that have an unusually large importance to specific reefs, and (iv) priority sites for mangrove reforestation projects. The algorithms generate a connectivity matrix among mangroves and coral reefs that facilitates the identification of connected corridors of habitats within a dynamic planning environment (e.g., reserve selection algorithms).

The SITES reserve selection system: A critical review

Numerous models have been put forth to help with the growing demand for the establishment of biodiversity reserves. One site selection model that has been used in several recent studies is SITES [S.J. Andelman, I. Ball, F.W. Davis and D.M. Stoms, SITES V 1.0: an analytical toolbox for designing ecoregional conservation portfolios, Unpublished manual prepared for the nature conservancy, 1999, 1–43. (available at http://www.biogeog.ucsb.edu/projects/tnc/toolbox.html )]. SITES includes two heuristic solvers: based on Greedy and Simulated Annealing. We discuss the formulation of the SITES model, present a new formulation for that problem, and solve a number of test problems optimally using off-the-shelf software. We compared our optimal results with the SITES Simulated Annealing heuristic and found that SITES frequently returns significantly suboptimal solutions. Our results add further support to the argument, started by Underhill [L.G. Underhill, Optimal and suboptimal reserve selection algorithms, Biol. Conserv. 70 (1994) 85–87], continuing through Rodrigues and Gaston [A.S.L. Rodrigues and K.J. Gaston, Optimization in reserve selection procedures – why not?, Biol. Conserv. 107 (2002) 123–129], for greater integration of optimal methods in the reserve design/selection literature.