Strategies of Reserve Selection


Existing protected areas contain only a biased sample of the Earth's biodiversity. This is because conservation has to compete with other land‐uses. Limitations in space and budget highlight the importance of effective and efficient conservation strategies. In this context, questions like what elements of biodiversity should we focus on, and where to protect them, are difficult to answer. The field of spatial conservation planning has evolved to address these questions, with attention to cost‐efficiency, representativeness, adequacy, flexibility, threat and vulnerability. It encourages politicians and conservation planners to set quantitative conservation goals, and offers tools to prioritize among large sets of species, ecosystem types and areas. The implementation of this approach is becoming essential in order to halt the loss of biodiversity in a world of increasing human population and increasing demand for natural resources.

Key concepts

  • Protected areas have long been established opportunistically resulting in inefficient spending of conservation funds.

  • Scoring methods rank individual sites according to their conservation value, but cannot guide the design of networks efficiently.

  • Complementarity‐based approaches instead, evaluate the value of the network as a whole, thus achieving cost‐efficiency in reserve network design.

  • Aiming at representation of species or ecosystems in reserves is insufficient. Conservation goals and their derived targets should aim at persistence if biodiversity loss is to be halted.

  • To achieve cost‐efficiency, targets have to be explicit and quantitative.

  • A large suit of tools exists to aid conservation decision‐making under conditions where data is incomplete or of poor quality – typical for biodiversity data. More or better data is indispensable, but meanwhile decision‐making can proceed.

  • Although the perfect biodiversity surrogate does not exist, basing the design of reserve networks on information for a number of taxa produces promising results.

  • Connectivity of reserve networks promotes the persistence of species in fragmented landscapes.

  • One‐size‐fits‐all solutions do not always exist. Species or habitats may require different conservation actions, leading to inevitable trade‐offs.

  • Although spatial prioritization has been mostly used to locate protected areas, it can be used to assist the identification and location of most suitable management actions.

Keywords: biodiversity representation; cost‐efficiency; reserve network design; spatial prioritization; systematic conservation planning

Figure 1.

Conservation priority areas selected in waters of trawlable depth in New Zealand's Exclusive Economic Zone. The gradation in colours indicates different levels of priority, with lowest ranks being top priorities. (a) Analysis ignoring costs, aiming at maximizing the protection of 96 fish species; (b) including cost constraints, depending on fishing intensity as recorded by fishermen. The table reflects costs and benefits from the top 10% priorities (dark blue) in the two analyses. When maximizing the protection of species, highest conservation value is achieved, but results in a notable reduction of fishing opportunities (22%). When constraining the analysis with fishing costs, the spatial solution is markedly different, achieving however only moderately less conservation returns (23.4%) than the biodiversity approach, but without loss of current fishing activity. Modified from Leathwick et al. .

Figure 2.

Effects of the use of weights to prioritize species differently on the average levels of representation, when applied to 75 vascular plant species characteristic of herb‐rich forest in Finland. Results are expressed as the mean percentage of populations represented for the species in each class (common, rare or red‐listed). When weights were used (dark grey), each species received a value depending upon regional rarity, threat status (re‐list classification) and taxonomic value. Courtesy of A. Arponen, based on Arponen et al. .

Figure 3.

Predicted average representation for 3 groups of species in 45 mesic semi‐natural grassland sites (Finland), for which the intensity of habitat management is optimized (three management options by cattle grazing: annual grazing, biennial grazing and no grazing). The species (Lepidoptera and plant species) are grouped by their grazing intensity preference (panels). The grazing intensity is optimized for all sites for the joint set of species, for combinations of budget (y‐axis) and weights (x‐axis). Next, species representation was estimated from the predicted responses of species to management intensity and other site conditions, for each combination of budget and weight. Management cost increased with grazing intensity and site area. Weights refer to the weight assigned to the species that prefer annual grazing; the most expensive management option. Other species received a weight of one. Representation for each species was scaled to their maximum possible representation. The figure shows trade‐offs in representation between different species groups; only higher weights and sufficient budget can increase the representation of species depending on annual grazing, although this goes at the cost of representation of species preferring less intense grazing. See also Van Teeffelen et al. .



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Further Reading

Margules CR and Sarkar S (2007) Systematic Conservation Planning. Cambridge: Cambridge University Press.

Moilanen A, Possingham HP and Wilson K (eds) (2009) Spatial Conservation Prioritisation: Quantitative Methods and Computational Tools. Oxford: Oxford University Press.

Pressey RL, Cabeza M, Watts ME, Cowling RM and Wilson KA (2007) Conservation planning in a changing world. Trends in Ecology & Evolution 22: 583–592.

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How to Cite close
Cabeza, Mar, and van Teeffelen, Astrid(Mar 2009) Strategies of Reserve Selection. In: eLS. John Wiley & Sons Ltd, Chichester. [doi: 10.1002/9780470015902.a0021224]