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36 Ting, C.T. et al. (1998) A rapidly evolving homeobox at the site of a hybrid sterility gene. Science 282, 1501–1504 37 Schmid, K.J. et al. (1999) Large number of replacement polymorphisms in rapidly evolving genes of Drosophila: implications for genome-wide surveys of DNA polymorphism. Genetics 153, 1717–1729 38 Hughes, A. et al. (1990) Positive Darwinian selection promotes charge profile diversity in the antigen-binding cleft of class I majorhistocompatibility-complex molecules. Mol. Biol. Evol. 76, 515–524 39 Yang, Z. et al. (2000) Codon-substitution models for heterogeneous selection pressure at amino acid sites. Genetics 155, 431–449 40 McAllister, B.F. and McVean, G.A.T. (2000) Neutral evolution of the sex-determining gene transformer in Drosophila. Genetics 154, 1711–1720 41 Parsch, J. et al. (2000) Deletion of a conserved regulatory element in the Drosophila Adh gene leads to increased alcohol dehydrogenase activity but also delays development. Genetics 156, 219–227 42 Chen, Y. et al. (1999) RNA secondary structure and compensatory evolution. Genes Genet. Syst. 74, 271–286 43 Jenkins, D.L. et al. (1995) A test for adaptive change in DNA sequences controlling transcription. Proc. R. Soc. London B Biol. Sci. 261, 203–207

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44 Akashi, H. et al. (1998) Mutation pressure, natural selection, and the evolution of base composition in Drosophila. Genetica 103, 49–60 45 Duret, L. and Mouchiroud, D. (1999) Expression pattern and, surprisingly, gene length shape codon usage in Caenorhabditis, Drosophila and Arabidopsis. Proc. Natl. Acad. Sci. U. S. A. 96, 4482–4487 46 Keightley, P.D. and Eyre-Walker, A. (2000) Deleterious mutations and the evolution of sex. Science 290, 331–333 47 Kondrashov, A.S. (1995) Contamination of the genome by very slightly deleterious mutations – why have we not died 100 times over? J. Theor. Biol. 175, 583–594 48 Keightley, P.D. and Eyre-Walker, A. (1999) Terumi Mukai and the riddle of deleterious mutation rates. Genetics 153, 515–523 49 Eichler, E.E. (1998) Masquerading repeats: paralogous pitfalls of the human genome. Genome Res. 8, 758–762 50 Charlesworth, B. et al. (1994) The evolutionary dynamics of repetitive DNA in eukaryotes. Nature 371, 215–220 51 Anagnostopoulos, T. et al. (1999) DNA variation in a 5-Mb region of the X chromosome and estimates of sex-specific/type-specific mutation rates. Am. J. Hum. Genet. 64, 508–517 52 Mullikin, J.C. et al. (2000) An SNP map of human chromosome 22. Nature 407, 516–520

53 Charlesworth, D. and Charlesworth, B. (1998) Sequence diversity: looking for effects of recombination rates. Curr. Biol. 8, R658–661 54 Przeworski, M. et al. (2000) Adjusting the focus on human variation. Trends Genet. 16, 296–302 55 Huntley, M. and Golding, G.B. (2000) Evolution of simple sequence in proteins. J. Mol. Evol. 51, 131–140 56 Long, M. and Langley, C.H. (1993) Natural selection and the origin of Jingwei, a chimeric processed functional gene in Drosophila. Science 260, 91–95 57 Long, M. et al. (1995) Intron phase correlations and the evolution of the intron/exon structure of genes. Proc. Natl. Acad. Sci. U. S. A. 92, 12495–12499 58 Brookes, A.J. et al. (2000) HGBASE: a database of SNPs and other variations in and around human genes. Nucleic Acids Res. 28, 356–360 59 Long, M. et al. (1999) Origin of new genes and source for N-terminal domain of the chimerical gene, jingwei, in Drosophila. Gene 238, 135–141 60 Eisen, J.A. (2000) Horizontal genome transfer among microbial genomes: new insights from complete genome sequences. Curr. Opin. Genet. Dev. 10, 606–611 61 Palmer, J.D. et al. (2000) Dynamic evolution of plant mitochondrial genomes: mobile genes and introns and highly variable mutation rates. Proc. Natl. Acad. Sci. U. S. A. 97, 6960–6966

Design of reserve networks and the persistence of biodiversity Mar Cabeza and Atte Moilanen Sophisticated computational methods have been developed to help us to identify sets of nature reserves that maximize the representation of regional diversity, but, until recently, the methods have not dealt explicitly and directly with the main goal of reserve networks, that of the long-term maintenance of biodiversity. Furthermore, the successful application of current methods requires reliable information about species distributions, which is not always available. Recent results show that data quality, as well as the choice of surrogates for biodiversity, could be critical for successful reserve design. Because of these problems and a lack of communication between scientists and managers, the impact of computational site-selection tools in applied conservation planning has been minimal.

Mar Cabeza* Atte Moilanen Dept of Ecology and Systematics, Division of Population Biology, PO Box 17 (Arkadiankatu 7), University of Helsinki, FIN-00014 Helsinki, Finland. *e-mail: [email protected]

Site-selection algorithms are computational methods that have been developed to identify a set of nature reserves containing as many species as possible, that is, methods maximizing the REPRESENTATION (see Glossary) of species diversity1–3. For this purpose, two general representation problems have been formulated. First, the minimum area problem: select the sites that represent all NATURAL FEATURES a given number of times with a minimum number of sites, area or cost (Box 1). Second, the maximum coverage

problem: given a limit on cost or area, select the combination of sites that maximizes the representation of natural features (Box 2). Advances in site-selection algorithms

Variants of these two problems have been tackled with different site-selection algorithms (Box 3). By the early 1980s, several authors1,3,4 independently developed iterative heuristic algorithms that attempt to solve the problem of combining sites into representative and efficient networks based on the concept of COMPLEMENTARITY5,6. Subsequently, authors began using tools from the field of OPERATIONS RESEARCH to contribute to the development of reserve design algorithms. In the 1990s, discussions about the optimality properties of site-selection algorithms became a central theme in the literature, which concluded that heuristic algorithms do not guarantee an optimal minimum area solution, in contrast to exact algorithms (such as integer programming techniques)7 (Box 3). Several studies2,3 have compared the optimality of different site-selection

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Box 1. Minimum area problem

Box 2. Maximal coverage problem

Minimize number of sites, total area, or cost, to represent all natural features (e.g. species) A is an M sites x N natural features matrix whose elements aij are a measure of the occurrence of the feature j in site i. Let Ii ∈{0,1} be an indicator variable which has value 1 if site i is included in the selection and 0 otherwise. Let each site have cost ci, and a desired representation level rj.. The optimization problem is:

Maximize the representation of natural features given a limit for the number of sites, cost or area Let S be the set of sites included in the site selection, N is the number of features. The object of optimization is to:

Minimize Eqn 1:

M

∑ ci Ii M

∑ aij Ii i =1

N

∑ V j (S)

[1]

∑ ci

[2]

j =1

[1]

i =1

subject to Eqn 2:

Maximize Eqn 1:

subject to Eqn 2:

≤R

i ∈S

≥ rj

[2]

for all j (each feature is represented at least rj times). Particular problems of representation can be derived from this general casea: (a) Minimize the number of sites needed to represent features: ci = 1 all sites are of the same size or cost aij = 0 or 1 if only the presence or absence of the feature is considered rj = 1 gives the basic single representation problem If rj ≥ 1 we have the multiple representation problem (b) Minimize the total area needed to represent each feature at a level rj. ci = areai the cost of a site is its area aij = area covered by feature j in site i rj = required representation level in area units (c) Minimize total cost to represent features. as (a) but the cost of a site is its precise real cost: ci = costI.

Vj (S) is the value of feature j in selection S. In the simplest form, Vj (S) is 1 if the desired representation level for feature j is achieved in selection S and 0 if it is not. Vj (S) could also be a measure of the predicted persistence of species j on selection S. The cost of patch i, ci , can be defined as 1 (if only the number of patches counts), as the patch area, or as the real patch cost depending on the objectives of optimization. R is the maximum available resource, counted in the same units as ci. (See Ref. a for detailed formulations.) Reference a Arthur, J.F. et al. (1997) Finding all optimal solutions to the reserve site selection problem: formulation and computational analysis. Environ. Ecol. Stat. 4, 153–165

Reference

a Pressey, R.L. et al. (1997) Effectiveness of alternative heuristic algorithms for identifying indicative minimum requirements for conservation reserves. Biol. Conserv. 80, 207–219

algorithms, giving mixed results. Although iterative heuristic algorithms are often suboptimal, the difference to the solution given by an exact algorithm depends on the data set2,3. The first systematic study comparing the solutions found by different algorithms in relation to data-set characteristics, such as rarity or nestedness of features, size variation of the selection units and the size of the data set, has only recently been conducted8. The number of rare features (land types in this study) in the data was an important factor in explaining differences between iterative heuristics and integer programming: the larger the number of rare features, the more sites are needed and the closer are their solutions. For instance, a high level of endemism forces the initial inclusion of many sites into the reserve network and, consequently, the remaining optimization problem becomes computationally simpler (and the results of different algorithms closer to each other). Size variation among the sites also appeared to reduce suboptimality when the goal was to minimize the total reserve area rather than the number of sites. Although integer programming techniques should be preferred for their guaranteed optimality, it has been argued that they are not convenient, because they may require unacceptably long computation http://tree.trends.com

times for large problems (hundreds or thousands of sites or patches), and because they cannot solve certain complex problem variants, including those with nonlinear object functions2. Nevertheless, an increasing fraction of conservation planning problems can be solved using exact algorithms9, and there is currently less emphasis on optimality, which is no longer a problem in many cases. Integer programming is preferred for both theoretical exercises and when the size of the data set and the conservation goal are appropriate9–11, whereas iterative heuristics are employed when solutions are required in seconds or minutes, and in conservationplanning projects requiring close interaction with decision makers4,12. Advanced heuristics, such as stochastic global search methods (Box 3), appear to represent a good compromise; these methods can deal relatively quickly with complex problems and their solutions are much less suboptimal than are those given by the simplest iterative heuristics13. Also, in recent reserve design literature, increasing attention has been given to the use of multicriteria methods4,13. Following algorithmic improvements and a great increase in hardware speed, topics other than algorithm efficiency and optimality are receiving increased attention in the current reserve design literature. These include the critical issue of how to deal with long-term persistence of biodiversity, the sensitivity of algorithms to data-set quality, the choice of surrogate species for reserve design, and how to

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Box 3. A phylogeny of site-selection algorithms Ordinary single criterion optimization minimizes (or equivalently maximizes) one quantity. Single criterion optimization methods that have been used for siteselection problems can be categorized into exact and inexact methods. The branch-and-bound methoda of integer programming is an exact method. It considers all possible site selections using implicit enumeration, and guarantees to find the best solution(s). Branch-and-bound methods require an exponentially increasing number of iterations as a function of problem size, and therefore dataset size limits their applicability. Inexact heuristics (optimality is not guaranteed and there is no estimate of the quality of the solution) can be divided into iterative heuristics and stochastic global search methods. Iterative heuristics repeatedly apply a set of rules, such as ‘add the site that most complements the current selection’, with no randomization used except occasionally for breaking ties.

Iterative heuristics are very fast to compute, but they can fail to find an optimal solution even for simple problemsb,c. Stochastic global search techniques seek good solutions using intelligent randomization. These techniques are faster than integer programming, but slower than iterative heuristics. They cannot guarantee optimality, but various difficult optimization problems have been solved successfully using stochastic global search techniquesd. Scoring methods are multicriteria optimization methodse,f that consider several objectives simultaneously (number of sites, cost or persistence, etc.). Typically, tradeoffs have to be made between different objectives; for example, low cost might prevent full biodiversity representation. A multicriteria optimization problem can be transformed into a single criterion problem by taking a weighted sum of the different objectives. Alternatively, a constrained single criterion problem can

establish conservation-scheduling priorities given the subset of selected sites. Performance of existing reserves

The performance of existing reserves has often been assessed by comparing them to the minimum number of sites (or area) needed to represent all diversity (efficiency sensu Pressey and Nicholls14), even though minimal site number might not have been the original goal of the reserve design. In practice, reserves have frequently been selected in an ad hoc manner12,15, without the use of explicit criteria. This explains the observation that fewer sites are needed to represent all diversity if the selection assumes no existing reserves rather than beginning by including existing reserves15. Although efficiency can be used as a measure of the performance of an algorithm searching for the minimum set (to compare solutions given by different algorithms for the same data set), the success of a reserve network depends on the initial conservation objectives and, hence, success can and should be measured in different ways. For example, Araújo16 and Rodrigues et al.10 have measured the success of existing reserves by considering the gap between current and complete representation, concluding that, although the existing protected areas do not sample all species, they do provide a better result than does choosing areas at random The assessment of the success of a reserve in terms of persistence requires monitoring over time to establish whether the conservation targets have http://tree.trends.com

be defined by setting limits to all objectives except one. Note that all the methods that have been used for the single criterion case can be used to solve ‘subproblems’ in multicriteria site-selection algorithms. However, in multicriteria optimization, the critical phase of optimization, is the determination of the criteria and the weights given to them. References a Nemhauser, G.L. and Wolsey, L.A. (1988) Integer and Combinatorial Optimization, John Wiley & Sons b Underhill, L.G. (1994) Optimal and suboptimal reserve selection algorithms. Biol. Conserv. 70, 85–87 c Pressey, R.L. et al. (1996) Optimality in reserve selection algorithms: when does it matter and how much? Biol. Conserv. 76, 259–267 d Bäck, T. et al. (1997) Handbook of Evolutionary Computation, Oxford University Press e Steuer, R. (1986) Multiple Criteria Optimization: Theory, Computation and Application, John Wiley & Sons f French, S. (1988) Decision Theory: An Introduction to the Mathematics of Rationality, Ellis Horwood

truly persisted10,16,17. A few recent studies have demonstrated how efficient reserve networks (efficient in representing numbers of species relative to the cost) might not protect the original set of features for many years, because species go locally extinct and colonize sites regardless of whether the site is included in the reserve network. Table 1 summarizes results from three studies that analysed how well different reserve design strategies would have maintained diversity over time. The first two studies18,19 evaluated the minimum area strategy in its ability to maintain diversity. Both concluded that species turnover was high, and that a large fraction of species could be lost even in a short time (Table 1). The third example11 analysed the long-term performance of several design strategies using the Common Birds Census data set in Britain. This is the first study that suggests ways of accounting for species turnover, and it concludes that fewer species are lost if sites are selected according to population density or local abundance. The density strategy might avoid selecting large sites with large populations, and favour smaller sites with denser populations, and it might, therefore, be a relatively efficient approach (Table 1). However, more studies are needed to establish whether focusing on good-quality (densely populated) but possibly small sites would be a good general strategy for reserve design. METAPOPULATION THEORY suggests that many sites are needed for species living as metapopulations in networks of

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Table 1. Studies demonstrating how species tend to be lost from reserves in minimum set designsa Study

Criteria

Species loss (%)b

Reserve size

Ref.

To minimize the number of sites that cover all plant species from limestone pavements (11 yr; 50 spp.; 77 sites)c

At least once At least twice for species with >1 population (78% of species)

36.0 34.0

18 sites 22 sites

18

For a combination of boreal lakes, find the minimum area that covers: (63 yr; 32 spp.; 25 sites)

Maximum number of plants Maximum restricted range diversity

18.0 18.0

5 sites 5 sites

19

Minimize reserve network area to selectd (10 yr; 47 spp.; 56 sites)

All species at least once All species at least at the site where they are most abundant All species at least at the site where they have the highest density

Min. 4.0 0.0 0.0

Max. 12.0 4.0 5.0

Mean 8.0 2.7 3.2

Min. 400 1500 1000

Max. 750 2250 1800

Mean 556.25 (ha) 1831.25 (ha) 1312.50 (ha)

11

aStudies analyzing the proportion of species that would have been successfully protected if the selection of the reserve had been done with the occupancy information at some previous point. bSpecies loss factors from Refs 18 and 19 are approximate values measured from graphs. cTime intervals, number of features (species) and number of available sites. dResults show minimum, maximum and mean data from eight analyses of data sets sampled ten years apart from a temporal data set of 20 years.

small patches, because the local extinction rates would inevitably be high20. Persistence of biodiversity in reserve networks

Researchers now agree that biodiversity persistence should be considered in reserve-network design4,12,13,17,21–23. So far, only a few studies have actually done this. Some have focused only on local (within site) persistence11,21,24,25, whereas others have looked at regional (within reserve network) persistence2,18,26. Spatial population dynamics, however, has yet to be treated explicitly in siteselection literature. Nevertheless, the general idea of keeping sites close together has been influential in spatial reserve design. Local and regional persistence

Several approaches to deal with local persistence have been suggested: (1) Only selecting sites larger than a threshold size when there is a known size limit for the presence of species (e.g. the Schonewald-Cox index gives an indication of the minimum area necessary for finding particular mammal species)25. (2) Basing site selection on abundance, either by prioritizing sites with the largest populations11, or by using population density as an indicator of site quality11,21,27. (3) Running population viability analysis for each species24. As far as we know, this approach has not been used in a multispecies site-selection exercise, undoubtedly owing to the huge amount of highquality data that it requires. If the focus of reserve design is on the regional persistence (i.e. maintain each species within the reserve network, even if local extinctions occur), a preferred approach (assuming that the cost allows it) is to include multiple representations of each natural feature within the set of sites2,18,28. Another recent study26 proposed a method that combines probabilities of local persistence among sites directly, http://tree.trends.com

until the required level of persistence probability for each species is reached. The difficult part here is how to obtain the probabilities of persistence. Araújo and Williams29 used niche-based regression models to estimate the local probabilities of occurrence of species, which were then transformed into probabilities of persistence with information on threat and susceptibility of species to the threats. Spatial reserve design

Economic considerations might require compact reserves because the cost of management often scales more closely with the length of reserve boundary than with its area13. Moreover, a compact reserve increases local persistence by reducing edge effects30 and, at the same time, having individual sites close together facilitates dispersal and recolonization of empty habitats, which increases the probability of regional persistence20,31. A few studies have considered the clustering of sites by • Including a rule that chooses sites close together when breaking ties in iterative algorithms32,33. • Minimizing a linear combination of reserve network area and boundary length13. Tight clustering of sites might not always be the best way to increase persistence when REGIONAL STOCHASTICITY is strong. Diseases, weather catastrophes and forest fires, etc. can increase the risk of simultaneous extinction of sites that are located very close to each other34. One ad hoc solution is to require multiple representations at sites separated by a minimum distance while maximizing reserve compactness13. Despite these approaches to promoting persistence, site-selection methods are essentially based on static criteria, because they mostly use information based on a single snapshot of species distributions. Spatiotemporal dynamics are not explicitly taken into account, even when species distributions are known to change over time. Given that many communities show high turnover18, the

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Glossary Complementarity: Property of two (sets of) sites that occurs when some of the natural features in a site differ from the features in another site. When (sets of) sites are highly complementary, they contain (almost) nonoverlapping representation of natural features. Irreplaceability: A measure of the likelihood that the site will be required as part of a reserve network that satisfies a specific conservation goal. A site is highly irreplaceable when it includes unique or rare natural features. Metapopulation theory: In general, spatial population dynamics of a species living in a spatially structured, possibly highly fragmented, landscape. A metapopulation may persist regionally in a stochastic balance between extinctions of local populations and recolonizations of empty habitat patches. Natural features: Attributes (i.e. species, plant communities or landscape types, etc.) valued for a conservation goal. Operations research (OR): A mathematical discipline concerned with the construction, mathematical analysis and solving of decision problems. OR seeks the determination of the best (i.e. optimal) course of action under limited resources. Regional stochasticity: Spatially correlated environmental stochasticity, caused for example by weather phenomena. Regional stochasticity induces correlation into local population dynamics and may cause simultaneous extinctions of closely spaced populations. Representation: The extent to which required natural features occur within a (set of) sites. Vulnerability: Risk of a site being transformed in a way that (some) natural features are lost from the site.

sites selected by a static approach might not protect as many species in the long term as the outcome of the algorithm would have us believe. Awareness of this problem has been growing and several authors13,17,21,23 have called for the integration of spatial population modelling in reserve network design, which would require more interaction between disciplines such as landscape ecology, metapopulation biology and conservation biology. In their recent study, Araújo and Williams29 attempt to deal with spatial dynamics in an implicit way. They use spatial aggregation measures to predict species occurrences, and they interpret spatial correlations as a result of colonization processes. The selected sites for the data set used in their study were also clumped together, supporting the spatial design approaches previously described. We are not aware, however, of any studies incorporating spatial dynamics explicitly in site-selection algorithms and the problem remains a major challenge for future reserve network designs. The critical part in such a study is likely to be the acquisition of properly parameterized species-specific models of spatial dynamics for multiple species. Data uncertainty

While site-selection algorithms continuously improve, the quality of the biodiversity distribution data needed for applying these methods remains poor. In some cases, with incomplete biodiversity surveys, data interpolation techniques have been used to approximate species distributions3,35,36. Risks associated with the use of incomplete data sets in site-selection procedures have been recently investigated28,37. In both studies, data sets were systematically modified to assess the effects of missing sites, missing taxa and missing records on various variables. The first study37 concluded that data deletions result in increased variation in http://tree.trends.com

selected networks and decreased efficiency because more sites are needed to achieve the goal when the level of deletion increases. The second study28 assessed the differences in selections in terms of the percentage of the initial goal achieved (i.e. five representations of each species from the complete data set covered by the set selected with the reduced data). In general, algorithms turned out to be relatively robust to randomly distributed reductions of records (up to 80–90% of the goal achieved) but much less so when the missing data were concentrated in particular sites or taxa. The important conclusion is that inventory efforts should be distributed as broadly as possible among sites and taxa. In practice, the problem is often that available data on species abundances and distributions are biased towards preferred sites or towards a few charismatic species. The use of umbrella species as surrogates for poorly known regional biota has been a common conservation strategy38, comparable to the use of indicator taxa in reserve design22. Andelman and Fagan39 evaluated several umbrella species schemes and found that none performed any better than did randomly selected species from the database. Williams et al.40 reached the same conclusion in a similar study, evaluating the use of flagship species to represent other taxa. Williams et al.40 also found that areas selected using flagships were no better for representing biodiversity than was randomly selecting the same number. Furthermore, sets of complementary sites for different taxa often do not overlap16,22,41,42, although results obtained in Uganda43 do seem encouraging. In Uganda, different taxa show similar biogeography, and therefore, representative sets of sites for one taxon also represent well other taxa. The conclusion from these few studies is that the degree of overlap among taxa varies depending on the geographical region and the taxa being considered. Another approach is to use habitat diversity as a surrogate for species diversity. Although the approach seems promising, results from recent studies40,44, using species and environmental data for Europe44 and Africa40 are not encouraging. When applied to the European data, the approach only performed well for plants, with other taxa not showing a consistent pattern of representation. The unrepresented species mainly had restricted range sizes, were narrow endemics, or were at the edges of their ranges. For the African data, using ecoregions to select sites was a better strategy to represent birds and mammals than was using flagships, but still not as good as using data for all birds and mammals. Where to go from here

After two decades of research, site-selection algorithms have improved to the point where they can deal with a wide variety of design objectives. Despite this, their impact in applied conservation planning

Review

Acknowledgements We are grateful to M. Araújo and I. Hanski for discussion and comments on this review. We also appreciate the critical comments of B. O’Hara, H. Possingham and R.I. Vane-Wright. Financial support to MC and AM was provided by the Academy of Finland, research projects #45125 and #71516, respectively.

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has been minimal. The work done so far remains mostly theoretical, with few exceptions2. Prendergast et al.31 have discussed the gaps between theory and practice in reserve design, and the lack of communication between scientists and managers appeared to be the main problem. Managers are not aware of existing computational and analytical tools and scientists remain unaware of many management goals and constraints; neither is the optimal set of sites selected by an algorithm generally readily available45. The acquisition and management of sites takes time. To cope with this, and to make decisions about the scheduling of conservation action, the use of IRREPLACEABILITY indices45,46 and VULNERABILITY indices has been suggested12. This information can be combined with other information (e.g. costs) in a multicriteria decision analysis, which has recently been suggested as a way of incorporating analytical tools into a management framework4,12,47. Another recent approach is the incorporation of site-selection algorithms into decision-support systems to guide negotiations between groups12. Analytical reserve design tools are not very helpful without high-quality data sets with which to apply them. Ways of improving data quality include the extension of surveys to avoid biases towards particular taxa or sites28 and the use of statistical tools to predict species distributions12,25. How to select

References 1 Pressey, R.L. et al. (1993) Beyond opportunism: key principles for systematic reserve selection. Trends Ecol. Evol. 8, 124–128 2 Pressey, R.L. et al. (1996) Optimality in reserve selection algorithms: when does it matter and how much? Biol. Conserv. 76, 259–267 3 Csuti, B. et al. (1997) A comparison of reserve selection algorithms using data on terrestrial vertebrates in Oregon. Biol. Conserv. 80, 83–97 4 Williams, P.H. (1998) Key sites for conservation: area–selection methods for biodiversity. In Conservation in a Changing World (Mace, G.M. et al., eds), pp. 211–249, Cambridge University Press 5 Vane-Wright, R.I. et al. (1991) What to protect? Systematics and the agony of choice. Biol. Conserv. 55, 235–254 6 Williams, P.H. Complementarity. In Encyclopedia of Biodiversity (Vol. 1) (Levin, S.A., ed.), Academic Press (in press) 7 Underhill, L.G. (1994) Optimal and suboptimal reserve selection algorithms. Biol. Conserv. 70, 85–87 8 Pressey, R.L. et al. (1999) Effects of data characteristics on the results of reserve selection algorithms. J. Biogeog. 26, 179–191 9 Arthur, J.F. et al. (1997) Finding all optimal solutions to the reserve site selection problem: formulation and computational analysis. Environ. Ecol. Stat. 4, 153–165 10 Rodrigues, A.S. et al. (1999) The performance of existing networks of conservation areas in representing biodiversity. Proc. R. Soc. London B Biol. Sci. 266, 1453–1460 11 Rodrigues, A.S. et al. (2000) Robustness of reserve selection procedures under temporal species turnover. Proc. R. Soc. London B Biol. Sci. 267, 49–55 http://tree.trends.com

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taxonomic groups used in reserve design remains another problem12,22. Tests of taxonomic and environmental surrogacy for species diversity are generally discouraging, although some positive results have been obtained43. The conclusion so far is that good biodiversity surrogates are very difficult to find. Although data about the performance of existing reserves are accumulating, more effort is needed in monitoring reserves designed with site-selection methods. All results from studies11,18,19 assessing the long-term performance of reserves designed by siteselection algorithms come from hypothetical situations and not from monitoring of established reserves. These studies have looked at how many species would have persisted if the reserve network had been designed using occupancy information at some point in the past. Although all studies11,18,19 concluded that species turnover can be high, the results concerning the performances of algorithms are still likely to be optimistic. More species might have disappeared if the remaining habitat not selected for the reserve had deteriorated in quality. Such habitat is likely to serve as a source of migrants that decreases extinction probabilities in the reserve sites. With this in mind, the explicit consideration of persistence and spatiotemporal dynamics in reserve design remains a major future challenge for the discipline.

12 Margules, C.R. and Pressey, R.L. (2000) Systematic conservation planning. Nature 405, 243–253 13 Possingham, H.P. et al. (2000) Mathematical methods for reserve system design. In Quantitative Methods for Conservation Biology (Ferson, S. and Burgman, M., eds), pp. 291–306, Springer-Verlag 14 Pressey, R.L. and Nicholls, A.O. (1989) Application of a numerical algorithm to selection of reserves in semi-arid New South Wales. Biol. Conserv. 50, 263–278 15 Pressey, R.L. (1994) Ad hoc reservations: forward or backward steps in developing representative reserve systems? Conserv. Biol. 8, 662–668 16 Araújo, M. (1999) Distribution patterns of biodiversity and the design of a representative reserve network in Portugal. Div. Distrib. 5, 151–163 17 Andelman, S. et al. Tools for conservation planning: general principles for reserve network design in an uncertain world. Bioscience (in press) 18 Margules, C.R. et al. (1994) Apparent species turnover, probability of extinction and the selection of nature reserves: a case study on the Ingleborough limestone pavements. Conserv. Biol. 8, 398–409 19 Virolainen, K.M. et al. (1999) Selecting networks of nature reserves: methods do affect the longterm outcome. Proc. R. Soc. London B Biol. Sci. 266, 1141–1146 20 Hanski, I. (1998) Metapopulation dynamics. Nature 396, 41–49 21 Nicholls, A.O. (1998) Integrating population abundance, dynamics and distribution into broad–scale priority setting. In Conservation in a Changing World (Mace, G.M. et al., eds),

pp. 251–272, Cambridge University Press 22 Pimm, S.L. and Lawton, J.H. (1998) Planning for biodiversity. Science 279, 2068–2069 23 Hanski, I. (1999) Metapopulation Ecology, Oxford University Press 24 Bedward, M. et al. (1992) A new approach for selecting fully representative reserve networks: addressing efficiency, reserve design and land suitability with an iterative analysis. Biol. Conserv. 62, 115–125 25 Kiester, R. et al. (1996) Conservation prioritization using gap data. Conserv. Biol. 10, 1332–1342 26 Williams, P.H. and Araújo, M.B. (2000) Using probabilities of persistence to identify important areas for biodiversity conservation. Proc. R. Soc. London B Biol. Sci. 267, 1959–1966 27 Winston, M. and Angermeier, P. (1995) Assessing conservation value using centres of population density. Conserv. Biol. 6, 1518–1527 28 Andelman, S. and Meir, E. Breath is better than depth. Biodiversity data requirements for adequate reserve networks. Conserv. Biol. (in press) 29 Araújo, M.B. and Williams, P.H. (2000) Selecting areas for species persistence using occurrence data. Biol. Conserv. 96, 331–345 30 Murcia, C. (1995) Edge effects in fragmented forest: implications for conservation. Trends Ecol. Evol. 10, 58–62 31 Prendergast, J.R. et al. (1999) The gaps between theory and practice in selecting nature reserves. Conserv. Biol. 13, 484–492 32 Nicholls, A.O. and Margules, C.R. (1993) An upgraded reserve selection algorithm. Biol. Conserv. 64, 165–169 33 Lombard, A. et al. (1997) Reserve selection in a species rich and fragmented landscape on the

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Agulhas Plain, South Africa. Conserv. Biol. 11, 1101–1116 Burgman, M.A. et al. (1993) Risk Assessment in Conservation Biology, Chapman & Hall Högmander, H. and Møller, J. (1995) Estimating distribution maps from atlas data using methods of statistical image analysis. Biometrics 51, 393–404 Polasky, S. et al. (2000) Choosing reserve networks with incomplete species information. Biol. Conserv. 94, 1–10 Freitag, S. and Van Jaarsveld, A.S. (1998) Sensitivity of selection procedures for priority conservation areas to survey extent, survey intensity and taxonomic knowledge. Proc. R. Soc. London B Biol. Sci. 265, 1475–1482 Simberloff, D. (1998) Flagships, umbrellas, and keystones: is single species management

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passé in the landscape era? Biol. Conserv. 83, 247–257 Andelman, S. and Fagan, W. (2000) Umbrellas and Flagships: efficient conservation or expensive mistakes? Proc. Natl. Acad. Sci. U. S. A. 97, 5954–5959 Williams, P.H. et al. (2000) Flagship species, ecological complementarity, and conserving the diversity of mammals and birds in sub-Saharan Africa. Anim. Conserv. 3, 249–260 Sætersdal, M. et al. (1993) How to maximize biological diversity in nature reserve selection: vascular plants and breeding birds in deciduous woodlands, Western Norway. Biol. Conserv. 66, 131–138 Van Jaarsveld, A.S. et al. (1998) Biodiversity assessment and conservation strategies. Science 279, 2106–2108

43 Howard, P.C. et al. (1998) Complementarity and the use of indicator groups for reserve selection in Uganda. Nature 394, 472–475 44 Araújo, M.B. et al. (2001) Would environmental diversity be a good surrogate for species diversity? Ecography 24, 103–110 45 Pressey, R.L. et al. (1994) Shades of irreplaceability: towards a measure of the contribution of sites to a reservation goal. Biodiv. Conserv. 3, 242–262 46 Ferrier, S. et al. (2000) A new predictor of t he irreplaceability of areas for achieving a conservation goal, its application to real-world planning, and a research agenda for further refinement. Biol. Conserv. 93, 3 03–325 47 Possingham, H.P. and Shea, K. (1999) The business of biodiversity. Aust. J. Zool. 31, 3–5

Linking plants to rocks: ectomycorrhizal fungi mobilize nutrients from minerals Renske Landeweert, Ellis Hoffland, Roger D. Finlay, Thom W. Kuyper and Nico van Breemen Plant nutrients, with the exception of nitrogen, are ultimately derived from weathering of primary minerals. Traditional theories about the role of ectomycorrhizal fungi in plant nutrition have emphasized quantitative effects on uptake and transport of dissolved nutrients. Qualitative effects of the symbiosis on the ability of plants to access organic nitrogen and phosphorus sources have also become increasingly apparent. Recent research suggests that ectomycorrhizal fungi mobilize other essential plant nutrients directly from minerals through excretion of organic acids. This enables ectomycorrhizal plants to utilize essential nutrients from insoluble mineral sources and affects nutrient cycling in forest systems.

Renske Landeweert* Thom W. Kuyper Sub-dept of Soil Quality, Wageningen University, Box 8005, NL-6700 EC Wageningen, The Netherlands. *e-mail: Renske.landeweert@ bb.benp.wag-ur.nl Ellis Hoffland Nico van Breemen Laboratory of Soil Science and Geology, Wageningen University, Box 37, NL-6700 AA Wageningen, The Netherlands. Roger D. Finlay Dept of Forest Mycology and Pathology, Swedish University of Agricultural Sciences, Box 7026, SE750 07 Uppsala, Sweden.

Physical and chemical WEATHERING (see Glossary) of the primary rock of the earth results in a release of dissolved mineral elements and residues into the biological environment. The rise of vascular land plants about 400 million years ago (Mya) presumably led to vastly increased mineral weathering1,2. Some of the photosynthetic products formed in plant leaves end up as organic acids that are exuded by plant roots into the surrounding soil. Together with CO2, which is pumped into the soil via root respiration and heterotrophic respiration of soil microorganisms, these organic acids greatly enhance the dissolution of primary silicate minerals (Box 1), thereby mobilizing essential LITHOPHILIC plant nutrients. Silicate minerals such as FELDSPARS, MICAS, HORNBLENDE and pyroxene provide calcium (Ca), magnesium (Mg) and potassium (K); APATITE is the main PRIMARY MINERAL source of phosphorus (P). The carbon-rich root exudates also support large communities of root-associated microorganisms (rhizosphere bacteria and fungi) that further

accelerate weathering of minerals by excreting organic acids, phenolic compounds, protons and SIDEROPHORES3,4. Such plant-induced mineral weathering must have facilitated the subsequent spread of land plants by increasing the availability of essential plant nutrients and by producing SECONDARY MINERALS, such as clays and iron (Fe) and aluminium (Al) oxides, providing soil material for anchorage and water holding. Continuing today, essential plant nutrients enter ecosystems through biogeochemical weathering of primary minerals. To understand soil ecological processes, knowledge of the potential influence of plant roots and their associated microbiota on weathering processes is essential. Organic acids as weathering agents

Soluble organic acids affecting mineral weathering in soils originate from various sources. Medium to high molecular weight organic acids, such as humic substances, are less effective in promoting mineral dissolution than are low molecular weight (LMW) organic acids produced by plant roots and soil microorganisms5. Although constituting only a minor fraction of the total organic acids in the soil solution, LMW organic acids are generally considered to be the most important biological weathering agents in soils, owing to their acidifying and complexing capacities5,6. Depending on the number and configuration of carboxylic and phenolic groups and the related acid strength, organic acids provide H+ for protonation of the mineral surface and chelate cations7 (Box 1). Concentrations of LMW organic acids in the bulk soil

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