BIOLOGICAL CONSERVATION
Biological Conservation 120 (2004) 471–480 www.elsevier.com/locate/biocon
Hotspots, complementarity or representativeness? designing optimal small-scale reserves for biodiversity conservation
Vassiliki Kati a,*, Pierre Devillers b, Marc Dufr^ene c, Anastasios Legakis d, Despina Vokou e, Philippe Lebrun f
a Department of Environmental and Natural Resources Management, University of Ioannina, Seferi 2, 30100 Agrinio, Greece b Institut Royal des Sciences Naturelles de Belgique, Section de Biologie de la Conservation, rue Vautier 29, B-1000 Bruxelles, Belgium c Minist ere de la R egion Wallonne, Centre de Recherche de la Nature, des For^ets et du Bois, Avenue Mar echal Juin, 23, 5030 Gembloux, Belgium d Department of Biology, University of Athens, Zoological Museum, Panepistimioupolis, 15784 Athens, Greece e Department of Ecology, Aristotle University of Thessaloniki, School of Biology, UPB 119 54124 Thessaloniki, Greece f Universit e catholique de Louvain, Unit e d’E cologie et de Biog eographie, Centre de Recherche sur la Biodiversit e, Place Croix du Sud, 5, 1348 Louvain-la-Neuve, Belgium
Received 23 July 2003; received in revised form 4 February 2004; accepted 26 March 2004
Abstract
Reserve networks are a major tool of ecological management aiming at biodiversity conservation. Maximizing the number of species conserved with the minimum land sacrifice is a primary requirement in reserve design. In this study, we examine the efficiency of five different scenarios to conserve: (i) the biodiversity of one target group and (ii) the overall biodiversity of an area. The study was conducted in Dadia Reserve, in northern Greece. Six groups of species were selected to represent its biodiversity: woody plants, orchids, Orthoptera, aquatic and terrestrial herpetofauna, and small terrestrial birds. The scenarios examined represent different conservation approaches to select network sites. For each approach, the starting point was one of the above six groups of species, considered as the target group. In scenario A, which reflects the hotspot approach, the sites richest in species are selected. Scenario B selects the sites most complementary in terms of species richness. The next two scenarios use the principle of environmental rep- resentativeness, expressed in terms of habitat (scenario C) or vegetation (scenario D). Under scenario E, sites forming the network are selected at random. The rank of scenarios in terms of preserving the species of the target group was always B > A > C > D > E, irrespective of the group considered as target group. Their rank, when preservation of the total biodiversity was the issue, was B, A > C, D > E. Ó 2004 Elsevier Ltd. All rights reserved.
Keywords: Reserve design; Ecological networking; Biodiversity; Conservation; Complementarity
1. Introduction rather than scientific criteria. Nevertheless, a multitude of methods and scientific approaches have been devel- Reserves have a major role as a tool for preserving oped to facilitate optimal reserve design; they are pri- biodiversity (Margules and Pressey, 2000). In designing marily based on hotspot identification and on reserve, one of the objectives is to maximize the number complementary and representative networking. of species conserved with the minimum land sacrifice, or Biodiversity hotspots are areas with a large number else satisfy the ‘‘minimal reserve set’’ requirement (re- of species or with large numbers of rare, threatened or view by Cabeza and Moilanen, 2001). Reserves are still endemic species. Because of these features, they are set up in response to political and economic interests considered of high conservation priority (Margules and Usher, 1981; Prendergast et al., 1993; Muyers et al., * Corresponding author. Tel.: +30-26510-60949; fax: +30-26510- 2000; Rodriguez and Young, 2000). Designing reserve 29477. networks on the basis of the richest-in-species areas re- E-mail address: [email protected] (V. Kati). flects the traditional practice.
0006-3207/$ - see front matter Ó 2004 Elsevier Ltd. All rights reserved. doi:10.1016/j.biocon.2004.03.020 472 V. Kati et al. / Biological Conservation 120 (2004) 471–480
Complementarity, a term invented by Vane-Wright 2. Methods et al. (1991), is considered a key-principle in reserve designing (Pressey et al., 1993; Margules and Pressey, 2.1. Study area 2000). Its application in site selection ensures that as much as possible new attributes will be added to an The study area is situated in northeastern Greece existing reserve system. These attributes can be species, (longitude 26°000 to 26°190, latitude 40°590 to 41°150). It endemic species (Kirkpatrick, 1983) or landscape units covers 430 km2, of which 424.6 km2 belong to the Re- (Pressey and Nicholls, 1989). A great number of itera- serve of ‘‘Dadia-Lefkimmi-Soufli Forest’’, abbreviated tive algorithms have been proposed to provide minimum as Dadia Reserve. After the CORINE typology (Devillers sets of complementary sites that can maintain biodi- and Devillers-Terschuren, 1996), nine main vegetation versity at its maximum (see review by Csuti et al., 1997; types occur in the sampled area, further divided to Pressey et al., 1997). nineteen sub-types (Table 1). A map of the main vege- Representativeness is an old and widely used princi- tation types of Dadia Reserve is presented in Kati et al. ple for reserve selection (Margules and Usher, 1981; (2004a). Franklin, 1993). The aim in applying it is to ensure that all environmental variation is well represented in the 2.2. Dataset selected reserve network (Faith and Walker, 1996). Standard typologies of habitats or vegetation types (see We used a dataset of 194 species (Kati et al., 2004b): Devillers and Devillers-Terschuren, 1996; Pienkowski 46 woody plant species, 19 orchid species, 39 Orthoptera et al., 1996; Stoms et al., 1998) can be used to represent species, 10 species of aquatic herpetofauna, 10 species of the diversity of the environment. terrestrial herpetofauna, and 70 species of small terres- The aim of this study is to examine the efficiency of trial birds. Species were sampled within 33 sites in the five different conservation approaches to preserve the study area, ranging in size from 0.02 km2 up to 0.2 km2, biodiversity (in terms of six groups of species) of a during the years 1998–1999. Sampling methodologies Mediterranean-type area. To this end, we conducted a are described in Kati et al. (2004b). None of the species study in Dadia Reserve, in northern Greece, an area sampled was endemic. For Orthoptera, terrestrial her- protected because of its high ornithological value. The petofauna species and birds, data were semi-quantita- groups selected to represent its biodiversity were the tive, whereas for woody plant species, orchids and woody plants (tree and shrub species), the orchids, aquatic herpetofauna species, data were of the type the Orthoptera, the aquatic herpetofauna (amphibians presence-absence. and freshwater turtles), the terrestrial herpetofauna (lizards and terrestrial tortoises), and the small terres- 2.3. Data analysis trial birds (Passeriformes, Columbiformes, Coracifor- mes and Piciformes). For each of the five conservation The diversity of sites was estimated in terms of species approaches examined, the starting point is one of the richness (S), weighted species richness (WS), and Shan- above six groups of species. This is considered as the nonindex (H 0). The weighted species richness (WS) is the target group, for the sake of which the reserve network sum of the vulnerability indices of all the species of the is primarily designed. The question we ask is how site. The vulnerability index of a species, taking values much biodiversity of each one of the other groups and 0–35, estimates its status in the European Union (EU). of the total biodiversity of the area is preserved in the It is calculated on the basis of its distribution, on both a networks, constructed following the different conser- coarse (in grids of 500 km 500 km) and a fine scale (in vation approaches. In scenario A, which reflects the grids of 10 km 10 km), of its population size (or its hotspot approach, the sites richest in species are se- habitat rarity), and of its population trend (for full de- lected. Scenario B selects the sites most complementary scription, see Bezzel, 1980). Vulnerability indices for in terms of species richness. The next two scenarios use orchids are published in Devillers et al. (1991), for Or- the principle of environmental representativeness, ex- thoptera in Kati et al. (2004a), and for herpetofauna and pressed in terms of habitat (scenario C) or vegetation birds in Kati (2001). (scenario D). Under scenario E, sites forming the re- We define target group the group of species for which serve network are selected at random. The five ap- we are designing the reserve network and as non-target proaches are evaluated according to their efficiency to groups all other groups. For every target group, we preserve non-target group and the total biodiversity of constructed a similarity matrix of the samples, where its the area. Our ultimate goal is to provide guidelines as members were found, using two coefficients widely used to the best practice for designing local, small-scale re- in ecological studies – Sørensen and the Steinhaus serve networks, under different regimes of data, budget, assymetrical coefficients of similarity. Sorensen coeffi- and time availability, particularly for the Mediterra- cient behaves very well for binary data and Steinhaus is nean region. its equivalent for semi-quantitative data (Legendre and V. Kati et al. / Biological Conservation 120 (2004) 471–480 473
Table 1 Description of sites samples in terms of their size and of the class to which they belong, according to the CORINE typology classification (the first two digits of the CORINE codes correspond to the main habitat types), and results of the clustering procedure Habitat types Corine code Description of Site area Number of cluster habitat types (ha) Orchids Orthoptera Aquatic Terrestrial Birds herpetofauna herpetofauna Broad-leaved 41.1B 41.19311 Beech wood 20 3 75–5 forests 2037635 41.76 Oakwoods 2026–35 (Quercus 2026–35 frainetto/cerris) 41.733 Oakwoods 20–7–36 (Quercus 20–3–26 pubescens) Oakwoods with 20–1–36 bush 2053–26 undergrowth Mixed forests 43.7 Mixed pine-oak 2027–35 woods 2027335 Coniferous 42.661(C) Pinewoods 357534 forests (Pinus nigra) 42.85 A Pinewoods 2057–13 (Pinus brutia) 557–24 Pinewoods with 1557–34 bush undergrowth Riverine 44.514 Riparian 20–7415 vegetation vegetation 20–7415 (Alnus glutinosa) 44.615 Riparian vegeta- 20–7111 tion (Populus sp.) 20–7111 Sclerophyllous 32.313 High maquis 1517––6 scrub (Arbutus sp.) 1517–16 32.161 Deciduous oak 2023–26 mattoral 32.21A4 Bushes (Phyllirea 10–4–23 latifolia) 32.32 Low ericaceous 10–4–27 maquis 10–4–27 Humid 37.4 Humid 1041236 grassland grasslands 341233 Dry grasslands 34.53 Xeric grasslands 10 – 5323 5–5–23 34.2 Heavy-metal 2–5–13 grasslands Rural mosaics 84.4 Rural mosaics 20 – 2112 20–2112 Crops 82.11 Field crops 20 – 2118 20–2118 Total 20 19 518 5 7638 Numbers given under each group of species represent the cluster within which the respective sites were grouped. Dashes indicate absence of the group in the respective sites.
Legendre, 1998). Sampling units were then ordinated ware package (Legendre and Vaudor, 1991) was used. across axes using the Principal Coordinate Analysis with The coordinates of Dist.P.Co.A were used as inputs into corrected eigenvalues (Dist.P.Co.A.) (Legendre and k-means clustering (Legendre and Legendre, 1998) using Anderson, 1998). For the above analyses the R3 soft- the FASTCLUS procedure (S.A.S., 1985). Wherever the 474 V. Kati et al. / Biological Conservation 120 (2004) 471–480 number of species included in the matrix was low (ma- group. This algorithm combines randomly any number trix for Orthoptera in shady sites and matrix for ter- of sites for 20,000 times, calculates the number of species restrial herpetofauna), we used the less robust Ward’s included in each combination and detects the site com- minimum variance method (Legendre and Legendre, bination with the maximum number of species of the 1998). The outputs were hierarchical dendrograms; the target group. Sometimes, the algorithm provides many distinct clusters that were produced represent the dif- solutions for the same number of sites. In this case, we ferent habitat types of the target group. opted for the set with the maximum cumulative weigh- ted species richness. Once the specific network B for 2.4. Reserve selection procedure group i and for a given number of sites was established, we calculated the number of species of the non-target Information concerning the construction of the five groups included in it. conservation scenarios, upon which choice of the opti- mal reserve network for the target group is based, is 2.4.3. Scenario C: habitat representativeness given in Table 2. Scenarios A and B are species-based The third scenario introduces a rather novel ap- and provide pragmatic solutions (specific sites selected). proach: the principle of habitat representativeness (Sa- The other scenarios provide a number of possible solu- etersdal and Birks, 1993). By this, it is meant that the tions satisfying specific requirements. All calculations final reserve network will include all habitat types of the were carried out with the help of the S.A.S. package. target group. In the current study, these habitats are defined after the distinct clusters produced by the clus- 2.4.1. Scenario A: the hotspot approach tering procedure. In practical terms, for each target For every target group i, we selected the richest sites group i, we ran an algorithm that picked randomly until all species of the group were represented. They (20,000 permutations) a given number of sites (n ¼ 1to formed the reserve network A for the group i. When two the number of clusters of each target group) under the sites had the same species richness (S), we opted for the condition that these sites belong to different clusters. For site with the highest weighted species richness (WS), and each network of n sites, the algorithm produced 20,000 in case of further equality, we chose the site with the outputs calculating the average, minimum and maxi- highest Shannon’sindex (H 0). Once the specific network mum number of species included. For each target group A for group i was established, we calculated the number i, the procedure ended when the final reserve network of species of the non-target groups included in it. consisted of as many sites as the number of clusters of the target group. 2.4.2. Scenario B: the principle of complementarity For every target group i, we ran an optimal selection 2.4.4. Scenario D: vegetation representativeness algorithm in order to identify the minimum set of sites Application of this approach ensures that a reserve that preserve the maximum number of species of the network will include the full spectrum of the environ- group. By definition, scenario B produces an optimal mental variability of the area, as expressed by the dif- network for any given number of sites, 1 to k, where k is ferent vegetation types occurring in it. We used the the number of sites required to protect all species of the CORINE typology system to express the environmental
Table 2 Methods used for the construction of conservation networks and outputs of the scenarios examined Scenario/criteria Sites selected Type of algorithm used Output applied Target group Non-target groups A/hotspot approach n Sites with the greatest number Non applicable Network A Species number conserved in network A of species (S) B/complementarity n Complementary sites with the Optimal algorithm Network B Species number conserved in network B greatest cumulative number of
species (Snetwork) C/habitat n Random sites from those Random selection Network C Mean, minimum, maximum number of representativeness belonging to different clusters algorithm respecting species conserved in it typology D/vegetation n Random sites from those Random selection Network D Mean, minimum, maximum number of representativeness belonging to different CORINE algorithm respecting species conserved in it types typology E/random choice n Sites randomly chosen Random selection Network E Mean, minimum, maximum number of algorithm species conserved in it V. Kati et al. / Biological Conservation 120 (2004) 471–480 475
Table 3 Parameters used for the comparison of networks’ efficiency; see text for explanation of symbols Target of the network Parameter Parameter calculation One group of species k Minimum number of sites needed to conserve all species of the group Species percentage (S) Number of species/total number of species
Species gain (g) gj ¼ Sj SE,(j¼ A, B, C, D) Biodiversity Total biodiversity (BD6)BD6 ¼ (species of all groups/194)*100 BD BD 6 6 Biodiversity gain (gj ) gj ¼ðBDj BDEÞ,(j¼ A, B, C, D) BD BD 6 6 Ideal biodiversity gain (G ) G ¼ðBDoptimal network BDEÞ 100
variability; this is represented by nine main habitat types biodiversity conserved within any network (A, B, C, and (corresponding to the first two digits of the coding sys- D), designed after a target group i, from that conserved tem, Table 1). For each target group i, we ran an al- in network E (Table 3). By use of the parameter gBD,we gorithm that picked randomly (20,000 permutations) a can compare the efficiency of each approach, regardless given number of sites (n ¼ 1–9), under the condition of the number of sites selected to form the network. To that these sites belong to different CORINE habitat types. do so, we applied the same procedure as above (for each For each network on n sites, the algorithm produced target group) using this time biodiversity gain (instead 20,000 outputs calculating the average, minimum and of species gain). We also designed the optimal biodi- maximum number of species included. The procedure versity network (G) by running the optimal selection ended when a network of n ¼ 9 sites was formed. algorithm, as described under scenario B, and we cal- culated the maximum biodiversity gain (GBD)asthe 2.4.5. Scenario E: random choice difference of the percentage of the total biodiversity Random selection of sites is still a very common conserved within the optimal network from that in the practice in designing reserve systems. This approach random network (Table 3). provides a measure of comparison for the efficiency of We examined whether the mean values of the biodi- BD the previous four conservation scenarios. For every versity gain gj (j refers to networks A–D) of the net- target group i, we ran a random selection algorithm works having n sites differed significantly (p < 0, 05) (20,000 permutations). The algorithm produced 20,000 from each other and from the maximum biodiversity outputs calculating the average, minimum and maxi- gain (GBD). To do so, we used one-way ANOVA and mum number of species included in the network of n Tukey and Dunnet T3 post hoc test (homogeneity of sites (n ¼ 1–33). The procedure ended when a network of variance satisfied or not, respectively) (SPSS). n ¼ 33 sites was formed.
2.4.6. Scenario comparison 3. Results We assessed the efficiency of the reserve networks developed under each of the five scenarios to conserve: The clustering procedure gave 5 habitat types for (a) any target group i, and (b) the biodiversity overall. orchids, 7 for Orthoptera, 6 for aquatic herpetofauna, 3 For every target group, and for a number of sites 1 to for terrestrial herpetofauna and 8 for birds (Table 1). k, we used species gain (g) as a measure to compare the randomly produced network E with the other four net- 3.1. Optimal reserve design for the conservation of one works. We define species gain gj as the difference Sj SE, target group where Sj is the percentage of species of the target group conserved within each one of networks A to D, and SE is Results in Table 4 show the species gain (g)ineachof the percentage conserved in the random network (Table the non-randomly constructed networks for the special 3). The parameter g is scale-independent and, therefore, case of k number of sites (given by scenario B), where k permits comparisons regardless of the number of sites is equal to 9 for the woody plants, 4 for orchids, 6 for forming each particular network. Using one-way AN- Orthoptera, 4 for aquatic herpetofauna, 2 for terrestrial OVA and Dunnet T3 post hoc test (SPSS), we further herpetofauna, and 8 for birds. examined whether the mean values of species gain gj Species gain (g) differed significantly among networks differed significantly (p < 0:05) among the four (F ¼ 70:732, p < 0:01; one way ANOVA). Dunnet T3 networks. post hoc tests (p < 0:05) showed that the hierarchy of The total biodiversity (BD6) is equal to the sum of networks was always the same regardless of the target species of all groups studied. We define biodiversity gain group. Networks were ranked as follows B(7%) > BD (gi ) as the difference of the percentage of the total A(17%) > C(5%) > D. Numbers in parentheses show 476 V. Kati et al. / Biological Conservation 120 (2004) 471–480
Table 4 Networks’ efficiency for the conservation of one target group in terms of species gain (g) Target group Network of k sites g ABCD Woody plants 9 30 41 – 0 Orchids 4 52 68 19 1 Orthoptera 6 27 32 11 5 Aquatic herpetofauna 4 37 57 19 5 Terrestrial herpetofauna 2 36 56 2 0 Birds 8 10 24 8 3 Average 32 46.3 11.8 2.3 Given are the corresponding values (percentages) for every target group, under every scenario and for the minimum surface reserve network of k sites (for definition of g and k, see Table 3).
Table 5 Scenarios’ rank after their efficiency to conserve biodiversity for a network of 1, 2, 3 up to k number of sites Target group Network efficiency k F statistic (p < 0:05) Post hoc test Woody plants G >B>A>D 9 155.810 Tukey Orchids G > BA>C D > E 4 37.153 Tukey Orthoptera G > BA>C D > E 6 95.296 Tukey Aquatic herpetofaua G > BA> CD>E 4 25.900 Dunnett T3 Terrestrial herpetofauna G B A C D > E 2 17.235 Dunnett T3 Birds G > AB> D C > E 8 60.147 Dunnett T3 Biodiversity G > BA>C D > E – 211.757 Dunnett T3 Networks underlined do not differ significantly among them.
how much more species on average are preserved in each from scenarios C and D, when the target group was the network compared to the next best alternative. Network aquatic herpetofauna, while scenario B did not differ D, the worst alternative, was compared with the ran- from scenarios C and D, when birds were the target domly constructed network E; species gain values dif- group. Besides, there were no significant differences fered significantly from 0 (t ¼ 4:250, 0 < 0:01; t test). among scenarios in the case of terrestrial herpetofauna; Network D preserved on average 2% more species than this is due to the fact that the optimal network for this network E. group consists of only two sites (k ¼ 2), thus making the sample size too small to demonstrate significant 3.2. Optimal reserve design for the conservation of relationships. biodiversity
Table 5 ranks the different networks after their effi- 4. Discussion ciency to conserve the biodiversity of the area overall. Biodiversity gain differed significantly (F ¼ 211:757, 4.1. Scenario efficiency p < 0:05) among networks designed after all six groups combined; the rank of networks was the following: The success of a reserve network to conserve the bi- G > B, A > C, D (post hoc tests, p < 0:05). Biodiversity- ological diversity of an area depends on the quantity and gain values differed in all cases from zero (t test: quality of biological data used to design it. Our results tA ¼ 7; 358, tB ¼ 12:583, tC ¼ 3:955, tD ¼ 3:591, show that, when conservation efforts target one group, tG ¼ 22:761, p < 0:05, for networks A, B, C, D and G, networks made in a complementary way (under scenario respectively). This proves that each of the non-randomly B) are much more efficient than those based on the constructed networks is more efficient in conserving richness hotspot approach (scenario A); this finding is in biodiversity than the random one (E). However, the agreement with results of a previous study (Williams above hierarchy was not always the same when net- et al., 1996). When species richness data are missing but works were designed after only one target group (Table there is information on the habitat preference of the 5). For instance, scenario A did not differ significantly target group within a region (e.g., the Mediterranean), it V. Kati et al. / Biological Conservation 120 (2004) 471–480 477 is preferable to implement the principle of habitat rep- der scenario A, will most probably belong to the same resentativeness (scenario C) rather than the principle of CORINE type. In such a case, each site will add only few vegetation representativeness (scenario D). Finally, in new species to the network. Therefore, the cumulative cases of emergency or whenever scarcity of resources species number in network A will increase very slowly does not allow collection of data that will support ap- and as a consequence the scenario efficiency will be ra- plication of any of the first three approaches (A, B, C), it ther low. On the contrary, the efficiency of scenarios B, is better to select sites in such a way as to represent the C and D will remain the same as the optimal selection environmental variability of the area (scenario D) rather algorithm is not affected and because only one site is than selecting them at random (scenario E). selected from each of the classes representing environ- When our aim is to conserve the whole biological mental variability. As for network E, with more sites per diversity of an area, there is not a unique solution. The habitat type, there would be a greater chance for similar ideal approach is to sample as many biological groups sites to be selected at random. If so, the average cu- as possible and design their complementary networks; mulative number of species in the network would be low in this way all sampled components of local biodiver- and in consequence the same would hold true for the sity will be included. Obviously, the principle of com- scenario efficiency. plementarity, as expressed in scenario B, is equally Though all non-randomly designed networks are useful when focusing on one group or on a set of found to preserve more efficiently both a target group groups. Though ideal, this approach is rather imprac- and total biodiversity than random ones, the difference tical in the field of conservation biology as, in general, is not always pronounced. The character of the study we need efficient but not time-consuming solutions area can explain this: it is a natural, well-diversified area, (Meffe and Carrol, 1994). Were we able to identify bi- with few anthropogenic disturbances, and therefore, the ological indicators that could represent the full spec- sites sampled represent natural or semi-natural habitats trum of an area’s biological diversity (Noss, 1990; Caro of different type. Any selected site is rich enough to and O’ Doherty, 1999; Soberon et al., 2000; Kati et al., contribute new species in the random network. 2004b), we would have a way to circumvent this Given the above, we can conclude that the networks’ problem. rank for conserving one target group is G > B > None of the groups that we studied can adequately C > D > E, whereas that for the whole biodiversity of the represent the whole biodiversity of the area. Neverthe- area is G > B > C, D > E; the exact position of network less, our results show that selection of the complemen- A in the above schemes depends on the sampling design tary network of any target group (scenario B) or of the and the character of the study area. richest-in-species sites, in which it occurs (scenario A), are the best possible approaches. Therefore, whenever 4.2. Combining scenarios time and resources allow it, we should opt for collection of data for at least one group of species. If we have the In our study, we evaluate and rank five different ap- possibility to collect data for two or more groups, then proaches as tools for designing local, small-scale reserve the most dissimilar in ecological requirements and spa- networks. This, however, does not mean that under any tial needs should be selected to better represent different circumstances, the rank will remain the same, or that facets of biological diversity (Noss, 1990). Birds and those low in the rank have no value as conservation invertebrates could be such a pair in our study, reflecting tools. In fact, the current trend in conservation biology very different spatial needs, patterns of distribution and is to combine many different approaches so as to meet ecological niches. In absence of any detailed data, ap- several criteria, in order to design operational and ef- plication of the principle of environmental representa- fective reserve systems (Belbin, 1995; Wessel et al., 1999; tiveness (in our case, expressed in terms of vegetation, Hoctor et al., 2000; Noss et al., 1999, 2002; Rodriguez scenario D) is the only available and acceptable choice. and Young, 2000). As our results show, networks derived under scenario D Although the richness hotspot approach (scenario A) are more efficient in preserving biodiversity than the was found with limited value at local scale, it has sub- random ones. stantial value at larger scales. Identification of biodi- We should note that results concerning the scenarios’ versity hotspots globally, in terms of species richness, rank depend on the sampling design and the character of endemism, rarity and threat is very important as it can the study area; a different sampling design or a study of direct conservation efforts towards such priority areas a less natural area may lead to different results, e.g., (Reid, 1998; Muyers et al., 2000). show a weaker efficiency of scenarios A and E as com- The principle of complementarity (scenario B) proves pared to B, C, and D (Tables 4 and 5). In our study, in very useful in ecological networking not only in the most cases, we represented each CORINE habitat type by frame of the current, small-scale study but also at larger two sites (Table 1). With more sites per CORINE type, it scales. For instance, Howard et al. (1998) represented is probable that the richest-in-species sites, selected un- biodiversity in the tropics with five biological groups 478 V. Kati et al. / Biological Conservation 120 (2004) 471–480 and proved that the complementary network of any 4.3. Reserve design complexity biological group conserves more biodiversity than a randomly selected network. Lombard (1995) repre- Reserve design for the long-term persistence of bio- sented biodiversity in reserves of South Africa with six diversity constitutes a complex problem involving many vertebrate groups. Targeting every group separately, she important parameters (Cabeza and Moilanen, 2001). found that more reserves were necessary to be included Our study dealt only partially with the problem of rare in the network for protection to be effective. These species and did not take into consideration important should not be hotspots but complementary to the ex- aspects related to conservation, such as edge effects, isting reserves. local ecosystem processes, metapopulation analysis, Hotspots defined after a certain biological group very and evolution. Neither did it take into account seldom coincide with hotspots defined after another non-ecological factors. But in practice, conservation (Prendergast et al., 1993; Lombard, 1995; Gaston and decisions are informed, not dictated by science. Deci- Williams, 1996; Howard et al., 1998; Ricketts et al., sion-makers have to design the shape, size, inter-dis- 1999). In consequence, establishing networks based on tance and connectivity of the reserves (Kunin, 1997; hotspots for one group does not safeguard conservation Shafer, 2001; Olson et al., 2002; Parks and Harcourt, of the whole biodiversity of an area. The greatest ad- 2002) under social, political, and economic constraints. vantage of the complementary reserve networks lies in All these factors, ecological or not, should be taken into their flexibility. Given the pressure of political and so- consideration in order to ensure the long-term conser- cio-economic factors against setting land apart for vation of biodiversity and maintenance of natural eco- conservation purposes, decision-makers often need al- system processes (Margules and Pressey, 2000; Purvis ternative solutions. The complementary approach sat- and Hector, 2000). Our study contributed to meeting the isfies this need up to a point and offers a tool for ‘‘minimal reserve set’’ requirement at a local, small- managing contradictions. scale. It can, therefore, provide a substantial basis for The complementary approach has the tendency to designing reserve networks at such a scale, offering a place reserves in areas of ecological transition, where number of alternative best possible approaches, de- the niches of many species overlap (Lombard, 1995). It pending on data, time, and budget availability. But as a is debated if transition zones can be important for the final remark, we should note that conservation practices long-term persistence of biodiversity or if only the must be as dynamic as ecosystems are; everything is non-transition zones can maintain viable populations changing, including the values that we assign to habitats (Araujo, 2002). In this context, the approach of habitat and taxa. Decisions concerning reserve networks should representativeness (scenario C) can indicate represen- be regularly revisited and modified accordingly to meet tative non-transition zones for conservation. Multivar- newly emerging values and concomitant needs. iate analysis is a powerful tool in conservation planning, since it pinpoints the atypical habitats, which should not be included in a reserve system (Belbin, 1995). Acknowledgements Scenario B can propose more than one optimal solu- tions, and scenario C can select the most representative The first author expresses her thankfulness to Bod- of them. ossakis Foundation and to A. Onassis Foundation, for The current study evaluated the vegetation-based supporting this research in the frame of Ph.D. scholar- approach (scenario D) as the least efficient method for ships for biodiversity issues. reserve selection, second only to random choice. Nev- ertheless, with minimum time and budgetary resources, it is the only realistic one. It is also favored by the fact References that remote sensing techniques (satellite images, GIS, gap analysis) have resulted in an increase of the quality Araujo, M.B., 2002. 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