Representing species in reserves from patterns of assemblage diversity

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1 Journal of Biogeography (J. Biogeogr.) (2004) 31, ORIGINAL ARTICLE Representing species in reserves from patterns of assemblage diversity M. B. Araújo 1 *, P. J. Densham 2 and P. H. Williams 3 1 Macroecology and Conservation Unit, University of Évora, Évora, Portugal, 2 Department of Geography, University College London, London, UK and 3 Biogeography and Conservation Laboratory, The Natural History Museum, London, UK ABSTRACT Aim A positive relationship between assemblage diversity (AD) equivalent to the biotic version of the environment diversity, ED, method and species diversity has been reported. This being true, reserve networks with many different assemblages would be expected to represent more species than reserve networks including fewer and less different assemblages. This idea was tested using European species atlas distributions of terrestrial vertebrates and plants. It is asked whether: (1) maximizing AD within one group would represent species diversity of this group better than expected by chance; and (2) maximizing AD within one group would represent species diversity of other groups better than expected by chance. Location Europe. Methods Three ordination techniques (non-metric multidimensional scaling, detrended correspondence analysis and correspondence analysis) are used to summarize patterns of compositional turnover within assemblages. p-median location-allocation models are then calculated from ordination space to measure the degree of expected species representation within the group being sampled as well as the expected representation within other groups. Results are compared with near-optimal solutions obtained with complementarity-based algorithms and to a null model obtained by simulating selection of areas at random. Matrix correlation analysis was also performed to investigate broad patterns of covariation in compositional turnover of assemblages of species belonging to different taxonomic groups and these values were compared with correlation in species richness scores between groups. Results The AD model did not always represent more species of the group being sampled than expected by chance (P < 0.05). Results were independent of the method and taxonomic group considered. Effectiveness of AD within one group to represent species of other groups varied, but in most cases it was worse than using complementarity-based algorithms as a surrogate strategy. Even when correlations indicated high coincidence between assemblages, taxonomic-based surrogates did not always recover more species than expected by chance (P < 0.05). *Correspondence: M. B. Araújo, Macroecology and Conservation Unit, University of Évora, Largo dos Colegiais, Évora, Portugal. mba@uevora.pt Main conclusions Results are discussed in the light of two possible explanations: (1) the AD model is based on unrealistic assumptions, namely that species have equal probability of having the centre of their distributions anywhere in ordination space and that species display unimodal, symmetrical, bell-shaped response curves to gradients; (2) particular implementation of methods may be inadequate to summarize useful complementarity among assemblages, especially for restricted-range species. We conclude that both arguments are likely to play a role in explaining results, but that opportunities exist to improve performance of existing surrogate strategies. ª 2004 Blackwell Publishing Ltd

2 M. B. Araújo et al. Keywords Assemblage diversity, biodiversity, complementarity, conservation planning, Europe, ordinations, p-median, reserve selection, surrogates. INTRODUCTION Knowledge on the distribution of species is generally poor. It follows that conservation priorities are usually forced to rely on surrogates expected to correlate with species, rather than on records of occurrence for all valued species. The use of habitats as surrogates, or indicators, for species has a long tradition in conservation. For example, UNESCO (1974) set up the Man and Biosphere (MAB) programme to conserve biosphere reserves throughout the world that represented a range of global biotic provinces. The World Wide Fund for Nature (WWF) launched the ecoregions programme (Olson & Dinerstein, 1998) in the expectation that regional classifications would provide useful representation of both species and biological phenomena within each of the world s major habitat types. The European Union s conservation policy is largely based on the assumption that conserving habitats, defined as assemblages of plants (97/62/EC habitat directive), represents many unsampled organisms. Likewise, the US Gap Analysis Programme begins with the idea that vegetation maps are useful to assess the degree to which biodiversity is represented within conservation area networks (Scott et al., 1993). There is also the expectation that conserving well-known charismatic organisms (e.g. Long et al., 1996), would perform well at preserving diversity among other species. Despite wide interest in surrogates, the usefulness of assemblage-based surrogates for species diversity remains largely untested. Here, we investigate the degree to which sampling pattern variation within assemblages (i.e. user-defined biotic groupings with no obligatory interactions amongst them) provides a good representation of species diversity. The assemblage diversity (AD) approach used in this paper is in effect the biotic version of the environmental diversity (ED) framework proposed by Faith & Walker (1996a,b). European atlas distribution records for plants and terrestrial vertebrates are used as test data. Faith & Walker (1996a,b) noted that sampling compositional variation [interpreted as being a surrogate for complementarity, i.e. a property of sets of objects that exists when at least some of the objects (species) in one set (areas) differ from the objects (species) in another set (areas), Williams, 2001] within one indicator group would predict compositional variation within another group, if it spanned a wider range of habitats or environments. Hence, sampling environmental pattern (or environmental diversity, ED) itself would be an alternative to selecting biodiversity-rich areas. Although ED-based approaches provide an attractive framework to select conservation areas when little species distribution data are available (e.g. Ferrier, 2002), most studies have advocated their use without testing their effectiveness empirically (e.g. DeVellice et al., 1988; Belbin, 1993, 1995; Saetersdal & Birks, 1993; Pressey et al., 1996; Woinarski et al., 1996; DeVellice & Martin, 2001; Fairbanks et al., 2001; Scott et al., 2001). A number of studies assessed the performance of ED-based approaches in representing species diversity, having reported occasionally good species representation results (e.g. Ward et al., 1999; Wessels et al., 1999; Lombard et al., 2003). However, these studies lack appropriate null models for comparison with surrogate strategies. For example, Lombard et al. (2003) concluded that broad habitat units (BHU) would be good surrogates for proteas in the Cape Floristic Region, because these units would represent 56 79% species; they also concluded that BHU were poor surrogates for terrestrial vertebrates because only 6 35% species would be represented. But in the absence of an appropriately designed null model it is difficult to judge whether representation results could have arisen by change alone (for discussion see Araújo et al., 2003). A limited number of studies provided such a null model for comparison (Ferrier & Watson, 1997; Araújo et al., 2001; Bonn & Gaston, in press). For example, Araújo et al. (2001) showed that European plants exhibited consistent non-random positive patterns of representation with ED, but that terrestrial vertebrates (especially herptiles) were consistently underrepresented (for discussion of these results see Araújo et al., 2003; Faith, 2003). One possibility is that terrestrial-vertebrate species diversity might be better predicted from fine-grained habitat variation, than from coarse-grained ED. Indeed, a number of studies suggested that the greater the number of habitats in an area, the greater the number of animal species found there (e.g. Kitchener et al., 1980; Rafe et al., 1985; Boecklen, 1986; Nichols et al., 1998), although other studies found no evidence in support of this relationship (e.g. Ambuel & Temple, 1983; McCollin, 1983; Bellamy et al., 1996; Diáz et al., 1998). A positive relationship between habitat and species diversity would arise from co-occupancy by species with different habitat requirements in a given area. Additionally, habitat diversity could also boost species diversity by reducing the negative effects of inter-specific competition and the chance of competitive exclusion (e.g. Young, 2001). A logical extension of this idea is to predict that to a greater number of habitats in a set of conservation areas (hereafter termed as reserves) would correspond a greater number of species represented within. However, teasing apart the relative influences of area and habitat diversity on species richness is difficult given that these factors are themselves highly correlated. Hence, the extent to which expected positive relationship between habitat diversity (in one area or set of areas) and species richness is an artefact of the species area relationship remains unclear (Gaston & Blackburn, 2000) Journal of Biogeography 31, , ª 2004 Blackwell Publishing Ltd

3 Representing species in reserves from patterns of assemblage diversity An additional difficulty arises from the definition of habitat itself. From an auto-ecological standpoint, habitats may be seen as the hyperspace where species shelter, forage and reproduce. Each species has its own habitat, which relates to a set of biotic as well as abiotic factors operating at different spatial and temporal scales (Knight & Morris, 1996). Thus, even if species share the same geographical location at a given time they may be using different resource gradients and responding to processes operating at different spatial scales. In other words, they may be using different habitats. Such an approach to habitat definition is useful when studying the ecology of a limited number of species, but is arguably unhelpful when trying to set conservation priorities from habitat surrogates for large numbers of species. An alternative is to define habitats as relatively uniform assemblages of plant species. This is common practice in conservation planning, where habitats are often classified according to their floristic composition, or to some other dominant features in the landscape (e.g. Jackson, 2000). A potential criticism is that animal species may not respond equally to variation in the floristic composition, or any other structural aspect of plant assemblages. Many species may indeed respond to gradients of habitat variation, other than changes in plant composition and structure (e.g. Christman & Culver, 2001). Particularly striking example are the invertebrates that constitute c. 99% of earth s total biodiversity (Heywood, 1995) with probably more species living upon dead and dying matter than upon green plants (Elton, 1949). Another related idea is that of biodiversity-indicator groups. This idea relies on the premise that the number of species in one well-studied group is well correlated with the number of species in other, less well-known groups (e.g. Pearson & Cassola, 1992; Prendergast & Eversham, 1997). However, simple tests of association between pairs of attribute richness-value ignore the identities of the attributes that comprise those richness values. Two areas with similar values for richness may, at one extreme, comprise taxonomically identical assemblages or, at the other extreme, comprise mutually exclusive sets of taxa (Gaston, 1996). The most important test is not, therefore, whether the spatial variation in the richness of one set of organisms predicts the spatial variation in the richness of another set of organisms, but whether compositional turnover within one set of organisms predicts turnover within another set (e.g. Williams & Gaston, 1998). This paper deals with a more general formulation of the environmental, or habitat-diversity, approach where assemblage diversity (AD) of any biological group is considered to summarize potentially useful indicator information for biodiversity. AD is equivalent to the biotic version of ED as defined by Faith & Walker (1996b). In particular we ask whether: 1. Maximizing AD within one group represents species diversity of this group better than expected by chance? 2. Maximizing AD within one group represents species diversity of other groups better than expected by chance? DATA AND METHODS Species distribution data Data included 868,960 records of occurrence for different groups of European terrestrial vertebrates and plants. These included 186 mammal (Mitchell-Jones et al., 1999), 440 breeding bird (Hagemeijer & Blair, 1997), 143 amphibian and reptile (Gasc et al., 1997), and 2294 plant species (Jalas & Suominen, ). Terrestrial vertebrates include all known species, whereas plants comprise c. 20% of the European flora. The grid used is based on the Atlas Florae Europaeae (Lathi & Lampinen, 1999), with cell boundaries typically following the 50 km lines of the Universal Transverse Mercator (UTM) grid, except near the border of the 6 UTM zones and at coasts (for more detailed information on the AFE grid see: The mapped area (2434 grid cells) includes western, northern and southern Europe, but excludes most of the eastern European countries (except for the Baltic States) where recording effort was both less uniform and less intensive. Within-group pattern variation Ordinations are used to summarize pattern variation among assemblages. Ordination is a collective term for multivariate techniques that arrange samples along axes on the basis of data for multiple attributes, such as species. The purpose of ordination space is that area samples that are close together in the derived summary space should be similar (expected to be less complementary), whilst areas that are far apart should be dissimilar (expected to be more complementary). Because ordination techniques have varying abilities to summarize useful patterns of compositional variation in data, we compared three different techniques: non-metric multidimensional scaling (NMDS), correspondence analysis (CA) and detrended correspondence analysis (DCA). NMDS is a form of distancescaling ordination and was performed with PRIMER v5 (Clarke & Gorley, 2001), while CA and DCA are variance-maximization methods and were performed with a specially compiled extended DOS version of CANOCO (Ter Braak & Šmilauer, 1998). Alternatives to NMDS would include principal coordinates analysis (PCoA), also known as metric multidimensional scaling or classical scaling (Gower, 1966), and hybrid multidimensional scaling (HMDS) (Faith et al., 1987) that combines metric and non-metric criteria. NMDS was used because it is reportedly better than metric analogues at reducing distance relationships among samples into fewer dimensions (Legendre & Legendre, 2000) and because HMDS was not available in commercial packages available to the authors. Between-group pattern variation Coincidence of pattern variation between assemblages can be explored by measuring the degree of association between two dissimilarity matrices. The significance of this relationship can Journal of Biogeography 31, , ª 2004 Blackwell Publishing Ltd 1039

4 M. B. Araújo et al. be determined by comparison with the distribution of the statistic found by randomly reallocating the order of the elements in one of the matrices (Manly, 1997). These elementby-element correlations between two dissimilarity matrices are known as Mantel tests (Mantel, 1967). Although Mantel s original proposal was restricted to standard Pearson product moment correlations (i.e. assuming linear relationships between elements), nonparametric matrix correlation statistics have been introduced allowing for a relaxation of the original assumptions of linearity (Manly, 1997). Here we use Spearman s nonparametric rank correlation coefficients q to measure the degree of association between two Bray Curtis dissimilarity matrices. Under the null hypothesis, of no relation between two matrices, q will be approximately zero. Its null distribution, either side of zero, can be obtained by permutating many times (here 999 times) one set of sample labels at random and recalculating q, to build up a frequency histogram with which the true value of q can be compared. Departures from the null hypothesis indicate the degree of association between matrices. Matrix correlation analyses were performed using PRIMER v5 (Clarke & Gorley, 2001). Testing the performance of AD The performance of AD to representing species diversity was tested by means of a three-step procedure: Step 1 Sampling pattern variation As a first step towards selecting AD areas from ordination space, pairwise Euclidean distances between pairs of axes scores are calculated. These distances are used to solve discrete p-median location-allocation models (Hakimi, 1965; Erkut, 1990), where p conservation areas are selected from n possible areas, so that the sum of the inter-area distances is minimized. This is conceptually equivalent to maximizing diversity, or difference, when sampling the ordination space (Faith & Walker, 1996a,b). Discrete p-medians are calculated for two- (2-D) and threedimensional (3-D) ordinations, using a heuristic vertexsubstitution algorithm called the Global-Regional Interchange Algorithm (GRIA) (Densham & Rushton, 1992). This algorithm is one of the most robust and efficient heuristic procedures to address large p-median problems (Church & Sorensen 1996), avoiding some of the shortcomings of early implementations of ED based on greedy discrete p-median approaches (for discussion see Araújo et al., 2003). GRIA selects p (or AD) areas (here 243) from among m candidate locations (here 2434) to represent an ordination space dispersed over n locations (here m ¼ n). The number of p areas selected is arbitrary, but follows an early IUCN (World Conservation Union) recommendation for regions to establish minimum conservation areas of up to 10% of their total area. AD areas are located to minimize the value of an objective function (z): the sum of all n areas weighted by the distance separating them from their closest AD area. GRIA has two phases. The first phase (global exchange) itself consists of two parts: first, identify the AD area to drop from the current solution that least increases the value of z and, secondly, find the candidate to add to the solution which most reduces the value of z. In its second phase (regional exchange), GRIA ensures that all areas in the ordination space are represented by their closest AD area and that each AD area is located at the local median of the areas it represents. The two phases are applied iteratively until three conditions are met: (1) each and every AD area is the local median of the areas it represents; (2) each area in the space is allocated to its closest AD area; and (3) removing an AD area from the solution and replacing it with a candidate area not in the solution yields an increase in the value of z. These properties are necessary but not sufficient for a globally optimal solution. Because of the heuristic nature of GRIA we calculated 10 solutions for each problem and chose the one minimizing the value of the z. Once the best AD area-set is identified, we recorded the achieved level of species representation. Step 2 Random selection A null model is obtained by simulating selection of a given number of areas (here 243) with records at random; the selection is repeated 1000 times to calculate the 5% upper tail of the random distribution. This is used as a simple test to assess departures in the observed representation of species in AD solutions from that expected by chance (P < 0.05). The WORLDMAP (Williams, 1999) software was used to generate random solutions. Step 2 Near optimal set (or complementarity hotspots) The efficiency of AD to predict the location of important areas for species diversity was also compared with that obtained with a more direct approach that maximizes species representation in a given area, here 243. The algorithm is based on Margules et al. (1988) and is implemented in WORLDMAP (Williams, 1999). It selects first all areas with taxa that are irreplaceable for a given representation goal. Then, it follows a simple set of rules to select areas with the greatest complementary richness in just the rarest taxa. If there are ties it proceeds by selecting areas among ties richest in the next rarest taxa and so on. If there are persistent ties, it then selects areas among persistent ties with the lowest grid-cell number. This is an arbitrary rule, rather than a random choice among ties, that ensures repeatability in tests. It then performs a test to reject any areas that, in hindsight, are redundant. It repeats all previous steps until the representation goal is achieved. Finally it reorders areas by complementary richness and chooses the first n areas from the reordered area list. RESULTS Within-group pattern variation NMDS provided a good representation of the original dissimilarity matrix in two or three ordination axes 1040 Journal of Biogeography 31, , ª 2004 Blackwell Publishing Ltd

5 Representing species in reserves from patterns of assemblage diversity (Appendix 1), although the addition of a third axis did not always improve the method s ability to represent the original ranked distances in ordination space. This was the case for breeding birds and mammals that showed constant minimum stress values after 10 simulations for both the 2- and 3-D ordinations (Appendix 1). CA and DCA recovered an important proportion of the total variation in the first three axes (Appendix 2), although the cumulative percentage variation represented in these axes was relatively low ( %). This is a common outcome of ordinations that summarize, in few axes, complex patterns of compositional variation for many species over large geographical areas. In DCA the length of a gradient is a measure of how unimodal the species responses are along an ordination axis; if over 4 SD, then there are species that show clear unimodal responses (Ter Braak & Šmilauer, 1998). All groups studied showed strong unimodal responses to the first axis of DCA (Appendix 2), with plants exhibiting the strongest unimodal response, followed by mammals, birds and herptiles, respectively. Between-group pattern variation Matrix correlation analyses revealed a strong correlation between plant and breeding bird assemblages, followed closely by the correlation between plant and mammal and breeding bird and mammal assemblages (Table 1). Weaker correlations are observed between herptile and the remaining taxonomic assemblages. To visualize ordination axes spatially, we plotted DCA sample scores on the European map (Fig. 1). The first Table 1 Matrix correlation among all biological groups considered. All correlations are significant at P < (after 999 permutations). Because of mismatches between matrices (due to missing values) 202 cases were excluded from analysis Plants Breeding birds Mammals Herptiles Plants 1 Breeding birds Mammals Herptiles Figure 1 Gradients of compositional variation for plants, birds, mammals and herptiles, respectively, summarized as DCA ordination scores for grid cells on the first three axes (see Appendices 2 and 3). Ordination scores are located on km grid cells. Individual axes scores are divided into 33 colour-scale classes of approximately equal size by numbers of grid cells, with maximum scores shown in red and minimum (nonzero) scores shown in blue. A 3-D overlay of DCA axes is also shown. Scores on each axis are divided into 16 colour-scale classes of equal score interval, with increasing scores on the first axis shown with intensities of green, increasing scores on the second axis shown with intensities of blue, and increasing scores on the third axis shown in intensities of red (the colour scale is shown simplified with only six colour classes on each axis, 216 classes in total). Consequently, black grid cells on the map show low scores on all three axes and white grid cells show high scores on all three axes. Journal of Biogeography 31, , ª 2004 Blackwell Publishing Ltd 1041

6 M. B. Araújo et al. ordination axis for plants, birds and mammals shows a consistent latitudinal gradient coincident to the European gradient of temperature (Araújo et al., 2001). Axis 2 for plants is also coincident with axes 2 and 3 for mammals (the second and third axes are coincident for this group in DCA analysis) and represents a longitudinal gradient of compositional turnover. Spatial patterns of compositional variation for the remaining ordination axes vary across taxa but seem to reflect softening maritime influences for plants and coastal/wetland habitat conditions for breeding birds. The first two axes of compositional turnover for herptiles inversed the order of importance in the gradients captured by DCA. The first axis represents a longitudinal gradient, while the second a latitudinal gradient. The third axis represents subregional gradients probably related to past extinction and speciation processes. By overlaying the three DCA axes on the map (Williams et al., 1999), we are able to depict four broad faunal regions for herptiles within Europe: (1) the Iberian Peninsula and France; (2) the Italic Peninsula; (3) the Balkan Peninsula; and (4) the rest of Europe. Similar regions are identified for the other three taxonomic groups, with faunal regions for mammals being similar to those of herptiles (Fig. 1). Compositional variation of assemblages (Table 1) did not always follow the same trends as variation in species richness (Table 2). Plants and birds, in particular, have the highest correlation for compositional pattern variation and the second lowest correlation in richness patterns. Performance of AD to representing species diversity As the same measurement (species representation within a target group) is calculated several times (different ordination techniques) in relation to each subject (surrogate group), a repeated factorial design measure is appropriate for testing differences in overall performance of p-median solutions to represent species diversity. Friedman rank sum tests, assuming no interaction between ordination technique and surrogate groups, were used. Results show that there is no significant effect of the ordination technique (2- and 3-D NMDS, DCA and CA) and surrogate group (plants, birds, mammals and herptiles) in the success of p-medians to representing species diversity within each of the assemblages (Appendix 4). Tests for plants showed slightly greater, although not significant (P ¼ ), differences in the contributions of ordination method and surrogate groups. Table 2 Spearman rank correlation (q) coefficients of species richness among groups of plants and terrestrial vertebrates in Europe Plants Birds Mammals Herptiles Plants 1 Breeding birds Mammals Herptiles Sampling compositional variation from one assemblage to represent species diversity of this assemblage did not always represent more species than expected by chance at P < 0.05 (Table 3, italic numbers). Results were particularly unstable for birds, mammals and herptiles, but not for plants as AD represented more plant species than expected by chance most of the time (exception for 2-D DCA). The general performance of p-medians to represent species from 2- and 3-D NMDS ordinations seem to be related with the method s ability to summarize original matrix dissimilarity values well (Table 1). For example, 3-D NMDS for plants had lower stress values (better performance) than 2-D NMDS ordinations, with a consequent increase in species representation. Addition of a third dimension to breeding bird and mammal ordinations did not reduce stress values; hence, no gains in species representation were achieved. Instead, inclusion of a third dimension to NMDS space weakened the ability of p-median to sample useful complementarity. The addition of a third dimension also reduced the performance of p-medians in all CA analyses, while it improved their performance in all DCA except for herptiles where the third axis decreased p-median performance with all methods. Sampling compositional variation from one assemblage to represent species diversity of other assemblages had varying consequences (Table 3, non-italic numbers). For example, selecting AD areas for plants represented almost as many bird and mammal species (sometimes more) than selecting AD areas directly for birds and mammals. Conversely, a poor representation of herptiles is achieved when selecting AD areas for plants. Strikingly, however, herptile assemblages were better surrogates for plant diversity than bird or mammal assemblages. This is surprising as patterns of compositional variation among herptile assemblages are poorly related to those of plants, while mammals and birds display rather coincident patterns of covariation with plants (Table 1). The highest level of plant representation obtained with a surrogate was achieved by sampling herptile assemblages with 2-D DCA. This pattern is completely reversed when sampling 3-D ordinations for herptiles, where herptiles provide some of the worse levels of species representation for plants. Complementarity hotspots, selected for one particular group, provided a better surrogate strategy to represent species, of other groups, than AD strategy, 74% of the times. AD outperformed complementarity hotspots when herptiles were used as surrogates for breeding birds and mammals and in few other ordination-dependent cases: herptile AD on 2-D DCA to represent plants; and breeding bird AD 2-D CA and 3-D NMDS and DCA to represent herptiles. It should be noted, however, that selected AD and complementarity-hotspot solutions identified are just but one of many equally flexible solutions. Results should, therefore, be regarded as exploratory of broad patterns rather than confirmatory of the strengths of particular surrogate strategy Journal of Biogeography 31, , ª 2004 Blackwell Publishing Ltd

7 Representing species in reserves from patterns of assemblage diversity Table 3 Percentage of species represented in 243 areas (10% of the total) selected with: (a) p-median solutions applied to 2- and 3-D NMDS, DCA and CA ordination spaces; (b) areas selected at random, with 1000 trials performed to calculate the 5% upper tail of the random distribution; and (c) complementarity hotspots. For complementarity hotspot solutions, species were represented 100% and the approximate minimum number of representations per species is shown in brackets. Areas selected for Plants Birds Mammals Herptiles 2-D 3-D 2-D 3-D 2-D 3-D 2-D 3-D Plants (a) NMDS DCA CA (b) Random* (c) Complementarity hotspots 100 (2) Birds (a) NMDS DCA CA (b) Random* (c) Complementarity hotspots (10) Mammals (a) NMDS DCA CA (b) Random* (c) Complementarity hotspots (9) Herptiles (a) NMDS DCA CA (b) Random* (c) Complementarity hotspots (14) *P < DISCUSSION The idea of using AD as a surrogate for species diversity is an appealing one. The underlying principle is that species diversity in conservation-area networks should increase with the number and degree of difference among the assemblages represented. It follows that if patterns of compositional variation between assemblages of different groups were correlated, then assemblages of one group would be expected to provide useful surrogate information for species diversity of other groups. In this study we found that patterns of compositional variation among European assemblages of plants, birds and mammals are fairly correlated (Table 1, Appendix 3). This pattern was coincident with another pattern of correlation among species richness of different groups, although there was the exception of plants against birds that showed the highest coincidence in compositional variation and one of the poorest coincidences in species richness. Herptile assemblages followed a dissimilar gradient of compositional variation and species richness. Given these broad patterns of coincidence, we would expect plants, mammals and birds to be potentially good predictors of diversity amongst each other, and herptiles to be poor predictors of diversity for these groups. However, this expectation was not fully supported by our analyses. AD areas selected for herptiles did not always represent fewer plant species than did AD areas selected for birds or mammals. Areas selected for herptiles sometimes represented more plant species than areas selected for plants themselves. Overall, we did not find a significant effect of the ordination method (including the option to use 2- or 3-D ordinations) and surrogate-group (i.e. plants, birds, mammals and herptiles) to represent species diversity (P > 0.05). There are at least two important aspects of the problem that deserve discussion. The first is asking whether the fundamental assumptions of AD models are realistic. The second is whether pattern-based methods, such as ordinations, coupled with particular implementations of p-median, capture meaningful complementarity. Are the assumptions realistic? P-median implementations of the environmental [or assemblage] diversity models are based on two fundamental assumptions. The first is that any species has equal probability of having the centre of its distribution at any point in environmental [or assemblage for the biotic version of ED framework] space (Faith & Walker, 1996a,b). The second is that species display unimodal, symmetrical, bell-shaped response curves to environmental [or assemblage] gradients. From these assumptions it follows logically that spreading Journal of Biogeography 31, , ª 2004 Blackwell Publishing Ltd 1043

8 M. B. Araújo et al. samples [or reserves] evenly across ordination space would maximize the probability of representing species diversity (Faith & Walker, 1996a,b). In principle distributions such as those hypothesized by Faith and Walker would occur if species were free from constraints, both contemporary and historical (Kirkpatrick & Brown, 1994; Araújo et al., 2001, 2003). But real species distributions are not free from constraints. Species have varying degrees of tolerance and adaptation to climate and substrate, as well as limited dispersal capabilities. Consequently, they do not have equal probabilities of having the centre of their distribution anywhere in ordination space neither can they spread across the whole of their environmental niche. Consequently, species diversity is distributed unevenly across regions and this causes important areas for biodiversity conservation to be spatially and environmentally clustered (e.g. Araújo et al., 2001; Lombard et al., 2003). The assumption of symmetrical bell-shaped responses of species to gradients is also questionable (Araújo et al., 2003). For example, Austin et al. (1990) examined 10 species distribution models and found that five models had skewed responses to one or more environmental variables, one was linear and two were bell-shaped. These results provide evidence for asymmetric responses of species to gradients. Indeed, there is no evidence that species responses to gradients should be preferentially unimodal as opposed, for example, to being skewed. However, the degree to which monotonic linear responses occur in one data set is likely to depend on the proportion of species that extend their ranges outside the region of interest and this is negatively related to the size of the studied area. An exploratory analysis of our data suggests that all groups had species with clear unimodal responses to the first axes of DCA, although unimodal responses could not be guaranteed in the subsequent axes. Nevertheless, these estimates are based on a multivariate assessment of responses and it remains unclear how they should be interpreted. Do ordinations and p-median models summarize useful complementarity? Departures from model assumptions have important implications for surrogate strategies. This is the case, for example, when ED is used to predict species diversity of unsampled organisms, or when assemblages of one group are used to predict species diversity of other groups. But such departures do not explain why sampling AD for one group does not always represent species of this group better than expected by chance. This particular problem is not about surrogacy but about the ability of ordinations and/or the particular implementations of p-median model to capture useful complementarity in the data. Ordinations summarize major patterns of environmental, or compositional variation among assemblages of a given data set. There is evidence that such gradients are often determined by the common especially intermediately common species. For example, Webb et al. (1967) found that 269 tree species were sufficient to recover original pattern-variation based on 818 plant species, while Day et al. (1971) found that 83 common polychaete species identified a depth gradient as clearly as the total 229 species present. Similarly, Callahan et al. (1979) (cited by Cao & Larsen, 2001) observed that the six most abundant river nematodes recovered a broad classification pattern based on 154 species. These results contrast with those of Faith & Norris (1989) who observed that rare species alone would reveal significant correlation between a species ordination and many physical chemical variables (see also Walsh, 1997). The importance of wide-ranging vs. restrictedrange species in ordinations is likely to be proportional to the length and strengths of gradients being studied and this relates with spatial scale. When particular analyses involve large spatial scales with strong gradients, common and intermediately common species may discriminate the most important gradients in the data (e.g. Cao & Larsen, 2001). In reality, common and intermediately common species discriminate first-order patterns of compositional variation (e.g. associated to broad climatic variation), while rare species may help to discriminate second-order, often local, patterns (e.g. associated with historical processes). Here, because we summarize the entire western European gradient and included a large number of species, only strong, first-order, gradients are summarized in the first three axes. These gradients discriminate broad floristic and faunal regions (Fig. 1), i.e. latitudinal, longitudinal, and moisture gradients as well as, for birds, land/water interfaces. Weaker, local, second-order gradients that are determined by distributions of restricted-range species (e.g. mountain refuges) are lost. The problem with this loss of information is that it affects the patterns most likely to play an important role in predicting complementarity. Indeed, if we accept that complementarity is mainly determined by the distribution of rare species (but also that of species richness, see Araújo & Williams, 2001), then it is second-order compositional variation that is most important and not first-order. These ideas coincide with observations of scale-dependency in the performance of biodiversity surrogates made by Reyers et al. (2002). They suggested that broadly defined land classes would miss more species than finely defined ones; this is closely related to the idea that first-order gradients, defining broad classes, may miss important local patterns of complementarity. Higher-dimensional ordinations are expected to provide additional information on second-order patterns of compositional variation. Hence, selecting areas with p-median including higher-dimensional ordinations should be expected to increase the ability of the p-medians to recover useful complementarity from ordinations. Our results suggest this may not always be the case. We found no evidence that calculating p-median solutions from higher-dimensional ordination axes would represent more species (Table 3). Indeed, a possible reduction in species representation might occur when AD areas are selected from higher-dimensional ordinations Journal of Biogeography 31, , ª 2004 Blackwell Publishing Ltd

9 Representing species in reserves from patterns of assemblage diversity This is because non-dominant gradients are difficult to detect. Consequently, the addition of more axes to ordination solutions may be arbitrary with no existing rules to stop the process. Additionally, there is a risk that by adding nondominant axes one may include random noise rather than second-order patterns of AD (Cao & Larsen, 2001). Another problem is that of weightings. Within analytic variance maximization methods, such as CA and DCA, ordination axes have different levels of importance in discriminating pattern variation. Each axis explains a given proportion of variation in the data with lower axes explain a greater proportion of compositional variation than do higherdimensional axes. However, standard p-median solutions minimize distances in Euclidean space without consideration for such weightings. By treating all axes as equal, and trying to identify evenly spread solutions in ordination space, p-medians are distorting the relative weight given by each axis. This problem should be particularly severe when solving p-medians from axes with strong variations in the proportion of explained variation. A problem that is inevitable when non-dominant axes are added. In principle, it should be possible to adjust p-medians to post-hoc considerations of patterns in the data (e.g. Faith, 2003), or calibrate p-median solutions with cost-functions from canonical ordination analysis (J. Lobo, pers. comm.). However, both approaches would suffer from a degree of circularity: if we knew where and how species were distributed then we could optimize p-median models to fit the pattern and represent species effectively; but if we knew where and how were species distributed, then why use ED/AD approaches to predict species diversity? The problem of weighting axes is not an issue with distance-scaling ordinations such as NMDS (or its metric, Gower, 1966, and hybrid, Faith et al., 1987, analogues). The issue, here, is whether adding additional axes increases the ability of ordinations to preserve ordering relationships among objects in the original triangular matrix of dissimilarity (the matrix used by distance scaling methods to create ordination space). An interesting possibility would be to calculate p-medians directly from these matrices. This would ensure that all information on assemblage (or environmental) dissimilarities would be taken into account with no losses of information associated to representing a truly multivariate space into a limited n-defined space. Future prospects However attractive the idea of using environmental/ad as a surrogate strategy for biodiversity might be, there is insufficient evidence that this would perform effectively in real-world applications (see also Kirkpatrick & Brown, 1994; Ferrier & Watson, 1997; Bonn & Gaston, in press). Here we showed that using a particular implementation of the assemblage-diversity idea for one assemblage could provide a suboptimal representation even for species belonging to the assemblage being sampled, although slightly more optimistic results were obtained when using AD as a surrogate strategy for other groups (see discussion in Faith, 2003). For example, AD areas selected for herptiles provided better results for mammals and birds than a surrogate-strategy based in complementarity hotspots for herptiles. However, contrary to previous predictions (e.g. Panzer & Schwartz, 1998; Wessels et al., 1999) we found that broad coincidence of pattern in species richness or AD between groups is insufficient to guarantee effective surrogacy (see also Moritz et al., 2001). Indeed, although AD areas for herptiles provided relatively good representation of mammals and birds, matrix correlation between herptiles and mammals or herptiles and birds were low. These results cast new uncertainties on the usefulness of using AD surrogatestrategies for species diversity or, alternatively, on using particular ordinations and p-median location-allocation models as a reserve-selection strategy. Improving the prospects of the ED and AD ideas would require a reconsideration of original equilibrium assumptions of Faith & Walker (1996a,b) and investigation of alternative methods to implement them. It would be particularly important to incorporate non-equilibrium factors responsible for departures of idealized species distributions in the ED/AD model. Effective surrogates will reflect general biogeographical patterns and the evolutionary processes that have given rise to these. Hence, their effectiveness is likely to be influenced not only by broad patterns of compositional turnover within assemblages (or associated environmental factors), but also by the overall congruence of the biogeographical history (e.g. Moritz et al., 2001; Williams et al., 2001). Standard ordinations may also be inadequate to summarize useful patterns of complementarity. This is because the role of rare species (those responsible for most complementarity) is down-weighted when analysing large gradients, which are better summarized by intermediately common species. Possible ways of tackling this problem include: (1) an investigation of the adequacy of different dissimilarity measures to represent second-order patterns of variation determined by rare species; (2) performing ordinations only with rare species and excluding common and intermediately common species from analysis or, as referred above; (3) calculating p-median solutions in the original dissimilarity matrices used to construct the ordination. Arguably, it may also be useful to consider hybrid approaches where available biological data are combined with environmental descriptions of areas (e.g. Kirkpatrick & Brown, 1994; Ferrier, 2002; Faith, 2003; Lombard et al., 2003). This could also include descriptions of past environments as surrogates for evolutionary processes. A candidate strategy deserving appropriate evaluation is the use of generalized dissimilarity modelling (GDM) (Ferrier, 2002; Ferrier et al., 2002) to predict compositional dissimilarity of areas based on biological and environmental (and possibly historical) attributes. The ED/AD ideas are in their infancy and there are still promising possibilities to explore. Only by intensifying research will we be in a better position to make progress and move towards more useful surrogate-based approaches to represent biodiversity. Journal of Biogeography 31, , ª 2004 Blackwell Publishing Ltd 1045

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