Input data, analytical methods and biogeography of Elegia (Restionaceae)

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1 Journal of Biogeography (J. Biogeogr.) (2006) 33, ORIGINAL ARTICLE Input data, analytical methods and biogeography of Elegia (Restionaceae) P. M. Moline* and H. P. Linder Institute of Systematic Botany, University of Zurich, Zurich, Switzerland ABSTRACT Aim The aim of this paper is to determine the optimal methods for delimiting areas of endemism for Elegia L. (Restionaceae), an endemic genus of the Cape Floristic Region. We assess two methods of scoring the data (presence absence in regular grids, or in irregular eco-geographical regions) and three methods for locating biogeographical centres or areas of endemism, and evaluate one method for locating biotic elements. Location The Cape Floristic Region (CFR), South Africa. Methods The distribution of all 48 species of Elegia was mapped as presence absence data on a quarter-degree grid and on broad habitat units (ecogeographical areas). Three methods to delimit areas of endemism were applied: parsimony analysis of endemism (PAE), phenetic cluster analysis, and NDM ( endemism ). In addition, we used presence absence clustering ( Prabclus ) to delimit biotic elements. The performances of these methods in elucidating the geographical patterns in Elegia were compared, for both types of input data, by evaluating their efficacy in maximizing the proportion of endemics and the number of areas of endemism. Results Eco-geographical areas perform better than quarter-degree grids. The eco-geographical areas are potentially more likely to track the distribution of species. The phenetic approach performed best in terms of its ability to delimit areas of endemism in the study area. The species richness and the richness of range-restricted species are each highest in the south-western part of the CFR, decreasing to the north and east. The phytogeographical centres identified in the present study are the northern mountains, the southern mountains (inclusive of the Riviersonderend Mountains and the Cape Peninsula), the Langeberg range, the south coast, the Cape flats, and the west coast. *Correspondence: P. M. Moline, Institute of Systematic Botany, University of Zurich, Zollikerstrasse 107, CH-8008 Zurich, Switzerland. phmoline@systbot.unizh.ch Main conclusions This study demonstrates that (1) eco-geographical areas should be preferred over a grid overlay in the study of biogeographical patterns, (2) phenetic clustering is the most suitable analytical method for finding areas of endemism, and (3) delimiting biotic elements does not contribute to an understanding of the biogeographical pattern in Elegia. The areas of endemism in Elegia are largely similar to those described in other studies, but there are many detailed differences. Keywords Areas of endemism, biogeography, biotic elements, Cape Floristic Region, ecogeographical regions, Elegia, Restionaceae, South Africa. ª 2005 Blackwell Publishing Ltd doi: /j x 47

2 P. M. Moline and H. P. Linder Cedarberg Atlantic ocean Northern Mts Piketberg Berg River Groot Winterhoek West coast Hawequas Mts Matroosberg Mts Klein Swartberg Rooiberg Groot Swartberg CAPE TOWN Bree d e River Cape Flats Southern Mts Peninsula Franshoek Mts Hottentots- Holland Mts Kogelberg Riviersonderend Mts Kleinrivier Mts Agulhas plain Indian ocean Figure 1 The Cape Floristic Region. Topography is indicated by grey shading, with darker grey indicating higher altitude. INTRODUCTION In the past 5 years, several methods for searching for areas of endemism have been proposed (e.g. Linder, 2001; Szumik et al., 2002; Mast & Nyffeler, 2003; Szumik & Goloboff, 2004). In addition, Hausdorf & Hennig (2003) developed an analytical method to obtain biotic elements. However, only scant attention has been given to the impact of the input data on the resulting areas. Traditionally, when evaluating spatial biodiversity patterns, distribution data are coded as presence absence data in a grid (e.g. Myers & Gillers, 1988; Morrone & Escalante, 2002). Grids have a number of analytical advantages and require a minimum of assumptions. Chorological studies in southern Africa have used quarter-degree squares (QDS), since most herbarium specimens had been geo-referenced to QDS rather than to point locations (Edwards & Leistner, 1971; Oliver et al., 1983; Linder, 2001). However, eco-geographical areas are more likely to track the distribution of species, and so may be more suitable (Morrone & Escalante, 2002). This leads to questions about the most appropriate methods and the most appropriate input data to use for defining areas of endemism or chorological units. We evaluate these problems using the genus Elegia L. (Restionaceae R. Br.). Elegia comprises 48 species, confined to the Cape Floristic Region (CFR) (Goldblatt, 1978; Fig. 1). The genus is present in all regions and most habitats in the CFR. The distribution patterns span from very range-restricted species such as E. fucata to very widespread species such as E. filacea. Moreover, the monophyly of the genus has recently been demonstrated, and a resolved, species-level phylogeny is available (Moline & Linder, 2005). The Cape Floristic Region is suitable for the investigation of biogeographical methods. It is small, covering c. 90,000 km 2, and harbours c plant species, with almost 70% endemism (Goldblatt & Manning, 2002). The flora is well known and well collected. The African Restionaceae constitute an important and characteristic element of the flora (Goldblatt, 1978; Taylor, 1978). They comprise the third largest of the Cape clades (Linder, 2003), with the majority of species and all genera present in the CFR, and with a distribution pattern comparable with other major Cape families, such as Proteaceae, Ericaceae, and Orchidaceae (Oliver et al., 1983). Here we determine the optimal methods for delimiting areas of endemism for Elegia. We investigate the influence of two factors. First, we evaluate the influence of scoring the distributions as presence absence in a grid system, or in irregular eco-geographical regions. Secondly, we evaluate the performance of the various analytical methods available for delimiting biogeographical patterns. These include three methods for locating biogeographical centres or areas of endemism, and one method for locating biotic elements. We use these results to describe the biogeographical pattern in Elegia, and compare it with previously published results. MATERIALS AND METHODS Distribution data We included the 48 known species in the genus Elegia s.l. (Moline & Linder, 2005, see Table 1). Distribution data were obtained from 1442 herbarium records from the Bolus Herbarium, University of Cape Town, geo-referenced to an 48 Journal of Biogeography 33, 47 62, ª 2005 Blackwell Publishing Ltd

3 Biogeographical patterns in the Cape flora Table 1 Species investigated in this study. The distribution ranges are indicated as the number of QDS and BHU in which each species occurs Species Presence in number of cells QDS Elegia acockii (Pillans) Moline and H.P.Linder 4 5 Elegia aggregata (Mast.) Moline and H.P.Linder 1 3 Elegia decipiens (Esterhuysen) Moline and H.P.Linder 2 2 Elegia deusta Kunth 9 12 Elegia ebracteata (Kunth) Moline and H.P.Linder Elegia elephantina H.P.Linder, ined. 4 5 Elegia hookeriana (Mast.) Moline and H.P.Linder 9 9 Elegia microcarpa (Kunth) Moline and H.P.Linder Elegia mucronata (Nees) Kunth 8 12 Elegia nuda (Rottb.) Kunth Elegia recta (Mast.) Moline and H.P.Linder 8 10 Elegia tectorum (L.f.) Moline and H.P.Linder Elegia macrocarpa (Kunth) Moline and H.P.Linder 5 8 Elegia amoena Pillans 1 2 Elegia asperiflora (Nees) Kunth Elegia atratiflora Esterhuysen 2 3 Elegia caespitosa Esterhuysen 4 4 Elegia capensis (Burm.f.) Schelpe Elegia coleura Nees ex Mast. 7 6 Elegia cuspidata Mast. 4 6 Elegia equisetacea Mast Elegia esterhuyseniae Pillans 7 13 Elegia extensa Pillans 4 3 Elegia fenestrata Pillans 3 4 Elegia filacea Mast Elegia fistulosa Kunth Elegia fucata Esterhuysen 1 1 Elegia galpinii N.E.Br. 5 6 Elegia glomerata H.P.Linder ined. 7 6 Elegia grandis (Nees) Kunth 5 8 Elegia grandispicata H.P.Linder 9 15 Elegia hutchinsonii Pillans 5 6 Elegia intermedia (Steud.) Pillans 1 1 Elegia juncea L Elegia muirii Pillans 7 6 Elegia neesii Mast Elegia persistens Mast. 9 8 Elegia prominens Pillans 9 12 Elegia racemosa Poir Elegia rigida Mast. 4 4 Elegia spathacea Mast Elegia squamosa Mast Elegia stipularis Mast Elegia stokoei Pillans 7 6 Elegia thyrsifera Rottb Elegia thyrsoidea (Mast.) Pillans 4 4 Elegia vaginulata Mast Elegia verreauxii Mast BHU accuracy of a minute. The species were scored as present or absent for each quarter-degree grid square (QDS; see Fig. 2a; Edwards & Leistner, 1971) as well as for the broad habitat units (BHU) as defined by Cowling & Heijnis (2001) (Fig. 2b). The QDS (which have been widely used in biogeographical analyses in the CFR: Oliver et al., 1983; Morrone, 1994; Linder, 2001) are arbitrary with respect to the distributions of the species. The BHU are much more likely to coincide with the distributional ranges of the species, as each BHU encompasses an area that is homogeneous for climate, topography and geology. We favoured the BHU over a grid system with a finer resolution (e.g. 1/8 grids, Rebelo & Siegfried, 1992) as a finer resolution exacerbates problems with under-collection. Species richness To determine the patterns of species richness for Elegia in the CFR, the number of species in each cell was plotted for both the QDS and the BHU data. The pattern of range-restricted species was determined by weighting presence with the inverse of the range before calculating the sum for each QDS or BHU. For this purpose, the presence coding of each species for each QDS or BHU was divided by its respective range. The range of each species was summarized as the sum of occupied QDS or BHU. Species accumulation curves were used to detect potential under-collection. The log-transformed number of collections per cell was plotted against the log-transformed number of species per cell for both the QDS and the BHU data. Potential under-collection in any cell will be evident in the lower than expected number of species as predicted from the number of collections. The distribution of species range sizes was explored using a modified species abundance plot (Preston, 1948): the number of species was plotted for each of seven range classes, instead of the number of individuals per species, as in the original Preston plot. Preston (1948) called the range classes octaves, with each consecutive octave comprising species with double the number of individuals in the previous octave. Here consecutive octaves comprised species with double the range of the previous one. The resulting graph is a histogram of cumulative species ranges. Areas of endemism We used a wide range of methods to seek areas of endemism, using as input data the presence absence matrices for QDS and BHUs. The methods can be ranked by the number of areas of endemism they locate, as well as by the number of species restricted to these areas of endemism. Two performance scores were calculated. The first performance score was calculated by multiplying the total number of areas by the total number of species endemic to these areas (score 1). The second performance score uses Nelson & Platnick s (1981) criterion that areas of endemism should have a minimum of two endemic species, by using the number of areas with more than Journal of Biogeography 33, 47 62, ª 2005 Blackwell Publishing Ltd 49

4 P. M. Moline and H. P. Linder a QDGS) 1.4 log (species number per R 2 = log (number of collections per QDGS) b log (species number per BHU) R 2 = log (number of collections per BHU) Figure 2 Patterns of species richness, and species-accumulation curves (inset) for (a) QDS and (b) BHU. Topography is given as grey shading, with darker grey indicating higher altitude. two endemic species instead of the total number of areas (score 2). Parsimony analysis of endemism (PAE) Rosen (1988) applied a cladistic framework to obtain area relationships directly from the species composition of the investigated areas. Morrone (1994) modified Rosen s approach to delineate areas of endemism based on their species composition. Widespread species cannot contribute to the recognition of areas of endemism (Platnick & Nelson, 1978; Rosen, 1988); consequently, it is desirable to reduce their influence by down-weighting them. This can be done by excluding taxa that are present in all areas (Rosen, 1988), or by weighting the distribution data to reduce the impact of widespread taxa on the analysis (Linder, 2001). Here we applied two weighting schemes to the data. The first weighting scheme used the power function e )axp (where a is a modifying parameter, x is the total number of areas occupied by a species, and p is a power function); consequently, the weight of the species follows a half-bell-shaped curve. The second weighting scheme used the inverse of the range for each species (x )1 ), and so the weight of the species follows a leptokurtic curve (Linder, 2001). The resulting real numbers were transformed to integers between 0 and 9 to make them suitable for input into PAUP (Swofford, 2000). Both the QDS and the BHU matrices were 50 Journal of Biogeography 33, 47 62, ª 2005 Blackwell Publishing Ltd

5 Biogeographical patterns in the Cape flora weighted by applying these weighting schemes with the parameter values suggested by Linder (2001). Linder (2001) found that the exclusion of areas with only one species improved the resolution of the analysis considerably. Therefore, all analyses were repeated after all areas with single species had been removed. All characters in the weighted data sets were treated as ordered. Prior to analysis, an artificial outgroup with an all-0 score was added to the data matrix. The data sets were analysed using PAUP version 4.0 beta 10 (Swofford, 2000). The search strategy comprised 500 random addition sequence replicates with TBR and MULTREES on and Acctran optimization in effect, but restricting the number of optimal trees per replicate to 5. The resulting shortest trees were kept in memory and swapped to completion, but restricting the number of trees to 20,000. Phenetic cluster analysis All phenetic analyses were carried out using NTSYS-pc version 2.11S (Rohlf, 1998). We used the Kulczynski coefficient (Shi, 1993), which, like the Jaccard coefficient (Jardine, 1972), does not take shared absences into account. The Kulczynski coefficient uses the average of the directional distances between two compared areas. The comparison of an area containing a large number of species with an area occupied by only a few species, which are also present in the first area, will return a very low Jaccard similarity, but a Kulczynski distance close to 0.5. This is more appropriate for data sets in which there might be large differences in the species richness between areas. The implementation of the Kulczynski coefficient is restricted to qualitative data, and therefore analysis of the weighted distribution data was precluded. upgma, single linkage and complete linkage were used to cluster the areas, based on the dissimilarity matrices calculated using the Kulczynski coefficients. A two-way Mantel test (Mantel, 1967) was used to assess the performance of the clustering algorithms. The cophenetic matrices resulting from upgma clustering provided the best fit to the original dissimilarity matrix. Consequently, only the upgma results are reported below. ndm (endemism) Szumik et al. (2002) and Szumik & Goloboff (2004) suggested a method ( NDM, derived from the word endemism) that finds areas of endemism that are based on groups of taxa with congruent ranges. The method does not test for a departure from a null model of co-occurrence, in contrast to the method of Hausdorf & Hennig (2003). The method of Szumik & Goloboff (2004) uses the presence and absence of taxa in grid cells of the study area as a representation of the range for the respective taxa. Sets of cells are selected to maximize the number of range-restricted taxa in the selected grid cells. The method allows the areas to overlap, when the species composition of two compared areas exceeds a certain degree of dissimilarity. The range of a taxon can be disjunct. The input data have to be in a grid format; consequently, we could not apply the method to the BHU data. Analysis parameters entered in the command line were -m2 -M1 - %50 -r.97 (C.A. Szumik, pers. comm.), with the individual program parameters explained as follows: -m2 let the program search for areas that were supported by more than two endemic species; -M1 let the program search for areas that had a minimum (endemism) score of 1; -%50 instructed the program to eliminate those areas (sets) that have more than 50% of their species in common and retain the set with the higher score; and -r0.97 instructed the program to retain sets that are 3% suboptimal during the search. All analyses were done with NDM version 1.5 (Goloboff, 2004). SIGCOT (significant co-occurrence of taxa) Mast & Nyffeler (2003) proposed a method ( SIGCOT, significant co-occurrence of taxa) that tests whether the co-occurrence of taxa is significantly non-random. Parsimony analysis of endemism is then carried out after excluding those taxa that are not involved in any instance of significant co-occurrence. However, it is assumed that there are no disjunct areas. Thus, the use of the program is precluded here: the mountainous habitats of many species of Elegia are fragmented by the intervening valleys, and potential under-collection as a consequence of the use of high-resolution distribution data also results in disjunct distribution ranges. Furthermore, the method is griddependent, thus making it impossible to analyse the BHU data. As a result of these limitations we were unable to apply the method to our data. Biotic elements Prabclus (presence absence clustering) Hausdorf & Hennig (2003) described a method ( Prabclus, presence absence clustering; Hennig, 2003) that aims to find groups of taxa whose ranges are significantly more similar to each other than to those of taxa of other such groups. The method is based on multidimensional scaling of Kulczynski distances (Shi, 1993). The areas found are called biotic elements, and are defined by the ranges of the species in each such group. These biotic elements are not areas of endemism, as overlap between areas is allowed. This is potentially important, as in a strictly vicariant framework species with overlapping ranges have to be considered widespread, and therefore carry no information (Henderson, 1991). We used the program PRABCLUS (an add-on package for the statistical program R, R Development Core Team, 2003), provided by its authors with the default settings. We used both the classical and the Kruskal methods, and applied these to both the complete data set and the data set without single-species areas, of the QDS and the BHU data, giving a total of eight analyses. Journal of Biogeography 33, 47 62, ª 2005 Blackwell Publishing Ltd 51

6 P. M. Moline and H. P. Linder RESULTS Species richness and endemism Although the CFR is a well-collected area, under-collection, especially of widespread species, is nevertheless a potential problem. The species accumulation curves for both the QDS (inset in Fig. 2a) and the BHU data (inset in Fig. 2b) did not indicate the presence of under-collection. For both data types, the variation in the number of collections explains more than 85% of the variation in the number of species. The coefficient of determination (r 2 ) for the QDS data (r 2 ¼ ) is higher than for the BHU data (r 2 ¼ ). The data revealed that five species (E. filacea, E. juncea, E. capensis, E. asperiflora and E. equisetacea) occur in more than 25% of the BHU, and 24% of the QDS. These species comprised just under 30% of all collections (see Table 1). The species of Elegia were not evenly distributed in the CFR. The majority of the species were located on the Peninsula, the Cape flats, the southern mountains, the Langeberg range, and the Agulhas plain (Fig. 2a for the QDS, Fig. 2b for the BHU). The species with restricted ranges were found primarily on the Peninsula, Kogelberg, the southern mountains, and the Riviersonderend Mountains (Fig. 3). The QDS data comprised two cells that contained one endemic species each: (1) QDS 3318CD (Table Mountain), with E. intermedia, and (2) QDS 3319DC (Riviersonderend Mountains), with E. fucata. Three BHU contained endemic species: (1) the Cape Peninsula Mountain Fynbos Complex, with E. intermedia, (2) the Hawequas Mountain Fynbos Complex, with E. amoena, and (3) the Riviersonderend Mountain Fynbos Complex, with E. aggregata and E. fucata. The histogram of cumulative species ranges for the QDS data (see Fig. S1a in Supplementary Material) shows the expected distribution of the majority of species having an intermediate distribution range, whereas the species with very restricted and those with very wide distributions are much less frequent. The histogram for the BHU data (see Fig. S1b) is characterized by a similar shape, but features a higher than expected number of rare species. One possible explanation could be that this is an artefact of the different area sizes of the BHU. Alternatively, this could indicate that habitat-restricted species are common in the genus. Areas of endemism Parsimony analysis of endemism Analysis of the unweighted QDS yielded 11 potential areas of endemism (AoE) for the complete area matrix and 10 AoE for the matrix with the QDS with areas with only one species excluded. Only one AoE in both analyses contained more than one endemic species (E. fenestrata and E. intermedia), namely that comprising the Peninsula, the coast at Kogelberg, and the western half of the Agulhas plain. Weighting by the inverse of the range resulted in two AoE with two endemic species: (1) E. aggregata and E. fucata defining an AoE in the Riviersonderend Mountains, and (2) E. decipiens and E. atratiflora defining an AoE in the Kogelberg and Kleinrivier mountains. When single-species areas were excluded, an additional clade was found comprising E. fenestrata and E. muirii defining an AoE that comprises the southern tip of the Peninsula and the western half of the Agulhas plain. The weighting with a power function resulted in two AoE with the same two endemic species for both the complete QDS data, and the data with the single-species QDS excluded. The respective species are (1) E. aggregata and E. fucata defining an AoE in the Riviersonderend Mountains, and (2) E. decipiens and E. atratiflora defining an AoE in the Kogelberg and Kleinrivier mountains. Analysis of the BHU data resulted in six potential AoE, with two of them with more than two species confined to them for the complete BHU set, and no clades when single-species BHU were excluded. The two clades in the complete BHU data comprise the species (1) E. aggregata, E. decipiens and E. fucata for the clade defining an AoE including the Riviersonderend Mountains and Kleinrivier Mountains, and (2) E. amoena and E. rigida for the clade defining an AoE from the Cedarberg to the Hawequas mountains. Weighting the BHU data by the inverse of the range retrieved one AoE to which E. amoena, E. rigida and E. stokoei are endemic and which ranges from the Cedarberg to the Hawequas mountains and the western part of the Langeberg. When QDS or BHU with single species were excluded, no AoE with two or more endemic species was found. The second weighting yielded one AoE with endemic E. fenestrata and E. intermedia, defining an AoE comprising the Peninsula, the coast at Kogelberg, and the western half of the Agulhas plain. The second weighting comprised no single-species BHU. The species endemic to the areas obtained and the performance scores for the various analyses are summarized in Table 2. The strict consensus trees for both data types (QDS and BHU) for the different weighting approaches contained basal polytomies comprising areas that could not be uniquely placed to any clade (area of endemism). Inspection of the set of most parsimonious trees from the respective analyses revealed that the basal polytomies in the strict consensus trees arose as a result of the conflicting placement of these areas. Phenetic clustering method The clustering of the QDS data found at least 9999 ties, the maximum possible number of ties reported in NTSYS. The resulting strict consensus was mostly unresolved. When the single-species QDS were excluded prior to analysis, 270 ties were found. The strict consensus comprised 12 AoE. Three of these included more than two endemic species: (1) E. acockii and E. intermedia in an AoE including the Cape flats and the northern end of the Peninsula; (2) E. fenestrata and E. muirii in an AoE from the Peninsula to the Agulhas plain; and (3) E. aggregata, E. amoena and E. fucata in an AoE from the Matroosberg to the Hawequas mountains to the Riviersonderend Mountains. The clustering of the BHU data yielded 26 ties. The strict consensus revealed 17 AoE. Two of these AoE included two endemic 52 Journal of Biogeography 33, 47 62, ª 2005 Blackwell Publishing Ltd

7 Biogeographical patterns in the Cape flora a b Figure 3 Endemism in Elegia: species presence is down-weighted by the inverse of range (a) for QDS data and (b) for BHU data. Numbers represent the sum of the down-weighted values for the species per cell (multiplied by 100 to obtain integer values). Increasing values are represented by increasingly darker background colour. species each: (1) E. atratiflora and E. intermedia in an AoE including the Peninsula and the Kogelberg; and (2) E. aggregata and E. fucata in an AoE in the Riviersonderend Mountains. Exclusion of single-species BHU prior to the analysis yielded a single tie (see Fig. S2) yielding six AoE. Two of these included more than two endemic species. The first, including E. amoena and E. rigida, ranges from the Cedarberg to the Hawequas mountains, while the second AoE includes five endemic species (E. aggregata, E. decipiens, E. atratiflora, E. fucata and E. intermedia), and comprises the Peninsula and the Kogelberg. The species endemic to the areas obtained and the performance scores for the different analyses are summarized in Table 2. NDM The analysis of the QDS data resulted in four AoE. The first AoE was defined by the distribution of two species (E. aggregata and E. asperiflora) and comprised three grid cells (score ¼ ). The same three cells were also identified by the second AoE, but defined by different species: E. asperiflora, E. elephantina and E. neesii (score ¼ 1.725). In both AoE, E. asperiflora was part of the definition. The next AoE was defined by E. glomerata and E. rigida (score ¼ 1.769), but the respective QDS were also a subset of the first AoE. The last AoE was defined by E. nuda and E. vaginulata Journal of Biogeography 33, 47 62, ª 2005 Blackwell Publishing Ltd 53

8 P. M. Moline and H. P. Linder Table 2 Overview of the various methods and data sets with the areas retrieved. Disjunct is the number of areas that are geographically disjunct; All endemic spp. is the count of species restricted to the areas obtained. Score 1 ¼ (areas retrieved all endemic species); Score 2 ¼ (areas with two endemic species the number of species endemic to them). Method Format Modification Areas Disjunct Areas with 2 end. sp. Species All endemic spp. Score 1 Score 2 PAE QDS Complete E. fenestrata, E. intermedia Reduced* E. fenestrata, E. intermedia InvW (1) E. aggregata, E. fucata (2) E. atratiflora, E. decipiens InvW red.à (1) E. aggregata, E. fucata (2) E. atratiflora, E. decipiens (3) E. fenestrata, E. muirii PwrW (1) E. aggregata, E. fucata (2) E. atratiflora, E. decipiens PwrW red (1) E. aggregata, E. fucata (2) E. atratiflora, E. decipiens BHU Complete (1) E. aggregata, E. decipiens, E. fucata (2) E. amoena, E. rigida Reduced InvW E. amoena, E. rigida, E. stokoei InvW red E. amoena, E. rigida, E. stokoei PwrW E. fenestrata, E. intermedia Clustering QDS Complete 0 Reduced (1) E. aggregata, E. amoena, E. fucata (2) E. fenestrata, E. muirii (3) E. acockii, E. intermedia BHU Complete (1) E. atratiflora, E. intermedia (2) E. aggregata, E. fucata Reduced (1) E. aggregata, E. decipiens, E. atratiflora, E. fucata, E. intermedia (2) E. amoena, E. rigida NDM QDS Complete Reduced *Single-species areas excluded. Weighted by the inverse of the range. àweighted by the inverse of the range and single-species areas excluded. Weighted with the power function. Weighted with the power function and single-species areas excluded. (score ¼ 1.718). Exclusion of the single-species QDS prior to the analysis yielded the same AoE as in the complete QDS data. In summary, the method found two AoE in the CFR (Fig. 4b). The first AoE was located in the southern mountains, including the Kogelberg. The second AoE was disjunct and included the coastal sand plains from the Agulhas plain, Peninsula, the Cape flats and the west coast. The species endemic to the areas obtained and the performance scores for the different analyses are summarized in Table 2. Biotic elements Prabclus Of the eight analyses, we obtained results for the complete BHU and QDS data using the classical algorithm, and for the reduced QDS data using both the classical and the Kruskal algorithms. The other four analyses failed to deliver results; that is, they could not detect any floristic elements. The complete QDS data sets used with the classical algorithm identified nine biotic elements with extensive overlap. These could be simplified to four general areas: (1) south coast and Peninsula, (2) southern mountains, (3) west coast, Piketberg, northern mountains, and W-Langeberg, and (4) the whole CFR. The exclusion of single-species QDS prior to analysis led to the identification of three biotic elements. The species in these biotic elements were located in three general areas: (1) south coast and Peninsula, (2) northern mountains, and (3) the whole CFR. The area south coast and Peninsula has a broader circumscription and so includes more species than in the previous analysis. The first two biotic elements do not overlap. The Kruskal analysis of the reduced QDS matrix located five biotic elements: two are found in the whole CFR, and one each in the south coast and Peninsula, northern mountains, and southern mountains. 54 Journal of Biogeography 33, 47 62, ª 2005 Blackwell Publishing Ltd

9 Biogeographical patterns in the Cape flora a b Figure 4 (a) Results from the PRABCLUS analysis of the BHU data. Circles represent collection localities. Different colours indicate the different biotic elements, with grey indicating species that could not be assigned to any of the biotic elements. (b) Results from the NDM analysis. The two AoE are indicated by blue and red, with overlap indicated by purple. Topography is given as grey shading, with darker grey indicating higher altitude. Scale bar represents 100 km. The complete BHU data used with the classical algorithm identified six biotic elements (Fig. 4a). These can be assigned to five groups according to their geographical location: (1) south coast and Peninsula, (2) southern mountains, (3) Riviersonderend Mountains, (4) northern mountains without Cedarberg, and (5) the Langeberg range. When single-species BHU are excluded prior to analysis no biotic elements are identified. The biotic elements northern mountains and southern mountains slightly overlap along the Breede river, and the biotic element Riviersonderend Mountains is included in southern mountains. The biotic elements with the component species are summarized in Table 3. DISCUSSION Biogeographical data format The advantage of the QDS data is that a minimum of assumptions have to be made, and that all grid cells have the same size. This facilitates some analytical procedures, where area could be important (potential correlation of species number with area size). However, QDS are arbitrary with regard to different habitats. An investigation of the grid boundaries reveals that the grids along the southern slopes of the Langeberg do not coincide with the boundary between the lowland and the mountain range. This is a serious problem for the identification of phytogeographical areas. This could be addressed by reducing the size of the grids, leading to areas that obviously are more homogeneous for the climate, soil, and vegetation. However, small grids introduce more noise, resulting from the larger number of false absences, and more cells with smaller species numbers. Consequently they are analytically more intractable. The BHU are defined as homogeneous areas for climate, geology and topography, thus circumventing the problem of environmental heterogeneity. Since the BHU are defined on their homogeneity for climatological and geological variables, their size should not influence the species richness, and this could be confirmed Journal of Biogeography 33, 47 62, ª 2005 Blackwell Publishing Ltd 55

10 P. M. Moline and H. P. Linder Table 3 Overview of the biotic elements and the comprising species as identified by PRABCLUS using the different data sets Method Format Elements retrieved General area Species QDS Complete 9 South coast and Peninsula (1) E. nuda, E. prominens, E. recta, E. squamosa, E. tectorum, E. verreauxii (2) E. extensa, E. fenestrata, E. microcarpa, E. muirii Southern mountains (3) E. ebracteata, E. coleura,, E. mucronata, E. persistens, E. spathacea (4) E. aggregata, E. fucata, E. grandis Northern mountains, and W-Langeberg (5) E. esterhuyseniae, E. hutchinsonii, E. macrocarpa, E. rigida, E. stokoei (6) E. elephas E. fistulosa, E. intermedia, E. vaginulata The whole CFR (7) E. atratiflora, E. cuspidata, E. glomerata, E. juncea, E. thyrsifera (8) E. asperiflora, E. capensis, E. decipiens, E. galpinii, E. grandispicata (9) E. equisetacea, E. filacea, E. thyrsoidea Reduced 3 Northern mountains (1) E. esterhuyseniae, E. hutchinsonii, E. rigida South coast and Peninsula (2) E. nuda, E. recta, E. verreauxii The whole CFR (3) E. amoena, E. asperiflora, E. caespitosa, E. capensis, E. coleura, E. cuspidata, E. decipiens, E. deusta, E. ebracteata, E. equisetacae, E. filacea, E. fistulosa, E. galpinii, E. glomerata, E. grandispicata E. juncea, E. macrocarpa, E. microcarpa, E. mucronata, E. neesii, E. prominens, E. racemosa, E. squamosa, E. stipularis, E. stokoei, E. tectorum, E. thyrsifera, E. thyrsoidea, E. vaginulata Reduced* 5 South coast and Peninsula (1) E. atratiflora, E. cuspidata, E. decipiens, E. deusta, E. hookeriana, E. stipularis Northern mountains (2) E. esterhuyseniae, E. hutchinsonii, E. rigida Southern mountains (3) E. amoena, E. coleura, E. ebracteata, E. equisetacae, E. grandis, E. persistens, E. racemosa, E. spathacea The whole CFR (4) E. galpinii, E. intermedia, E. prominens, E. recta, E. squamosa, E. vaginulata, E. verreauxii (5) E, asperiflora, E. caespitosa, E capensis, E. filacea, E. grandispicata, E. juncea, E. neesii, E. nuda, E. tectorum, E. thyrsifera BHU Complete 6 South coast and Peninsula (1) E. nuda, E. prominens, E. verreauxii (2) E. cuspidata, E. fistulosa Southern mountains (3) E. persistens, E. spathacea Riviersonderend Mountains (4) E. aggregata, E. fucata Northern mountains without Cedarberg (5) E. hutchinsonii, E. rigida, E. stokoei Langeberg range (6) E. galpinii, E. thyrsoidea. *A different algorithm is used for the multidimensional scaling. Differences between data sets are explained in Materials and Methods. 56 Journal of Biogeography 33, 47 62, ª 2005 Blackwell Publishing Ltd

11 Biogeographical patterns in the Cape flora with a regression of species richness for each BHU against its areal extent (data not shown). The use of such eco-geographical units is dependent on three factors. The most important requirement is that sensible eco-geographical regions are available. If these are poorly delimited, it will probably be better to use arbitrary grids. Secondly, the distribution data have to be geo-referenced, and not only be assigned to grids. Thirdly, a GIS should be available to assign the point localities to the eco-geographical regions. Species richness and endemism The highest species richness in Elegia is found in the western part of the CFR. It is the highest in the southern mountains, lower for the Peninsula, and species numbers decrease to the north and the east (Fig. 2). The same pattern is found for the range-restricted species, with the eastern end of the CFR being predominantly represented by widespread species (Fig. 3b). There is a strange drop in the degree of endemism in the Drakenstein Mountains, between Jonkershoek and Du Toitskloof, which is also reflected in the species-richness data. Since this area is very well collected indeed, the explanation must be found elsewhere: maybe it is just stochastic variation in a relatively small data set. The high endemism of the area in the Riviersondereinde Mountains relative to its richness is explained by the presence of two endemic species (E. fucata and E. aggregata) in the area. The gradient from the Peninsula to the north and the east was first described by Levyns (1964), and was confirmed by Oliver et al. (1983). Cowling & Lombard (2002) and Proches et al. (2003) showed that this pattern is also expressed at the beta-diversity level, and suggested that this could be accounted for by the predictable, seasonal rainfall and topographically complex environments of the southwestern CFR. The species number collection number plots (QDS inset in Fig. 2a, BHU inset in Fig. 2b) indicate that the variation in the collection numbers explains the variation in the species numbers better for the QDS data (r 2 ¼ ) than for the BHU data (r 2 ¼ ). This may be the result of the traditional use of QDS to map the biodiversity in the CFR. Thus collecting effort in the CFR is biased towards QDS, as individual researchers and herbarium managers base their decisions regarding whether or not to keep collections on whether they are new records for those QDS. Nevertheless, the distribution of range-restricted species is much better represented by the BHU. This is illustrated by the narrow endemics E. aggregata and E. amoena, both known only from three localities c. 15 km and 32 km apart, respectively. For E. aggregata each locality is in a separate QDS, and for E. amoena they are in two QDS. The BHU data, on the other hand, places each of these species in a single BHU. Given the results obtained by the current study, we advocate the use of BHU instead of QDS to study questions of biogeography and diversity in the CFR. Biogeographic patterns Areas of endemism All methods applied (Linder, 2001; Szumik et al., 2002; Hausdorf & Hennig, 2003) use objective procedures to search for patterns in plant distributions, but the underlying theoretical frameworks are fundamentally different. The phenetic approach is more a chorological method that will tend to maximize areas, assigning grids to areas on the basis of shared combination of species, rather than shared endemics. NDM and PAE find proper areas of endemism, defined by endemic species. The results of the various methods differ in: (1) the number of areas identified, (2) the proportion of species restricted to the areas, (3) the congruency of distributions of species restricted to the areas, (4) the number of areas of endemism that have a minimum of two species endemic to them, and consequently (5) the performance score calculated. Based on these differences, the applied methods are evaluated for their ability to locate areas that have endemic species, and their ability to find a biogeographical pattern in the CFR using Elegia. Of all the methods, the phenetic clustering performed best in terms of its ability to retrieve the biogeographical pattern in the study area, expressed as the number of retrieved areas and the number of endemic species (summarized in Table 2). Clustering applied to the complete data for both the QDS and the BHU yielded only poorly resolved phenograms, and exclusion of the single-species areas improved the resolution for both data sets. The highest score was obtained by clustering of the reduced QDS, but this approach retrieved six areas with disjunct ranges. Ideally, each identified area should have a contiguous range. The second highest score was obtained by clustering the complete set of BHU, but this included three disjunct areas. The third highest score was for clustering BHU with single-taxon areas excluded: this approach retrieved no disjunct areas. PAE yields fewer AoE than the clustering approaches, and these are congruent with those from the phenetic analysis. The AoE identified by the PAE are located in the south-western part of the CFR, which is characterized by high species numbers and the highest number of range-restricted species (Goldblatt & Manning, 2002). The weighting considerably improved the ability of the PAE to retrieve AoE. The cladograms retrieved by PAE consistently had little resolution, with large basal polytomies. Logically this is to be expected, since many areas will fall outside the AoE defined by PAE. These are QDS or BHU that have only widespread species, and contribute to a number of alternative solutions. Furthermore, the results for the unweighted data yielded several areas that show a disjunct distribution. This is probably a result of the low level of resolution in the respective strict consensus tree. A better resolved tree would presumably group the respective clades together. The consequence is that the retrieved AoE comprise a relatively small proportion of the input areas, and cover only a fraction of the CFR. Journal of Biogeography 33, 47 62, ª 2005 Blackwell Publishing Ltd 57

12 P. M. Moline and H. P. Linder The areas defined by NDM (Fig. 4b) are consistent with the areas discovered by the previously described methods. NDM finds basically two areas, which comprise only a small fraction of the whole CFR. NDM sometimes excludes species with similar distributions from recognized areas; for example, one of the recognized areas is defined by E. nuda and E. vaginulata, but E. microcarpa, which has a distribution almost identical to that of E. nuda, is not included in the area definition by NDM. In addition, the area located in the southern mountains and the Kogelberg includes the narrow endemic E. fucata, but disregards this species for the definition of the area. This might be a result of the fact that NDM aims to maximize the number of species with a maximal number of congruent grid cells over the total extent of the area. The consequence is that species that occupy only a fraction of a potential area of endemism will not be considered by NDM when it calculates the optimality of the respective area. A comparison of the two performance scores (Table 2) reveals that the phenetic clustering performed best in terms of its ability to retrieve the biogeographical pattern. The phenetic approach clearly outperformed the other methods when performance was calculated using the total number of areas and the number of endemic species for these areas (score 1). If instead the calculation is done with the number of areas that comprise more than two endemic species, the phenetic approach still yields the highest scores, but the PAE using the complete BHU data and the inversely weighted QDS data show an only slightly poorer performance. The general problem is that only two to three identified areas per analysis satisfy the criterion of two species being endemic to them. The most debatable of the criteria used to determine the quality of the areas of endemism is the criterion for an area of endemism to have two endemic species. This was suggested by Nelson & Platnick (1981), and Morrone & Crisci (1995), but was originally proposed for flora-level studies, where several unrelated clades were included, and it might not be appropriate for single clades. In a study of 350 species of Restionaceae, Linder (2001) found just six areas that comprised more than two endemic species per area. Using QDS generally resulted in better scores than using BHU only for PAE using the complete data set did BHU outperform QDS. However, the analyses using grids more often resulted in the recognition of small geographically disjunct areas. We consider that areas of endemism consisting of several geographically disjunct areas are not optimal. Consequently we rank the use of BHU as preferable to QDS. Phytogeographical centres We chose the phenetic cluster analysis of the reduced set of BHU as the optimal method, and this leads to the recognition of six phytogeographical areas for Elegia. These phytogeographical centres are the northern mountains, the west coast, the Cape flats, the southern mountains (with the Riviersonderend Mountains and the Peninsula), the south coast, and the Langeberg range (Fig. 5). The Langeberg centre is somewhat problematic, as several species (e.g. E. caespitosa, E. ebracteata, E. grandispicata) that are common in the southern mountains have more or less restricted distributions in the Langeberg range, with E. caespitosa and E. grandispicata restricted to the westernmost corner of the Langeberg centre, but E. ebracteata spanning across half of the centre. A possible explanation for this pattern is the increasing aridity and a gradual shift from a winter-rainfall to a summerrainfall regime from the western end to the eastern end of the Langeberg centre. A comparison with the centres delimited by Weimarck (1941), Oliver et al. (1983), Linder & Mann (1998), and Goldblatt & Manning (2002), respectively panels (a) to (d) of Fig. 6, shows that the northern mountains, the southern mountains, and the south-eastern Cape are delimited by all of these authors. This suggests that these constitute the core phytogeographical areas in the Cape flora. Even though all authors retrieved the northern mountains, the southern mountains and the south-eastern Cape as separate centres, the exact position of the boundaries between them differ. The boundary between the northern mountains and the southern mountains coincides with the Little Berg river in Weimarck (1941), Oliver et al. (1983), and Goldblatt & Manning (2002), but lies north of the Matroosberg in Linder & Mann (1998), and just south of the Hawequas Mountains in the present study. The boundary between the northern mountains and the south-eastern Cape was placed west of the Klein Swartberg by Weimarck (1941), Oliver et al. (1983), and Goldblatt & Manning (2002), but between the Klein Swartberg and the Groot Swartberg by Linder & Mann (1998) and in this study. The south-eastern Cape was subdivided into a dry inland centre (Karoo Mountains), the Langeberg, and a South- Eastern centre by Weimarck (1941) and Goldblatt & Manning (2002), whereas Oliver et al. (1983) and the present study recognize one contiguous centre, and Linder & Mann (1998) recognize only the Langeberg centre, and include part of the dry inland mountains (Klein Swartberg) in their northern centre. Weimarck (1941) included the coastal plains in the southwest centre (Fig. 6a), while Linder & Mann (1998) and Goldblatt & Manning (2002) separated the Agulhas plains, and Oliver et al. (1983) the west coast. Weimarck s inability to detect a centre in either the west coast or the Agulhas plain could be an artefact of the lack of data: the coastal plains were at the time of his study poorly collected. Based on the accumulation of new collections and an improved taxonomy, Oliver et al. (1983) were able to subdivide Weimarck s southwest centre into four phytogeographical centres: the west coastal, the Peninsula, the Bredasdorp, and the south-western centre. Their south-western centre includes only the Hottentots-Hollands Mountains, the Kogelberg and the Kleinrivier Mountains (Fig. 6b). Linder & Mann (1998) retrieved the Agulhas plain, but included the west coast in their northern 58 Journal of Biogeography 33, 47 62, ª 2005 Blackwell Publishing Ltd

13 Biogeographical patterns in the Cape flora West coast Northern Mts Cape flats * Langeberg range Southern Mts Coast Figure 5 Phytogeographical centres identified in the present study, taken from the phenetic clustering based on the BHU data with singlespecies areas excluded. Northern mountains are Cedarberg, Piketberg, Groot Winterhoek, Hawequas and Matroosberg mountains. Southern mountains are Hottentots Holland Mountains, Franshoek, Riviersonderend Mountains, Peninsula and Kogelberg including the Kleinrivier Mountains. Langeberg range comprises the mountains from the Koo Langeberg Mountains to the Tsitsikama Mountains. Cape flats are the lowlands between the Peninsula and the southern and northern mountains. The west coast comprises the coastal plains in the north-west. The south coast includes the Agulhas and Albertina plains and the sand dunes on the west coast just to the north of the Peninsula (indicated by the asterisk). Topography is given as grey shading, with darker grey indicating higher altitude. Scale bar represents 100 km. centre. Their southern centre included the Peninsula, the Cape flats and the Kogelberg (including the Kleinrivier Mountains, Fig. 6c). The south-western centre recognized by Goldblatt & Manning (2002) was more similar to that of Weimarck (1941) in that they included the west coast, but followed Oliver et al. (1983) in the recognition of a separate centre on the Agulhas plains (Fig. 6d). The differences between the boundaries of the phytogeographical centres could be an artefact of the different analytical methods, comparable to the differences found between the different analytical methods in this study. The differences in species composition between the three core phytogeographical areas in the CFR, the northern mountains, the southern mountains and the south-eastern Cape, are probably linked to the differences in edaphic factors, such as the seasonality of precipitation (winter rainfall vs. summer rainfall) and differences in the amount of the annual mean precipitation, whereas the coastal plains are distinguished from the remaining CFR by a different soil type (limestone-derived soils vs. sandstonederived soils). Biotic elements The four successful analyses located surprisingly different biotic elements. All four located a northern mountain element, as well as a coast and Peninsula element. In most cases these elements have similar species composition. However, there are always exceptions, and as a consequence the exact geographical delimitation differs. Three of the four analyses located a southern mountain element, but again the species composition varies among the three analyses. A ubiquitous CFR element was also located in three analyses. In one analysis it appeared to act as a catch all for species not included in the other elements. The biotic elements identified by PRABCLUS bear resemblance to Weimarck s (1941) approach, identifying Cape elements, or groups of species with the same general distribution pattern. However, it is difficult to establish precise correspondences between the elements delimited by Weimarck, and the elements located by the various PRABCLUS analyses. The BHU analysis results are the easiest to interpret, and also fit our intuitive estimation of the most sensible elements. This implies recognizing five elements. The south coast and Peninsula element encompasses the whole coastal plain, interrupted by the southern mountains. Weimarck found no such element, but it can be detected in all of our analyses, albeit with different species assigned to it. The northern mountains element is common to all phytogeographical analyses of the Cape flora, but with no common boundaries. This element is centred on the mountains north of the Breede River. Similarly, the southern mountains are also generally identified here the question is rather whether the Journal of Biogeography 33, 47 62, ª 2005 Blackwell Publishing Ltd 59