The influence of recent geography, palaeogeography and climate on the composition of the fauna of the central Aegean Islands

Blackwell Science, LtdOxford, UKBIJBiological Journal of the Linnean Society0024-4066The Linnean Society of London, 2005? 2005 844 785795 Original Article INFLUENCES ON THE COMPOSITION OF THE FAUNA OF THE AEGEAN ISLANDS B. HAUSDORF and C. HENNIG Biological Journal of the Linnean Society, 2005, 84, 785 795. With 3 figures The influence of recent geography, palaeogeography and climate on the composition of the fauna of the central Aegean Islands BERNHARD HAUSDORF 1 * and CHRISTIAN HENNIG 2 1 Zoologisches Museum der Universität Hamburg, Martin-Luther-King-Platz 3, 20146 Hamburg, Germany 2 Universität Hamburg, Fachbereich Mathematik SPST, Bundesstrasse 55, 20146 Hamburg, Germany Received 12 September 2003; accepted for publication 28 June 2004 Partial Mantel tests and structural equation models were used to investigate the influence of recent geography, palaeogeography and climate on the composition of the fauna of the central Aegean Islands. The composition of land snail and isopod island faunas was significantly influenced by recent and by Pliocene geography. Only Pleistocene palaeogeography had a significant influence on the composition of tenebrionid beetle island faunas. The composition of butterfly island faunas was influenced by recent and by Miocene geographical distances. The composition of reptile island faunas was correlated with recent and Pliocene geography as well as with Pleistocene and/or Miocene geographical distances. Island area influenced only the composition of the island faunas of the volant butterflies, and not that of the less mobile land snails, land isopods, tenebrionid beetles and reptiles. This might indicate that butterflies are able to colonize large islands with suitable habitats even if such islands are comparatively far from source areas more frequently than can the nonvolant groups. Influence of a climatic parameter, namely annual precipitation, on faunal composition was found only for reptiles. 2005 The Linnean Society of London, Biological Journal of the Linnean Society, 2005, 84, 785 795. ADDITIONAL KEYWORDS: biogeography land isopods land snails Lepidoptera reptiles Tenebrionidae. INTRODUCTION The Aegean Islands have become one of the model areas for biogeographical studies (Heller, 1976; Beutler, 1979; Mylonas, 1982; Sfenthourakis, 1996a, b; Foufopoulos & Ives, 1999a; Sfenthourakis, Giokas & Mylonas, 1999; Welter-Schultes & Williams, 1999; Dennis et al., 2000; Fattorini, 2002a, b), because they consist of a large number of islands of various sizes and because the systematics and distribution of some taxa are comparatively well known. Some authors have supposed that the distribution patterns of the groups they have studied in the Aegean Islands reflect palaeogeographical patterns (Heller, 1976; Sfenthourakis, 1996a; Subai, 1996; Welter- Schultes & Williams, 1999; Fattorini, 2002a, b) and *Corresponding author. E-mail: hausdorf@zoologie.uni-hamburg.de there are different lines of evidence that support this hypothesis for Crete (Douris et al., 1998; Welter- Schultes, 2000, 2001). However, so far this hypothesis has not been tested statistically. We used partial Mantel tests and structural equation models (SEMs) to investigate the influence of recent geography, palaeogeography and climate on the composition of the fauna of the central Aegean Islands. MATERIAL AND METHODS DISTRIBUTION DATA We analysed distribution data of land snails, land isopods, tenebrionid beetles, butterflies and reptiles on the Cyclades, Chíos, Ikaría, Sámos and the adjacent Dodecanese (Fig. 1). One hundred and fifty-two land snail species are known from the following 34 central Aegean Islands: 785

786 B. HAUSDORF and C. HENNIG A B 0 50 100km 0 50 100km C D 0 50 100km 0 50 100km E 0 50 100km Figure 1. Central Aegean Islands from which the following taxa were analysed (marked in black): A, land snails; B, land isopods; C, tenebrionid beetles; D, butterflies; E, reptiles. Andros, Tínos, Míkonos, Dílos, Makrónisos, Kéa, Kíthnos, Síros, Sérifos, Sífnos, Kímolos, Políegos, Mílos, Folégandros, Síkinos, Ios, Iráklia, Andíparos, Páros, Náxos, Koufonísi, Kéros, Amorgós, Thíra, Anáfi, Astipálea, Chíos, Ikaría, Sámos, Léros, Kálimnos, Psérimos, Kos and Nísiros. The dataset (Supplementary Material; Table A1) has been compiled from the following sources: Bank (1997), Bank & Maassen (1981), Bank & Menkhorst (1992), Bank & Neuteboom (1988), Bar & Butot (1986), Cameron, Mylonas & Vardinoyannis (2000), Fuchs & Käufel (1936), Hausdorf (2000, 2003), Mylonas (1982), Mylonas & Vardinoyannis (1989), Nordsieck (1999), Reischütz (1985), Riedel (1992, 1999), Riedel & Mylonas (1997), Subai (1996), Wiktor (2001) and unpublished records of R.A. Bank, E. Neubert and F.W. Welter-Schultes (pers. comm.). We analysed the complete dataset as well as a dataset in which we excluded the following species, which were probably introduced into the central Aegean Islands by man: Ferussacia folliculus (Gmelin), Rumina saharica (Pallary), Paralaoma servilis (Shuttleworth), Schistophallus cyprius (L. Pfe-

INFLUENCES ON THE COMPOSITION OF THE FAUNA OF THE AEGEAN ISLANDS 787 iffer), Tandonia cristata (Kaleniczenko), Tandonia totevi (Wiktor), Limacus flavus (Linnaeus), Deroceras panormitanum (Lessona & Pollonera), Deroceras sturanyi (Simroth), Caracollina lenticula (Michaud), Cochlicella acuta (O.F. Müller), Cochlicella barbara (Linnaeus), Monacha cartusiana complex (including M. cartusiana (O.F. Müller) and M. claustralis (Menke)), Monacha ocellata (Roth), Trochoidea pyramidata (Draparnaud), Xeropicta krynickii (Krynicki), Xerotricha apicina (Lamarck), Xerotricha conspurcata (Draparnaud), Microxeromagna lowei (Potiez & Michaud), Theba pisana (O.F. Müller), Eobania vermiculata (O.F. Müller), Cantareus apertus (Born), Cornu aspersum (O.F. Müller), Helix asemnis Bourguignat and Helix melanostoma Draparnaud. It is difficult to establish whether a species that occurs mainly synanthropically is native or introduced. Therefore, it is possible that some of the species (or some occurrences of some species) that we did not exclude are actually also the result of introductions. Sixty-seven land isopod species are known from 23 central Aegean Islands of which the snail fauna is known (Supplementary Material; Table A2; after Sfenthourakis, 1996a; supplemented by Schmalfuss, 2000). Land isopod species have also been recorded from some additional small central Aegean Islands which were not considered in our study. Sixty-five native tenebrionid beetle species are known from 22 central Aegean Islands (Supplementary Material; Table A3; after Fattorini, 2002a; Dendarus records updated following Chatzimanolis et al., 2003; pers. comm.). Some synanthropic, transient and exotic species and species of uncertain distribution were omitted by Fattorini (2002a). Seventy-seven butterfly species are known from 14 central Aegean Islands (Supplementary Material; Table A4; after Dennis et al., 2000). Thirty-two reptile species are known from 20 central Aegean Islands (Supplementary Material; Table A5; after Foufopoulos & Ives, 1999a, b; supplemented by data from Chondropoulos, 1986 for Anguis fragilis Linnaeus and Lacerta oertzeni Werner, which were excluded by Foufopoulos & Ives, 1999a, b; because they occur only on a single of the considered islands). FAUNAL DISTANCES Various distance measures have been proposed to quantify differences in faunal composition (e.g. Shi, 1993). One of the most popular measures is the Jaccard distance, defined as 1 - a/(a + b + c), where a denotes the number of taxa present in both faunas, b denotes the number of taxa present only in the first fauna and c denotes the number of taxa present only in the second fauna. However, the Jaccard distance has an important drawback. The comparison of faunas with similar composition but very different richness for example, a poorer fauna that is a subset of a richer fauna leads to a large value of b compared with a and c ª 0. The Jaccard distance approaches 1 in such situations, which we call the richness dependency of the Jaccard distance. This does not match our interpretation of faunal similarity. Instead, we prefer the second Kulczynski distance (no. S28 in Shi, 1993), which is defined as 1 - (a/(a + b) + a/(a + c))/2. Its maximum value in the situation described above is 0.5, while disjunct sets of taxa have the maximal distance of 1. In contrast to the Jaccard distance, the second Kulczynski distance is judged to be of low overall suitability by Shi (1993) on the ground of nine ideal conditions for dissimilarity coefficients (note that Shi discussed the corresponding similarity coefficients). Of these conditions, it fulfils the five properties which we consider as basic: it takes the value 0 only for perfectly matching sets and the value 1 only for disjunct sets, it ranges between 0 and 1, and it is symmetrical and independent of the number of negative matches. The second Kulczynski distance is not a metric, but this is not needed for our analyses. The remaining three conditions were judged on the basis of single experiments with particular simulation designs by Shi (1993), and we comment on only one of them. The experiment corresponding to Shi s condition (8) ( sensitivity against differences in sample size ) was conducted in such a way that measurements sharing the inadequate richness dependency of the Jaccard distance were favoured over relatively richness-independent coefficients such as the second Kulczynski distance. Thus we disagree with Shi (1993) about the operationalization of sample size dependency in this respect. Nevertheless, we repeated all analyses with the Jaccard distance to investigate the influence of choice of measure for faunal distances on our results. GEOGRAPHICAL, PALAEOGEOGRAPHICAL AND CLIMATIC DATA Recent geographical distances were measured from coast to coast on a map of the islands (Supplementary Material; Table A6). Palaeogeographical distances were taken in the same way from the palaeogeographical maps for the Pleistocene (cold ages), Pliocene and Late Miocene (Tortonian Messinian) presented by Dermitzakis (1990) (Supplementary Material; Tables A7 A9). Nonvolant terrestrial taxa might disperse much faster across land than they do across the sea. Therefore, we also calculated the shortest overwater distances between all islands (Supplementary Material; Tables A10 A13) by searching for the minimum sums of distances across the sea considering different paths

788 B. HAUSDORF and C. HENNIG between islands. The overwater distances were computed iteratively. In the first step, we checked for each pair of islands whether there was a path via a third island such that the sum of coast-to-coast distances between the third island and the two initial islands was smaller than the distance between the two initial islands. If such a path existed, the original distance between the two initial islands was replaced by the sum of the path distances. The same procedure was then performed on the resulting distances repeatedly, until the distance matrix no longer changed (after six iterations for the recent distances, four iterations for the Pleistocene distances and five iterations for the Pliocene distances). In this way, paths with small overwater distances via more than one island were also discovered. We transformed all geographical distances by y = ln(x + 1) to improve the linearity in the relationships between the variables (which is needed for the SEM). The areas of the islands (Supplementary Material; Table A14) were transformed to distances by taking absolute values of differences and the resulting distances were logarithmically transformed. Annual precipitation and mean annual temperature data (Supplementary Material; Table A14) were taken from Mariolopoulos (1961) and were similarly transformed to distances to make them comparable with the geographical and faunal distances by taking absolute values of the differences. STRUCTURAL EQUATION MODELS (SEMS) SEMs (see e.g. Duncan, 1975) can be used to assess the relative influence of variables in a path diagram which shows the causal relationships between the variables. Our model of the causal structure between the variables is shown in Figure 3. The double-headed arrows represent a correlation between the variables mean annual temperature and annual precipitation without a causal relationship from one variable to the other. Because there are no feedback loops in the model, this is a so-called recursive model, whose coefficients can be computed by recursive linear regressions on the standardized variables, i.e. transformed to zero mean and unit variance. This technique was known formerly as path analysis. The assumptions of the SEM were not fulfilled, particularly because our data consisted of distances, which violate the assumption of stochastic independence. Therefore, the relative size of the path coefficients could only be interpreted in an exploratory manner and the usual hypothesis tests in SEM and linear regression could not be applied. Instead, we used partial Mantel tests to test the significance of the influences on the faunal distances. A similar strategy, namely path analysis in connection with partial Mantel tests, was used by Leduc et al. (1992). PARTIAL MANTEL TESTS The Mantel test for correlation of distance data can be carried out without distributional assumptions by means of a Monte Carlo simulation, in which the distribution of the correlation coefficient is simulated by holding one distance matrix fixed and permuting randomly the rows and the corresponding columns of the other one (Cliff & Ord, 1981). Smouse, Long & Sokal (1986) generalized this method to partial correlation coefficients, in which the principle of the Mantel test is applied to the matrices of residuals of two linear regressions, with the distance matrices to be correlated as the dependent variables and the distance matrices to be partialized out as the independent variables. The necessity of the presence of an arrow in a path diagram, i.e. the presence of a direct influence (as long as the direction of the influence is assumed to be known), can be tested by testing the hypothesis that a certain partial correlation is zero. To do this, we interpreted our path diagram as a graphical model and used the results of Sections 3.2.2 and 3.2.3 of Lauritzen (1996) (changing the bidirectional arrow into an undirected edge). According to these results, two variables have to be independent (and therefore uncorrelated) conditional on all sets of other variables that separate the two variables in the moral graph, which is built by linking all pairs of variables with arrows pointing to a common third variable and omitting the directions of all edges afterwards. For example, to test the null hypothesis that the arrow between Pliocene distances and faunal distances does not exist, we first have to link all variables with remaining arrows pointing at the faunal distances. Then we make all edges in the graph undirected and observe that the Pliocene distances can be separated from the faunal distances by conditioning on the Pleistocene and Miocene distances. Thus, we have to test the partial correlation between Pliocene and faunal distances to be zero, while Pleistocene and Miocene distances have to be held fixed. To test the arrow between area and faunal distances, even a simple Mantel test suffices according to these rules. Because of possible departures from normality, we used Spearman s rank correlation as the test statistic. We only performed tests for the arrows pointing at the faunal distances, because these arrows were our primary interest. The smaller the number of tests, the higher the power, since we accounted for multiple testing by using Bonferroni correction of the P-values. The use of partial Mantel tests was criticized recently by Raufaste & Rousset (2001; Rousset, 2002; but see Castellano & Balletto, 2002). They argued that if dependence between two variables ( target variables hereafter) is tested conditional on further variables ( conditionizers hereafter), then the dependence

INFLUENCES ON THE COMPOSITION OF THE FAUNA OF THE AEGEAN ISLANDS 789 of the target variables on the conditionizers can be such that under the null hypothesis (conditional independence) the uniform distribution over the row and column permutations as used for the Mantel test may no longer be valid. The uniform distribution does not take into account that some permutations may be less likely than others because of information given in the conditionizers. Raufaste & Rousset (2001) illustrated this by an example testing partial regression coefficients, which differs from our use of partial Mantel tests for testing partial correlations. Their argument in general also holds for testing partial correlations, i.e. for the use of matrices of residuals stemming from linear regressions as described above. However, there is a situation under which the partial Mantel test is still valid. It is possible that the whole dependence between the target variables and the conditionizers manifests itself in the two linear regressions. If the residuals of the linear regressions, i.e. the entries of the matrices on which the test is performed, are independent of the conditionizers (as is the case in the usual linear regression model for non-distance data), a uniform distribution of permutations of the residual matrices can be used, because there is no information about the matrix entries left in the conditionizers. The independence has to be row-wise, i.e. dependence coming from the fact that certain entries in the distance matrices stem from the same island is handled adequately by the permutation procedure and does not affect the partial Mantel test. It is complicated to assess such an independence assumption in full detail. However, some insight can be obtained from diagnostic plots in which the regression residuals of a single row are plotted against the corresponding values of the conditionizers, i.e. there is one such plot for every combination of row, target variable and conditionizer for every partial Mantel test. As far as we have considered such plots (e.g. Fig. 2), no clear dependence has been detected. Thus, we consider the partial Mantel test to be at least approximately valid. All calculations were done with the statistical software system R (available at http://www.r-project.org). The package sem of R was used to estimate the SEMs. We implemented the partial Mantel test in R based on the function mantel in the package vegan, which was used to compute the non-partial Mantel tests. RESULTS INFLUENCE OF OVERWATER DISTANCES VS. TOTAL DISTANCES ON FAUNAL COMPOSITION We used Mantel tests and partial Mantel tests to investigate whether the examined terrestrial groups disperse much faster across land compared with across the sea. For land snails, tenebrionid beetles and reptiles, faunal (Kulczynski) distances were more strongly correlated with recent overwater distances than they were with recent total geographical distances (Table 1). If overwater distances were controlled for, the partial correlation of faunal distances with total geographical distances was not significantly different from zero. Consequently, we used overwater distances in the following analyses for land snails, tenebrionid beetles and reptiles. On the other hand, for land isopods and butterflies, faunal distances were more strongly correlated with recent total geographical distances than they were with recent overwater distances (Table 1). If total geographical distances were controlled for, the partial cor- A Regression residual of faunal distance 0.2 0.1 0.0 0.1 0.2 B Regression residual of log-transformed recent overwater distance 0.5 0.0 0.5 1.0 1.5 2.0 2.5 0.0 0.5 1.0 1.5 2.0 2.5 3.0 3.5 Log-transformed Pleistocene overwater distance 0.0 0.5 1.0 1.5 2.0 2.5 3.0 3.5 Log-transformed Pleistocene overwater distance Figure 2. Diagnostic plots for the partial Mantel test for influence of recent overwater distances on land snail faunal distances: plots of Pleistocene overwater distances against residuals of faunal Kulczynski distances (A) and recent overwater distances (B) from linear regression on the conditionizers temperature and Pleistocene overwater distance for the island Páros (geographical distances have been log-transformed).

790 B. HAUSDORF and C. HENNIG Table 1. Correlations (cor) and partial correlations (parcor) of faunal Kulczynski distances (d K ) with log-transformed recent total distances (d tot ) and log-transformed recent overwater distances (d ow ), with the matrix that was partialled out following the bar ( ), for central Aegean land snail, land isopod, tenebrionid beetle, butterfly and reptile datasets Land snails Land snails (native species only) Land isopods Tenebrionid beetles Butterflies Reptiles r P r P r P r P r P r P cor (d K, d tot ) 0.642 0.000 0.693 0.000 0.514 0.000 0.210 0.018 0.702 0.000 0.785 0.000 parcor (d K, d tot d ow ) 0.110 0.062 0.112 0.061 0.212 0.009-0.117 0.918 0.517 0.000-0.002 0.499 cor (d K, d ow ) 0.664 0.000 0.712 0.000 0.485 0.000 0.268 0.002 0.618 0.000 0.831 0.000 parcor (d K, d ow d tot ) 0.178 0.004 0.204 0.001-0.011 0.538 0.182 0.019 0.046 0.364 0.408 0.000 r: Spearman s rank correlation rho; for partial correlations: Mantel statistic based on Spearman s rank correlation rho. Significance was assessed from partial Mantel tests. Table 2. Partial Mantel tests of the correlations of faunal Kulczynski distances with log-transformed recent geographical distances (d rec ), log-transformed Pleistocene geographical distances (d pleist ), log-transformed Pliocene geographical distances (d plio ), log-transformed Miocene geographical distances (d mio ), log-transformed area distances (d area ), mean annual temperature distances (d temp ) and annual precipitation distances (d prec ) for the central Aegean land snail, land isopod, tenebrionid beetle, butterfly and reptile datasets, using geographical overwater distances for land snails, tenebrionid beetles and reptiles and total geographical distances for land isopods and butterflies Land snails Land snails (native species only) Land isopods Tenebrionid beetles Butterflies Reptiles r P r P r P r P r P r P d rec 0.333 0.000 0.379 0.000 0.481 0.000 0.127 0.063 0.464 0.000 0.329 0.001 d pleist 0.114 0.115 0.180 0.016-0.157 0.895 0.239 0.010 0.134 0.217 0.230 0.001 d plio 0.398 0.000 0.410 0.000 0.386 0.001 0.015 0.400-0.060 0.640 0.354 0.001 d mio 0.084 0.142 0.106 0.076-0.033 0.619 0.098 0.170 0.423 0.001 0.260 0.000 d area 0.034 0.348 0.066 0.249-0.113 0.856-0.031 0.607 0.230 0.037 0.191 0.065 d temp 0.097 0.204 0.159 0.083-0.123 0.829-0.070 0.740-0.044 0.624 0.161 0.115 d prec 0.087 0.076 0.016 0.391-0.009 0.535 0.038 0.318 0.196 0.095 0.316 0.000 r: Mantel statistic based on Spearman s rank correlation rho. relation of faunal distances with overwater distances was not significantly different from zero. Consequently, we used total geographical distances in the following analyses for land isopods and butterflies. The stronger correlations of faunal distances with overwater distances than with total geographical distances indicate that dispersal across the sea is more difficult than is dispersal across land areas for land snails, tenebrionid beetles and reptiles, whereas this is not the case for the volant butterflies and, more surprisingly, for land isopods. These results remained unchanged if Jaccard instead of Kulczynski distances were used. These results should be considered tentative, because the high correlation between overwater and geographical distances rendered the proper separation of the influences of the two dispersal modes difficult. Moreover, we did not take into account the possibility that dispersal modes between the species belonging to a group can differ. For example, small land snails can be blown over the sea up to several kilometres (Kirchner, Krätzner & Welter-Schultes, 1997), whereas larger snail species cannot. INFLUENCE OF RECENT GEOGRAPHY, PALAEOGEOGRAPHY AND CLIMATE ON FAUNAL COMPOSITION The results of the partial Mantel tests (Table 2) and the SEM analyses (Fig. 3) using faunal Kulczynski distances and geographical overwater distances for land snails, tenebrionid beetles and reptiles, and total

INFLUENCES ON THE COMPOSITION OF THE FAUNA OF THE AEGEAN ISLANDS 791 Figure 3. Structural equation models of the relationships between faunal Kulczynski distances and log-transformed recent geographical distances, log-transformed Pleistocene geographical distances, log-transformed Pliocene geographical distances, log-transformed Miocene geographical distances, log-transformed area distances, mean annual temperature distances and annual precipitation distances. We used geographical overwater distances for land snails, tenebrionid beetles and reptiles, and total geographical distances for land isopods and butterflies. The estimates of the influences indicated at the arrows should be interpreted only tentatively, because the model s assumptions are not met with distance data. The significance of the direct influences on faunal distance were tested with partial Mantel tests (Table 2). Influences on faunal distances that proved to be significant at the Bonferroni-corrected probability level of 0.007 (overall significance level of 0.05 over seven simultaneous tests) are marked with an asterisk. A, model for the land snail data; B, model for the land snail data (introduced species excluded); C, model for the land isopod data; D, model for the tenebrionid beetle data; E, model for the butterfly data; F, model for the reptile data (the influences of Pleistocene and Miocene distances on the faunal distances could not be separated in the structural equation model for the reptile data, because for the island set for which reptile data were available, Pleistocene and Miocene overwater distances were informationally identical; thus, a value is indicated only for Miocene distances). geographical distances for land isopods and butterflies (as explained above) were in several respects similar for the five groups examined, although there were some differences. The composition of the island faunas was found to be significantly influenced by present or past geographical distances between the islands for all groups. However, the faunal composition of the five taxa on the

792 B. HAUSDORF and C. HENNIG central Aegean Islands was influenced differently by the geography of different time periods. Distances between land snail island faunas were correlated significantly with recent and Pliocene distances (and also with Pleistocene distances, if introduced species were excluded; however, this correlation lost its significance if Bonferroni correction with k = 7 was applied); distances between land isopod island faunas were correlated significantly with recent and Pliocene distances; distances between tenebrionid beetle faunas were correlated significantly only with Pleistocene distances (however, this correlation lost its significance if Bonferroni correction with k = 7 was applied); distances between butterfly faunas were correlated significantly with recent and Miocene distances; distances between reptile faunas were correlated significantly with distances of all four time periods. There was no significant influence of recent climatic parameters, mean annual temperatures and annual precipitation on the composition of the land snail, land isopod, tenebrionid beetle and butterfly faunas of the central Aegean Islands, if recent distances between the islands were controlled for. In contrast, the composition of reptile faunas was correlated significantly with annual precipitation. The area of the islands had no significant influence on the faunal distances for land snails, land isopods, tenebrionid beetles and reptiles. In contrast, the distances between butterfly faunas were correlated significantly with island area (though this correlation lost its significance if Bonferroni correction with k = 7 was applied). The proportions of unexplained error variance of the faunal Kulczynski distances in the SEM were 0.425 (land snails), 0.357 (land snails, native species only), 0.632 (land isopods), 0.848 (tenebrionid beetles), 0.320 (butterflies) and 0.276 (reptiles). These values should be interpreted with caution, because the explanatory power of distance data can be expected to be smaller than that of independent data as assumed by the SEM. We repeated all analyses with Jaccard distances instead of Kulczynski distances to investigate the influence of the choice of measure for faunal distances on the results. Only in the following cases did the use of Jaccard distances affect the significance in the partial Mantel tests (after Bonferroni correction): the correlation with Pliocene distances for land isopods was not significant after Bonferroni correction (P = 0.009, k = 7), the correlation with precipitation for butterflies became significant (P = 0.007) and the correlations with Pleistocene (P = 0.263) and Miocene (P = 0.146) distances for reptiles became nonsignificant. DISCUSSION The hypothesis that the distribution patterns of recent taxa on the Aegean Islands reflect palaeogeographical patterns (Heller, 1976; Sfenthourakis, 1996a; Subai, 1996; Welter-Schultes & Williams, 1999; Fattorini, 2002a, b) has so far not been tested statistically. We tested the hypotheses that the composition of the fauna of the central Aegean Islands has been influenced by recent, Pleistocene, Pliocene and Late Miocene geography and by climatic parameters using partial Mantel tests and SEMs. These statistical procedures make possible an estimation of the influence of different factors on the distribution of organisms. Our results (Table 2) showed that the composition of land snail island faunas has been influenced by recent and by Pliocene distances. If introduced species were excluded, the influence of Pleistocene distances on the composition of land snail island faunas also became apparent (but significance was lost after Bonferroni correction). Heller (1976) and Subai (1996) recognized the importance of palaeogeography for the distribution of land snails on the Aegean Islands, but supposed that some land snail groups were influenced by Miocene and Pleistocene palaeogeography. We found that the composition of land isopod island faunas was influenced by recent and by Pliocene distances. Sfenthourakis (1996a) found a relationship between isopod distribution and Pliocene palaeogeography. On the contrary, we found that only Pleistocene distances had a significant influence on the composition of tenebrionid beetle faunas (but significance was lost after Bonferroni correction). In fact, Fattorini (2002a, b) supposed that Pleistocene palaeogeography was responsible for the present distribution patterns of tenebrionid beetles. In accordance with the conclusion of Dennis et al. (2000) that contemporary geography is the dominant influence on the butterfly faunas of the Aegean archipelago, we found that the composition of butterfly island faunas was correlated most strongly with recent distances (Table 2). However, there was also a strong correlation with Miocene distances. At first glance, the correlation between the composition of island faunas of highly mobile butterflies and Miocene palaeogeography is surprising. In the Late Miocene there were only two landmasses in the central Aegean area (Dermitzakis, 1990). The islands off the Anatolian coast were connected with the Anatolian mainland and all Cyclades were connected with the Greek mainland. The correlation between the distances between the butterfly faunas on the central Aegean Islands and Late Miocene geographical distances does not mean that the distribution patterns of the butterfly species have not changed since the Late Miocene and it probably cannot be attributed exclusively to the Late Miocene marine ingression separating the western and eastern Aegean landmasses. Rather, it reflects the continued geographical separation of Anatolian and European faunas since the Late Miocene and the

INFLUENCES ON THE COMPOSITION OF THE FAUNA OF THE AEGEAN ISLANDS 793 strong influence of the different proportions of Anatolian and European species on the islands on the composition of the butterfly faunas. When the various Anatolian and European lineages really diverged is unknown. Some of these lineages might already have diverged in the Late Miocene. The lack of a correlation between the composition of land snail, land isopod and tenebrionid beetle island faunas with Miocene palaeogeography does not mean that these groups were not affected by Miocene palaeogeography. It indicates only that such an influence cannot be separated statistically from the stronger influence of Pliocene or Pleistocene palaeogeography on the composition of the island faunas of these groups. The influence of Pleistocene and Miocene palaeogeography could not be separated at all for the reptile dataset, because Pleistocene and Miocene overwater distances were informationally identical for the island set for which reptile data were available. In contrast, no influence of Pliocene or Pleistocene palaeogeography on the composition of butterfly faunas on the central Aegean Islands was statistically detectable (Table 2). This is due to the lack of butterfly species characterizing island groups which formed land masses during the Pleistocene or Pliocene. Such species might be missing because they became extinct, as supposed by Dennis et al. (2000), because they dispersed later across the areas where the Pleistocene and Pliocene landmasses were separated by sea-ways, or because these sea-ways did not act as barriers to the highly mobile butterflies. Pleistocene or Pliocene sea-ways did, however, form barriers for some of the less mobile land snail, land isopod, tenebrionid beetle and reptile species which did not disperse later and are still extant. There was a weak correlation between the distances between the butterfly faunas on the central Aegean Islands and island area (Table 2). This was not true for land snails, land isopods, tenebrionid beetles and reptiles. The reason for this difference might be that the highly mobile butterflies are able to colonize large islands with suitable habitats more frequently than can the nonvolant groups examined, even if such islands are comparatively far from source areas. Recent climatic parameters, mean annual temperatures and annual precipitation were hardly found to influence faunal distances between island faunas of land snails, land isopods, tenebrionid beetles and butterflies, if recent distances between the islands were controlled for. Only for the reptiles were faunal distances correlated significantly with annual precipitation. The minor influence of climatic parameters on faunal composition might indicate either that most of the examined groups are not affected by the variation of these parameters across the islands or that they could not disperse across the barriers between the islands to colonize other islands with suitable climatic conditions. For the highly mobile butterflies at least, the latter explanation is unlikely. An analysis of the land snail fauna of Israel and Palestine, where geographical barriers play a minor role, also provides tentative support for the first hypothesis. Kadmon & Heller (1998) found that the variation in the composition of the land snail fauna can be attributed to the variation in precipitation only in the very dry parts of Israel with less than approximately 450 mm annual rainfall. In our study area precipitation was below this threshold only in the southern Cyclades. Much variation in the faunal distances between the islands remained unexplained. This might be due to several reasons. The correlation between faunal distances and the examined parameters could be reduced by stochastic extinctions and colonizations, introduction of species by man and failures to record a species which is present on an island, as well as by taxonomic errors. The palaeogeographical reconstructions presented by Dermitzakis (1990) are only rough approximations. We can test only the significance of a few discrete palaeogeographical stages, although geography changes continuously and similar land configurations occurred several times (e.g. as a result of the Pleistocene sea-level fluctuations). Moreover, geographical distances are only approximations of the strength of barriers to the dispersal of organisms. The actual strength of a barrier is influenced by several additional factors depending on the mode of dispersal. For example, it can be influenced by the direction of wind or bird migration for organisms that are distributed by wind or birds, or by the direction of ocean currents for organisms that are distributed by rafting. The importance of different means of dispersal is not well known for most organisms. Especially for palaeogeographical reconstructions, data about the direction of predominant winds and ocean currents are hardly available. The modelling of the influence of wind and ocean current directions is further complicated by seasonal changes in their direction. The amount of unexplained variation in the faunal distances between the islands decreases if species introduced by man are excluded, as we have shown for the land snails. Moreover, the influence of Pleistocene distances on the composition of the land snail island faunas becomes apparent only after the exclusion of introduced species. This demonstrates that additional information can be gained by reducing noise created by introduced species. On the other hand, our main results remained unchanged. This indicates that the applied methods can detect the main factors to influence the faunal composition of the islands, even if the pattern is partly obscured by the introduction of species by man.

794 B. HAUSDORF and C. HENNIG ACKNOWLEDGEMENTS We are grateful to R. A. Bank, E. Neubert and F. W. Welter-Schultes for providing additional distribution data for some land snails, to S. Chatzimanolis for notes on the distribution of Dendarus species, to H. Schmalfuss for checking our terrestrial isopod data matrix and to F. W. Welter-Schultes and an anonymous referee for helpful comments on the manuscript. SUPPLEMENTARY MATERIAL The following material is available from: http://www.blackwellpublishing.com/products/ journals/suppmat/bij/bij467/bij467sm.htm Appendix containing Tables A1 A14: Table A1. Distribution of land snails on the central Aegean Islands. Table A2. Distribution of land isopods on the central Aegean Islands. Table A3. Distribution of Tenebrionidae on the central Aegean Islands. Table A4. Distribution of butterflies on the central Aegean Islands. Table A5. Distribution of reptiles on the central Aegean Islands. Table A6. Recent geographic distances between the central Aegean Islands. Table A7. Pleistocene geographic distances between the central Aegean Islands. Table A8. Pliocene geographic distances between the central Aegean Islands. Table A9. Late Miocene geographic distances between the central Aegean Islands. Table A10. Recent overwater distances between the central Aegean Islands. Table A11. Pleistocene overwater distances between the central Aegean Islands. Table A12. Pliocene overwater distances between the central Aegean Islands. Table A13. Late Miocene overwater distances between the central Aegean Islands. Table A14. Area, annual precipitation and mean annual temperature of the central Aegean Islands. REFERENCES Bank RA. 1997. Notes on the radiation of the genus Mastus Beck, 1837 (Pulmonata) in the Aegean archipelago (Greece). Heldia 4: 29 30. Bank RA, Maassen WJM. 1981. Beiträge zur Molluskenfauna der Präfektur Samos (Griechenland, östliche Ägäis). De Kreukel 34: 45 80, pl. 1 2. Bank RA, Menkhorst HPMG. 1992. Notizen zur Familie Enidae, 4. Revision der griechischen Arten der Gattungen Ena, Zebrina, Napaeopsis und Turanena (Gastropoda Pulmonata: Pupilloidea). Basteria 56: 105 158. Bank RA, Neuteboom WH. 1988. Zur Molluskenfauna der Dodekanes-Inseln Kos, Kalymnos, Pserimos und Nisiros (Griechenland). De Kreukel Jubileumnummer: 45 62, pl. 1 2. Bar Z, Butot LJM. 1986. The land snails of Chios. De Kreukel 22: 65 93. Beutler A. 1979. General principles in the distribution of reptiles and amphibians in the Aegean. Biologia Gallo-Hellenica 8: 337 348. Cameron RAD, Mylonas M, Vardinoyannis K. 2000. Local and regional diversity in some Aegean land snail faunas. Journal of Molluscan Studies 66: 131 142. Castellano S, Balletto E. 2002. Is the partial Mantel test inadequate? Evolution 56: 1871 1873. Chatzimanolis S, Trichas A, Giokas S, Mylonas M. 2003. Phylogenetic analysis and biogeography of Aegean taxa of the genus Dendarus (Coleoptera: Tenebrionidae). Insect Systematics and Evolution 34: 295 312. Chondropoulos BP. 1986. A checklist of the Greek reptiles. I. The lizards. Amphibia-Reptilia 7: 217 235. Cliff AD, Ord JK. 1981. Spatial processes: models and applications. London: Pion. Dennis RLH, Shreeve TG, Olivier A, Coutsis JG. 2000. Contemporary geography dominates butterfly diversity gradients within the Aegean archipelago (Lepidoptera: Papailionoidea, Hesperioidea). Journal of Biogeography 27: 1365 1383. Dermitzakis MD. 1990. Paleogeography, geodynamic processes and event stratigraphy during the late Cenozoic of the Aegean Area. Atti dei Convegni Lincei 85: 263 288. Douris V, Cameron RAD, Rodakis GC, Lecanidou R. 1998. Mitochondrial phylogeography of the land snail Albinaria. Crete: long-term geological and short-term vicariance effects. Evolution 52: 116 125. Duncan OD. 1975. Introduction to structural equation models. New York: Academic Press. Fattorini S. 2002a. Biogeography of the tenebrionid beetles (Coleoptera, Tenebrionidae) on the Aegean Islands (Greece). Journal of Biogeography 29: 49 67. Fattorini S. 2002b. A comparison of relict versus dynamic models for tenebrionid beetles (Coleoptera: Tenebrionidae) of Aegean Islands (Greece). Belgian Journal of Zoology 132: 55 64. Foufopoulos J, Ives AR. 1999a. Reptile extinctions on landbridge islands: life-history attributes and vulnerability to extinction. American Naturalist 153: 1 25. Foufopoulos J, Ives AR. 1999b. Reptile distributions and island dendrograms for the islands of the Aegean and Ionian Seas. http://www.wisc.edu/zoology/faculty/fac/ive/ extinctions.html Fuchs A, Käufel F. 1936. Anatomische und systematische Untersuchungen an Land- und Süßwasserschnecken aus Griechenland und von den Inseln des Ägäischen Meeres. Archiv für Naturgeschichte, N. F. 5: 541 662.

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