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Fig. 2. The results of the CCA of the effect of island charac- tionships were negative and positive. No variable was
teristics in determining the species composition of tenebrionid distinctly associated with axis 4. Of these variables, dis-
beetles on circum-Sicilian islands. The relative importance of tance to Sicily and distance to Africa have significant
individual geographical variables is expressed by the length of conditional effects, whilst water depth to Africa was mar-
the respective vectors. A – Area; Ds – Distance to Sicily; Da – ginally non-significant. Area and distance to Africa
Distance to North Africa; Di – Distance to the nearest island; showed similar increases in eigenvalue, whereas distance
SDs – Sea depth to Sicily; SDa – Sea depth to Africa; SDi – to Sicily had a substantially higher value of additional fit.
Sea depth to the nearest island. Island distance to Sicily was particularly important in
determining species composition on Pantelleria and the
Variation in species composition Pelagie Islands. By contrast, area was particularly impor-
tant for the Maltese Islands.
The constrained ordination (CCA) biplot for geo-
graphical variables (Fig. 2) resulted in relatively high Using raw values of land cover categories, the con-
eigenvalues and cumulative percentage variances, indica- strained ordination biplot resulted in low eigenvalues and
tive of a well structured data set (Table 4). Moreover, cumulative percentage variances, indicative of a “noisy”
there were strong species-geography correlations with all data set (Table 5). However, there were strong species-
four axes, which together accounted for about 90% of the environment correlations with all three axes, which
variance explained by the geographical data. Table 4 also together accounted for 84% of the variance explained by
shows the correlation coefficients of the geographical the environmental data. Use of percentage values of land
variables with four axes of the ordination, the results of cover produced even worse results (Table 5). No variable
the automatic forward selection of the geographical vari- had a significant effect when raw values were used,
ables, additional fit given by each step and their statistical whilst the percentage of cultivated and grassland areas
significance. Distance to Sicily was very strongly related was the only variable that had a (marginally) significant
to axis 1, whereas island area was associated with axis 2. value (F-ratio = 1.55, P = 0.05) when percentage data
Axis 1 was also negatively related to maximum water were used.
depth between the island in question and Africa (referred
to as “depth to Africa”). Distance to Africa and the Inter-island biogeographical similarity
nearest island were related to axis 3; the respective rela-
Cluster analysis based on Kulczynski 2 inter-island
faunal similarity and UPGMA clustering method pro-
duced a dendrogram that reflects the geographical group-
ings of the islands (Fig. 3). The first basic split separates
the Maltese Islands from all other islands. The latter are
subdivided into two main clusters: in one are the islands
of the Sicilian Channel (Pantelleria and Pelagie) and in
the other larger one are the islands closer to Sicily. In this
large cluster, two smaller clusters can be identified: one
grouping the Egadi Islands with Ustica and another
including the Aeolian Islands and islets. Although three
dimensions were retained from NMDS and introduced in
CANCORs, a biplot of the first two dimensions indicates
that these are sufficient to reflect inter-island relationships
(Fig. 4). The third dimension was also not significant in
CANCORs (see below).

TABLE 4. Results (F-statistics) of CCA for the geographical variables. O a indicates the increase in eigenvalue (additional fit). P
indicates the significance level of the conditional effects based on Monte Carlo tests (999 random permutations). Variables are the
same as in Table 1.

Variable Oa P F Weighted correlations

2.682 Axis 1 Axis 2 Axis 3 Axis 4
1.900 0.901 –0.177
Ds 0.51 0.004 1.930 0.211 –0.051 –0.142 –0.336
A 0.35 0.083 1.720 –0.594 –0.355
Da 0.34 0.016 1.516 –0.744 0.687 0.221 –0.420
SDa 0.29 0.060 0.987 0.139 –0.327
Di 0.25 0.134 0.845 –0.255 0.208 –0.614 –0.231
SDi 0.16 0.480 –0.414 –0.447
SDs 0.14 0.576 0.555 –0.224 –0.297 0.207
Eigenvalues 11.3 32.6
Cumulative % variance 27.1 –0.224 0.682 77.8
Cumulative species-geography relationship 0.964 0.899
Species-geography relationship –0.281 0.000

–0.415 –0.228

0.456 0.376

20.6 28.3

49.3 67.7

0.961 0.963

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