**3. Results**

156 The Dynamical Processes of Biodiversity – Case Studies of Evolution and Spatial Distribution

possibility for vegetation data is to regress similarity of units regarding species composition against their geographical separation (Nekola and White, 1999; Steinitz et al., 2006). To test for the influence of different vegetation types on patterns of compositional similarity data was divided into subsets. As the plots can be assigned to 2 geographically distinct regions, the distance decay of different vegetation types and subsets based on other categorical variables

The similarity values of the subsets are compared with an ANOVA-like function (mrpp[vegan]), (Oksanen et al., 2007)) and tested for significant differences using a permutation procedure (diffmean[simba]). Normal tests and ANOVA might fail here

The Multiple Response Permutation Procedure (mrpp) allows testing whether there is significant difference between two or more groups of sampling units. The method is insofar similar to anova, in that it compares dissimilarities within and among groups. If two groups of sampling units are really different (e.g. in their species composition), then average withingroup compositional dissimilarities ought to be less than the average of the dissimilarities between two random collections of sampling units drawn from the entire population. The mrpp statistic delta gives the overall weighted mean of within-group means of the pairwise dissimilarities among sampling units. The mrpp[vegan] algorithm first computes all pairwise distances in the entire dataset, then calculates delta. Then the sampling units and their associated pairwise distances are permuted, and delta is recalculated based on the permuted data. The last steps are repeated N times. N defaults to 1000 which provides a possible significance-level of p<0.001 as significance is tested against the distribution of the

After testing for significant differences between subsets, the differences in mean similarity are tested with a permutation procedure (diffmean[simba]). The difference in mean similarity between two sets is calculated. The two sets are joints together and two random sets the same size as the original sets are selected and their difference in mean is calculated. Then the sampling units and their associated pairwise distances are permuted, and the difference in mean is recalculated based on the permuted data. The last steps are repeated N times. N defaults to 1000 which provides a possible significance-level of p<0.001 as

To answer the question if distance decay is significantly different between the various evaluated subsets of the data, the slopes of the distance decay relationship have been calculated for the three subsets and compared. A permutation procedure following Nekola & White (1999) has been implemented as an R function (diffslope[simba]) to test for significance. For each subset compositional similarity between plots is regressed against their geographical separation. Before calculating the difference in slope between two subsets the values of compositional similarity are rescaled to a common mean. So testing the difference in slope of the distance decay relationship is independent of differences in the mean (Nekola & White, 1999; Steinitz et al., 2006). Linear regression is carried out on both of the subsets, and the difference in slope is calculated and stored. Then the variable pairs (geographical separation, compositional similarity) are randomly reassigned to the two data-subsets. Regression is calculated for each of the random subsets and the difference in slope is obtained again. The last steps are repeated 1000 times. Finally the difference between the observed slopes is compared to the differences based on random reassignment. Number of times when randomization are being produced differences in slope which is higher than the original data are summed up and

(fragmentation, slope, disturbance, etc.) is compared between the two regions.

because the similarities are not independent from each other.

significance is tested against the distribution of the permuted values.

divided by the number of permutations to get a p-value.

permuted deltas.
