**4.2.2 Distance decay**

Similar findings are to be stated regarding the rate of the decrease of similarity with distance. Compared to data from the Neotropics (e.g. Condit *et al.*, 2002, 0.0019-0.00055/km) the distance decay rate in zone-1 (0.00022/km) fits nicely in. Astonishingly the rate is much lower in zone-2 (0.000088/km) and as already discussed in the previous paragraph, the intercept is also very low. This means that it doesn't matter how far two plots are from each other. There is always the chance that two plots can be very different regarding their tree species composition.

But even at this low decay rate a phenomenon occurs which seems to be ubiquitous to all distance decay data: There is comparably faster decrease on short distances. This has also been reported by (Jurasinski, 2007) who attributed it to the predominance of dispersal over niche assembly in the short range around vegetation samples. In the investigated data it can be found on all evaluated subsets of the data. This supports the idea of a ubiquitous pattern

Spatial Patterns of Phytodiversity - Assessing Vegetation Using (Dis) Similarity Measures 179

shows that the differentiation in species composition is largely driven by the position in sites. However, on smaller scale vegetation types (or better groupings based on tree species

The methodology developed for the comparison of multiple plots has been applied to a data set of vegetation in the Eastern Ghats of Andhra Pradesh to assess vegetation dis(similarity) and also evaluate transitional ecosystem. Whittaker's (1960) concept for assessing diversity has triggered a lot of development in ecology. However, especially the term 'beta-diversity' has begun to take on relatively different meanings and thus is a rather confusing concept. The terminological ambiguity is an obstacle to the development in all fields requiring more than inventory data ('alpha'or 'gamma-diversity'sensu Whittaker). Compositional (dis) similarity between samples ('differentiation diversity') and the variation of inventory diversity across scales ('proportional diversity') are important fields for future research,

Research and data recording in vegetation ecology should be spatially and temporally explicit. Even when no spatial analysis is intended, this might provide for the incorporation of data in later meta-analyses. Different sample designs needs (random, stratified and hexagonal grid) to be validated for different ecosystems prior applying this method, as it is efficient for long-term monitoring purpose. Furthermore it allows tracking temporal changes in spatial patterns through periodically repeated sampling. The changes in spatial patterns can be assessed statistically (Jurasinski & Beierkuhnlein, 2006) and suggested hexagonal grids are efficient method for the investigation and monitoring of spatio-

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Ayyappan N. and Parthasarathy N. 1999. Biodiversity inventory of trees in a large-scale

Badano EI, Cavieres LA, Molina-Montenegro MA, Quiroz CL. 2005. Slope aspect influences

Black G.A., Dobzhansky T., Pavan C. 1950. Some attempts to estimate species diversity and population density of trees in Amazonian forests. Bot. Gaz. 111: 413-25. Campbell D.G., Daly D.C., Prance G.T., Maciel U.N. 1992. A comparison of the

permanent plot of tropical evergreen forest at Varagalaiar, Anamalais, Western

plant association patterns in the Mediterranean matorral of central Chile. Journal of

phytosociology and dynamics of three floodplain (varzea. Forest of known ages, Rio Jurna, Western Brazilian Amazon. Botan. Jr. of the Linn. Soc. 108: 213 – 237. Campbell D.G., Douglas C.D., Prance G.T. and Maciel U.N. 1986. Quantitative ecological

inventory of terra firme and varzea tropical forest on the Rio Xingu, Brazilian

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Ghats, India. Biodiversity and Conservation 8: 1533-1554.

data) might be identifiable.

which should not be neglected due to unclear concepts.

temporal patterns on various scales.

Arid Environments 62:93-108.

Amazon. Brittonia 38: 369- 393.

**6. References** 

**5. Conclusion** 

in species assembly that bases on the predominance of neutral (Hubbell, 2005) versus niche assembly (Leibold, 1995) on different spatial scales.

The results of the distance decay evaluation indicate that the change of compositional similarity between plots with geographic distance follows different paces depending on fragmentation/disturbance. However the most impressive difference exists between the two zones. So the question arises, what is responsible for the differences in species similarity and distance decay relationship between the two zones? We have already learned above, that this cannot be directly attributed to differences in richness. And in the NMDS the grouping is not as clear with richness as the grouping variable compared to the zones (Fig. 8).
