**3.1.6 Statistical analysis on Dis(Similarity measures)**

In the following significance levels are represented with asterisks: \* = p ≤ 0.05, \*\* = p ≤ 0.01, \*\*\* = p ≤ 0.001, not significant values simply sport no asterisks.

### **3.1.6.1 Species richness**

Apparently species richness is only very slightly influenced by fragmentation and disturbance (Fig. 5). There is even less impact of the fragmentation and disturbance categories a plot falls in onto species richness when the zones are considered separately (Fig. 6). In the light of this finding it is interesting whether beta-diversity is in contrast to species richness - influenced by fragmentation and disturbance and whether this effect might be also only visible when all data are considered together.

#### **3.1.6.2 Compositional similarity**

Richness differs only slightly between the zones (Fig. 7) and although it is not apparent in the Fig. 7, the difference in mean is significant according to a regular t-Test applied to the data. However, a NMDS plot of the data suggests an apparent and direct relationship between zone membership and species composition (Fig. 8). The plots of zone-1 are clearly separated (along the NMDS axes 2) from the plots of zone-2. Fig. 8 also hints at the reasons for the faster decay of similarity in zone-1 (as seen in Fig. 9). The majority of the plots of zone-1 is closely clumped together which means that they are all relatively similar in their species composition. However, there is also some spread in direction of the NMDS-axes 2. In geographic terms a higher likelihood of two plots further apart is being more different than close plots results. The three other tested grouping variables (which have been found to

the total number of individuals (i.e. G ≥10cm) (Fig. 4). We observed girth class of 10-30cm was having high number of individuals (39%). These species itself doesn't support larger biomass and density as a result there is very less representation in girth class above 200cm.

> Site 1 Site 2 Site 3 Site 4 Site 5 Site 6

10-30 30-60 60-90 90-120 120-150 150-180 180-210 210-240 240-270 270-300 >300

Fig. 4. Girth Class distribution for the six transects in Northern and Southern Eastern Ghats

In the following significance levels are represented with asterisks: \* = p ≤ 0.05, \*\* = p ≤ 0.01,

Apparently species richness is only very slightly influenced by fragmentation and disturbance (Fig. 5). There is even less impact of the fragmentation and disturbance categories a plot falls in onto species richness when the zones are considered separately (Fig. 6). In the light of this finding it is interesting whether beta-diversity is in contrast to species richness - influenced by fragmentation and disturbance and whether this effect might be

Richness differs only slightly between the zones (Fig. 7) and although it is not apparent in the Fig. 7, the difference in mean is significant according to a regular t-Test applied to the data. However, a NMDS plot of the data suggests an apparent and direct relationship between zone membership and species composition (Fig. 8). The plots of zone-1 are clearly separated (along the NMDS axes 2) from the plots of zone-2. Fig. 8 also hints at the reasons for the faster decay of similarity in zone-1 (as seen in Fig. 9). The majority of the plots of zone-1 is closely clumped together which means that they are all relatively similar in their species composition. However, there is also some spread in direction of the NMDS-axes 2. In geographic terms a higher likelihood of two plots further apart is being more different than close plots results. The three other tested grouping variables (which have been found to

**3.1.6 Statistical analysis on Dis(Similarity measures)** 

also only visible when all data are considered together.

\*\*\* = p ≤ 0.001, not significant values simply sport no asterisks.

of Andhra Pradesh

**3.1.6.1 Species richness** 

**3.1.6.2 Compositional similarity** 

explain most of the explainable variation in species richness in multiple regressions with backward selection) do not show a clear pattern in the NMDS plots (Fig. 8).

The mrpp() function of package vegan (Oksanen *et al.*, 2007) for the R Statistics System has been implemented to evaluate whether the plots of the two zones can be attributed to different vegetation types. The two zones are clearly distinct in their vegetation composition: The observed delta is significantly lower (0.89) than the expected delta (0.93, as determined by permutation) although the difference is not very large. Furthermore A = 0.036. A is a chance-corrected estimate of the distances explained by group identity. It can be compared to a coefficient of determination of a linear model (R2). Thus, it shows that the grouping into the two zones based on species composition is not very clear. With other words they are less distinct in species composition than the NMDS suggests (Fig. 8, upper left panel). Therefore another, more robust test has been employed as well. The function anosim [vegan] provides basically the same test but acts on ranks instead of the original data. It reports an R of -0.28\*\*\*: The similarity among plots of one zone is significantly higher than the similarity between plots of different zones.

Because the zones showed considerable grouping in the NMDS, further NMDS plots were drawn for each zone separately, to evaluate whether the species composition could than be more clearly attributed to the membership to categories of fragmentation, disturbance and richness. Fig. 10 shows that this is not the case. When the zones are considered separately no clear groupings according to the categories of the mentioned variables occur.

Fig. 5. Richness (species number inside plots) is significantly lower in highly fragmented plots. With disturbance there is no influence. The categorization is very coarse. Note, that there the numbers of plots involved in the categories differ considerably. Overall significance was tested with simple anova (for fragmentation: F = 0.13\*\*, for disturbance: F = 0.83ns). Inference regarding the difference between the classes was obtained with pairwise t-Tests (α = 0.05). Bonferroni correction was applied.

Spatial Patterns of Phytodiversity - Assessing Vegetation Using (Dis) Similarity Measures 165

Fig. 7. Difference in richness between zones 1 and 2 (Inference obtained with t-Test)

The 3d-plot of the factor variables fragmentation (x-axis), disturbance (y-axis) and the continuous variable richness (z-axis) shows that there is no clear relation between these three. It was also experimented with just putting the categories together (leads to 21 different possibilities). However, these categories explain nothing, because there is no explainable way in which they influence for instance the distance decay relations (see Fig.

There is only relatively slow distance decay of similarity when all data is analyzed together for each zone. However, beta-diversity structure is different for the two zones (Fig. 9). In Zone-1 similarity decreases much faster (-0.00022/km) compared to zone-2 (-0.000088/km). This is more than one order of magnitude and is also reflected in the intercept. The linear regression line of the distance decay relationship intersects the y-axis at a similarity value (Sørensen) of 0.23 for zone-1, whereas the intercept is only 0.11 for zone-2. Not only the distance decay after linear regression but also the spline regression lines show considerable differences between zone-1 and 2. In zone-1 the rate of decay changes heavily and after a rapid decrease from 0 to 100 km distance, the similarity declines much slower. This holds also true when the subsets are further subsetted and e.g. different fragmentation classes are

Within the zones the slope of the distance decay relationship differs only slightly but significantly between different fragmentation categories (Fig. 12). Only the slopes of the linear regression lines describing the distance decay in the fragmentation classes 1 and 2 of zone-1 are not significantly different (Table 5). The smoothed regression lines cannot be tested in this way but from the illustrations (Fig. 12) it is apparent that there are important

**3.1.6.3 Combined index of species richness, fragmentation, and disturbance** 

11).

**3.1.6.4 Distance decay of similarity** 

differences between the different fragmentation classes.

considered (Fig. 12).

Fig. 6. The relation between richness (species number inside plots) and fragmentation/disturbance is much less clear when the zones are considered separately: Only fragmentation classes in zone 2 artly show a significance impact on species richness. Note, that here as well the numbers of plots involved in the categories differ considerably. Overall significance was tested with simple anova. Zone 1: fragmentation: F = 1.25ns, disturbance: F = 0.80ns; Zone 2: fragmentation: F = 7.13\*\*\*, disturbance: F = 0.027ns. Inference regarding the difference between the classes was obtained with pairwise t-Tests (a = 0.01) and Bonferroni correction was applied.

Fig. 7. Difference in richness between zones 1 and 2 (Inference obtained with t-Test)
