Results

Potential suitable Habitat analysis of Great hornbill (GH) and Rufous-necked hornbill (RNH):The habitat suitability map of GH and RNH is divided into four classes as highly suitable, moderately suitable, and marginally suitable and least suitable. Each following classes are indicated by red, yellow, blue and green, respectively. The MaxEnt result showed that the area wise class percentage analysis for GH comprised of 2%, 8%, 10% and 80% (fig.3; i, ii) whereas RNH comprised 3%, 6%, 15% and 76% (Fig. 3; iv, v) representing highly suitable, moderately suitable, marginally suitable and least suitable respectively. The predictor variables performance with the highest percentage contribution and permutation importance in predicting the species presence location for GH were Elevation, Bio_13-Precipitation of Wettest Month, and Aridity index (Fig. 3; iii) whereas variables: Precipitation of Wettest Month (Bio_13) with 44.7 aspect, and Aridity Index (Fig. 3; vi) are major controlling distribution of RNH.
Figure 3: Habitat suitability map of GH and RNH with percentage of area suitability for species and permutation importance of of contributing variables
The curves below in figure 4 illustrates how the logistic prediction changes as each environmental variable are varied while keeping all other environmental variables at their average sample value. Red lines are the mean response of the 100 replicates and blue is the ± one standard Deviation. The X-axis represents the ranges of values of the environmental variables whereas Y-axis represents the probability of occurrences on the scale (low probability – high probability). Thegraphs represent the effect of an individual environmental variable on the distribution of the great hornbill (RH) and Rufous-necked hornbill (RNH). Precipitation of wettest month identifies the total precipitation that prevails during the wettest month in mm. The probability of occurrence of GH and RNH is high in 800-850 mm and 450-850 mm (Fig.4; i & ii). The range of BIO_13 in the study area is from 79-1181mm. The probability of occurrence of both GH and RNH is high in slightly higher precipitation of the wettest month. The contribution of Aridity Index in predicting the species presence location for GH and RNH was 19% and 11.5% respectively (Fig.4; iii & iv). The response curve shows that the probability of occurrence of GH is high at 14000 AI (Fig.4; iii), it shows GH prefers humid climatic condition. However, the response curve of RNH shows negative relationship (Fig.4; iv). The probability of occurrences of RNH is high in semi-arid and arid region. The contribution of elevation in predicting the species presence location of GH was highest with 30.4%. The curve shows a negative relationship i.e. elevation increases, the probability of occurrences of species decrease (Fig.4; v) and we can seespecies occurrence probability is high at the elevation below 3000 m. The aspect ranges from 1-360 degree representing the direction between east, west, north and south. The aspect ranging from 340-360 degree is the most suitable for the species which is south-east aspect – (fig. 4; vi). The jackknife test of variable importance shows the highest gain when the variable elevation is used in isolation (Fig. 4 xi & xii). The environment variable that decreases the gains the most when aspect is omitted in case of RNH and GH elevation is omitted which indicates that, this variable are most effective to define the model compared to any others (Fig.4 vi & vii).