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).