Discussion
Birds are thought to perform the widest range of ecological functions in
the ecosystem (Sekercioglu, 2006). Birds benefit humans by providing a
variety of important ecosystem services, including provisioning (food,
feathers), regulating (seed dispersal, pollination), cultural (art,
religion), and supporting (soil formation, nutrient cycling) services
(Millennium Ecosystem Assessment, 2005). Hornbills are frugivore, which
means they eat fruit. Frugivores play a critical role in the ecosystem’s
structure and function (Nathan and Muller-Landau, 2000). However, due to
the gradually increasing human population following anthropogenic
disturbance, there has been a severe decline in the population of these
birds.
Species distribution models (SDMs) are widely used in biogeography,
biodiversity, and macro-ecology research to model species’ geographic
distribution based on the correlation between known occurrence records
and environmental conditions at occurrence locations (Elith et al.,
2011). SDM creates geographic maps of environmental suitability for a
species(Raes, 2012) . We chose the MaxEnt model to predict the
distribution of the Great Hornbill and the Rufous-necked hornbill
because its distribution prediction provides a powerful new tool that
uses only presence data for species distribution modeling. Since its
inception, MaxEnt has grown in popularity (Renner and Warton, 2013).
MaxEnt is a presence-only model that allows scientists to tap into the
wealth of natural history data while avoiding the high cost of sampling
species across their entire range (Phillips et al., 2006). Data on
presence is plentiful, but data on absence is difficult to come by and
frequently unreliable due to a lack of survey effort (Liu et al., 2013).
MaxEnt compares the distribution of presences along environmental
gradients to the distribution of background points, which are drawn at
random from the study area (Renner and Warton, 2013). Furthermore, it
considers interactions between environmental variables and appears to
perform reasonably well with small amounts of occurrence data when valid
occurrence data and appropriate predictor variables are chosen(Phillips
et al., 2006).
The distribution model for the great hornbill and the rufous-necked
hornbill was created using 21 environmental variables and three
topographical variables (elevation, aspect, and slope). However, after
performing a multi-collinearity test in the ENM tool, only thirteen
variables were used to prepare a habitat suitability model and conduct
further analysis. The results revealed that the most important
environmental variables for predicting the species presence location for
GH and RNH were elevation (30.4%), precipitation of wettest month
(27.2%), Aridity Index (19%), and precipitation of wettest month
(44.7%), aspect (22.3%), and aridity index (11.5%), respectively.
The probability of GH occurrence and elevation has a negative
relationship, meaning that the likelihood of seeing great hornbills
decreases as elevation rises (fig 6& 7). According to the current
study, the probability of GH occurrence is high below 3000 masl. The
great hornbill is known to frequent wet evergreen and mixed deciduous
forests, ranging out into open deciduous areas to visit fruits and
ascend slopes to at least 1,560 masl in south India (Mudappa and Raman,
2009) and up to 2,000 masl in Thailand(Poonswad et al., 2013). The RNH
is found in primary subtropical evergreen and deciduous forests between
600 and 2000 masl worldwide but have been seen as high as 2900 masl
(Chimchome et al., 1997). RNH presence was predicted in the species
occurrences predicted map at Lhuentse, Yangtse, Punakha, and even up to
Gasa, which is over 3000 masl. Habitat suitability analysis shows that
GH and RNH are highly suitable for 2 and 3% of the total country’s
area, respectively. Their occurrence and distribution are affected by
factors such as vegetation type, habitat, temperature, availability of
food, forest size, and tall old trees with holes(Wagh et al., 2015).
Figure 6: Predicted GH occurrence map from low to high
probability values
Figure 7: Predicted RNH occurrence map from low to high
probability values
Conclusion
The distribution potential of great hornbill and Rufous-necked Hornbill
was delineated using species distribution modeling. This model is a very
effective tool for mapping the suitable habitats of different species
under the influence of topographic and climatic factors. In comparison,
Great hornbill has 2 % of the area of Bhutan suitable for habitat
whereas Rufous necked hornbill has total of 3% of most suitable habitat
in Bhutan. However Rufous-necked hornbill habitat range is high in
central, south-east and north-east region of Bhutan. The probability of
occurrence of the great hornbill is high in the southern region and
elevation below 3000 msl. Rufous-necked hornbill prefers semi-arid and
arid region whereas great hornbill prefers humid condition. The model
also revealed that the highest contributing variables for predicting
this threatened species were aspect and precipitation of wettest month.
The baseline information generated from the present study can be useful
facilitating fieldwork, planning and future scientific management of
these species in the country.