Jinamoy et al.: Predictive distribution modelling for rufous-necked hornbill in Western Forest Complex
Conservation & Ecology
RAFFLES BULLETIN OF ZOOLOGY 62: 12–20
Date of publication: 21 February 2014
http://zoobank.org/urn:lsid:zoobank.org:pub:6A289E8C-422C-4CE6-AA25-C2945039C613
Predictive distribution modelling for rufous-necked hornbill Aceros
nipalensis (Hodgson, 1829) in the core area of the Western Forest
Complex, Thailand
Sitthichai Jinamoy1, Yongyut Trisurat1*, Anak Pattanavibool2, Chatchawan Pisdamkham3, Sompong
Thongsikem3, Vittaya Veerasamphan3, Pilai Poonswad4 & Alan Kemp5
Abstract. The rufous-necked hornbill, Aceros nipalensis (Hodgson, 1829), is listed as vulnerable and is found only
in the Western Forest Complex. The objectives of this research were: 1) to estimate the geographical distribution
for the rufous-necked hornbill at the Thung Yai-Huai Kha Khaeng World Heritage Site in both the breeding and
non-breeding seasons; and 2) to determine seasonal changes in its habitat use. We collated the occurrence records of
the rufous-necked hornbill from long-term monitoring data and conducted additional surveys during 2004–2008. In
addition, spatial layers for potential environmental variables that might affect hornbill distribution were developed
and Maximum Entropy (MaxEnt) modelling technique was used to generate potential distributions. The results
indicated that MaxEnt models performed very well and the overall accuracies of the predicted maps in breeding and
non-breeding seasons derived from the contingency matrix were 81% and 85% respectively. In addition, altitude
and land cover were considered significant variables in the species distribution model. Suitable habitats for the
rufous-necked hornbill were predicted in the high altitude evergreen forest and were clustered into three patches in
the center of Thung Yai Naresuan West, Huai Kha Khaeng, and along the western boundary of Huai Kha Khaeng
adjoining Thung Yai Naresuan East. Suitable habitats covered 11.7% of the world heritage site, of which 6.6% and
9.2% were in the breeding and non-breeding seasons respectively, owing to the fact that the home range during
breeding season is smaller compared to non-breeding season. Future conservation efforts should focus on enhancing
the connectivity between suitable large and small patches within the distribution range, the installation of artificial
nests, and patrolling to minimise poaching.
Key words. MaxEnt, maximum entropy modelling, species distribution modelling, Thung Yai–Huai Kha Khaeng
World Heritage Site
INTRODUCTION
the many forests they inhabit, where they have a significant
role in seed dispersal (Kemp, 1995). Large hornbills are also
good indicators of forest condition and human disturbance
because they require large tracts of contiguous primary forest
with large trees for nesting sites and food resources, and
they are targeted for hunting (Poonswad & Kemp, 1993).
Meanwhile, hornbills also control populations of small seed
predators such as insects and mice, thus helping to maintain
forest structure and the productivity of forest ecosystems
(Poonswad & Kemp, 1993).
Hornbills are recognised as the largest bird species in the
tropical forests of Asia and are classified in the family
Bucerotidae of the order Bucerotiformes (Sibley & Monroe,
1990). Thirty species of hornbills are found in tropical
Asia, with a further 22 species in Africa. Besides being a
flagship species for conservation because of their striking
appearance and intriguing nesting behaviour, with the female
imprisoning herself inside a cavity in a tree (Poonswad et
al., 1987), hornbills are among the primary frugivores of
However, due to severe deterioration of tropical forests,
eight out of 31 Asian hornbill species are threatened
species (BirdLife International, 2001; Kinnaird & O’
Brien, 2007). The rufous-necked hornbill (RNH, Aceros
nipalensis [Hodgson, 1829]) was originally distributed in
the mountainous regions in Bhutan, north-eastern India,
Myanmar, Thailand, Laos, Vietnam, southern Yunnan,
and south-eastern Tibet in China. Its range has declined
dramatically and it is now very rare across much of its
historical range (BirdLife International, 2001). The RNH is
classified as a vulnerable species at the global level and an
endangered species in Thailand (Vidhidharm et al., 1995;
Round, 2000; Sanguansombat, 2005) because its original
habitat has disappeared from many areas, and it is now
1
Department of Forest Biology, Faculty of Forestry, Kasetsart University, Chatuchak,
Bangkok 10900, Thailand; Email: fforyyt@ku.ac.th, Tel: +662 579 0176, Fax: +662
942 8107 (*corresponding author)
2
Wildlife Conservation Society Thailand Program, Pak Kret District, Nonthaburi
Province 11120, Thailand
3
Department of National Parks, Wildlife and Plant Conservation, Bangkok 10900,
Thailand
4
Department of Microbiology, Faculty of Science, Mahidol University, Bangkok
10400, Thailand
5
Naturalists & Nomads, Postnet Suite 38, Private Bag X19, Menlo Park, 0102,
South Africa
© National University of Singapore
ISSN 2345-7600 (electronic) | ISSN 0217-2445 (print)
12
RAFFLES BULLETIN OF ZOOLOGY 2014
1), which cover approximately 6,488 km2 and designated
as a UNESCO Natural World Heritage Site in 1991. Thung
Yai Naresuan has been divided into East and West parts for
effective administration. Thung Yai Naresuan and Huai Kha
Khaeng formed the core area of the 18,727 km2 WEFCOM,
which comprise of six wildlife sanctuaries and 11 national
parks. The WEFCOM is the largest forest complex in
Thailand and more importantly, the largest conservation area
in mainland Southeast Asia. It is especially important for
its high capacity to support large and endangered mammal
species (Trisurat et al., 2010) and birds. At least six hornbill
species have been observed in the World Heritage Site,
including the great hornbill Buceros bicornis (Linnaeus,
1758), RNH, wreathed hornbill Rhyticeros undulatus (Shaw,
1811), plain-pouched hornbill Rhyticeros subruficollis (Blyth,
1843), Tickell’s brown hornbill Ptilolaemus tickelli (Blyth,
1855), and Oriental pied hornbill Anthracoceros albirostris
(Shaw & Nodder, 1807).
found mainly in the Western Forest Complex (WEFCOM),
particularly in the hill evergreen forest of the Huai Kha
Khaeng Wildlife Sanctuary at elevations above 1,000 m
(Chimchome et al., 1998; Ouithavon et al., 2005). Previous
studies of the RNH in Thailand were confined to describing
its general habitat, breeding biology, and ecology (Lekagul
& Round, 1991; Poonswad, 1993; Poonswad & Kemp, 1993;
Kemp, 1995; Chimchome et al., 1998; Poonswad et al., 1999;
Ouithavon et al., 2005). Recently, Tifong (2007) studied the
home range, feeding, and roosting sites of one female and
two male RNHs using radio-telemetry during the breeding
and non-breeding seasons. The results indicated that aspects
of fruit availability, vegetation type, slope and altitude within
the area, and distance from water source affected their daily
movements, feeding, and roosting sites.
Much research has attempted to predict species distributions
in the landscape. In addition, several approaches have been
applied to species-distribution modelling using presenceabsence data and a geographic information system (GIS;
Agresti, 1996; Corsi et al., 2000), with popular and frequently
used methods being Generalised Linear Model (GLM) or
Generalised Additive Model (GAM; Guisan & Zimmermann,
2000; Pearce & Ferrier, 2000; Guisan et al., 2002; Beck
et al., 2005; Trisurat et al., 2010). A problem found when
using logistic regression is that true absence data were
often not available and in many cases, pseudo-absence data
were created. However, the determined pseudo-absence
may appear in the presence localities (Brotons et al., 2004;
Phillips et al., 2006) because species that are listed as near
extinction (endangered or critically endangered species) are
often difficult to detect (Engler et al., 2004), or the survey
may not have covered the habitat(s) of the species (Trisurat
et al., 2010).
Species occurrence data. Occurrence records of the RNH
were gathered along 14 transects, each 9 km in length
(Fig. 1). Transects 1–4 were located in Huai Kha Khaeng,
5–9 in Thung Yai Naresuan West, and 10–14 in Thung
Yai Naresuan East. Forty-five point-count sampling spots
at 200-m intervals were designated at each transect and
trained personnel spent 10 minutes at each spot to record
hornbill presence based on sightings and calls (Fig. 1). Field
surveys along each transect were conducted twice in each
breeding and non-breeding season between January 2004
to December 2008. The breeding season commences in
January and lasts until May while the non-breeding season
is between June and December (Chimchome et al., 1998;
Ouithavon et al., 2005; Tifong, 2007). Additional records
of hornbill occurrence were obtained from long-term
monitoring studies of hornbills jointly conducted by the
Thailand Hornbill Project in association with the Wildlife
Conservation Society–Thailand Program.
Recently, there has been progress in the development of
models to predict species distribution using only presence
data. Most research indicated that Maximum Entropy
(MaxEnt) model was superior in performance (Phillips et
al., 2006; Sérgio et al., 2007) to other models (e.g., Artificial
Neural Networks [ANN; Pearson et al., 2002], Ecological
Niche Factor Analysis [ENFA; Chefaoui et al., 2005;
Peterson, 2006; Santos et al., 2006], Genetic Algorithm for
Rule-set Production [GARP; Stockwell & Peters, 1999]).
Phillips et al. (2006) reported that MaxEnt model performed
well even for small sample sizes.
Dataset. Environmental variables that may influence
hornbill distribution at landscape levels were identified from
The Thailand Hornbill Project conducted long-term studies
of hornbills over 30 years, where only occurrence data was
recorded and the MaxEnt model was adopted. The objectives
of this research were: 1) to estimate the geographical
distribution of the RNH in the Thung Yai–Huai Kha Khaeng
World Heritage Site in both the breeding and non-breeding
seasons; and 2) to determine the seasonal habitat use of
the RNH.
Fig. 1. Map of research locations showing the study areas where
the point counts were conducted along14 line-transects, using a
hand-held GPS to accurately measure the count locations or extent
and position of evergreen forest where the RNH lives, in order to
predict RNH distribution.
MATERIAL AND METHODS
Study areas. The research was conducted in the Thung Yai
Naresuan and Huai Kha Khaeng wildlife sanctuaries (Fig.
13
Jinamoy et al.: Predictive distribution modelling for rufous-necked hornbill in Western Forest Complex
and non-breeding season (11.16 km2) determined in Huai
Kha Khaeng by Tifong (2007) as a minimum habitat size
to eliminate smaller habitat areas (noise) for preparation of
the final distribution maps.
previous research (Kinnaird & O’ Brien, 2007; Tifong, 2007;
Trisurat et al., 2013). These variables consisted of climate
(minimum and maximum temperature, precipitation), land
cover, topography (digital elevation model (DEM), slope),
proximity to roads, proximity to streams, and proximity to
ranger stations. A land cover map in 2008 and the locations
of ranger stations were obtained from the Department of
National Parks, Wildlife and Plant Conservation. Climatic
variables were downloaded from the WORLDCLIM database
(see http://www.worldclim.org/), while topographic feature
layers were obtained from the WEFCOM GIS database at
a scale of 1:50,000 (WEFCOM, 2004). All environmental
variables were converted to a raster (grid) with a spatial
resolution of 100 × 100 m and ArcGIS 9.2 software was
used for all spatial analyses.
RESULTS
Species occurrence sampling points. In total, 330 presence
data were obtained for the study areas, of which 169 were
for breeding and 161 for non-breeding seasons (Table 1).
In the breeding season, 127 presence records were used to
generate distribution model, 42 for testing and, in the nonbreeding season, 121 and 40 records were used, respectively.
In addition, 40 absence points were randomly chosen from
all transects in the breeding season and 39 points were
chosen for the non-breeding season. These data were used as
independent absence data to validate the species distribution
model accuracy.
Species distribution model. We selected the MaxEnt model
because it was developed specifically to model species
distributions with presence-only data (Phillips et al., 2006),
which is the only hornbill data available for Thailand. We
ran the MaxEnt model using the following parameters:
random test percentage = 25%, regularisation multiplier =
0.2, maximum iteration = 1,000, convergence threshold =
0.001, maximum number of background points = 10,000.
From the surveys, we found the occurrence of the RNH in
Huai Kha Khaeng over all four trails used for point counts
(trails 1–4), but only along trail 4 in the non-breeding
season. In Thung Yai Naresuan West, we observed RNH in
both seasons over all five trails (trails 5–9). In Thung Yai
Naresuan East, the RNH was detected on trails 10–12 in
both seasons but was absent along trails 13 and 14 (Fig. 1).
The output of the MaxEnt model was the continuous
probability of occurrence of the species’ distribution model
output (0.00–1.00). In this study, the more conservative
cut-off value of “equal training sensitivity and specificity”
was used to classify the continuous probability into a binary
prediction (0–1) of presence-absence. If the probability
value was equal to or greater than this threshold value, it
was classified as present, if less, it was classified as absent.
Presence-absence data were separated between the breeding
and non-breeding seasons.
Model performance and favorable habitat factors. Among
the 10 possible environmental factors contributing to RNH
presence in the breeding season, the MaxEnt model indicated
that DEM was the highest contributor (33%), followed by
land cover (29%), and minimum temperature (13%; see
Table 2 for details). The contributions of proximity to ranger
stations, villages, and roads were moderate (6%), while
proximity to streams, annual precipitation, and maximum
temperature contributed the least (<5%). However, in the
non-breeding season, while the contributions of elevation and
minimum temperature were still significant, the contribution
of land use had dropped from 29% in the breeding season
to 9%. In both seasons, the vegetation type used by the
RNH was evergreen forest, which includes hill evergreen
Previous studies normally used the area under the curve
(AUC) of a receiver operating characteristic (ROC; Zweig
& Campbell, 1993; Trisurat et al., 2011) to determine the
accuracy of the predicted distribution models. However, the
AUC has several shortcomings that were pointed out by
Lobo et al. (2008) and Allouche et al. (2006). In the current
research, the presence data were randomly partitioned into
two datasets. Seventy percent of the data were used as training
data to generate the distribution model and the remaining 25%
were used as independent points to validate the model. In
addition, the same amount of absence data from all transects
was randomly selected and used as an independent test.
Then, the contingency matrix was developed to calculate
omission and commission errors and the kappa coefficient for
assessing the accuracy of predicted distribution maps rather
than the AUC (Sim & Wright, 2005; Allouche et al., 2006).
The predicted distribution maps for the RNH were generated
for both the breeding and non-breeding seasons. In addition,
the extents of predicted presence-absence in both seasons and
estimated seasonal shifts in distribution were compared and
discussed (Fig. 2). It was noted that preliminary predicted
distribution maps were generalised using the average home
range size for a RNH male in the breeding season (6.19 km2)
Fig. 2. Diagram showing the conceptual framework used to model
the distribution of the rufous-necked hornbill in the Western Forest
Complex (WEFCOM), Thailand.
14
RAFFLES BULLETIN OF ZOOLOGY 2014
Table 1. Rufous-necked hornbill occurrence data in the breeding and non-breeding seasons in the core areas of the Western Forest Complex.
Protected Area Name
Number of Occurrences
Trail No.
Huai Kha Khaeng
Thung Yai Naresuan West
Thung Yai Naresuan East
Breeding Season
Non-Breeding Season
31
7
6
0
18
20
16
23
2
18
28
0
0
28
8
2
10
15
19
27
5
18
13
16
0
0
169
161
1
2
3
4
5
6
7
8
9
10
11
12
13
Total
Table 2. Contributions of environmental factors to rufous-necked hornbill distribution in the breeding and non-breeding seasons, in
descending order of importance.
Breeding Season
Variables
Non-breeding Season
% Contribution
Variables
% Contribution
Altitude (m)
33.4
Altitude (m)
47.6
Land use
28.8
Minimum temperature (°C)
17.5
Minimum temperature (°C)
12.9
Land use
8.5
Proximity to ranger station (m)
9.0
Proximity to road (m)
6.9
Proximity to village (m)
6.7
Proximity to ranger station (m)
5.3
Proximity to road (m)
6.0
Maximum temperature (°C)
4.3
Proximity to stream (m)
1.2
Annual precipitation (mm)
4.2
Annual precipitation
1.0
Proximity to village
2.6
Slope (%)
0.8
Proximity to stream (m)
2.1
Maximum temperature (°C)
0.3
Slope (%)
1.0
Total
100.0
AUC values
0.98
Total
100.0
0.96
due to the lower omission and commission errors for both
presence and absence classes.
forests (montane), degraded hill forests, and dry evergreen
forests. Hill evergreen forest occurs from about 1,000–1,700
m above mean sea level (asl). No single tree species or
family is dominant, but the families of Fagaceae, Myrtaceae,
Lauraceae, Theceae, and Magnoliaceae are well represented
in the mountainous WEFCOM. Dry evergreen forest generally
occurs from 600–1,000 m asl and is sometimes found along
streams at altitudes as low as 400 m asl. Common species at
canopy level include Dipterocarpus spp. (e.g., D. costatus
and D. turbinatus; WEFCOM, 2003, 2004).
Species distribution maps. The presence-absence maps
for the RNH in the breeding and non-breeding seasons
were derived from equal training sensitivity, and specificity
threshold values of 0.245 in the breeding season and 0.247
in the non-breeding season. The preliminary predicted
distribution areas for the RNH in the breeding and nonbreeding seasons covered 476 and 675 km2 respectively;
the areas of suitable habitat gain and habitat loss were 86
and 285 km2 respectively; and the seasonal shifts were 371
km2 (5.7%; Table 4). If the suitable habitats in both seasons
were combined, their total area was approximately 13% of
the study area. In addition, the overlapped suitable habitats of
the RNH in both breeding and non-breeding seasons covered
390 km2 or 6.0% of the study area. After the preliminary
habitat remnants that were smaller than a home range size
(Tifong, 2007) were eliminated, the final distribution areas
The accuracies of the predictive models were tested by the
confusion matrix and the kappa coefficient. The contingency
matrix shown in Table 3 indicates that the overall accuracy
of prediction in the breeding season was 83.53% and in nonbreeding season was 87.34%. It was noted that the kappa
coefficient value in the breeding season was approximately
67% while it was 74% in the non-breeding season. The
higher kappa coefficient in the non-breeding season was
15
Jinamoy et al.: Predictive distribution modelling for rufous-necked hornbill in Western Forest Complex
Table 3. Contingency matrix of predicted distributions in breeding and non-breeding seasons for rufous-necked hornbill.
Breeding Season
Observation
Predicted Class
Presence
Absence
Total
Commission errors
KHAT
Non-breeding Season
Observation
Presence
Absence
Total
Omission errors
34
6
40
8
37
45
42
43
85
19.05
13.95
15.00
17.78
83.53
0.67
Predicted Class
Presence
Absence
Total
Commission errors
KHAT
Presence
Absence
Total
Omission errors
34
4
38
6
35
41
40
39
79
15.00
10.26
15.53
14.63
for the RNH in the breeding and non-breeding seasons were
421 and 595 km2 respectively. In addition, the habitat use
for both seasons and the shifting habitats were 351 and 313
km2 respectively (Table 4).
87.34
0.74
for RNH male #15 in the non-breeding season was 1.8 times
greater than in the breeding season (11.16 km2 versus 6.19
km2). The fact that the male hornbill can feed the female up
to 73 times a day during the breeding season (Chimchome
et al., 1998) limits the male’s range distance from nest tree.
The distribution patterns appeared to be similar in both
seasons, predicting the RNH’s absence from the southeastern
corner of the study area with the main distribution being
in the center of Thung Yai Naresuan West and along the
western boundary of Huai Kha Khaeng adjoining Thung Yai
Naresuan East (Fig. 3A, 3B). The largest patch was predicted
in Thung Yai West, followed by Thung Yai Naresuan East and
Huai Kha Khaeng. The predicted habitat sizes and patterns
of distribution for RNH in Huai Kha Khaeng between the
breeding and non-breeding seasons were quite stable, while
in the other two sanctuaries these were quite different. For
example, the predicted habitat sizes in the non-breeding
season were greater than in the breeding season (30% for
Thung Yai East and 35% for Thung Yai West) due to the
fact that they are connected with the remaining evergreen
forest, while suitable habitat patches in Huai Kha Khaeng
are isolated and surrounded by deciduous forest (WEFCOM,
2004).
Several previous studies (Poonswad & Kemp, 1993;
Pattanavibool & Dearden, 2002; Sitompul et al., 2004;
Kinnaird & O’Brien, 2007; Trisurat et al., 2013) indicated
that landscape patterns and human disturbance are significant
factors affecting hornbill distribution. Rufous-necked hornbill
was formerly recorded in northern Thailand, such as in Doi
Inthanon and Doi Suthep-Pui national parks, Chiang Mai
Province (Poonswad & Kemp, 1993). Unfortunately, it is
now locally extinct at these two parks owing to hunting
and deforestation by the Hmong and Karen tribes, with
a 2005–2007 survey led by the Thailand Hornbill Project
finding only Oriental pied hornbills in some protected areas.
These patterns support the finding from this research where,
even though the study area is large, the distribution of the
RNH was limited to specific areas of evergreen forest,
particularly large patches of hill evergreen forest, and is also
dependent on particular combinations of altitude, temperature,
and available foods. In this research, hornbills were found
on trails 10–12 in both seasons but not on trails 13 and
14 (Fig. 1) because the topography of trails 13 and 14 is
generally flat and easily accessible by people living nearby
(WEFCOM, 2004).
DISCUSSION
Species occurrence data for RNH. The species occurrence
data recorded for RNH in the breeding season were more
restricted to certain areas and/or less frequent than in the
non-breeding season. We detected RNH on three trails in the
breeding and four in the non-breeding season in Huai Kha
Khaeng. Although we sighted the species over all five trails
in both seasons in Thung Yai Naresuan West, its detection in
the breeding season was lesser than the non-breeding season
(e.g., two versus 18 records on trail nine, respectively). This
phenomenon may be explained by the ecological niche
concept, which is defined as a species only being able to
establish populations in areas that match the ecological
conditions that limit its native distribution (Peterson, 2003),
which, in this case, produce seasonal movements in response
to variations in availability of fruits and hence, home range
size. Tifong (2007) found that the average home range size
Species distribution model. We selected the MaxEnt model
to predict the RNH distribution because its distribution
prediction provides a powerful new tool that uses only
presence data. MaxEnt estimates the most uniform distribution
(maximum entropy) of the occurrence points of the RNH
across the study areas, given the assumption that an expected
value for each environmental predictive variable within this
estimated distribution matches its empirical average (average
values for a set of RNH occurrence data). In addition, it takes
into consideration the interactions between environmental
variables and seems to perform relatively well with small
sample sizes of occurrence data (Phillips et al., 2006). Our
research demonstrated that this approach can be applied to
16
243.59
(3.75%)
313.19
(4.83%)
many taxa in Thailand that have only presence data, such
as plant specimens (Trisurat et al., 2011).
Species distributions for hornbills were conducted previously
at either regional (Kinnaird & O’ Brien, 2007) or national
levels (Trisurat et al., 2013), and analyses were undertaken
at a resolution of 1 km. However, previous studies did not
consider seasonal movement. This research used a pixel
resolution of 100 m for the estimation of the distribution of the
RNH—a scale appropriate to study the species’ distribution
at the microhabitat level in response to variations in the
habitat factors used in the model.
In this study, altitude (m), slope, land use, temperature,
precipitation, and the proximity to ranger stations, to villages
and to roads were used to generate the distribution models
for the RNH because they had been defined previously as
potential environmental factors (Kinnaird & O’ Brien, 2007;
Tifong, 2007; Trisurat et al., 2013). However, the results
derived from the MaxEnt model indicated that altitude,
land use, and minimum temperature were preferable habitat
factors in the breeding season, and altitude and land use
were identified as habitat preferences in the non-breeding
season (Table 2). The contribution of altitude was very high
for both seasons (48% in the non-breeding season and 33%
in the breeding season). The current findings indicated that
the elevation range of the RNH in the breeding season was
748–1,640 m and 493–1,640 m asl in the non-breeding
and breeding seasons, respectively, and was concentrated
in an altitudinal range between 1,000–1,400 m asl for both
seasons. These findings were quite different from previous
research (Chimchome et al., 1998; Ouithavon et al., 2005;
Tifong, 2007), except the concentration areas, which indicated
that the RNH normally occurs above 1,000 m. In addition,
the response curves derived from MaxEnt indicated that
evergreen forest was a preferable habitat for the RNH in
both seasons. Chimchome et al. (1998) and Ouithavon et
al. (2005) revealed that there were 13 and 15 plant species
(mainly figs) in the dry evergreen and hill evergreen forests
of the Huai Kha Khaeng Wildlife Sanctuary identified as
fruit-foods for the RNH in the breeding season and these
contributed approximately 80% of the total fruit in the
RNH diet. Sitompul et al. (2004) and Kinnaird & Brien
(2007) reported that figs have been recognised as “keystone
species” for tropical frugivorous vertebrates, especially birds
(hornbills) and primates. However, after the RNH female and
chick emerge from the nest, they need more food, especially
proteins for the growth of the fledged chicks (Poonswad,
1998; Ouithavon et al., 2005; Tifong, 2007), and they range
much farther afield. Therefore, the contribution of land use
factor on a particular hill evergreen forest was substantially
reduced from 29% in the breeding season to 8% in the nonbreeding seasons (Table 4).
Note: * Predicted presence derived from the MaxEnt model,
** Predicted presence greater than the home range of RNH#15 (breeding season = 6.19 km2; non-breeding season = 11.16 km2),
(1) Number of patches in the breeding season = 990 and in the non-breeding season = 1,092.
(2) Number of patches in the breeding season = 9 and in the non-breeding season = 6.
421.17
(6.49%)(2)
6,487.52
equal training sensitivity and specificity,
and generalised using home range**
595.16
(9.17%)(2)
351.57
(5.42%)
69.60
(1.07%)
371.66
(5.73%)
285.20
(4.40%)
86.46
(1.33%)
675.37
(10.41%)(1)
476.63
(7.35%)(1)
equal training sensitivity
and specificity*
Study areas
Huai Kha Khaeng,
Thung Yai Naresuan East and
and West Wildlife Sanctuary
Site
390.17
(6.01%)
Habitat
Loss
Habitat
Gain
Uses in
Both Seasons
Non-Breeding
Season
Distribution Threshold
(cut off value)
Total Area
(km2)
Table 4. Comparison of data from presence-absence maps in the breeding and non-breeding seasons.
Breeding
Season
Habitat Suitability
Area in km2 (percent per study area)
Seasonal
Shifts
RAFFLES BULLETIN OF ZOOLOGY 2014
Habitat suitability and seasonal shifts. The results of the
MaxEnt models indicated that suitable habitats for the RNH
after generalisation were located in three forest patches (Fig.
3). The first patch was consistent with Tifong (2007), who
depicted that suitable habitats were located in intact and
degraded evergreen forests, at altitudes 800–1,400 m asl,
17
Jinamoy et al.: Predictive distribution modelling for rufous-necked hornbill in Western Forest Complex
mostly near streams. The second patch was situated along
the junction west of Huai Kha Khaeng and east of Thung Yai
Naresuan East. Even though it was located in a large patch of
evergreen forest (238 km2), not all of this area was defined
as suitable RNH habitat (Figure 3B), though other hornbill
species that are less threatened than the RNH were found,
such as the great hornbills, Tickell’s brown hornbills, and
Oriental pied hornbills (BirdLife International, 2001). The
third patch in the central zone of Thung Yai Naresuan West
was the largest extent of suitable RNH habitat found in this
study and thus vital in maintaining local RNH populations
in this area (Bailey, 2007).
These preliminary suitable habitats consisted of 990
sub-patches in the breeding season and 1,092 in the nonbreeding season. Nevertheless, the predicted areas may be
overestimated since the RNH did not appear in many of the
small patches of suitable habitats estimated as used from the
MaxEnt model. This was especially so on mountain tops
where patch sizes were smaller than the home range and
distance to other suitable patches was greater than the average
daily movement. In addition, Akçakaya (2005) suggested
that the estimation of metapopulations for endangered
species with a small population size and limited distribution
should employ the smallest home-range size in the analyses.
Therefore, this study used the average home range size
for RNH male #15 in Huai Kha Khaeng to generalise the
preliminary map. Hence, the number of sub-patches was
substantially reduced in both seasons (Table 4).
When suitable habitats in the breeding season were subtracted
from habitats in the non-breeding season, the predicted
suitable habitats for RNH before and after generalisation
differed by 29%. This estimate conforms to the studies by
Chimchome et al. (1998) and Tifong (2007), which found
that during the breeding season a male RNH utilised a
habitat that is 45% smaller than that in non-breeding season
because of sufficient availability of fruits around the nest
tree. In addition, this finding is consistent with Poonswad
et al. (1987), who noticed that differences in habitats used
by different hornbill species potentially correlated with
differences and variations in the availability of fruit foods
and the stages of the reproductive period.
Conservation and protection measures. Poonswad et al.
(1999) indicated that nest trees, home range, food source,
and roosting sites during flocking are limiting factors for
hornbills. The results of this research clearly indicated that
in the non-breeding season, RNH used extended contiguous
patches in the study area. On the other hand, in the breeding
season, patches were clearly segregated, which resulted in
three sub-populations (Fig. 3A) that can be best explained
by the concept of “habitat fragmentation” (Akcakaya, 2005).
The predicted habitat patches situated along the junction west
of Huai Kha Khaeng and east of Thung Yai Naresuan East
can be used by RNH as stepping stones for dispersion to
adjacent and larger suitable habitats (Sitompul et al. 2004)
located in Mae Wong National Park and Umphang Wildlife
Sanctuary even though these patches are small.
Poonswad (1993) indicated that the availability of nesting
cavities of appropriate size may be the most important
population-limiting factor for hornbills as hornbills are large
birds and require large nesting cavities that exist naturally
only in large trees. Most hornbills’ nesting holes occur in
trees of the genus Dipterocarpus. Before Thung Yai Naresuan
was declared as a wildlife sanctuary in 1957, indigenous hill
tribes had settled in the area and converted the forest into
agricultural lands. The GIS database (WEFCOM, 2004) and
Fig. 3. Presence-absence binary models for RNH distributions
after introducing equal sensitivity-specific threshold values to the
continuous MaxEnt model based on the smallest home range size
of the male RNH #15, as derived from Tifong (2007), during: a,
the breeding season; b, the non-breeding season; and c, showing
the combined habitat classification.
18
RAFFLES BULLETIN OF ZOOLOGY 2014
vegetation assessment (WEFCOM, 2003) reported that 30%
of Thung Yai East and 19% of Thung Yai West are classified
as degraded evergreen forest. Hence, big trees that are both
potential nest trees and sites with suitable cavities were
removed (Duengkae & Chimchome, 2007) and this became
a critical factor for the RNH. Recent attempts to install
artificial nests to assist hornbill conservation at the BudoSu-Ngai Padi National Park, Southern Thailand showed that
the number of nests visited by hornbills steadily increased
after installation (Pasuwan et al., 2011). This conservation
effort and a proper artificial nesting design can be applied
to the study area in order to increase the carrying capacity
and enhance degraded habitats.
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ACKNOWLEDGEMENTS
For the success of this research, we thank the Department of
National Parks, Wildlife and Plant Conservation of Thailand
for granting permission to conduct this research in the Western
Forest Complex. We greatly appreciate the support received
from the staff at the Huai Kha Khaeng and Thung Yai (East
and West) wildlife sanctuaries for providing information
and accommodation and from the park rangers during the
field work. We are also overwhelmingly grateful to the
Thailand Hornbill Project for scholarship support through
their project on the genetics of hornbills in fragmented-forest
landscape and their population and habitat status in Thailand,
funded by the National Center for Genetic Engineering and
Biotechnology (BIOTEC), Thailand. We thank the Wildlife
Conservation Society (WCS) Thailand for assistance with
field surveys and materials. Special thanks are given to the
Associate Editor, Frank Rheindt, and to two anonymous
reviewers for raising important points and for providing
constructive comments to improve the manuscript.
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