Using maxent to assess the impact of climate change on the distribution of southern yellow-cheeked crested gibbon (Nomascus gabriellae)

Management of Forest Resources and Environment 
JOURNAL OF FORESTRY SCIENCE AND TECHNOLOGY NO. 2 - 2018 131
USING MAXENT TO ASSESS THE IMPACT OF CLIMATE CHANGE 
ON THE DISTRIBUTION OF SOUTHERN YELLOW-CHEEKED 
CRESTED GIBBON (Nomascus gabriellae) 
Vu Tien Thinh1, Tran Van Dung2, Luu Quang Vinh3, Ta Tuyet Nga4 
1,2,3,4Vietnam National University of Forestry 
SUMMARY 
Climate change has a variety of impacts that might have negative impacts on wildlife species, especially their 
distribution. Species with narrow distributions are are more sensitive than the other species. In this study, we 
used ecological niche modelling species (MaxEnt software), species occurrence data, and environmental 
variables to assess the impacts of climate change on the distribution of Southern yellow-cheeked crested gibbon 
(Nomascus gabriellae) - an endemic and rare primate species of Vietnam and Cambodia with narrow 
distribution range. We used environmental variables to generate the potential distribution of Southern Yellow-
cheeked Crested Gibbon at current and two times in future (2050 and 2070). In addition, two scenarios of 
climate change (RCP4.5 and RCP8.5) and three climate models (ACCESS1 - 0; GFDL - CM3; MPI - ESM - 
LR) were used to evaluate the changed of suitable distribution in the future. The results show that the 
distribution of this species was predicted to decrease dramatically under the effects of climate change. 
Futhermore, the projections indicate that a larger suitable area will disappear. The suitable areas are likely to 
shift toward the center of current distribution range and areas with high elevation above sea level. In addition, 
we assessed the priority of protected areas in gibbon conservation under climate change context. 
Keywords: Climate change, Gibbon, ecological niche modelling, Maxent, Nomascus. 
I. INTRODUCTION 
Southern Yellow-cheeked Crested Gibbon 
(SYCCG) (Nomascus gabriellae) is an 
endemic primate species of Indochina, this 
species is only recorded in the Southern of 
Vietnam and the Northeast of Cambodia (Van 
Ngoc Thinh et al., 2010; Rawson et al., 2011). 
Recently, the population of SYCCG has been 
rapidly decreasing. The main threats to the 
species are habitat loss, hunting (Geissmann et 
al., 2000; Rawson et al., 2011). This species is 
listed as Endangered on the IUCN Red List 
(Geissmann et al., 2008). Gibbons are highly 
sensitive to living environment because of 
narrow ecological niche. They are often 
recognized in tall evergreen and semi-
evergreen forest (Geissman et al., 2000) and in 
the cool climate area (Pham Nhat, 2002). 
Climate change is one of the main causes 
of biodiversity loss, that is the direct impact 
component and the consequences are obvious. 
To adapt to the changes of climate, wildlife 
species might shift their distribution poleward 
or shift to higher areas where the ecological 
conditions are more suitable for them (Root 
and Schneider, 2002). Recent studies have 
shown that a variety of primate species are 
significantly affected by climate change in the 
21st century, especially in Southeast Asia as a 
hot spot (Graham et al., 2016) due to small 
distribution range and narrow ecological niche 
(Estrada et al., 2017; Sesink-Clee et al., 
2015). Especially, gibbons are predominantly 
frugivorous, but the diet also includes leaves, 
shoots and flowers (Nadler and Brockman, 
2014). Therefore, climate change is also likely 
to impact on their food sources (Wiederholt 
and Post., 2010). Climate change also results 
in fragmentation or loss of habitat, which is 
one of the most serious threats to the primate 
populations. In this study, we of assess the 
impact of climate change on the distribution 
of SYCCG. The study aims to achieve the 
following objectives: (1) predicting the 
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JOURNAL OF FORESTRY SCIENCE AND TECHNOLOGY NO. 2 - 2018 132
potential distribution of SYCCG at current 
and the future; (2) assessing the change of 
the potential distribution caused by climate 
change; (3) determining the priority areas for 
gibbon conservation in the climate change 
context. 
II. RESEARCH METHODOLOGY 
2.1. MaxEnt model 
MaxEnt is software that uses predictive 
methods to simulate the potential distribution 
of species from existing information (Phillips 
et al., 2006). Species occurrence data is used as 
an input (called occurrence data), along with 
the use of environmental condition variables 
(such as temperature, rainfall, etc.) to 
interpolate the likelihood of occurrence for 
each grid cell. This model is the most popular 
among ecological niche modeling programs. 
Several studies have used MaxEnt to assess the 
effect of climate change on primate’s species 
such as Sesink-Clee et al. (2015), Gouveia et 
al. (2016). In addition, the newest MaxEnt 
version can be downloaded free from 
ource/maxent/. In this study, the following 
indexes were used: percentage of random 
sample to test = 20%, regularization multiplier 
= 0.2, maximum iteration = 1,000, 
convergence threshold = 0.001, maximum 
number of background points = 10,000. 
The area under the response curve 
(AUC), with values ranging from 0 to 1 
was used under application of the Receiver 
Operator Characteristic model (ROC) to 
determine model suitability (Phillips, 
2006). In this context, models with AUC 
values > 0.75 (larger values meaning 
higher model suitability) are usefull in 
modeling species distribution (Elith, 2000). 
When the AUC = 1, the predictive power of 
the model is considered perfect. If the AUC 
< 0.5, the predictive power of model is low 
(Phillips, 2006). 
MaxEnt generated a projection showing 
levels of suitability for SYCCG with the value 
ranging from 0 to 1 for each pixel. Cells with 
greater values represented higher suitability. 
This projection was generated in ASCII (*.asc) 
format, then it was converted into raster format 
(*.tif) by ArcMap10.1. In this study, we used 
the value "equal training sensitivity and 
specificity" to classify suitable level (> 0.1) 
and unsuitable level (0 - 0.1). Then the suitable 
level was divided into 3 categories: Highly 
potential (> 0.5); moderately potential (0.3 - 
0.5); and low potential (0.1 - 0.3). 
Finally, to assess the priority areas for 
conservation of SYCCG, we used 02 criteria. 
Firsts, we calculated the area of species 
distribution in each protected area lost due to 
the effect of climate change. All protected 
areas were evaluated with scores ranging from 
1 - 5 points for different scenarios of climate 
change. If the suitable area decreased by less 
than 20% of the current distribution range, the 
protected area was assigned 5 points. 
Similarly, the protected area was assigned 4 
points (21 - 40%); 3 points (41 - 60%); 2 
points (61 - 80%) and 1 point (more than 
80%), respectively. The second criteria used 
the number of gibbon group in each protected 
area. The area will receive 5 points, 3 points if 
the number of gibbon group in this area is 
larger 10 groups and less than 10 groups, 
respectively. If the gibbon was previously 
recorded in the protected area but no recent 
records were confirmed, the protected area was 
assigned 1 point. The maximum point for each 
protected areas was 65 points. Therefore, we 
divided the protected areas into 3 levels: high 
priority (41 - 65 points), medium priority (21 - 
40 points) and low priority (1 - 20 points). 
2.2. Species occurrence data 
We gathered a total of 431 independent 
localities at that the occurrence of N. 
gabriellae during field surveys and from 
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previous studies, including Dong Thanh Hai et 
al. (2011); Pollard et al. (2008); Hoang Minh 
Duc (2010); Hoang Minh Duc et al. (2010a), 
(2010b); Ngo Van Tri (2003); Nguyen Manh 
Ha et al. (2010); Channa and Gray (2009); 
Tran Van Dung (unpublished), Vu Tien Thinh 
et al. (2016); and Cat Tien National Park 
(2004). 
2.3. Environmental variables 
* Present climate data 
We gathered environmental variables from 
Wordlclim ( (Hijmans 
et al., 2005) (table 1). The spatial resolution of 
the variables is 0.83 x 0.83 km. The range of 
climate data used to run the model covered the 
Indochina region, the Southern of China and a 
part of Thailand. 
To eliminate highly correlated variables, 
data from 2,000 randomly selected points in 
the region was exported to Excel for 
calculating the correlation coefficient. The 
Pearson correlation coefficient was used to 
calculate the correlation between pairs of 
variables. We used only one variable in the 
pairs having a coefficient of correlation | r | > 
0.85 for subsequent analysis. Finally, we used 
8 variables, including: 04 temperature 
variables and 04 precipitation variables (Table 
1) for final modelling. 
Table 1. The environmental variables used to run model 
Variables Source Data type 
BIO1 = Annual Mean Temperature Worldclim Continuous 
BIO2 = Mean Diurnal Range (Mean of monthly = max temp - min temp) 
BIO3 = Isothermality (BIO2/BIO7) (*100) 
BIO4 = Temperature Seasonality (standard deviation *100) 
BIO5 = Max Temperature of Warmest Month 
BIO6 = Min Temperature of Coldest Month 
BIO7 = Temperature Annual Range (BIO5 - BIO6) 
BIO8 = Mean Temperature of Wettest Quarter 
BIO9 = Mean Temperature of Driest Quarter 
BIO10 = Mean Temperature of Warmest Quarter 
BIO11 = Mean Temperature of Coldest Quarter 
BIO12 = Annual Precipitation 
BIO13 = Precipitation of Wettest Month 
BIO14 = Precipitation of Driest Month 
BIO15 = Precipitation Seasonality (Coefficient of Variation) 
BIO16 = Precipitation of Wettest Quarter 
BIO17 = Precipitation of Driest Quarter 
BIO18 = Precipitation of Warmest Quarter 
BIO19 = Precipitation of Coldest Quarter 
* Variable in bold are used for final analysis 
 Climate scenario 
To predict the changed of SYCCG's 
distribution in the future, we used climate change 
scenarios from Worldclim (Hijmans et al., 2005). 
The data was calculated from future climate 
projection of General Circulation Models (GCM) 
of the Coupled Model Intercomparison Project 
Phase 5 (CMIP5). Base on the study conducted 
by McSweeney et al. (2014), which evaluated the 
suitability of different GCMs to predict the 
Southeast Asia's climate, we selected the three 
best available GCMs (ACCESS1 - 0; GFDL - 
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CM3 and MPI - ESM - LR), which was then run 
under two different greenhouse gas concentration 
trajectories (RCP4.5 and RCO8.5) 
(Representative Concentration Pathways). 
RCP4.5 is an intermediate emission scenario, 
which is developed by Pacific Northwest 
National Laboratory in the US. RCP8.5 is a high 
emission scenario and it is developed by the 
International Institute for Applied System 
Analysis in Austria. 
III. RESULTS AND DISCUSSION 
3.1. Predicting the suitable distribution of 
SYCCG at the present 
The AUC values were higher than 0.92 for 
all climate scenarios. Therefore, this model can 
be used to predict the potential distribution of 
SYCCG. The projection of this model 
indicated the SYCCG's suitable distribution 
range lies in the Dak Lak, Dak Nong, Lam 
Dong, Dong Nai, and Binh Phuoc provinces 
(Vietnam) and Mundulkiri (Cambodia). Past 
distribution of this gibbon species covered the 
Southern Central Highland region and a part of 
Southeastern region of Vietnam and the
Eastern region of Cambodia (Van Ngoc Thinh 
et al., 2010; Rawson et al., 2011). Therefore, 
the distribution was generated by MaxEnt is 
congruent with our understaning on the species 
distribution. 
The potential distribution of SYCCG can be 
divided into two sections. The first section lies 
in the Da Lat plateau withelevation ranges 
from 1,200 - 2,200 m als. The main habitat in 
this area is broad-leaved evergreen. The 
second section lies in the Binh Phuoc, Dong 
Nai province (Vietnam) and Muldokiri 
(Cambodia). The topology of this area is quite 
flat. There are two separate seasons in the 
region: dry and rain seasons. Arcording Dao 
Van Tien (1983), this is the suitable habitat for 
gibbon species (Rawson et al., 2011). 
At present, the total of suitable area for the 
species is approximately 52,527.92 km2, 
including: highly suitable (11,781.09 km2), 
moderately suitable (23,184.30 km2), and low 
suitable (17,562.53 km2) (Fig. 1). 
. 
Figure 1. The present potential distribution of N. gabriellae generated by MaxEnt 
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3.2. The shifts of distribution of SYCCG 
under climate change scenarios 
The extent of SYCCG distribution 
decreased much under RCP4.5 and RCP8.5 
senerios. In addition, while the larges in the 
current distribution range become unsuitable, 
the species distribution range did not extend to 
new areas in the future. The suitable area 
shrinked toward the center and mountainous 
areas (Fig. 2, 3). 
ACCESS1 - 0 GFDL - CM3 MPI - ESM - LR 
2050 (a) 
2050 (b) 
2050 (c) 
2070 (d) 
2070 (e) 
2070 (f) 
Figure 2. The potential distribution of SYCCG under RCP4.5 
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ACCESS1 - 0 GFDL - CM3 MPI - ESM - LR 
2050 
2050 2050 
2070 2070 2070 
Figure 3. The potential distribution of SYCCG under RCP8.5 
Under RCP4.5 scenario, on average the 
distribution range reducedby about 60.64% in 
2050 and 64.23% in 2070. Under RCP8.5 
scenario, the species lost 62.77% and 72.83% 
it’s distribution range in 2050 and 2070, 
respectively (Table 2). 
Table 2. The change of potential distribution of SYCCG area under the impact of climate change 
Area: km2 
Model RCP 
2050 2070 
Area Change % Area Change % 
Present 52,527.92 52,527.92 
ACCESS1-0 4.5 21,826.00 -30,701.92 -58.45 19,999.52 -32,528.40 -61.93 
GFDL-CM3 4.5 18,758.27 -33,769.65 -64.29 17,492.45 -35,035.47 -66.70 
MPI_ESM 4.5 21,433.69 -31,094.23 -59.20 18,868.09 -33,659.83 -64.08 
ACCESS1-0 8.5 23,533.99 -28,993.93 -55.20 11,421.28 -41,106.64 -78.26 
GFDL-CM3 8.5 17,511.23 -35,016.69 -66.66 18,976.46 -33,551.46 -63.87 
MPI_ESM 8.5 17,628.28 -34,899.64 -66.44 12,422.67 -40,105.25 -76.35 
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The General Circulation Models (GCM) 
GFDL-CM3 is the most influential model to the 
distribution of this SYCCG. Under RCP4.5, the 
estimated loss of suitable area was 46.29% in 
2050 and roughly 66.70% in 2070. Furthermore, 
the highly suitable distribution dropped 
considerably to less than 1,000 km2 in 2070 
(Figure 5). Regarding RCP8.5, the potential 
distribution of SYVVG was most affected also 
by GFDL-CM3 (35,016.69 km2) in 2050. By 
contrast, ACCESS1-0 was the best model. It was 
predicted that 41,106.64 km2 current suitable 
area can be lost by climate change. 
RCP8.5 had more impacts on the suitable 
distribution of SYCCG than RCP4.5. In 
addition, the model under RCP8.5 projected 
that the suitable distribution can be severely 
fragmented and was divided in to two separate 
sections. The first section was in the South of 
Dak Lak province and the North of Lam Dong 
province. This area had the largest natural 
forest in Vietnam, including: Chu Yang Sin 
NP, Bidoup - Nui Ba NP and Phuoc Binh NP. 
Furthemore, this area can be connected with Ta 
Dung NR to form a biodiversity corridor (Vu
Tien Thinh, 2014). The other section is in the 
West of Dak Nong Province. This area 
connects with the East of Muldukiri Province, 
Cambodia, in which the largest SYCCG 
population was found (Rawson et al., 2011). 
Climate change can have impacts on the 
distribution a variety of species. However, an 
endemic species or narrow distribution species 
are more likely to be vulnerable. Thus, their 
future distribution was predicted to decerease 
dramatically than that of species with larger 
distribution range (Levisky et al., 2007). 
SYCCG is an endemic and restricted-range 
primate species of Indochina. Gibbons prefer 
to live in cool climate area and predominately 
feeds on plants. Therefore, their distribution 
depends on the types and quality of the forest 
cover (Pham Nhat, 2002). Global warming can 
also affect the distribution of vegettation 
(IPCC, 2013; Virginia et al., 2001). Therefore, 
the distribution of this gibbon can also be 
affected considerably by climate change 
throught the change of forest ecosystems. 
However, in this study, we restricted our 
invironemtal variables to only climatic factors. 
Figure 4. The extent of suitable distribution of 
SYCCG under RCP 4.5 
Figure 5. The extent of suitable distribution 
levels of SYCCG under RCP 8.5 
3.3. The pritority protected areas for 
Southern yeallow-cheeked gibbon 
The modelled distribution of SYCCG 
decreased significantly, especially within 
protected areas. Six protected areas were 
considered high prority, including: Chu Yang 
Sin NP, Bidoup - Nui Ba NP, Ta Dung NR, 
Nam Kar NR, Nam Nung NR and Pnom 
Namlear Wildlife Sanctuary (Table 3). 
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Table 3. The pritority protected areas for Southern yeallow-cheeked gibbon under 
climate change context 
No Protected areas 
Total 
point 
Priority 
level 
No Protected areas 
Total 
point 
Priority 
level 
1 Bidoup - Nui Ba NP 65 High 10 
Seima Protected 
Forest 
19 Low 
2 Chu Yang Sin NP 64 High 11 
Mundulkiri Protected 
Forest 
18 Low 
3 Ta Dung NR 63 High 12 
Nam Cat Tien 
(Cat Tien NP) 
17 Low 
4 Nam Nung NR 59 High 13 Dong Nai C & NR 17 Low 
5 Nam Kar NR 50 High 14 
Phnom Prich Wildlife 
Sanctuary 
17 Low 
6 
Pnom Namlear 
Wildlife Sanctuary 
50 High 15 Yok Don NP 15 Low 
7 Bu Gia Map NP 31 Medium 16 Easo NR 13 Low 
8 Phuoc Binh NP 29 Medium 17 Nui Ong NR 13 Low 
9 Cat Loc (Cat Tien NP) 24 Medium 18 
Snoul Wildlife 
Sanctuary 
13 Low 
The priorty rangking for long-term 
conservation of SYCCG is important for 
directing conservation effort to save this 
species. Priority areas are less affected by 
climate change. Additionally, these areas 
contain the large population of SYCCG, for 
example: Chu Yang Sin NP (166 groups, Vu 
Tien Thinh et al., 2016), Bidoup - Nui Ba (at 
least 25 groups, Rawson et al., 2011), Nam 
Nung NR (at least 11 groups, Rawson et al., 
2011). Soe protected areas are holding a large 
populaiton of SYCCG, such as Cat Tien NP 
(149 groups), Bu Gia Map NP (176 groups, 
Rawson et al., 2011), Seima protected forest 
(432 - 832 groups, Pollard et al., 2007), Phnom 
Prich Wildlife Sanctuary (149 groups, Channa 
and Gray, 2009), however, environment factors 
in these protected areas were predicted to be 
less suitable with SYCCG. 
IV. CONCLUSIONS 
The MaxEnt software generated the 
potential distribution of SYCCG using 
occurence data and environment variables. The 
current potential distribution area covers 
52,527.92 km2 in the South of Central 
Highland, Southeastern region (Vietnam) and 
Southeastern region of Cambodia. 
The model predicted that the future 
potential distribution of SYCCG was 
afftected by climate change under RCP4.5 
and RCP8.5 scenarios. While large areas in 
the species distribution range will potentially 
become unsuitale in the future, no new areas 
for this species are added to it’s distribution 
range. SYCCG The species distribution 
range will shrink toward the center and 
mountainous areas. 
High priority areas for long-term 
conservation of SYCCG in climate change 
context include Chu Yang Sin NP, Bidoup - 
Nui Ba NP, Ta Dung NR, Nam Kar NR, 
Nam Nung, NR and Pnom Namlear 
Wildlife Sanctuary. 
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ỨNG DỤNG MÔ HÌNH MAXENT ĐỂ ĐÁNH GIÁ ẢNH HƯỞNG CỦA 
BIẾN ĐỔI KHÍ HẬU ĐẾN VÙNG PHÂN BỐ CỦA LOÀI VƯỢN MÁ VÀNG 
PHÍA NAM (Nomascus gabriellae) 
Vũ Tiến Thịnh1, Trần Văn Dũng2, Lưu Quang Vinh
3, Tạ Tuyết Nga4 
1,2,3,4Trường Đại học Lâm nghiệp 
TÓM TẮT 
Biến đổi khí hậu đang có nhiều tác động tiêu cực tới các loài động vật hoang dã, trong đó có ảnh hưởng đến 
vùng phân bố của chúng. Các loài có vùng phân bố hẹp thường bị ảnh hưởng bởi biến đổi khí hậu nặng nề hơn 
so với các loài có vùng phân bố rộng. Trong nghiên cứu này, chúng tôi đã sử dụng mô hình ổ sinh thái (phần 
mềm MaxEnt), cùng với dữ liệu về sự có mặt của loài và các biến khí hậu để đánh giá ảnh hưởng của biến đổi 
khí hậu đến loài Vượn má vàng phía Nam (Nomascus gabriellae), một loài linh trưởng đặc hữu, quý hiếm của 
Việt Nam và Campuchia. Các dữ liệu về khí hậu được sử dụng bao gồm thời điểm hiện tại và hai thời điểm 
trong tương lai (2050 và 2070). Hai kịch bản khí hậu RCP4.5 và RCP8.5 cùng với ba mô hình khí hậu 
ACCESS1 - 0; GFDL - CM3 và MPI - ESM - LR được sử dụng để chạy mô hình. Kết quả cho thấy, vùng phân 
bố của loài Vượn má vàng phía Nam bị giảm mạnh bởi biến đổi khí hậu. Nhiều vùng phân bố thích hợp bị biến 
mất, đặc biệt là các diện tích có mức độ thích hợp cao và rất cao. Các vùng phân bố thích hợp còn lại có xu 
hướng dịch chuyển về phía trung tâm và các khu vực có núi cao hơn. Đồng thời, chúng tôi cũng đánh giá mức 
độ ưu tiên của các khu rừng đặc dụng trong bảo tồn loài vượn dưới ảnh hưởng của Biến đổi khí hậu. 
Từ khóa: Biến đổi khí hậu, Maxent, Nomascus, ổ sinh thái, Vượn. 
Received : 07/01/2018 
Revised : 27/3/2018 
Accepted : 03/4/2018