POLISH JOURNAL OF ECOLOGY
(Pol. J. Ecol.)
60
4
827–837
2012
Regular research paper
Ji JIAN 1,2, Hong JIANG 1,3*, Zishan JIANG 1, Guomo ZHOU 3, Shuquan YU 3,
Shaoling PENG 4, Shirong LIU 5, Shaoyin LIU 6, Jinxi WANG 6
1
International Institute for Earth System Science, Nanjing University, Hankou Road 22, Nanjing 210093, China
2
Institute of RS and GIS, Chengdu University of Technology, Chengdu, 610059, China
3
International Research Center of Spatial Ecology and Ecosystem Ecology, Zhejiang Forestry University,
Hangzhou, 311300, China
4
State Key Laboratory of Biocontrol, School of Life Science, Sun Yat-Sen University, Guangzhou 510275,
Guangdong, China
5
The Institute of Forest Ecology, Environment and Protection, Chinese Academy of Forestry, Beijing, 100091, China
6
Sichuan Academy of Forestry, Chengdu, 610081, China
*e-mail: [email protected] (corresponding author)
EVALUATING THE HABITAT QUALITY OF GIANT
PANDA (AILUROPODA MELANOLEUCA)
USING HABITAT SUITABILITY INDEX (HSI)
ABSTRACT: Habitat quality for many wildlife populations has a spatial component related to
the arrangement of habitat elements across large
geographic areas. With remote sensing and GIS
technology, this paper presents an approach to
calculate Habitat Suitability Index (HSI) for Giant
Pandas to evaluate the habitat quality. In this paper,
a buffer of a given distance (30 km or more) to the
Giant Panda distribution area estimated in three
national surveys (1974, 1989 and 2002), which is
located in Sichuan, Gansu and Shanxi provinces in
western China, was used as the study area. In order
to study different species group’s habitat quality,
the study area is divided into five parts: the Qinling mountain systems, located in the southeast in
Shanxi province, the Minshan mountain systems,
located in the south in Gansu province and northwest in Sichuan province, the Qionglai mountain
systems, the Xiangling mountain systems and the
Liangshan mountain systems, located in the west
of Sichuan province, conforming to the five big
Giant Panda species groups. Three physical environmental factors (elevation, slope and aspect),
one ecological factor (vegetation distribution)
and several human-influence factors (distances
to highways, general roads, inhabitants and rural
areas) are selected as the influence factors to cal-
journal 32.indb 827
culate HSI. Each factor was reclassified by grid-cell
(30 × 30 m per cell) to the suitability index scale
from 0 to 1 based on habitat affinities before final
calculation. After analyzing the HSI values on the
most Giant Panda distribution area, 0.0144 was
considered as the threshold habitat quality. Then,
HSI was calculated for five mountain systems for
three periods conforming to three national surveys (1974, 1989 and 2002). Several benefits to
the approach can be highlighted. Firstly, HSI can
be used as the standard to evaluate the quality of
Giant Panda habitat. Secondly, by using HSI maps
from 1974, 1989 and 2002, we can see that the
Giant Panda habitat was the largest in 1974, and
was then reduced much before 1989. However, by
2002, it had recovered to some extent, which conforms to the habitat data from the three national
surveys. Thirdly, the habitat changes in the five
mountain systems examined in the study are different. Finally, nature reserves play an important
role in the protection of Giant Panda habitat; there
are more suitable habitats in nature reserves than
non-protected areas.
KEY WORDS: Habitat Suitability Index
(HSI), Giant Panda, remote sensing, wildlife habitat
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Ji Jian et al.
828
1. INTRODUCTION
Effective conservation of threatened species requires knowledge of distribution and
an understanding of the ecological factors
determining habitat suitability (Gray et al.
2009). A Habitat Suitability Index (HSI) is
such a conceptual model that relates measurable environmental variables to the suitability
of a site for a species (Bro oks 1997, Vinag re
et al. 2006). HSI values are combinations of
environmental variables, each represented by
a suitability index (SI) and typically scaled
from 0 (unsuitable habitat) to 1 (suitable habitat). HSI models are widely used in North
America and allow wildlife to be represented
with other natural resource information by
recording or predicting the response of a species to its surroundings. Various factors, aside
from habitat, may determine animal presence
or abundance. Measures of habitat suitability
may include only a limited number of factors
that determine population levels. Thus, HSI
models attempt to quantify habitat quality using habitat attributes considered important to
the wildlife species (Rüger et al. 2005).
The Giant Panda (Ailuropoda melanoleuca, named by Armand David, literally meaning “cat-foot black-and-white”), a bear native
to central-western and south western China
(Wi k i m e d i a - c ont r ibutors 2010-02-09),
is a peculiar and endangered animal. It is
the mark of the World Wildlife Foundation
(WWF) and is considered a national treasure
in China. Since the 1950s, it has been designated as top priority for species protection in
China. The Giant Panda habitat was widely
distributed in southwestern China, including
Hunan, Hubei, Sichuan, Shaanxi and Gansu
provinces in the 16th to 19th centuries (Z hu
and L ong 1983). But today, with the human
population increase, agriculture expansion
and transportation development, Giant Pandas (with a total number of 1596) only live
in the Qinling mountain systems (with a
total number of 275 on the area 3529 km2),
located in the southeast in Shanxi province,
the Minshan mountain systems (with a total
number of 708 on the area 9603 km2), located
in the south in Gansu province and northwest
in Sichuan province, the Qionglai mountain
systems (with a total number of 437 on the
area 6101 km2), the Xiangling mountain sys-
journal 32.indb 828
tems (with a total number of 61 on the area
1602 km2) and the Liangshan mountain systems (with a total number of 115 on the area
2204 km2), located in the west of Sichuan province (Nat iona l-Forest - Minist r y 2006).
Comparing the data from the third survey
(1999–2003) with the data from the second
survey (1985–1988), which were carried out
by State Forestry Administration, (Nat i ona l
Forest Mi n ist r y 1989; Nat i ona l Forest
Mi n ist r y 2006), we can find that the panda
distribution area expanded from 11 counties
to 45 counties. Between these two surveys,
the panda habitat also expanded by 43% from
13922 km2 to 23050 km2 and the panda population increased by 66% from 1114 to 1596
km2 in 2002. The continuing decline in the
panda population and habitat area has been
preliminary curbed, and the survival environment is improving (Nat i ona l Fore st
Mi n ist r y 2006). Because of the large human
population, recessive economy and poor facilities in the distribution area of the pandas,
there remain many difficulties, problems and
challenges to overcome and resolve in the
process of panda protection in China. Maintaining and promoting panda habitat quality
is the only way to go. Panda habitat quality
is the degree of amenity of panda’s surroundings, which can be measured by panda habitat
suitability. The HSI is therefore an excellent
model to evaluate panda habitat quality.
By using the existing and potential panda habitats as the study area, along with the
Landsat ETM data contemporary with the
third national survey and the data from the
third survey used as the main data source,
this paper presents a HSI model to map the
habitat quality in the study area. It provides a
detailed view on the habitat quality and some
new methods and ideas for habitat protection. This paper is significant for three reasons. First, it provides a scientific basis for
strengthening the protection of existing nature reserves. Though many scholars have
thoroughly studied the panda habitat, nobody has made a systematic analysis on the
changes of panda habitat (L iu et al. 1999, An
et al. 2001, G ong et al. 2005, L i et al. 2005,
L i and Z ha o 2005). Therefore, the changing
dynamics of panda habitat remains unclear
and the panda habitat quality is in dispute.
Second, this paper is a guide to adjust and op-
2012-12-14 08:26:43
Evaluating the habitat of Giant Panda using HSI
timize the existing nature reserves. Some nature reserves are too small to be effective and
some adjacent nature reserves, due to separate local administration, are divided into
several parts which degrades their function.
Third, this paper provides a scientific basis
for establishing new nature reserves. The new
nature reserves can be established in the areas
with a relative bigger HSI.
2. STUDY AREA
In this paper, a buffer of a given distance
(30 km or more) to the Giant Panda distribution area from the three national surveys
is used as the study area (shown in Fig. 1).
The study area, which is located between
27°52´N–34°18´N and 101°19´E–109°10´E,
covers all the distribution points of the four
Giant Panda species groups, including three
counties of Gansu Province, five counties of
Shanxi Province, one county of Yunnan Province and more than thirty counties of Sichuan
Province.
The study area is divided into five parts
conforming to the five species groups: the
Qinling mountain systems (1), the Minshan
mountain systems (2), the Qionglai mountain
829
systems (3), the Xiangling mountain systems
(4) and the Liangshan mountain systems (5),
shown as Fig. 1.
The study area is an expansion of the
existing and historic habitat of the Giant
Panda. It is located in the transition from the
Sichuan basin to the Qinghai-Tibet Plateau,
characterized by a mountain ravine area and
a sparse human population. This area is suitable for the survival of the Giant Panda physiologically. Thus, we can study the existing
panda habitats, the historical habitat, and the
potential habitat, to provide a scientific basis
for the expansion of Giant Panda habitat.
3. METHODS
3.1. Data collection
Landsat ETM data from 2002 of the study
area, together with the Digital Elevation
Model (DEM), road network maps, panda
location, panda habitat area and settlement
place of study area from the three national
surveys data, which were carried out by
State Forestry Administration, (Nat i ona l
Forest Mi n ist r y 1989; Nat i ona l Fore st
Mi n ist r y 2006), are collected to study the
1
2
3
4
5
Fig. 1. The location of study area, the Giant Panda distribution area surveyed in years 1974–2002 in
China and the five mountain systems (1 – Qinling mountain systems, 2 – Minshan mountain systems,
3 – Qionglai mountain systems, 4 – Xiangling mountain systems, 5 – Liangshan mountain systems).
journal 32.indb 829
2012-12-14 08:26:43
Ji Jian et al.
830
habitat quality of Giant Panda. Also, Landsat
MSS data from 1975 and Landsat TM data
from 1989 are collected to validate the habitat quality of Giant Panda.
3.2. Selection of Panda habitat variables
According to the publications on the Giant Panda’s ecology and behavioral ecology,
the three national surveys on Giant Panda
population and habitat (Nat iona l Forest
Minist r y 2006), and the analysis of the habitat characteristics (An et al. 2001, Zhang et
al. 2004, L i and Zhao 2005), the influencing
factors of Giant Panda’s habitat can be classified into three types: physical environment
factors, biological factors and human-influence factors (Peng et al. 2005, Yang et al.
2006, Zhang et al. 2006, Pasher et al. 2007,
Zhang et al. 2007, Zhang et al. 2008).
(1) Physical Environmental Factors: physical environmental factors include elevation,
geomorphic type, slope, etc. According to the
data from the three national surveys, almost
all the Giant Pandas lived in the area with the
elevation from 1200 meters to 3800 meters
with a slope less than 30 degrees (Nat i ona l
Fore st Minist r y 2006, R an et al. 2006).
(2) Biological Factors: biological factors
include vegetation type, edulis bamboo (Fargesia or Bashania) distribution and richness,
natural enemy distribution, etc. For Giant
Panda, mountainous coniferous forest is the
best habitat, broadleaf forest and shrubby
are also suitable. In this paper, the vegetation
types used to evaluate habitat are retrieved
from Landsat remotely sensed data. The information on the distribution and richness of
edulis bamboo came from the three surveys
on the Giant Panda’s population and habitat.
(3) Human Influence Factors: human influence factors include logging, transportation, rural activity and daily life of local inhabitants. These activities either destroy the
Giant Panda’s habitat or lead to a lower habitat quality.
3.3. HSI calculation
HSI models typically evaluate habitat
quality by using weighted linear combinations and factors considered to be important
to the wildlife species (Bro oks 1997). Then
journal 32.indb 830
the HSI model is scaled so that the HSI values
range from 0 for unsuitable habitat to 1 for
optimal habitat.
In this paper, we select elevation, slope
and aspect as the physical environmental factors, vegetation distribution as the ecological
factors and distances to highways, general
roads, inhabitants and rural areas as the human-influence factors to calculate HSI. Each
factor was reclassified by grid-cell to the suitability index scale (e.g. elevation in meters
was converted to a 0–1 suitability scale) based
on habitat affinities of panda derived from
published sources on panda biology as well
as on the survey data from the three national
surveys. The evaluation standards of physical
environmental factors and ecological factors
are listed in Table 1. Suitable habitat was assigned to 1, less suitable was assigned to 0.6
and unsuitable was assigned to 0.1. The distribution of edulis bamboo was taken as a
reference factor for the survey area. Because
of human influence factors, four levels were
classified according to the distance to the activity center. The evaluation standards of human influence factors are listed in Table 2, no
influence was assigned to 1, weak influence
was assigned to 0.7, middle influence was
assigned to 0.4 and strong influence was assigned to 0.1.
Thus, we can get eight suitability indices
for various factors:
SIele=B1100-1800+0.6*B1100-1500,2800-3800+0.1*BLT1100,GT3800
SIslope=BLT30+0.6*B30-45+0.1*BGT45
SIaspect=Bsouthest, south, southwest+0.6*Bothers
SIveg=Bconiferous+0.6*Bshrubby, broad leaved+0.1*Bothers
SIhighway=BGT720+0.7*B300-720+0.4*B180-300+0.1*BLT180
SIroad=BrGT720+0.7*B210-720+0.4*B60-210+0.1*BLT60
SIinhabitants=BGT1920+0.7*B1410-1920+0.4*B900-1410+0.1*BLT900
SIrural=BGT750+0.7*B240-750+0.4*B90-240+0.1*BLT90
(1)
(2)
(3)
(4)
(5)
(6)
(7)
(8)
where: B represents “binary,” which
means each degree is turned into binary before calculating the SI.
As all factors are the contributors to HSI,
then, HSI for Giant Panda can be calculated as:
HSI= SIele * SIslope * SIaspect * SIveg * SIhighway * SIroad * SIinhabitants * SIrural (9)
The spatial processing of the suitability index values was calculated by grid cell
2012-12-14 08:26:43
Evaluating the habitat of Giant Panda using HSI
831
Table 1. Evaluation standards of physical environmental factors and ecological factors used in Habitat
Suitability Index (HSI).
Influence types
elevation(m)
Physical environslope(degree)
mental factors
aspect
Ecological factors
vegetation
bamboo
Suitable
Less suitable
1100–2800
1100–1500, 2800–3800
<30
30–45
south-east, south, west, north-west, north, north-east,
south-west
east
coniferous forest
broad leaved forest and shrubby
edulis bamboo
-
Unsuitable
<1100,>3800
>45
–
others
no bamboo
Table 2. Evaluation standards of human-influence factors used in Habitat Suitability Index (HSI).
human influence types
Distance to national highway and
province highway (m)
Distance to general road (m)
Distance to inhabitants (m)
Distance to rural area (m)
Strong influence
Middle influence
Weak influence
No influence
<180
180–300
300–720
>720
<60
<900
<90
60–210
900–1410
90–240
210–720
1410-1920
240-750
>720
>1920
>750
(30 × 30 m per cell) overlaying the environmental maps. This resulted in a map of the
composite Habitat Suitability Index value.
The calculation process is shown in Fig. 2.
4. RESULTS
4.1. Determination of the threshold value
The Giant Panda is a non-social animal
(Wang 2001, Zhang et al. 2004). Except for
the rut and the vertical migration season, its
movement distance is limited and relatively
stable within a given period. Thus, there is little probability that a panda will appear outside
a certain circle (always 2 km) around each
active trace spot within a given period (Nat iona l Forest Minist r y 1989, Nat iona l
Forest Minist r y 2006). From the third national survey data, Sichuan province has the
largest Giant Panda population and habitat,
about 75% and 77%, respectively. Thus, the
locations of Giant Pandas in Sichuan province
from the third national survey data are used to
get the threshold value. In practice, we buffer
every location within 5 km to create the actual
territory map for all the Giant Pandas (Wang
2001, Nat iona l Forest Minist r y 2006).
Then the map is used to overlay the HSI map
of the study area to create a HSI map for the
actual territory (Fig. 3). Supposing that 80%
of the area is suitable for the Giant Panda, a
threshold value of 0.0144 was got (Fig. 4; Nat iona l Forest Minist r y 2006). Therefore,
a location with a HSI value larger than 0.0144
is considered being suitable for Giant Pandas.
Fig. 2. HSI Calculation diagram.
journal 32.indb 831
2012-12-14 08:26:43
Ji Jian et al.
832
than that in 1974. This also corresponds to
the panda population data from the three national surveys.
4.3. HSI map for five mountain systems
Fig 3. HSI map in Minshan, Qionglai, Xiangling
and Liangshan mountain systems’s panda distribution area.
4.2. HSI map for study area
With the HSI calculation model listed
in section 2.4, HSI maps of the study areas
in 1974, 1989 and 2002 are created (Fig. 5).
The distribution plots for the three maps
are shown in Fig. 6. We can see that the areas with HSI less than 0.0144 are 22% of the
whole study area in 1974 (about 32338 km2),
23% in 1989 (about 34300 km2) and 23% in
2002 (about 33925 km2). This means that the
area suitable for the Giant Panda in 1974 is
larger than that in 2002, and the area in 2002
is larger than that in 1989, which corresponds
to the data from the three national surveys.
From Fig. 6, we can see that the plot of 1989
is the highest, followed by that of 2002, and
then 1974. This means that the quality of panda habitat in 2002 is better than that in 1989,
but is worse than that in 1974. From 1974 to
1989, the quality had declined sharply due to
deforestation and road construction. In 2002,
it recovered to some extent, but was still less
journal 32.indb 832
Due to the expansion of human activities,
the Giant Panda habitat is divided into several
parts (mountain systems of Qinling, Minshan,
Qionglaishan, Xiangling and Liangshan) and
each part has its own level of development and
protection. Thus, in order to protect and expand the habitat with corresponding actions, it
is necessary to evaluate the quality of each part.
Table 3 shows the HSI statistical results for all
parts in 1974, 1989 and 2002. Fig. 7 presents
the HSI maps in 1974, 1989 and 2002, respectively. The table and figures indicate that few
changes have taken place in Minshan, Qionglai and Qinling mountain systems, although
they have experienced a decline and then
a steady improvement. Liangshan mountain
system has a similar situation, but the change
is much more prominent, which show serious
disturbance in this area. Xiangling mountain
system is also improving steadily.
The situations of all the mountain systems at the time of 2002 can be retrieved
from Table 3. The average HSI in the Minshan mountain systems is 0.0649, which is
lower than the study area average. However,
the areas with a HSI larger than 0.0144 cover
52% of the whole Minshan mountain system
and have an average HSI of 0.123. The average HSI in Qinling is 0.131, which is still lower
than the study area average. The areas with a
HSI above 0.0144 occupy 64% of the whole,
with an average HSI of 0.202. The average HSI
in Qionglai is 0.131, which is lower than the
study area average. The area with HSI above
0.0144 covers 64% of the whole system, with
an average HSI of 0.211. The average HSI in
Xiangling is 0.059, which is lower than the
study area average. The area with HSI above
0.0144 includes 38% of the whole system, with
an average HSI of 0.152. The average HSI in
Liangshan is 0.103, which is lower than the
study area average, but the areas with HSI
above 0.0144 occupy 51% of the whole system
and have an average HSI of 0.199.
With these statistical results, the quality
of all mountain systems can be evaluated with
the panda population density calculated by
2012-12-14 08:26:44
Evaluating the habitat of Giant Panda using HSI
833
Fig. 4. Plot of HSI in Minshan, Qionglai, Xiangling and Liangshan mountain systems’ panda distribution area.
Fig. 5. Calculated HSI-map for study area (A – 1974, B – 1989, C – 2002).
Fig. 6. Plot of calculated HSI in 1974, 1989 and 2002 for the study area.
journal 32.indb 833
2012-12-14 08:26:44
Ji Jian et al.
834
kernel density estimation method from the
panda distribution (Wang 2001, Nat i ona l
Forest Minist r y 2006). Qinling and Qionglaishan mountain systems have the best
quality and the largest percentage of habitat,
hence the highest population density (with
0.078 per km2 and 0.072 km2 from the third
national survey data, respectively). Xiangling
mountain system bears the worst quality and
the smallest percentage of habitat. While Liangshan mountain systems hold a medium
percentage, it has the smallest area among
the five mountain systems, hence the smallest
area for the pandas. Thus, both systems have
a relatively low density (with 0.040 per km2
and 0.052 per km2 from the third national
survey data, respectively). Minshan mountain system has a different situation. It has
been divided into two species groups by the
highways. Moreover, it has the largest human
population and has a relatively low HSI. However, due to its large area, Minshan mountain
system provides more suitable habitat for the
pandas leading to a relatively high density
(with 0.074 per km2 from the third national
survey data) (Nat ional Forest Ministr y
2006). Fig. 7C indicates that Qinliang and
Qionglaishan mountain systems have the best
quality habitat in 2002. Xiangling mountain
system has the worst quality habitat, demanding special protection. Liangshan mountain
system has a medium quality habitat, but due
to the smallest area, special protection is still
needed. Minshan mountain system has the
largest habitat, but Fig. 7C indicates that the
panda habitat is being depleted due to the
fast development of the economy. Therefore,
habitat protection in this area requires much
attention.
4.4. HSI map for nature reserves
Nature reserves are the primary base for
biodiversity protection (Wang 2001, Nat i ona l Fore st Mi n ist r y 2006). They play
a significant role in maintaining biological
function, strengthening the regional eco-capacity and sustainable development capacity,
encouraging international communication
and scientific research, and implementing
the scientific development outlook comprehensively (National Forest Ministry 2006).
As the Giant Panda is a protected species,
panda habitat protection becomes the main
concern of the nature reserves where pandas
are distributed (Nat ional Forest Minist r y 2006). These nature reserves also become
the major base for panda protection. Thus,
it is necessary to evaluate the quality of the
nature reserves where pandas are distributed.
The nature reserves in Sichuan province were
selected to evaluate because this province has
the most natural reserves, the widest distribution and the most variety.
Compared with other areas, nature reserves have a higher percentage of suitable
habitats. Wolong Nature Reserve, as an example, has a suitable habitat percentage in the
reserve (83% in 2002), but the percentage of
the whole Qionglai mountain system, where it
is located, only reaches 63%. Xiaozaizigou and
Huanglong Nature Reserves have a relative
small percentage of suitable habitat (63% and
57%, respectively), while the percentage only
reaches 52% for the whole Minshan mountain
system where they are located. The establishment of the two nature reserves is relatively
late (Nat iona l Forest Minist r y 2006).
Thus, we can infer that nature reserves play
an essential role in the protection of pandas.
Table 3. Statistics (HSI Avg.– average value of HIS; HSI>0.0144 (%) – the percentage of the area covered
with HSI greater than 0.0144; HSI Avg. (>0.0144) – it’s average value of HSI) for five mountain systems
(see Fig. 1) years 1974, 1989 and 2002 (with threshold value = 0.0144).
Mountain
systems
Minshan
Qinling
Qionglai
Xiangling
Liangshan
journal 32.indb 834
HSI
Avg.
0.07
0.16
0.14
0.06
0.09
1974
HSI>0.0144 HSI Avg.
(%)
(>0.0144)
53.57
0.12
64.63
0.24
62.42
0.22
35.95
0.15
55.08
0.16
HSI
Avg.
0.07
0.17
0.13
0.06
0.09
1989
HSI>0.0144 HSI Avg.
(%)
(>0.0144)
51.27
0.12
63.73
0.26
61.43
0.20
36.62
0.15
49.91
0.18
HSI
Avg.
0.06
0.13
0.13
0.06
0.10
2002
HSI>0.0144 HSI Avg.
(%)
(>0.0144)
52.41
0.12
64.01
0.20
63.43
0.21
38.03
0.15
51.02
0.20
2012-12-14 08:26:44
Evaluating the habitat of Giant Panda using HSI
835
Fig. 7. Plots of calculated HSI for the five mountain systems in 1974, 1989 and 2002 (A – 1974, B – 1989,
C – 2002. Five mountain systems locations see Fig.1).
5. DISCUSSION AND CONCLUSIONS
5.1. Changing tendency of the
panda habitat quality
Intuitively, large-bodied species, assumed
to have the greatest spatial requirements,
should be most prone to suffering the effects
of habitat loss (Ho cke y and Cu r t is 2009).
Thus, to make clear of the changing tendency of the panda habitat quality is important
to protect panda. With the HSI calculated
above, the changing tendency of the panda
habitat quality can be deduced: the panda
journal 32.indb 835
habitat quality hits an all-time high in 1974
(with the largest area and best quality), and
then declines in 1989 (with a smaller area and
worse quality), but has started to rebound
steadily since then. Though the rise has not
reached the highest point in 1974, the ascending trend continues due to the national
policies and the contribution of local people
in the later years. This changing tendency
corresponds to the data from three national
surveys and other published sources. However, the habitat changes vary among the five
mountain systems. In Qionglai and Qinling
mountain systems, the change was steady and
2012-12-14 08:26:44
Ji Jian et al.
836
minor. The situation in Minshan mountain
system fluctuated during the period 1974 to
2002, while Liangshan and Xiangling mountain systems suffered a major change, which
also corresponds to the data from the three
national surveys and other literature. Thus,
the HSI can be used as an essential parameter to evaluate the quality of panda habitat
(K liske y et al. 1999). Meanwhile, because
the land cover status is retrieved from remote
sensing data, the human-induced factor,
which was used to be the main factor impacting the evaluated result, is reduced to a controlled level. From the calculation process of
HSI, we can see that the major driving factors
of habitat change are land cover and elevation. Highways and roads are the second major driving factors of habitat change because
they cause fragmentation of the habitat (Na t i ona l Forest Minist r y 2006).
5.2. Role of nature reserves in quality
assurance of Giant Panda habitat
There are 40 nature reserves in China that
function to protect Giant Panda. These nature reserves cover 45% of the area of the total panda distribution area, and contain 62%
of the total panda population. This means
that nature reserves are playing an essential
role in panda habitat protection, not as that
habitats had become worse because of the establishment of the nature reserve (L iu et al.
2001). Using the HSI maps from 1974, 1989
and 2002, we can see that the nature reserves
have a higher percentage of habitats suitable
for pandas than that of other areas. Though
the quality and quantity of Giant Panda habitat have recovered in recent years, in the view
of species group protection, the Giant Panda
habitat needs to be further expanded. Thus,
HSI can be used to evaluate the quality of
panda habitat as well as to provide scientificbased information to expand existing nature
reserves and support the foundation of new
nature reserves.
5.3. Merits and deficiencies of HSI
HSI models are based on the assumptions that a species will select and use areas
that are best able to satisfy its life requisites,
and consequently, greater use will occur in
journal 32.indb 836
higher quality habitat (Bro ok s 1997, Ray
and Burg man 2006). Thus, HSI models
have their own limitations. Limitations of
HSI models include the lack of linearity between wildlife density and individual habitat
parameters, the inferiority of simple indices
to those based on multivariate analysis, the
variability of habitat use regardless of life
stage or season, and inadequacy of a species’
observed density as an indicator of habitat
quality (Bro ok s 1997, K l iske y et al. 1999,
Vi nag re et al. 2006, Vi ncen z i et al. 2007).
In addition to the above limitations, the
HSI model presented in this paper has its own
limitations. First, HSI maps are unable to indicate the fragmentation of habitats. In future
research, HSI should be combined with landscape features to evaluate the fragmentation
of the habitats. Second, others factors, which
may have some impact on the quality of habitats, such as logging, harvesting herbs, picking
bamboo shoots, and bamboo flowering, have
not been taken into account. Third, the distribution of panda-edible bamboo has not been
taken into consideration, yet it is a crucial factor for the distribution of pandas. In the future,
distribution and changes of edible bamboos for
pandas will be combined with HSI to improve
the precision of the panda habitat evaluation.
ACKNOWLEDGMENTS: Funding support partially from The State Key Fundamental
Science Funds of China (No. 2002CB111504,
2002CB410811, 2005CB422208), The State Data
Synthesis and Analysis Funds of China (No.
2006DKA32300-08), and NSF-China Project
(40671132).
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Received after revision July 2011
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