Animal Cells and Systems

ISSN: 1976-8354 (Print) 2151-2485 (Online) Journal homepage: http://www.tandfonline.com/loi/tacs20

Developing and testing a habitat suitability
index model for Korean water deer (Hydropotes
inermis argyropus) and its potential for landscapemanagement decisions in Korea
Jihyang Jung, Yo Shimizu, Kenji Omasa, Sungsu Kim & Sangdon Lee
To cite this article: Jihyang Jung, Yo Shimizu, Kenji Omasa, Sungsu Kim & Sangdon Lee (2016)
Developing and testing a habitat suitability index model for Korean water deer (Hydropotes
inermis argyropus) and its potential for landscape management decisions in Korea, Animal
Cells and Systems, 20:4, 218-227, DOI: 10.1080/19768354.2016.1210228
To link to this article: http://dx.doi.org/10.1080/19768354.2016.1210228

Published online: 21 Jul 2016.

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ANIMAL CELLS AND SYSTEMS, 2016VOL. 20, NO. 4, 218–227
http://dx.doi.org/10.1080/19768354.2016.1210228

Developing and testing a habitat suitability index model for Korean water deer
(Hydropotes inermis argyropus) and its potential for landscape management
decisions in Korea
Jihyang Junga, Yo Shimizub, Kenji Omasab, Sungsu Kimc and Sangdon Leed
Research Institute for Energy, Environment & Economy, Kyungpook National University, Daegu, South Korea; bDepartment of Biological andEnvironmental Engineering, Graduate School of Agriculture and Life Science, The University of Tokyo, Tokyo, Japan; cSchool of Business
Administration, Kyungpook National University, Daegu, South Korea; dDepartment of Environmental Science and Engineering, College ofEngineering, Ewha Womans University, Seoul, South Korea

ECOLOGY, POPULATIONBIOLOGY & ANIMALBEHAVIOR

Downloaded by [University of Tokyo] at 18:06 07 August 2016a

ABSTRACTARTICLE HISTORY

Geographic information system (GIS) and landscape-level data offer a new opportunity formodeling and evaluating the quality of wildlife habitats. Models of habitat quality have not beendeveloped for some species, and existing models could be improved by incorporating updated
information on wildlife–habitat relationships and habitat variables. We developed a GIS-based
habitat suitability index (HSI) model for the Korean water deer (Hydropotes inermis argyropus),which often causes human–wildlife conﬂicts in the Chungnam Province of Korea because ofindustrialization and urbanization. The model is based on logistic regression analysis, which
addresses the impact of multiple habitat variables, such as habitat components, topographic
characteristics, and human disturbances. The model yielded a p-value of .289 (χ 2 = 9.672) and
65.4% correct prediction level with the overall observation–prediction comparison data. The
model demonstrated that a large portion of the province (61.6%) could be regarded as a poor
habitat (mean HSI value of the province = 0.22), while the current habitats of the province could
be considered of moderate quality (mean HSI value = 0.31). In addition, the chance ofobservation of the deer increases as the HSI level increases, which means that the model yields a
good predictive power. Lastly, we used the model to produce a habitat suitability map. Our HSI
model enabled us to quantify habitat preferences, which could be the basis for decision-making
on habitat protection, mitigation, and enhancement of the Korean water deer. The proposedmodel is also applicable for improving and enhancing the existing management practices, aswell as for establishing an effective wildlife protection policy.

Received 22 July 2015
Revised 9 June 2016
Accepted 24 June 2016

Introduction
Damage by wildlife to ﬁeld crops is a widespread
concern across South Korea, and the assessment andcontrol of this damage to agricultural produce havebecome important components of wildlife management.
Losses caused by wildlife to agriculture have beenreported particularly with respect to small grains, corn,
vegetables, tree fruits, forest regeneration, and landscape planting. According to nationwide surveys conducted between 2005 and 2009, wildlife-related
economic losses in the agriculture industry in South
Korea exceed 99.5 million dollars (Cho 2010). Wild boar
(Sus scrofa), Korean water deer (Hydropotes inermis argyropus), and Korean magpie (Pica pica) are often considered to be the most common wildlife species thatroutinely damage agricultural crops (Han et al. 2015). In
particular, damage by deer to farm produce creates aserious and potentially confrontational situation
CONTACT Sangdon Lee

lsd@ewha.ac.kr, leesd02@gmail.com© 2016 Korean Society for Integrative Biology

KEYWORDS

Geographic information
system; habitat suitability
index model; Korean water
deer (Hydropotes inermis
argyropus); logistic regression
analysis; wildlife
management

between the agricultural community and environmentalorganizations.
The water deer (Hydropotes inermis) is evidently an‘edge’ species, and it prefers habitats characterizedby shrubs and small trees. It is also found in lowland
habitats, speciﬁcally along river shores, streams, andcoastal areas with reeds and tall grasses, as well as inpaddy areas, grasslands on hills, and areas near lakes(Rhim & Lee 2007). The deer is relatively small,ranging in length from 775 to 1000 mm. Both males
and females do not have antlers. The males havelong upper tusks, which can reach up to 5 cm inlength. The adults are relatively small (9−14 kg) andhave restricted home ranges (10−25 ha). The water
deer is mainly solitary, although stable pairs andeven small groups are sometimes observed. The populations have the capacity to increase quickly, as adultfemales begin to reproduce within the ﬁrst year andDownloaded by [University of Tokyo] at 18:06 07 August 2016

ANIMAL CELLS AND SYSTEMS

can produce 2−5 fawns per litter (Cooke & Farrell 1983;
Zhang et al. 2006).
There are two distinct subspecies of the water deer in
East Asia: one in China (Hydropotes inermis inermis) andthe other in Korea (H. i. argyropus). The Chinese water
deer is on the IUCN Red List, and it is classiﬁed as ‘Vulnerable’ on a world scale; thus, its population density has
become a critical issue from the perspective of conservation (IUCN 2001). A similar threat exists for the Koreanwater deer in certain regions of Korea because of aserious decline in the population due to poaching andhabitat destruction (Woo et al. 1990). However, thenumber of Korean water deer has slightly increased from1982 to 2011, in part because of reforestation programs,
absence of predators, and prohibition of hunting (Kanget al. 2016). The population density of Korean water deer
per 100 ha is 7.8, and it varies from 3.7 to 11 (Han et al.
2015). The Korean water deer population has particularly
ﬂourished in the modern agricultural landscape, where
fragmented mosaic of wood-lots, hedgerows, and cultivated crops seem to be particularly favorable (Kim et al.
2011). Farmers have been appealing to the government
for actions to reduce the deer population; however, thegovernment has not yet responded in the light of wildlife
conservation, highlighting the necessity for implementing
an appropriate species management practice.
To prevent the crop damage caused by the Koreanwater deer, it is essential to understand its ecology, suchas distribution, density, and habitat use. Above all, information on its patterns of habitat use is critical for developing effective management strategies, which would appeal
to land managers because it facilitates the understandingof habitat preferences of the deer. The Korean water deer
live in ﬁelds of tall reeds, rushes along a river, and ﬁelds oftall grasses on mountains and cultivated ﬁelds. They also
inhabit swampy regions and open grasslands. They are
adept at hiding, and any cover seems sufﬁcient for
shelter. When the cultivated ﬁelds that they occupy are
cut, they may be found lying in the furrows and hollows
of open ﬁelds (Brown 1991).
Across different ecoregions, the core components of adeer habitat are consistent: water, food, and cover (Fulbright & Alfonso 2006). Cover is deﬁned as either thermalor hiding cover: two habitat components required bydeer (Deperno et al. 2003). Thermal cover is the vegetation used by animals to lessen the effects ofweather, particularly during winter and summer. Hiding
cover is used for security while foraging and moving
between areas, and it can provide animals with a senseof security when habitat quality is less than the
optimum and vulnerability to harassment and weather
is high. The deer favor forest habitats with over 90%
canopy cover. The most frequently used bedding sites

219

are located in the areas with high canopy cover,whereas feeding sites are often found in the areas withlow canopy cover (0–25%) (Zhang et al. 2006).
The Korean water deer has a four-chamberedstomach, but the rumen pillars are poorly developed;
this prohibits the deer from efﬁciently digesting thecarbohydrates from plant materials and therefore they
select foods low in ﬁber but high in soluble carbohydrates, proteins, and fats. The Korean water deer is ahighly selective feeder, particularly consuming herbs,
forbs, and young sweet grasses rather than the coarser
and more ﬁbrous mature grasses. Locally, it may
browse the tops of root crops in winter when otherfood sources are in short supply (Cooke & Farrell 1983;
Nowak 1991). The Korean water deer drinks free waterfrom ponds, creeks, rivers, springs, and seeps. Besides
free water, it obtains water from the food it consumes,
and it receives an annual average of 1577 mm of precipitation. The Korean water deer prefers to be within 1 km
from open waters (streams, ponds, lakes, etc.), as it uses
such areas for feeding and hiding at night (Brown 1991;
Congalton et al. 1993; Zhang et al. 2006).
Land managers need to understand how a species
will respond to a change in the habitat so that appropriate strategies can be implemented to maintain thespecies population. One approach that has been successful is the habitat suitability index (HSI) model,
which describes large-scale habitat use patterns. HSI
modeling is the process of formulating the suitabilityof a particular habitat on the basis of measurable
habitat variables that affect the growth, survival, abundance, distribution, behavior, or other measures of wellbeing of animals (Brown et al. 2000; Clark et al. 2004).
HSIs are generated through the application of wildlife–habitat relationships to relevant geospatialenvironmental data within a geographic information
system (GIS) to develop a composite HSI score with arange of 0.0–1.0 (representing unsuitable to optimal
habitat) (Roloff & Kernohan 1999). Using informationsuch as the relevance of the selected habitat variables,
availability of geospatial environmental data, andreliability of the applied wildlife–habitat relationships,
the HSI model can serve as a robust spatial tool toassist species management via identifying the key
habitat areas at a large spatial scale (Margules et al.
2002; Rouget et al. 2006). Although the developmentof technologies such as GIS and remote sensing has
allowed scientists to analyze habitat patterns in detail(Li et al. 2000; Ortigosa et al. 2000; Kim et al. 2014),
such indices generally require ﬁeld data to create aneffective model for a species. Many countries, including
Korea, have been trying to develop an effective andwidespread wildlife census; however, the challenge is

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to create HSIs with the ﬁeld data, which can then beleveraged for wildlife management.
The general purpose of this study was to address thefundamental habitat characteristics for the managementof the Korean water deer. Speciﬁcally, the three goals of
the study were to: (1) identify the relevant habitat variables that affect deer distribution, (2) develop an HSI
model, and (3) propose how the model can be used to
guide management decisions. Further, HSI models
enable us to quantify habitat preferences; therefore,
this study can be the groundwork to assist decisionmaking on habitat protection, mitigation, and enhancement for the Korean water deer. In addition, theKorean government would be able to use the model
results to improve and enhance current management
practices for the deer.

Materials and methods
Study area
Chungnam Province (about 8600 km2) is located in the
middle of South Korea (Figure 1 left) in a temperatemonsoon zone characterized by a cold and dry winterand a hot and humid summer. Topographically, the province is ﬂat and low in the west and steep and mountainous in the east, with a maximum elevation of 904 m at
the Gyeryongsan National Park. Forests in the eastern
mountains are composed of coniferous species, while
those in the mountainous areas of the south are composed of mixed deciduous broad-leaved and coniferous
trees (Figure 1 right). A wildlife survey (Han et al. 2015)
reported that the Korean water deer population in theprovince has been constantly increasing since the late
1990s, and the province has the highest deer density(11 deer per 100 ha) in South Korea. Substantial losses
in agricultural revenue have been estimated in Chungnam Province because of crop damage by the deer,
and it has resulted in a rapid increase in agriculturaland environmental conﬂicts.

Habitat variable selection and HSI modeling
To predict the distribution of the water deer in Chungnam Province, we collected existing information on the
habitat preferences of the water deer in Korea andChina, where it occurs naturally, as well as in the
United Kingdom, where it has been introduced. Comprehensive knowledge and understanding of the habitat
requirements of the Korean water deer are necessary
to accurately assess habitat quality and proactively
manage the population through habitat manipulation.
It is also necessary to evaluate the relative inﬂuence ofeach habitat variable on habitat suitability (HS) for effective habitat conservation at a large landscape level(Hirzel & Lay 2008). Because the Korean water deer isknown to require temporally and spatially diverse
habitat components, such as food, water, and cover,
key habitat components were incorporated as habitat
variables into the model.
The Korean water deer is usually observed at places
where two different habitat types meet, for example,
forest and meadow; it thrives in an environment wherea forested land is adjacent to an open area (Won 1967).
Edge areas generally contain more vegetative diversity
than either forested or open areas, and many positive
attributes of both types are available in these relatively
small and readily accessible areas. In other words,
woody cover and food requirements can be fulﬁlled by
the associated forested area, and herbaceous food andcover requirements can be satisﬁed by the associatedopen place.
Water availability is presumably the most criticalfactor for the deer. Wild mammals generally require a
supply of fresh water around their habitat. In particular,
the water deer prefers areas around wetlands or
streams. Although the Chinese water deer has beenreported to prefer areas that are 200–599 m away fromwater sources (Zhang et al. 2006), the Korean water
deer prefers habitats within 1 km from water (Kim et al.
2011).
In addition, topographic characteristics and humandisturbance were considered as habitat variables.
Habitat destruction mainly occurs because of theharvest of natural resources for industrial production
and urbanization. Habitat losses and fragmentation
caused by urban development have become a concern
for the recovery and management of species thatbelong to the family Cervidae (Folk 1991). With anincrease in urban development across the province, it
is important to understand how Korean water deer are
likely to respond to different levels of urbanizationpressure. In this study, the effect of human disturbanceon habitat quality was considered with two factors, thedistance to roads and the distance to urbanized areas.
Topographic features such as slope, elevation, andaspect are known to inﬂuence the habitat selection patterns of the species (Kim et al. 2011; Zhou & Zhang 2011).
In addition, land use was utilized to identify the topographic characteristics of Korean water deer habitats.
Using information on habitat requirements, wedeﬁned a function for each habitat variable. An individual habitat variable was represented by a single suitability index (SI). The Korean water deer commonly
traverses 1000 m in 24 h; therefore, SI values wereassigned within 1000 m. Overall, SI variables based on

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ANIMAL CELLS AND SYSTEMS

221

Figure 1. The study area in Chungnam Province (8598 km2) located 150 km to the south of Seoul, Korea (left). Land use and distribution
map for the Korean water deer in the province (right).

the natural history traits were as follows: SI for the distance to water areas (SIv1), canopy cover (SIv2), distanceto wetlands (SIv3), slope (SIv4), distance to urbanizedareas (SIv5), distance to roads (SIv6), land use (SIv7),
aspect (SIv8), and elevation (SIv9) (Figure 2). The government database was used to establish these SI variables.
The ﬁeld data were collected from 1997 to 2006 as partof the Long Term Ecological Research by the Ministryof Environment in Korea. The ﬁeld data on wildlife
were collected for 30−40 days of each season every
year in the decade. Observation of Korean water deer
was registered at 292 sites, from identiﬁcation of tracks,
droppings, rubbings on trees, and scent markings toother signs of the species’ presence in the province.
The deer were counted at sites in several sections of
the province, and the number at each section was converted into percentage. Then, the SI values were standardized in the range of 0.0–1 by dividing the calculated
percentage value for all the sections by the maximum
percentage, with 0.0 being the lowest suitability and 1
being the highest. The SI values were then classiﬁed
into ﬁve categories of 0.0−0.2, 0.2−0.4, 0.4−0.6, 0.6−0.8, and 0.8−1, which are categorized as poor, moderate, fair, good, and excellent, respectively, according to
the U.S. Fish and Wildlife Service (1981) habitat
description.
We developed the HSI model by using the identiﬁed
habitat variables to predict the distribution of Koreanwater deer habitats at a landscape level, and we tested
the model to validate its application in the province.

The HSI model evaluated habitat quality and classiﬁed
HS in the province, differentiating potentially suitableareas from unsuitable areas. A composite HSI value fora given area was obtained by mathematically combining
individual SI values of the habitat variables to generate acomposite index of HS on a scale of 0.0–1. If the index
was high at a particular location, then the chance ofthe species to occur was high. We used the following
types of spatial data to develop the model: 1/5000
scaled level in the land cover map1; vegetation map1;
digital elevation model1; stream map2; and the Korean
Transport database,3 which includes national and local
roads. Data analysis was performed using ArcView 3.1
and ArcGIS 9 (Environmental Systems Research Institute,
Inc. 2000).

HSI modeling
The HSI model directly incorporated habitat variables(SIvn) regarded as critical in evaluating HS. For HSI modeling, a backward stepwise logistic regression wasapplied (Hosmer & Lemeshow 2000; Menard 2002). All
habitat variables for either analysis of variance or bivariate analysis at a signiﬁcance level of .05 or less were
included in the initial logistic regression model.
The probability that a particular habitat is selected by
the species was assumed to be taken in the form of thelogistic regression model. A binary response variable (y)was deﬁned for each observation, such that y = 1 if
observed and y = 0 if not. It is similar to a linearDownloaded by [University of Tokyo] at 18:06 07 August 2016

222

J. JUNG ET AL.

Figure 2. SI for the habitat variables: Distance to water area (SIv1), canopy cover (SIv2), distance to wetland (SIv3), slope (SIv4), distanceto urbanized area (SIv5), distance to roads (SIv6), land use (SIv7), aspect (SIv8), and elevation (SIv9).

regression model but is suited to a case where thedependent variable is dichotomous. The logistic
regression equation estimates the probability that y = 1
if the sample region is the most suitable habitat, whiley = 0 if not. This predictive probability was regarded as
the HSI value, differentiating potentially suitable areasfrom unsuitable areas in the province. The HSI model
describing the probability of use conditioned on habitats
was deﬁned as follows:P(y = 1|x) =exp (b′ x ),1 + exp (b′ x )(1)

where β′ = (β0, β1, … , βn) is a vector of coefﬁcients relating the probability of use to the habitat covariates via therelationship b′x = b0 + b1 SIv1 + . . . + bn SIvn. The HSI
model is intrinsically bound within the interval from 0
to 1. Model parameters were estimated by maximizing
the log likelihood proposed by Hosmer and Lemeshow(2000). Development of the model was performed
using SPSS ver. 13.
The HSI model was statistically developed by comparing its distribution predictions with the actual distributions of Korean water deer in the province on thebasis of the ﬁeld data (total, 292 observations; 1997–
2006). The low number of observations is due to the nocturnal habits, secretive nature, and small size of thespecies, as well as its tendency to avoid roads andpeople. To conﬁrm the distribution of the SI values, we
divided the province into two parts: Kernel and nonKernel, where ‘Kernel’ is the region around each point
location containing some likelihood of animal presence.
The technique using Kernel is regarded as the mostrobust approach to determine probabilistic distributionsof wildlife (Hooge et al. 1999). The output of the Kernel
home range displayed 75% probability regions, calculatedwith the default smoothing parameter value (h = 1) in the
Animal Movement Extension of Arcview (Rodgers & Carr

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ANIMAL CELLS AND SYSTEMS

223

1998) (Figure 3). The 75% contour had the maximum variation from random space use and was the area mostintensely used by the species. We determined 300
random points outside the 75% Kernel areas and thencreated a square buffer around each of the 292 observation points and 300 random point features (25 haunits). The outputs of the buffering operation for observation and random points were used as habitat andnon-habitat samples, respectively.

number of non-habitats), and correct classiﬁcation rate(the ratio of correctly predicted habitats and non-habitats to the total number of sampling sites) – were estimated from a confusion matrix to assess agreement
between the observed and predicted distributions. A ttest was also conducted to test the differences
between the two groups: actual habitats where thewater deer were observed and non-habitats chosen at
random on the basis of the regression estimates.

HSI model validation

Results

Logistic regression analysis was performed to determine
the best ﬁtting model that effectively described therelationship between the HSI values and a set of
habitat variables. The Hosmer–Lemeshow test was performed to determine the goodness of ﬁt of the model.
Moreover, the model predictive accuracy was validated
using a test habitat and a non-habitat sample data set
based on the kappa statistic (Scott et al. 2002). The classiﬁcation ratio is another method to evaluate the predictive accuracy. To make comparisons between observed
and predicted data, the continuous probability generated by the HSI model should be converted to a binary
one, that is, habitat versus non-habitat. For thispurpose, a threshold was set to 0.5, so that the predictedprobability was reclassiﬁed into habitat (over 0.5 probability) or non-habitat (below 0.5). Parameters for themodel evaluation – sensitivity (the ratio of correctly predicted habitat to the total number of habitats), speciﬁcity(the ratio of correctly predicted non-habitat to the total

The HSI model for Korean water deer
After overlaying habitat variables on the habitats andnon-habitats with 25 ha units, 465 sub-region sample
data (habitat = 180 and non-habitat = 285) wereobtained out of 592 because of some overlaps. We
applied this approach to identify the distribution ofpotential habitats at a higher resolution (25 ha), which
provided us with feasibility to directly map the target
species. Using the stepwise method with 465 sample
data, the signiﬁcant logistic regression model with −2
log-likelihood values (−2LL) of 577.37 and overall accuracy of 65.4% emerged (Table 1). The resulting equation
for the habitat variable data is as follows:
In {Y/(1 − Y)} = 0.268SIv1 + 0.501SIv2+ 1.550SIv3 + 1.988SIv4+ 0.695SIv5 + 1.118SIv6+ 0.011SIv7 − 2.627.

(2)

The predicted value, Y, denotes the probability ofobserving Korean water deer in the area, ranging from
0.0 to 1. In this study, the predicted values were regarded
as HSI values, describing suitability of given habitat bycombining the interactions of all key environmental variables. The signiﬁcance of each habitat variable wasmeasured using the Wald statistic (p < .05). Of all thehabitat variables, SIv3, SIv4, and SIv6 had the largest
impact on the HSI model, indicating that these threecomponents are major contributors to the HSI and particularly effective at predicting Korean water deer occurrence. It also implied that the deer tend to avoid roads
and prefer wetlands and forest areas.

Application of the HSI model to Chungnam
Province
Figure 3. Habitats and non-habitats for the Korean water deer in
Chungnam Province.

The mean HSI value for the province was 0.22, with arange from 0.02 to 0.88. Only 414.91 km2 (4.8% of
the province) had HSI values ranging from 0.6 to 1,

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J. JUNG ET AL.

Table 1. HSI model summary for the Korean water deer.
Hosmer–Lemeshow test

Correct classiﬁcation ratio

Model

−2LL

Cox and Snell
R2

Nagelkerke
R2

Chi-square (χ 2)

df

Sig.

Speciﬁcity

Sensitivity

Overall accuracy

Null model (Only constant)
HSI model

620.711577.367

–
0.089

–
0.121

–
9.672

–
8

–
0.289

–
85.3%

–
33.9%

–
65.4%

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Note: −2LL = −2 log likelihood. HSI model = 0.268SIv1 + 0.501SIv2 + 1.550SIv3 + 1.988SIv4 + 0.695SIv5 + 1.118SIv6 + 0.011SIv7 − 2.627,
where SIv1 = SI for the distance to water area; SIv2 = SI for canopy cover; SIv3 = SI for the distance to wetland; SIv4 = SI for slope; SIv5 = SI for the distance tourbanized area; SIv6 = SI for the distance to roads; and SIv7 = SI for land use.

whereas 748.07 km2 (8.7%) had values from 0.4 to 0.6
and 7467.41 km2 (86.5%) had values from 0.0 to 0.4.
Majority of the studied areas were unsuitable as
Korean water deer habitats; therefore, 61.6% of theprovince could be regarded as a poor habitat (Table2). Most deer were observed in the mountainous
areas, and high HSI values were largely concentrated
in the forest areas (Figure 4); this indicated that theforest provides quality habitats that can offer foodand shelter. This is particularly true for the nationalparks, which have been well managed and provide a
canopy cover of suitable density. The HSI modelyielded signiﬁcantly larger HSI values where the
Korean water deer were actually observed, relative torandomly generated non-habitat areas in the sameprovince (mean HSI value = 0.31). The areas with HSI
values greater than 0.6 had a relatively higher percentage of habitats (11.5%) than non-habitats (1.3%; Table2). The average SI value for every habitat variable considered was signiﬁcantly higher in the actual habitatsor sites in which the Korean water deer were observed
than the average values for those not observed or nonhabitats (p < .001; Table 3). The average SI values forfour habitat variables, SIv2, SIv3, SIv4, and SIv7, allexceed 0.6 (over 50% of the habitat areas), indicating
good quality as habitat components. In addition, theaverage SI values for three habitat variables, SIv1,
SIv5, and SIv6, were in the range of 0.4–0.6. The SI
for SIv1 had a lower average (SI = 0.46) than theentire province (SI = 0.56), suggesting that it is noteasy for the deer to access water.

Validation of the HSI model for Korean water deer
The model was validated to estimate the accuracy ofmodel prediction when compared with a set of observations. Classiﬁcation accuracy was assessed using sensitivity, speciﬁcity, and overall accuracy with a modest setof observations, allowing us to predict where there
would be concentrations of Korean water deer in theprovince.
The predictive performance of the model was determined by comparing the HSI mean values between the
habitats and non-habitats. The results of the t-test forthe predicted values of the logistic regression model
indicated that there were distinct differences between
the habitat (mean = 0.31) and non-habitat sites (mean= 0.14) (p-value < .001; Table 2). The distribution of thedeer could also be compared visually to the map for
HS (Figure 4). The deer were observed in areas withboth high HSI values and high SI values for roads(SIv6). They tend to be observed in areas with highdensity forests and far from roads. One exception is in
the far northwest of the province, which apparently
has a suitable habitat despite a lack of observations.

Table 2. Distribution of HSI values for habitat, non-habitat, andthe entire province (unit: km2) and the observation possibility
in the HSI ranges.
HSI range

HabitatNon-habitat

Chungnam
Province

Observation
possibility

0.0–0.2
17.68 (38.0%) 65.11 (83.5%) 5317.17 (61.6%) 38.0/61.6 = 0.6
0.2–0.4
15.54 (33.4%) 9.67 (12.4%) 2150.24 (24.9%) 33.4/24.9 = 1.4
0.4–0.6
7.98 (17.1%) 2.22 (2.9%)748.07 (8.7%) 17.1/8.7 = 2.0
0.6–0.8
4.51 (9.7%) 0.80 (1.0%)
363.21 (4.2%)
9.7/1.0 = 9.7
0.8–1.0
0.84 (1.8%) 0.22 (0.3%)
51.70 (0.6%)
1.8/0.6 = 3.0
Mean (SD)* 0.31 (0.20)
0.14 (0.11)
0.22 (0.17)
Min–Max 0.02–0.88
0.02–0.88
0.02–0.88*Signiﬁcant difference at p = .001.

Figure 4. The HS map for the Korean water deer in Chungnam
Province.

ANIMAL CELLS AND SYSTEMS

225

Table 3. Habitat variables (SIvn) for the HSI model and distribution of the SI values for habitat, non-habitat, and the entire province(unit: km2).
Habitat variables(SIvn)
Distance to water area (SIv1)*

Canopy cover (SIv2)*

Distance to wetland (SIv3)*

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Slope (SIv4)*

Distance to urbanized area (SIv5)*

Distance to roads (SIv6)*

Land use (SIv7)*

SI range

HabitatNon-habitat

Chungnam Province

0.0–0.2
0.2–0.4
0.4–0.6
0.6–0.8
0.8–1.0
Mean (SD)
0.0–0.2
0.2–0.4
0.4–0.6
0.6–0.8
0.8–1.0
Mean (SD)
0.0–0.2
0.2–0.4
0.4–0.6
0.6–0.8
0.8–1.0
Mean (SD)
0.0–0.2
0.2–0.4
0.4–0.6
0.6–0.8
0.8–1.0
Mean (SD)
0.0–0.2
0.2–0.4
0.4–0.6
0.6–0.8
0.8–1.0
Mean (SD)
0.0–0.2
0.2–0.4
0.4–0.6
0.6–0.8
0.8–1.0
Mean (SD)
0.0–0.2
0.2–0.4
0.4–0.6
0.6–0.8
0.8–1.0
Mean (SD)10.99 (23.6%)
6.10 (13.1%)
7.02 (15.1%)
8.87 (19.0%)
13.58 (29.2%)
0.46 (0.35)
5.40 (11.6%)
4.55 (9.8%)
5.59 (12.0%)
7.77 (16.7%)
23.26 (50.0%)
0.67 (0.34)
9.02 (19.4%)
0.64 (1.4%)
0.83 (1.8%)
2.63 (5.7%)
33.43 (71.8%)
0.78 (0.40)
1.68 (3.6%)
3.12 (6.7%)
15.88 (34.1%)
16.21 (34.8%)
9.66 (20.8%)
0.71 (0.23)
13.44 (28.9%)
12.61 (27.1%)
7.87 (16.9%)
3.69 (7.9%)
8.95 (19.2%)
0.46 (0.35)
4.13 (8.9%)
8.47 (18.2%)
5.99 (12.9%)
10.45 (22.5%)
17.51 (37.6%)
0.56 (0.37)
2.32 (5.0%)
1.15 (2.5%)
17.94 (38.5%)
10.42 (22.4%)
14.73 (31.6%)
0.69 (0.31)

26.32 (33.7%)
9.86 (12.6%)
10.77 (13.8%)
9.68 (12.4%)
21.38 (27.4%)
0.40 (0.37)
44.47 (57.0%)
9.52 (12.2%)
8.37 (10.7%)
5.75 (7.4%)
9.92 (12.7%)
0.30 (0.29)
47.35 (60.7%)
0.19 (0.2%)
0.33 (0.4%)
1.10 (1.4%)
29.06 (37.2%)
0.39 (0.48)
0.43 (0.6%)
0.90 (1.2%)
57.76 (74.0%)
11.90 (15.3%)
7.03 (9.0%)
0.46 (0.14)
47.90 (61.4%)
19.33 (24.8%)
6.67 (8.6%)
2.08 (2.7%)
2.0 (2.6%)
0.20 (0.37)
17.53 (22.5%)
31.46 (40.3%)
12.76 (16.4%)
11.82 (15.1%)
4.45 (5.7%)
0.26 (0.24)
14.37 (18.4%)
1.73 (2.2%)
44.02 (56.4%)
4.14 (5.3%)
13.76 (17.6%)
0.44 (0.33)

1734.57 (20.1%)
640.52 (7.4%)
1013.80 (11.7%)
1451.71 (16.8%)
3789.80 (43.9%)
0.56 (0.36)
3160.49 (36.6%)
976.54 (11.3%)
953.94 (11.1%)
985.01 (11.4%)
2554.42 (29.6%)
0.47 (0.37)
3600.16 (41.7%)
31.99 (0.4%)
77.71 (0.9%)
224.42 (2.6%)
4696.11 (54.4%)
0.57 (0.47)
158.56 (1.8%)
283.51 (3.3%)
4858.09 (56.3%)
2086.70 (24.2%)
1243.54 (14.4%)
0.59 (0.19)
4011.37 (46.5%)
2336.58 (27.1%)
1078.11 (12.5%)
541.56 (6.3%)
662.78 (7.7%)
0.31 (0.32)
1125.22 (13.0%)
2339.14 (27.1%)
1226.10 (14.2%)
1701.52 (19.7%)
2238.41 (25.9%)
0.45 (0.35)
917.23 (10.6%)
148.20 (1.7%)
4449.70 (51.6%)
1068.61 (12.4%)
2046.68 (23.7%)
0.56 (0.34)*Signiﬁcant difference at p = .001.

The model had a correctly classiﬁed rate of 65.4%,
with a sensitivity of 33.9% and speciﬁcity of 85.3%(Cox–Snell R 2 = 0.089; Nagelkerke R 2 = 0.121; Table 1).
In addition, chances of observation increase throughout
the province with the increase in the HSI values. This indicates that the deer would be found in the areas with high
HSI values, and the model proves the high overall prediction accuracy (Table 2).

Discussion
This study was conducted to understand the habitat
requirements for the Korean water deer on the basis of
HSI modeling for Chungnam Province, Korea. The HSI
model was developed by leveraging the environmental
geographic information and ecology of the water deerin Korea, China, and the United Kingdom. This HSI modeling could provide information on the types of habitat

favored by the Korean water deer. Of all the habitat variables considered, three components of the model(wetland accessibility, slope, and distance from roads)
are particularly effective in predicting the presence ofthe Korean water deer. Cooke and Farrell (1983) reported
that the water deer prefers watery areas or nearby.
However, water features in the province have received
developmental pressures for many years, resulting in
the interference of the approach of the Korean water
deer to the water areas. Thus, habitat variables for thewater areas could not be used as the habitat indicators
for the region strongly affected by anthropogenic
factors, and sufﬁcient evidence regarding the existenceof water deer will be required to assess the level of
their accessibility to water.
The water deer and other Korean ungulate species (orsubspecies) are facing the risk of anthropogenic disturbances such as roadkill, lack of habitat, or habitat

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226

J. JUNG ET AL.

fragmentation, especially in or near large cities (Kim et al.
2011). High levels of some types of human activity
indeed limit deer abundance, implying that the deer
tend to avoid roads and prefer wetland and forest
areas. In addition, local topographic features, such as
the slope and aspect, play a crucial role in a number ofmorphological, ecological, and hydrological processes(Vico & Porporato 2009). However, the studied area isrelatively low and ﬂat with a maximum elevation of
904 m above sea level (a.s.l.). The distribution of Koreanwater deer in areas below 300 m a.s.l. does not appear
to be affected by local differences in elevation. In
addition, there are almost no signiﬁcant differences in
aspect. Given the geographical conditions, the variablesof aspect and elevation could be less important thanother habitat variables for the province.
The proposed model is particularly useful in the casewhere a wildlife habitat has not been previously identiﬁed
in a broad area. It allows us to predict the concentrationof Korean water deer at a large scale. It demonstrates
that only 13.5% of the province can be considered as ahigh-quality habitat (HSI values > 0.4), mainly in forest
areas. Because the forest areas in parks have been wellmanaged and they offer a forest canopy cover of suitabledensity, most deer have been observed in mountain areas
close to cultivated or open areas (Figures 1 and 4), indicating that the ﬁeld crops can be damaged or destroyed by
the deer. The deer are more likely to stay and rest in theforest or a dense vegetation area during daytime andfeed on the farmlands at night.
The HS map was created by ﬁtting the logistic
regression to the environmental and species distribution
data. However, the species distribution data were not
sufﬁcient and complete at a high spatial resolution andlarge spatial extent. Constructing a distribution modelby using the species data could cause an issue related
to the model accuracy if the species has not beenrecorded at all the sites that are actually suitable (Tyreet al. 2003; Guisan & Thuiller 2005). With respect tothe presence/absence data for the Korean water deer,
the absence information was generated at random. We
randomly selected 300 sites outside 75% of the Kernelareas to compare with the observation sites as the presence information. The Kernel density estimation basedon the available ﬁeld data provides a probabilistic
measure of how the animals use space and allows us
to distinguish between areas with different intensities
of use. Calculating a core area (area of 50% Kernel
contour) and an area of active use (area of 90% Kernel
contour) will help to clarify the assessment of preferences and qualities of habitats for the Korean water deer.
To identify all suitable locations, long-term location
data on changes in the deer population with respect tothe environment may be required (Welk 2004; Herben
et al. 2006). Historical records of population may correspond to sites where the species’ habitat has been subsequently destroyed or changed. For the past few
decades, areas near streams or wetlands have undergone
substantial changes because of development projects,
such as building a residential or industrial complex.
Although Korean water deer prefer this lowland habitat,
habitat modiﬁcation would cause them to migrate toforested areas and locations at a higher elevation.
Another limitation is that, because of the lack of dataon shrubs and herbs, certain key food variables thatprobably affect habitat quality could not be fully considered in the model. Since the feeding pattern of thedeer is known to selectively increase the abundance ofunpalatable species (Suzuki et al. 2008), in the future,
we would like to investigate the impact of the deer onvegetation. The HSI model is also limited in terms of
information on dispersal, competition, niche issues ofsingle species, and interaction with other species.
Despite these limitations, the HSI model shows thatthe population of Korean water deer increases as thelevel of HSI values increases (Table 2). The model
enables us to identify key environmental variables thatcan be used to predict the distribution of Korean water
deer as well as the focus areas where the habitat
should be managed. It also allows us to identify areasthat may or may not have a signiﬁcant population of
the deer but have suitable habitats. In addition, themodel could provide guidance to government ofﬁcials
who are required to protect the wildlife species and to
farmers whose crops are often being damaged by the
species. We believe it is important to implement
actions that achieve a better coexistence between
humans and wildlife, and this study could be an initial
step in that effort.

Notes
1. The source of the data set is the Ministry of Environment,
Korea.
2. The source of the data set is the Korea Water Resources
Corporation.
3. The source of the data set is the Korea Transport
Institute.

Acknowledgments
This paper was prepared as part of a Master’s Thesis submitted
to Ewha Womans University in Korea by Jung, Jihyang in 2007.
Financial support was from KEITI (2014-0001-30010) and SEST(2016). The authors thank Richard Primack, Libby Ellwood, Elizabeth Platt, and Rachel Morrison for their helpful comments andsuggestions.

ANIMAL CELLS AND SYSTEMS

Disclosure statement
No potential conﬂict of interest was reported by the authors.

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