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Assessing Climate Change Impact on Forest Habitat Suitability and Diversity in the Korean Peninsula

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Abstract and Figures

Habitat changes in temperate forests are more vulnerable to climate change than tropical or boreal forests. This study assessed forest habitat suitability and diversity to determine the impact of climate change on the Korean Peninsula. We used the MaxEnt (Maximum Entropy) species distribution model, three key climate indices, and two representative climate change scenarios, using short and long-term data. Two of the three key climate indices related to temperature were more capricious than the precipitation-related index in the future. In the baseline prediction, both statistical and qualitative validation using the actual vegetation map showed excellent results. Regarding forest habitat suitability, northward migration and substantial increase were definitely distinctive in warm temperate evergreen forest. On the other hand, subalpine forest areas decreased significantly due to climate change; the suitable area for Representative Concentration Pathways (RCP) 8.5 2070s decreased by more than half. With regard to forest habitat diversity, regions with high diversity declined due to climate change. In the RCP 8.5 scenario, areas where all three forest types are suitable no longer appeared; however, in the case of RCP 4.5 2050s, suitable areas for two forest types increased, which implies climate change is not only negative in terms of diversity. As this negative prediction of future change is discouraging, active mitigation and adaptation are required to prevent these changes. The sustainability of future ecosystems is still dependent on our efforts.
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Article
Assessing Climate Change Impact on Forest Habitat
Suitability and Diversity in the Korean Peninsula
Chul-Hee Lim 1,2 ID , Somin Yoo 2, Yuyoung Choi 2, Seong Woo Jeon 2ID , Yowhan Son 2
and Woo-Kyun Lee 2,*
1Institute of Life Science and Natural Resources, Korea University, Seoul 02481, Korea;
limpossible@korea.ac.kr
2Department of Environmental Science and Ecological Engineering, Korea University, Seoul 02481, Korea;
somin215@naver.com (S.Y.); cuteyu0@korea.ac.kr (Y.C.); eepps_korea@korea.ac.kr (S.W.J.);
yson@korea.ac.kr (Y.S.)
*Correspondence: leewk@korea.ac.kr; Tel.: +82-02-3290-3016
Received: 4 April 2018; Accepted: 8 May 2018; Published: 10 May 2018


Abstract:
Habitat changes in temperate forests are more vulnerable to climate change than tropical or
boreal forests. This study assessed forest habitat suitability and diversity to determine the impact
of climate change on the Korean Peninsula. We used the MaxEnt (Maximum Entropy) species
distribution model, three key climate indices, and two representative climate change scenarios,
using short and long-term data. Two of the three key climate indices related to temperature were more
capricious than the precipitation-related index in the future. In the baseline prediction, both statistical
and qualitative validation using the actual vegetation map showed excellent results. Regarding
forest habitat suitability, northward migration and substantial increase were definitely distinctive in
warm temperate evergreen forest. On the other hand, subalpine forest areas decreased significantly
due to climate change; the suitable area for Representative Concentration Pathways (RCP) 8.5 2070s
decreased by more than half. With regard to forest habitat diversity, regions with high diversity
declined due to climate change. In the RCP 8.5 scenario, areas where all three forest types are
suitable no longer appeared; however, in the case of RCP 4.5 2050s, suitable areas for two forest types
increased, which implies climate change is not only negative in terms of diversity. As this negative
prediction of future change is discouraging, active mitigation and adaptation are required to prevent
these changes. The sustainability of future ecosystems is still dependent on our efforts.
Keywords:
climate change impact; forest habitat suitability; forest habitat diversity; Korean
Peninsula; MaxEnt
1. Introduction
In the Intergovernmental Panel on Climate Change (IPCC) Fifth Assessment Report (AR5),
climate change impacts are expressed as unequivocal facts, and forests and ecosystems in temperate
regions have been suggested to respond more sensitively [
1
]. The boundary between the tropics and the
temperate zone is expected to migrate northward, and climate variability will increase in the temperate
regions where seasonality is high [
2
4
]. In accordance with recent knowledge, ecosystem changes in
temperate regions, particularly mid-latitude regions, are noteworthy. The habitat suitability of forest
ecosystems is defined according to various environmental factors, such as climate, topography, distance
from water-flow, and type of rocky outcrop [
5
,
6
]. Although some species are heavily dependent
on specific factors such as groundwater or drought stress, the overall species distribution is most
dependent on climate, especially on the macro scale [
6
,
7
]. In other words, the climate is a basic factor
for forest ecosystems, above other environmental factors such as soil characteristics and land use [
7
,
8
].
Forests 2018,9, 259; doi:10.3390/f9050259 www.mdpi.com/journal/forests
Forests 2018,9, 259 2 of 16
Many recent studies have indicated that forest habitats are changing globally or locally in response to
changing climate [6,7,9].
The habitat suitability of a forest can be understood as a concept similar to the potential species
distribution of a forest, in that it analyzes the area favorable for plant growth [
10
12
]. Many studies
using future climate data that predict changes in forest habitat suitability or species distribution due to
climate change have been performed at national and regional levels [
13
15
]. The species distribution
model (SDM) is the most commonly used tool to predict these changes. SDM finds suitable growth
areas for the changing environment through the past occurrence data of species. The representative
tools for the SDM are maximum entropy (MaxEnt), generalized linear model (GLM), generalized
additive model (GAM), and random forest (RF) [11,1618].
With the increased awareness of the importance of biodiversity, the diversity of forest ecosystems
is also being continually discussed [
19
,
20
]. Specifically, many studies have shown that forest variety
can be reduced in response to climate change, and the results of recent monitoring in past decades
have supported this [
21
,
22
]. For instance, species turnover occurs when subtropical species shift to
current temperate regions or temperate species shift to current boreal regions by climate change. In this
process, species that fail to adapt or fail to migrate become extinct and biodiversity decreases [
19
21
].
However, quantitative studies on diversity changes in forest species or habitats are lacking, and very
few studies have used spatial prediction. There has been a lack of consideration of the changes in
diversity resulting from habitat changes.
The Korean Peninsula has a densely forested area with various species, ranging from subtropical
evergreen forests to alpine forests [
14
,
23
], and is evidently experiencing the impacts of climate change
on forest habitats. To be specific, recent studies have shown that pine trees, the major species,
are expected to decline, and subalpine forests in the mountains have already declined [
13
,
24
]. However,
existing studies have not much dealt with forest habitat suitability and diversity simultaneously.
The purpose of this study is to assess the forest habitat suitability and diversity under climate change
in the Korean Peninsula. To this end, we use the MaxEnt model, which is the most widely used SDM
model, and predict future changes using two representative climate change scenarios, and short and
long-term data. Ultimately, this study tries to suggest the necessity of active adaptation to climate
change and importance of greenhouse gas reductions for forest habitats conservation.
2. Data and Methods
2.1. Study Area
This study covers the entire Korean Peninsula, including South Korea and North Korea (Figure 1).
The Korean Peninsula is approximately 1100 km in length from south to north and approximately
300 km in width from east to west; the total area is approximately 220,000 km
2
[
25
]. The Korean
Peninsula corresponds to the mid-latitude region (33–43
N) and is located in the east of the Asian
continent; it belongs to the temperate monsoon climate, which is affected by seasonal winds [
26
]. It is
characterized by climatic characteristics of hot, humid summers and cold, dry winters. Geographically,
the high-altitude highland of the Gaema Plateau is located in the northeast, and the plains are situated
around the west coast [
27
]. An altitude of the Gaema Plateua region is more than 1500 m with the
mountains over 2000 m, and the mountain ranges located along the east coast to the southern part of
South Korea (Figure 1). Halla Mountain, 1950 m high, is located in Jeju Island, the southernmost part
of the Korean Peninsula.
Forests 2018,9, 259 3 of 16
Forests 2018, 9, x FOR PEER REVIEW 3 of 16
Figure 1. Study area ((a): forest species sites used in this study, (b): administrative boundary with
topographical information).
2.2. Forest Habitat Suitability, Diversity, and Forest Species Data
Habitat suitability of each forest type is influenced by climate, soil condition, topography, land
use, and socioeconomic conditions; of these, climatic condition is the most basic factor underlying
suitability [11,23]. The concept of forest habitat suitability assessed in the present study is evaluated
for each forest type as suitability in terms of climatic conditions in a spatial unit, and only considers
climatic factors. Many previous studies have also found a forest habitat suitable area, or forest
potential distribution, using only major climate variables [13,14,17,18,24].
Forest habitat diversity is assessed by overlapping areas of forest habitat suitability. The more
overlapped areas of forest habitat suitability, the higher the habitat diversity. Many previous studies
have shown that the diversity of forest ecosystems is higher in two or more types of overlapped
vegetation [22,31]. According to the forest type, the lowest habitat diversity is where there are no
suitable regions, and the higher habitat diversity is commonly found where there are two or more
suitable regions.
For assessing the forest habitat suitability and diversity, we grouped the forest species on the
Korean peninsula into three forest types. In this study, we trained the model to predict the
Figure 1.
Study area ((
a
): forest species sites used in this study, (
b
): administrative boundary with
topographical information).
The Korean Peninsula is generally temperate, and temperate forests are present, but subtropical
evergreen forests are found on the southern coast, and subalpine forests are distributed in mountainous
areas [
24
,
28
]. Although the urbanization rate is very high in South Korea, and many forest areas have
been deforested and converted into cropland in North Korea, forests still have the highest land coverage
in the Korean Peninsula [
29
]); in South Korea, more than 60% of land cover is forest, and approximately
58% of North Korea is forest [
30
]. In this forest habitat study, research was conducted across the whole
area, without distinguishing land cover types.
2.2. Forest Habitat Suitability, Diversity, and Forest Species Data
Habitat suitability of each forest type is influenced by climate, soil condition, topography,
land use, and socioeconomic conditions; of these, climatic condition is the most basic factor underlying
suitability [
11
,
23
]. The concept of forest habitat suitability assessed in the present study is evaluated
for each forest type as suitability in terms of climatic conditions in a spatial unit, and only considers
Forests 2018,9, 259 4 of 16
climatic factors. Many previous studies have also found a forest habitat suitable area, or forest potential
distribution, using only major climate variables [13,14,17,18,24].
Forest habitat diversity is assessed by overlapping areas of forest habitat suitability. The more
overlapped areas of forest habitat suitability, the higher the habitat diversity. Many previous studies
have shown that the diversity of forest ecosystems is higher in two or more types of overlapped
vegetation [
22
,
31
]. According to the forest type, the lowest habitat diversity is where there are no
suitable regions, and the higher habitat diversity is commonly found where there are two or more
suitable regions.
For assessing the forest habitat suitability and diversity, we grouped the forest species on the
Korean peninsula into three forest types. In this study, we trained the model to predict the distribution
of these three forest types rather than individual species. As shown in Section 2.1, temperate forests
are the most widely distributed forests in the Korean Peninsula, with warm temperate evergreen
forests and subalpine forests being distributed locally. In the Korean peninsula, the terms alpine forest
and subalpine forest are used interchangeably, but it is known that subalpine forest is more generally
distributed. Thus, we grouped them into terms and categories of subalpine forests. In this study,
forest suitability and diversity were assessed for three forest types, and representative species were
selected for each forest type:
Warm temperate evergreen forest: Camellia japonica L., Castanopsis sieboldii (Makino) Hatus,
Quercus acuta Thunb., Machilus thunbergii Sieb. & Zucc., and Pinus thunbergii Parl.
Temperate forest: Pinus densiflora Siebold & Zucc., Quercus dentate Thunb., Quercus variabilis
Blume, Castanea crenata Siebold & Zucc., Robinia pseudoacacia L., Quercus mongolica Fisch. ex Ledeb.,
Zelkova serrate (Thunb.) Makino, Acer mono Maxim., Quercus acutissima Carruth., Carpinus laxiflora
(Siebold & Zucc.) Blume, Quercus serrate Murray, and Carpinus tschonoskii Maxim.(Betulaceae)
Subalpine forest: Abies koreana E.H.Wilson, Abies nephrolepis (Trautv. ex Maxim.) Maxim.,
and Taxus cuspidate Siebold & Zucc.
For the species distribution modeling, we required species occurrence data for each forest species.
In the case of South Korea, much information can be obtained about forest species, but only very limited
information is available for North Korea. In order to simulate a balanced distribution for the entire
Korean peninsula, we excluded South Korea’s precise data, and all the data for each species were taken
from the Global Biodiversity Information Facility (GBIF) database. GBIF collects biodiversity data
from all over the world, and provides them as spatial information, including information on species
distribution in North Korea. The distribution of each forest type was estimated using information
on the distribution of about 20 species belonging to that forest type over 79 locations (Figure 1).
As Figure 1and Figure 5 show, the GBIF data we used are mostly consistent with the actual vegetation
of South Korea.
2.3. Climate Data
Climate data used in this study were obtained from the WorldClim database (http://www.
worldclim.org/ Accessed on 5 April 2017). The WorldClim database collects global climate data
for basic climatic factors, and provides downscaled data of up to a 30-arcsec resolution [
32
].
Future downscaled climate data for each Representative Concentration Pathways (RCP) scenario,
and up to a 30-arcsec resolution per typical Global Climate Model (GCM) are also available. In the
present study, we used the basic (maximum, minimum, and average temperature, and precipitation)
climatic data of 1970–2000 (WorldClim version 2) for past weather data. For future climate data,
we used the short-term future (2040–2060) and long-term future (2060–2080) basic climate data for the
RCP 4.5 and 8.5 scenarios of HadGEM2-AO GCM. Based on climate data, 1970–2000 was defined as
the baseline period, the short-term future as the 2050s, and the long-term future as the 2070s. All the
data were processed and converted to a 1 km2spatial resolution.
Forests 2018,9, 259 5 of 16
The climate variables used to determine habitat suitability and diversity for the three forest types
were taken from the climate index suggested by Choi et al. [
13
]. They used the Minimum Temperature
of the Coldest Month Index (MTCI), the Warmth Index (WI), and the Precipitation Effectiveness
Index (PEI) to predict the potential species distribution for forested areas. Prediction of potential
forest distribution in the Korean Peninsula using these three climate indices has already been verified
through being applied to various studies since Choi et al. [
13
,
14
,
23
,
24
]. In this study, the MTCI was
employed [
33
] following the logic of Neilson [
34
], converting the MTC (Minimum Temperature of the
Coldest month) using Equation (1):
MTCI = ((MTC tmid)/(thi tmid )) ×100. (1)
The MTCI is an important temperature-related index which is a significant factor used to explain
the vegetative limit of tree species [34,35].
The WI is an important thermal index also associated with the effective heat of vegetation [
35
];
therefore, WI has been used to predict potential vegetation distribution and spatial movement [
36
].
The WI was calculated using Equation (2), which determines the annual sum of the positive differences
between monthly mean temperature and 5 C:
WI = Σ(t5) (2)
where tis the monthly mean temperature >5 C.
The PEI is an index devised by [
37
], which is based on the principle that both precipitation
and evaporation are important for the growth of natural vegetation. As an index representing the
long-term efficiency of precipitation, it is calculated as the sum of 12 monthly PE ratios (monthly
precipitation/monthly evaporation):
PE ratio (i) = 0.165 ×(Pi/(Ti + 12.2))10/9, (3)
where i is the number of the month (from 1 = January to 12 = December), Pi is the normal monthly
precipitation in mm, and Ti is the normal monthly temperature in
C. All temperatures <
2
C are
given the value
2
C, and PE ratios >40 are counted as 40 [
38
]. In this study, PEI was applied because
it affects vegetation distribution indirectly by influencing the productivity of vegetation [13].
These three powerful bioclimatic variables can predict the forest habitat more effectively
than general climate variables [
13
,
14
,
24
]. Since only the core variables are used, the problem of
multicollinearity caused by similar variables can be prevented [39,40].
2.4. MaxEnt Modeling and Classification
The MaxEnt model is a non-linear model used to make predictions or inferences from incomplete
information based on statistical mechanics. It has been used to estimate the probability distribution of
maximum entropy by evaluating the contrasts between observations and background variables [
41
].
The model is known to be highly accurate with a statistically significant value [
42
,
43
]. MaxEnt has a
number of significant advantages, including the capacity to analyze complex response functions by
combining various function types (e.g., linear, quadratic, product, threshold, and hinge; [44]).
The MaxEnt model has been used for various species distribution modeling studies including
forest habitats, and in many cases, these have focused on the Korean peninsula [
15
,
28
,
45
]. In this study,
MaxEnt model 3.3.3 version (Developed by Steven J. Phillips: Florham Park; NJ, USA) was used and
data was processed with ArcGIS 10.3 version (ESRI Inc.: Redlands, CA, USA). We replicated the runs
15 times using the subsample method, and the random test percentage was set to 25. The averaged
value of 15 outputs was used for the main result. For the maximum iterations, 5000 circuits were set,
and the results were calculated and analyzed using a logistic output format. We trained the model
of each forest type through MaxEnt with evenly distributed tree species occurrence data in the entire
Forests 2018,9, 259 6 of 16
Korea Peninsula. Thus, the geographical extent of this study can be regarded as the whole area of the
Korean Peninsula.
The Natural Break (Jenks) Classification method was used to classify the probability distribution
of the forest type calculated using the MaxEnt model; it identifies break points by picking the class
breaks that best group similar values and maximize the differences between classes [
46
]. The values are
divided into classes whose boundaries are set, which are relatively large jumps in the data values [
46
,
47
].
The results of each species distribution model estimated with a probability value between 0 and 1 are
classified into two classes by the Natural Break method: a high value region is designated as suitable
and a low value region as non-suitable. In order to explain the change in forest habitat suitability
of each forest type according to climate change, priority is given to areas where suitability overlaps,
in the order of subalpine forest, warm temperate evergreen forest, and temperate forest. It gives
priority to the forest type, which is distributed in a more restrictive region in the Korean peninsula,
and overlapping areas are shown according to this priority. For analyzing the area where multiple
forest habitat suitabilities overlap, we presented the forest habitat diversity result separately.
2.5. Evaluation of Model Performance
Statistical methods and qualitative methods were used to verify the suitability of each forest type
estimated by the model. First, Area Under the Curve (AUC) of Receiver Operating Characteristic (ROC)
curves was used as a statistical method. The AUC represents the probability that a randomly chosen
forest species occurrence exceeds that of randomly choosing an absence. The AUC value is within the
range (0.5–1.0), where the minimum value represents the performance of a random prediction and the
maximum value correspond to a perfect prediction. The AUC value ranged between: 0.5–0.69, poor;
0.7–0.79, reasonable; 0.8–0.89, excellent; and >0.9, exceptional [48].
For qualitative evaluation, an actual vegetation map produced by the Ministry of Environment,
Korea was used. The actual vegetation map is a representative map of the forest species distribution
of South Korea, in the form of a map created using high resolution satellite imagery and aerial
photographs, and corrected by field surveys and remote sensing techniques. The natural forest on
the actual vegetation map is divided into the three forest types classified in this study, and their
performance is evaluated by overlapping with the habitat suitability map simulated in this study.
3. Results and Discussion
3.1. Calculating Three Climate Indices
Calculation of the three key climate variable, revealed that the future change and variation in
temperature-related variables was large. In the MTCI results using the minimum temperature of the
coldest month, the range area from 0 to
30, which was only distributed in the southern region, is
predicted to increase northward as the future approaches. This means that climate conditions will
be changed to widely distribute warm temperate plants. In the baseline period, the value was found
to be greater than zero in the southern part of the Korean Peninsula; in the future, the MTCI value,
which showed a low value from
60 to
90 in the mountainous region of South Korea, changed to
30 to
60 in most cases. Spatial changes in MTCI, which have a large impact on the growth limitation
of vegetation, can change the vegetation in both subalpine and coastal forests (Figure 2).
The WI calculated from the cumulative value of mean temperatures gradually increased as
with MTCI, but the distribution was slightly different. Because WI considers the possible growth
temperature of vegetation, high altitude mountain areas and plains regions were clearly distinguished,
and the difference according to altitude was larger than the difference between north and south
(latitude). In the baseline period, there were very few areas with high WI values above 150, but in
RCP 4.5 2070s, Seoul in the middle of the Korean Peninsula was above WI 150, and in RCP 8.5 2070s,
it extended to some parts of North Korea (Figure 2).
Forests 2018,9, 259 7 of 16
The PEI calculation that accounted for the ratio of meteorological water content on land, showed
no significant difference compared to the temperature indices. The highest change in PEI was observed
in the Gaema Plateau, in the northern part of the Korean Peninsula, where the evapotranspiration
amount was lowest compared with the precipitation level. There is also a tendency for the effective
precipitation rate to decrease due to the increase in evapotranspiration in the South Korean interior,
northwest coast, and northeast coast (Figure 2).
As in previous studies that used climate change scenario data, the distribution of temperature
indices was prominent, and was even higher in RCP 8.5 than in the RCP 4.5 scenario [
25
,
49
]. RCP 4.5
2070s and RCP 8.5 2050s exhibited a similar level of variation, and the largest change was seen in RCP
8.5 2070s.
Figure 2. Three climate indices calculated for baseline and future periods.
3.2. Estimating Baseline Forest Habitat Suitability
The estimation of habitat suitability of the three forest types for the baseline period was similar
to that of many previous research studies and surveys [
16
,
17
,
50
,
51
]. In the case of warm temperate
evergreen forest, suitable areas were found in Jeju Island and the southern coast of the Korean Peninsula
(Figure 3). Temperate forests were most suitable for the entire peninsula, but non-suitable for northern
mountains (Figure 3). In the real environment, temperate forests are indigenous to most parts of the
peninsula. In the case of subalpine forests, most of the main highlands of South Korea and most of the
North Gaema Plateau were suitable areas (Figure 3). As a result of overlaying all suitable forest type
areas, the only non-suitable area was found in the northern part of North Korea. This area is also the
Forests 2018,9, 259 8 of 16
boundary between the temperate forest and the subalpine forest [
52
], and it is likely that the lack of
forest occurrence points in North Korea affected our result.
Figure 3. Results of forest habitat suitability by type at the baseline period.
3.3. Evaluation of Model Performance
3.3.1. Evaluation Using AUC and ROC
AUC and ROC curves were examined to evaluate the statistical accuracy of the forest habitat
modelling. AUC values obtained with a high overall statistical confidence were 0.975 for the warm
temperate evergreen forest, 0.748 for the temperate forest, and 0.958 for the subalpine forest (Figure 4).
Figure 4.
Evaluating habitat modeling performance using ROC and AUC ((
a
): warm temperate
evergreen forest; (b): temperate forest; (c): subalpine forest).
Compared to the warm temperate evergreen and subalpine forests, temperate forest exhibited a
relatively low accuracy, which was interpreted as a result of the low spatial correlation with location
information due to the nature of temperate forests being distributed over a wide area.
In the context of an AUC value of MaxEnt output, a reasonable level is >0.7, and an exceptional
level is >0.9; the overall statistical accuracy was more than reasonable and mostly exceptional.
3.3.2. Evaluation Using the Actual Vegetation Map of South Korea
Comparison with the actual vegetation map of South Korea showed that the present distributions
of the target forest types were included within the predicted suitable area (Figure 5). In the case of
warm temperate evergreen forests, actual forests were located at Jeju Island and the south coast area
of the Korean Peninsula, and some islands of the southwestern coast area. All these were included
within the predicted range of suitable areas by this study. In the case of temperate forests, 12 species
were compared, and our predicted range of suitable areas for temperate forest was the whole of South
Korea. However, the actual distribution of temperate forests throughout the entire Korean Peninsula,
and the whole of South Korea, confirmed that we can be confident in our results. The three sub-alpine
Forests 2018,9, 259 9 of 16
forest species were present in Mt. Halla in Jeju Island, Mt. Jiri in the southern region, and the eastern
mountainous regions (Mt. Seolak, Mt. Odae, Mt. Taebaek, etc.). The actual subalpine forest areas also
showed a tendency to match our results precisely. In all three forest types, the actual forest ranges
and the predicted suitable areas overlapped, and all the actual forest locations were included in the
predicted ranges. The results of this study were expected to be broader than the actual ranges because
of the potential habitat suitability. Even though only climate indices were used, our baseline habitat
suitability results for the three forest types simulated the distribution of actual vegetation well, so this
approach proves to be sufficient for future prediction.
Figure 5.
Comparison with actual vegetation maps for evaluation of habitat modeling performance
((a): warm temperate evergreen forest; (b): temperate forest; (c): subalpine forest).
3.4. Climate Change Impact on Forest Habitat Suitability
The climate change scenarios impacted on the habitat suitability of all three forest types. In the
RCP 4.5 scenario 2050s, the warm temperate evergreen forest extended smoothly to the northern
area. The temperate forest was almost similar to the baseline period. In the case of subalpine forest,
South Korea and North Korea appeared different; in South Korea, the Jeju Island reduced a suitable area
for subalpine forest only, while other regions were almost the same as in the baseline period; however,
the Gaema Plateau in North Korea, decreased the area suitable for subalpine forest significantly. In the
RCP 4.5 2070s scenario, the warm temperate evergreen forest was more northward, extending widely
to the northern Hwanghae-do Province of North Korea, and beginning to spread inland in South
Korea. The temperate forest was fairly similar to the baseline period, as in the 2050s, but the subalpine
Forests 2018,9, 259 10 of 16
forest began to decrease gradually in South Korea. In particular, in South Korea, non-suitable areas
began to appear because the temperate forest suitable area had extended northward (Figure 6).
Forests 2018, 9, x FOR PEER REVIEW 10 of 16
suitable areas began to appear because the temperate forest suitable area had extended northward
(Figure 6).
In the RCP 8.5 scenario 2050s, suitable areas for the warm temperate evergreen forest are
increasingly found to the north, and the distribution of the subalpine forests gradually decreases; this
is broadly similar to the distributions seen in RCP 4.5 2070s. The largest change compared to the
baseline period was observed in RCP 8.5 2070s, and a suitable area for warm temperate evergreen
forests began to appear on the east coast of North Korea. The suitable area for warm temperate
evergreen forest became widespread in the interior of South Korea, and the subalpine forest greatly
decreased in South Korea. Notably, only a small fraction remained in RCP 8.5 2070s in Jeju Island,
where the subalpine forest rate was greatest at the baseline period (Figure 6).
Figure 6. Spatial distribution of forest habitat suitability in baseline and future climates.
A large difference can also be seen when comparing the size of the area. The suitable area for
subalpine forest, which accounted for 17.18% of the total area of the Korean Peninsula at the baseline
period, was only 7.37% at RCP 8.5 2070. In contrast, the warm temperate evergreen forest habitat
suitable area, which was only 3.68% at the baseline period, increased more than five times to 19.21%
in RCP 8.5 2070s (Table 1). The habitat suitable area for temperate forest also decreased by
approximately 5.5% compared to the baseline, suggesting that major vegetation changes can be
predicted for the Korean Peninsula. This result indicates that South Korea is in danger of eventually
Figure 6. Spatial distribution of forest habitat suitability in baseline and future climates.
In the RCP 8.5 scenario 2050s, suitable areas for the warm temperate evergreen forest are
increasingly found to the north, and the distribution of the subalpine forests gradually decreases; this is
broadly similar to the distributions seen in RCP 4.5 2070s. The largest change compared to the baseline
period was observed in RCP 8.5 2070s, and a suitable area for warm temperate evergreen forests began
to appear on the east coast of North Korea. The suitable area for warm temperate evergreen forest
became widespread in the interior of South Korea, and the subalpine forest greatly decreased in South
Korea. Notably, only a small fraction remained in RCP 8.5 2070s in Jeju Island, where the subalpine
forest rate was greatest at the baseline period (Figure 6).
A large difference can also be seen when comparing the size of the area. The suitable area for
subalpine forest, which accounted for 17.18% of the total area of the Korean Peninsula at the baseline
period, was only 7.37% at RCP 8.5 2070. In contrast, the warm temperate evergreen forest habitat
suitable area, which was only 3.68% at the baseline period, increased more than five times to 19.21% in
RCP 8.5 2070s (Table 1). The habitat suitable area for temperate forest also decreased by approximately
5.5% compared to the baseline, suggesting that major vegetation changes can be predicted for the
Forests 2018,9, 259 11 of 16
Korean Peninsula. This result indicates that South Korea is in danger of eventually losing its subalpine
forest, and that warm temperate evergreen forests are likely to encroach into North Korea due to
climate change.
Table 1. Habitat suitable area statistics of each forest type and period, km2(%).
Forest Type Baseline RCP 4.5 2050s RCP 4.5 2070s RCP 8.5 2050s RCP 8.5 2070s
Evergreen 8372 (3.68) 22,302 (9.79) 28,100 (12.34) 27,254 (11.97) 43,731 (19.21)
Temperate 149,811 (65.79) 139,074 (61.08) 138,652 (60.09) 132,375 (58.14) 137,245 (60.28)
Subalpine 39,128 (17.18) 26,938 (11.83) 24,907 (10.94) 29,906 (13.13) 16,779 (7.37)
Non-suitable 30,383 (13.34) 39,380 (17.29) 36,035 (15.83) 38,159 (16.76) 29,939 (13.15)
In terms of altitude and latitude, it can be seen that the habitat suitability area shifts to higher
places as the climate changes. In the case of warm temperate evergreen forests, it gradually goes
northward up to a high latitude around the low-altitude area. Temperate and subalpine forests, on the
other hand, are shifting to increasingly higher altitudes in order to find suitable areas for growth as the
climate changes dramatically. As this phenomenon expands, especially in the RCP 8.5 scenario 2070s,
the suitability area of the subalpine forest in South Korea becomes highly fragmented and the habitat
is expected to be destroyed in most areas.
3.5. Converting to Forest Habitat Diversity Analysis under Climate Change
In terms of habitat diversity, there are currently areas where the three forest types overlap on Jeju
Island in the southern part of the Korean Peninsula (Figure 7). Though it is only a small area, it can
be classified as having a high diversity. There are also areas on the southern coast, and in the central
mountainous region of the Korean Peninsula, where conditions suitable for temperate forest-subalpine
forest and temperate forest-warm temperate evergreen forest overlapped.
When climate change effects were analyzed, the RCP 4.5 scenario in the 2050s produced an area
where the three forest types could co-exist on Jeju Island, but this had disappeared by the 2070s and
was not present at all in the RCP 8.5 scenario. The size of the suitable area that could support two
different forest types in the RCP 4.5 scenario had increased by the 2050s, but it decreased after that
period. In the RCP 8.5 scenario in the 2070s, the size of the suitable area for two forest types fell
significantly, and for the first time, areas that were unsuitable for all three forest types appeared in
South Korea. This habitat modeling did not simulate individual species, but these results indicate that
future climate change could alter the Korean environment and reduce ecosystem biodiversity.
Overall, suitable areas with a highest forest habitat diversity, which could support three different
forest types, are expected to disappear in the future and suitable areas supporting two forest types are
also expected to decline significantly from 6.12% (baseline period) to 2.76% (RCP 8.5 2070s) (Table 2).
Table 2. Habitat diverse area statistics of each type and period, km2(%).
Diversity Baseline RCP 4.5–2050s RCP 4.5–2070s RCP 8.5–2050s RCP 4.5–2070s
Not suitable 30,383 (13.34) 39,926 (17.53) 36,035 (15.83) 38,159 (16.76) 29,939 (13.15)
1 suitable 183,099 (80.41) 159,309 (69.97) 181,911 (79.89) 177,624 (78.01) 191,466 (84.09)
2 suitable 13,925 (6.12) 28,304 (12.43) 9748 (4.28) 11,911 (5.23) 6289 (2.76)
3 suitable 287 (0.13) 155 (0.07) - - -
Forests 2018,9, 259 12 of 16
Forests 2018, 9, x FOR PEER REVIEW 12 of 16
Figure 7. Spatial distribution of forest habitat diversity in baseline and future climates.
3.6. Implications of Forest Suitability and Diversity Assessment Attributable to Climate Change
Most previous studies on future forest distributions in the Korean Peninsula show detailed
changes based on individual species changes, whereas this study predicted overall changes through
representative forest types [13,16,17,24,53]. Nevertheless, our results on habitat suitability of
subalpine forest showed a decrease as in Koo et al. [52]’s study, habitat suitability of warm temperate
forests increased as in studies of Park et al. [16] and Koo et al. [28], and spatial distributions also
showed the same patterns. This suggests that the same results can be achieved by simulating group
level forest types, without taking into account the growth characteristics of individual species, and
this may be effective in studying regions where data is lacking, such as North Korea.
In this study, we proposed a method to evaluate habitat diversity through the overlap of habitat
suitability. Although this method is simple, Jeju Island, which is known to have the highest ecosystem
diversity, gave the highest level of confidence in the results. Indeed, biogeographically, biodiversity
is highly likely to be greatest at the boundary between subtropical and temperate zones, or between
the temperate zone and the alpine zone [20,21]. Jeju Island is the only latitude where subtropical and
temperate vegetation overlap in the Korean Peninsula, and subalpine forest is distributed in the high
mountains. The concept of this study is based on these theories.
Changes in habitat diversity, as well as habitat suitability of forest types, due to climate change
can have a significant impact on the conservation of biodiversity on a regional scale. While these
forest habitats do not have a direct impact on human food or industry, they can reduce the
Figure 7. Spatial distribution of forest habitat diversity in baseline and future climates.
3.6. Implications of Forest Suitability and Diversity Assessment Attributable to Climate Change
Most previous studies on future forest distributions in the Korean Peninsula show detailed
changes based on individual species changes, whereas this study predicted overall changes through
representative forest types [
13
,
16
,
17
,
24
,
53
]. Nevertheless, our results on habitat suitability of subalpine
forest showed a decrease as in Koo et al. [
52
]’s study, habitat suitability of warm temperate forests
increased as in studies of Park et al. [
16
] and Koo et al. [
28
], and spatial distributions also showed the
same patterns. This suggests that the same results can be achieved by simulating group level forest
types, without taking into account the growth characteristics of individual species, and this may be
effective in studying regions where data is lacking, such as North Korea.
In this study, we proposed a method to evaluate habitat diversity through the overlap of habitat
suitability. Although this method is simple, Jeju Island, which is known to have the highest ecosystem
diversity, gave the highest level of confidence in the results. Indeed, biogeographically, biodiversity
is highly likely to be greatest at the boundary between subtropical and temperate zones, or between
the temperate zone and the alpine zone [
20
,
21
]. Jeju Island is the only latitude where subtropical and
temperate vegetation overlap in the Korean Peninsula, and subalpine forest is distributed in the high
mountains. The concept of this study is based on these theories.
Changes in habitat diversity, as well as habitat suitability of forest types, due to climate change
can have a significant impact on the conservation of biodiversity on a regional scale. While these forest
habitats do not have a direct impact on human food or industry, they can reduce the provisioning,
regulating, supporting, and culture functions provided by ecosystems and can lead to the destruction
Forests 2018,9, 259 13 of 16
of ecosystems through infestation by pests or alien species [
19
,
54
57
]. This negative prediction of
future change is disappointing, and means that active mitigation and adaptation to prevent these
changes are required. If changes are maintained at the RCP 4.5 level, in the 2050s, Jeju Island should
still be a suitable area for three forest types, and the suitable area for two forest types in South Korea
will be greater than at present. Even if the habitat suitable areas change, subalpine forests such as
Abies koreana and Abies nephrolepis should be found in major mountain areas through the management
of conservation species under the RCP 4.5 scenarios [
23
,
24
,
58
]. In other words, the sustainability of
future ecosystems is still dependent on our efforts.
Despite these implications, our study has some limitations. First, since only one GCM and SDM
were used, the uncertainties of the data and models remained. However, since our results are highly
similar with the results of other ensemble studies, it is considered that it is enough to explain future
conditions [
16
,
17
,
28
]. Also, SDM studies at the macro or regional level have structural limitations that
do not reflect microclimate variability or specific small-scale habitat attributes. Therefore, our results
might be useful to interpret the regional scale rather than small habitat scale.
4. Conclusions
Close assessment of the impacts of climate change has significant implications for the specific
impacts of greenhouse gas (GHG) mitigation, and the level needed for adaptation to climate
change. Changes in habitat suitability by forest type, and subsequent changes in habitat diversity,
were confirmed by two climate change scenarios. Three climate indices and MaxEnt models were used
for this purpose, and quantitative and qualitative evaluations were conducted. First, the results of
the three climate indices showed significant variation in the those related to temperature. Both the
statistical and the qualitative validation using the current actual vegetation map showed excellent
results. In terms of forest habitat suitability, a substantial increase and northward migration of warm
temperate evergreen forest areas was prominent, and the subalpine forest decreased significantly due
to the effects of climate change. The subalpine forest habitat suitable area for RCP 8.5 2070s decreased
to less than half that of the baseline period, while warm temperate evergreen forest habitats increased
by more than five times. In terms of forest habitat diversity, regions with a high diversity declined
due to the impact of climate change. In the RCP 8.5 scenarios, suitable areas for all three forest types
did not appear at all; however, in the RCP 4.5 2050s, suitable areas for two forest types increased, i.e.,
this was not negative in terms of diversity. This study highlights the necessity of adapting to climate
change, and the importance of greenhouse gas reduction to minimize the impact on habitat change
and diversity throughout forests and ecosystems.
Author Contributions:
C.-H.L. designed this research, analyzed the results, and wrote the paper; S.Y. and Y.C.
participated in analyzing the results and processing the data. S.W.J., Y.S., and W.-K.L. gave comments and
improved the final manuscript.
Acknowledgments:
This work was supported by “Public Technology Development Project based on
Environmental Policy” (2016000210001) provided by Korea Environmental Industry and Technology Institute,
and a Korea University Grant.
Conflicts of Interest: The authors declare no conflict of interest.
References
1.
Intergovernmental Panel on Climate Change (IPCC). Climate Change 2014: Impacts, Adaptation, and
Vulnerability. Part A: Global and Sectoral Aspects. Contribution of Working Group II to the Fifth Assessment
Report of the Intergovernmental Panel on Climate Change; Cambridge University Press: Cambridge, UK, 2014.
2.
Anadon, J.D.; Sala, O.E.; Maestre, F.T. Climate change will increase savannas at the expense of forests and
treeless vegetation in tropical and subtropical Americas. J. Ecol. 2014,102, 1363–1373. [CrossRef]
3.
Wernberg, T.; Bennett, S.; Babcock, R.C.; de Bettignies, T.; Cure, K.; Depczynski, M.; Dufois, F.; Fromont, J.;
Fulton, C.J.; Hovey, R.K.; et al. Climate-driven regime shift of a temperate marine ecosystem. Science
2016
,
353, 169–172. [CrossRef] [PubMed]
Forests 2018,9, 259 14 of 16
4.
Schlaepfer, D.R.; Bradford, J.B.; Lauenroth, W.K.; Munson, S.M.; Tietjen, B.; Hall, S.A.; Wilson, S.D.;
Duniway, M.C.; Jia, G.; Pyke, D.A.; et al. Climate change reduces extent of temperate drylands and
intensifies drought in deep soils. Nat. Commun. 2017,8, 14196. [CrossRef] [PubMed]
5.
Isbell, F.; Craven, D.; Connolly, J.; Loreau, M.; Schmid, B.; Beierkuhnlein, C.; Bezemer, T.M.; Bonin, C.;
Bruelheide, H.; de Luca, E.; et al. Biodiversity increases the resistance of ecosystem productivity to climate
extremes. Nature 2015,526, 574–577. [CrossRef] [PubMed]
6.
Prieto-Torres, D.A.; Navarro-Sigüenza, A.G.; Santiago-Alarcon, D.; Rojas-Soto, O.R. Response of the
endangered tropical dry forests to climate change and the role of Mexican Protected Areas for their
conservation. Glob. Chang. Biol. 2016,22, 364–379. [CrossRef] [PubMed]
7.
Dyderski, M.K.; Pa´z, S.; Frelich, L.E.; Jagodzi´nski, A.M. How much does climate change threaten European
forest tree species distributions? Glob. Chang. Biol. 2017,24, 1150–1163. [CrossRef] [PubMed]
8.
Akhter, S.; McDonald, M.A.; van Breugel, P.; Sohel, S.; Kjær, E.D.; Mariott, R. Habitat distribution modelling
to identify areas of high conservation value under climate change for Mangifera sylvatica Roxb. of Bangladesh.
Land Use Policy 2017,60, 223–232. [CrossRef]
9.
Mair, L.; Harrison, P.J.; Räty, M.; Bärring, L.; Strandberg, G.; Snäll, T. Forest management could counteract
distribution retractions forced by climate change. Ecol. Appl. 2017,27, 1485–1497. [CrossRef] [PubMed]
10.
Clark, J.S.; Gelfand, A.E.; Woodall, C.W.; Zhu, K. More than the sum of the parts: Forest climate response
from joint species distribution models. Ecol. Appl. 2014,24, 990–999. [CrossRef] [PubMed]
11.
Koo, K.A.; Patten, B.C.; Madden, M. Predicting effects of climate change on habitat suitability of red spruce
(Picea rubens Sarg.) in the southern Appalachian Mountains of the USA: Understanding complex systems
mechanisms through modeling. Forests 2015,6, 1208–1226. [CrossRef]
12.
De Rigo, D.; Caudullo, G.; San-Miguel-Ayanz, J.; Barredo, J.I. Robust Modelling of the Impacts of Climate
Change on the Habitat Suitability of Forest Tree Species; Publication Office of the European Union: Luxembourg,
2017; 58p.
13.
Choi, S.; Lee, W.K.; Kwak, D.A.; Lee, S.; Son, Y.; Lim, J.H.; Saborowski, J. Predicting forest cover changes
in future climate using hydrological and thermal indices in South Korea. Clim. Res.
2011
,49, 229–245.
[CrossRef]
14.
Nam, K.; Lee, W.K.; Kim, M.; Kwak, D.A.; Byun, W.H.; Yu, H.; Kwak, H.; Kwon, T.; Sung, J.; Chung, D.J.;
Lee, S.H. Spatio-temporal change in forest cover and carbon storage considering actual and potential forest
cover in South Korea. Sci. China Life Sci. 2015,58, 713–723. [CrossRef] [PubMed]
15.
Kang, W.; Minor, E.S.; Lee, D.; Park, C.R. Predicting impacts of climate change on habitat connectivity of
Kalopanax septemlobus in South Korea. Acta Oecol. 2016,71, 31–38. [CrossRef]
16.
Park, S.U.; Koo, K.A.; Seo, C.; Hong, S. Climate-related range shifts of Ardisia japonica in the Korean
Peninsula: A role of dispersal capacity. J. Ecol. Environ. 2017,41, 38. [CrossRef]
17.
Koo, K.A.; Park, S.U.; Seo, C. Effects of Climate Change on the Climatic Niches of Warm-Adapted Evergreen
Plants: Expansion or Contraction? Forests 2017,8, 500. [CrossRef]
18.
Huang, J.; Li, G.; Li, J.; Zhang, X.; Yan, M.; Du, S. Projecting the Range Shifts in Climatically Suitable Habitat
for Chinese Sea Buckthorn under Climate Change Scenarios. Forests 2018,9, 9. [CrossRef]
19.
Alsterberg, C.; Roger, F.; Sundbäck, K.; Juhanson, J.; Hulth, S.; Hallin, S.; Gamfeldt, L. Habitat diversity and
ecosystem multifunctionality—The importance of direct and indirect effects. Sci. Adv.
2017
,3, e1601475.
[CrossRef] [PubMed]
20.
Rosenzweig, M.L. Species diversity gradients: We know more and less than we thought. J. Mammal.
1992
,73,
715–730. [CrossRef]
21.
Paquette, A.; Messier, C. The effect of biodiversity on tree productivity: From temperate to boreal forests.
Glob. Ecol. Biogeogr. 2011,20, 170–180. [CrossRef]
22.
Müllerová, J.; Hédl, R.; Szabó, P. Coppice abandonment and its implications for species diversity in forest
vegetation. For. Ecol. Manag. 2015,343, 88–100. [CrossRef] [PubMed]
23.
Kim, M.; Lee, W.K.; Choi, G.M.; Song, C.; Lim, C.H.; Moon, J.; Piao, D.; Kraxner, F.; Shividenko, A.;
Forsell, N. Modeling stand-level mortality based on maximum stem number and seasonal temperature.
For. Ecol. Manag. 2017,386, 37–50. [CrossRef]
24.
Yoo, S.; Lee, W.K.; Kim, M.; Lim, C.H.; Song, C.; Kim, S.J. Predicting Endangered Coniferous Tree Species
Distribution under Climate Change: Implication of HyTAG Model. J. Environ. Impact Assess.
2018
,
under review.
Forests 2018,9, 259 15 of 16
25.
Lim, C.H.; Kim, S.H.; Choi, Y.; Kafatos, M.C.; Lee, W.K. Estimation of the Virtual Water Content of Main
Crops on the Korean Peninsula Using Multiple Regional Climate Models and Evapotranspiration Methods.
Sustainability 2017,9, 1172. [CrossRef]
26.
Lamchin, M.; Lee, W.K.; Jeon, S.W.; Wang, S.W.; Lim, C.H.; Song, C.; Sung, M. Long-term trend and correlation
between vegetation greenness and climate variables in Asia based on satellite data.
Sci. Total Environ. 2018
,
618, 1089–1095. [CrossRef] [PubMed]
27.
Lim, C.H.; Choi, Y.; Kim, M.; Lee, S.J.; Folberth, C.; Lee, W.K. Spatially Explicit Assessment of Agricultural
Water Equilibrium in the Korean Peninsula. Sustainability 2018,10, 201. [CrossRef]
28.
Koo, K.A.; Park, S.U.; Hong, S.; Jang, I.; Seo, C. Future distributions of warm-adapted evergreen trees,
Neolitsea sericea and Camellia japonica under climate change: Ensemble forecasts and predictive uncertainty.
Ecol. Res. 2018,33, 313–325. [CrossRef]
29.
Lim, C.H.; Choi, Y.; Kim, M.; Jeon, S.W.; Lee, W.K. Impact of deforestation on agro-environmental variables
in cropland, North Korea. Sustainability 2017,9, 1354. [CrossRef]
30.
Lim, C.H. A Cross-Sectoral Approach in the Water-Food-Ecosystem Nexus to Climate Change in the Korean
Peninsula. Ph.D. Thesis, Korea University, Seoul, Korea, 2017.
31.
Chao, A.; Chiu, C.H.; Jost, L. Statistical challenges of evaluating diversity patterns across environmental
gradients in mega-diverse communities. J. Veg. Sci. 2016,27, 437–438. [CrossRef]
32.
Fick, S.E.; Hijmans, R.J. Worldclim 2: New 1-km spatial resolution climate surfaces for global land areas. Int.
J. Climatol. 2017,37, 4302–4315. [CrossRef]
33.
Bachelet, D.; Lenihan, J.M.; Daly, C.; Neilson, R.P.; Ojima, D.S.; Parton, W.J. MC1: A Dynamic Vegetation Model
for Estimating the Distribution of Vegetation and Associated Ecosystem Fluxes of Carbon, Nutrients, and Water;
Pacific Northwest Station General Technical Report PNW-GTR-508; USDA: Washington, DC, USA, 2001.
34.
Neilson, R.P. A model for predicting continental-scale vegetation distribution and water balance. Ecol. Appl.
1995,5, 362–385. [CrossRef]
35.
Kira, T. A New Classification of Climate in Eastern Asia as the Basis for Agricultural Geography; Horticultural
Institute Kyoto University: Kyoto, Japan, 1945.
36.
Yim, Y.J. Distribution of forest vegetation and climate in the Korean Peninsula: III. Distribution of tree species
along the thermal gradient. Jpn. J. Ecol. 1977,27, 177–189.
37.
Thornthwaite, C.W. The climates of North America: According to a new classification. Geogr. Rev.
1931
,21,
633–655. [CrossRef]
38.
Vörösmarty, C.J.; Douglas, E.M.; Green, P.A.; Revenga, C. Geospatial indicators of emerging water stress:
An application to Africa. Ambio 2005,34, 230–236. [CrossRef] [PubMed]
39.
Choi, Y.; Lim, C.H.; Ryu, J.; Jeon, S.W. Bioclimatic Classification of the Northeast Asia Reflecting Social
Factors: Development and Characterization. Sustainability 2017,9, 1137. [CrossRef]
40.
Lim, C.H.; Kim, G.S.; Lee, E.J.; Heo, S.; Kim, T.; Kim, Y.S.; Lee, W.K. Development on Crop Yield Forecasting
Model for Major Vegetable Crops using Meteorological Information of Main Production Area. J. Clim.
Chang. Res. 2016,7, 193–203. [CrossRef]
41.
Deblauwe, V.; Barbier, N.; Couteron, P.; Lejeune, O.; Bogaert, J. The global biogeography of semi-arid periodic
vegetation patterns. Glob. Ecol. Biogeogr. 2008,17, 715–723. [CrossRef]
42.
Phillips, S.J.; Dudík, M. Modeling of species distributions with MaxEnt: New extensions and a comprehensive
evaluation. Ecography 2008,31, 161–175. [CrossRef]
43.
Elith, J.; Phillips, S.J.; Hastie, T.; Dudík, M.; Chee, Y.E.; Yates, C.J. A statistical explanation of MaxEnt for
ecologists. Divers. Distrib. 2011,17, 43–57. [CrossRef]
44.
Chen, F.; Du, Y.; Niu, S.; Zhao, J. Modeling forest lightning fire occurrence in the Daxinganling Mountains of
Northeastern China with MAXENT. Forests 2015,6, 1422–1438. [CrossRef]
45.
Ikegami, M.; Jenkins, T.A. Estimate global risks of a forest disease under current and future climates using
species distribution model and simple thermal model–Pine Wilt disease as a model case. For. Ecol. Manag.
2018,409, 343–352. [CrossRef]
46.
Xiaofeng, L.; Yi, Q.; Diqiang, L.; Shirong, L.; Xiulei, W.; Bo, W.; Chunquan, Z. Habitat evaluation of wild
Amur tiger (Panthera tigris altaica) and conservation priority setting in north-eastern China. J. Environ. Manag.
2011,92, 31–42. [CrossRef] [PubMed]
47.
Jenks, G.F. The Data Model Concept in Statistical Mapping. In International Yearbook of Cartography; George
Philip: London, UK, 1967; Volume 7, pp. 186–190.
Forests 2018,9, 259 16 of 16
48.
Vilar, L.; Gómez, I.; Martínez-Vega, J.; Echavarría, P.; Riaño, D.; Martín, M.P. Multitemporal modelling of
socio-economic wildfire drivers in central Spain between the 1980s and the 2000s: Comparing generalized
linear models to machine learning algorithms. PLoS ONE 2016,11, e0161344. [CrossRef] [PubMed]
49.
Kafatos, M.C.; Kim, S.H.; Lim, C.-H.; Kim, J.; Lee, W.-K. Responses of Agroecosystems to Climate Change:
Specifics of Resilience in the Mid-Latitude Region. Sustainability 2017,9, 1361. [CrossRef]
50.
Kim, T.; Song, C.; Lee, W.K.; Kim, M.; Lim, C.H.; Jeon, S.W.; Kim, J. Habitat Quality Valuation Using InVEST
Model in Jeju Island. J. Korea Soc. Environ. Restor. Technol. 2015,18, 1–11. [CrossRef]
51.
Cui, G.; Kwak, H.; Choi, S.; Kim, M.; Lim, C.H.; Lee, W.K.; Kim, J.; Chae, Y. Assessing vulnerability of forests
to climate change in South Korea. J. For. Res. 2016,27, 489–503. [CrossRef]
52.
Koo, K.A.; Kong, W.S.; Nibbelink, N.P.; Hopkinson, C.S.; Lee, J.H. Potential effects of climate change on the
distribution of cold-tolerant evergreen broadleaved woody plants in the Korean Peninsula. PLoS ONE
2015
,
10, e0134043. [CrossRef] [PubMed]
53.
Lim, C.H; Lee, W.K. Climate-Environmental Impacts on Agriculture and Water Balance; LAP: Beau Bassin,
Mauritius, 2018.
54.
Kim, M.; Lee, W.K.; Kim, Y.S.; Lim, C.H.; Song, C.; Park, T.; Son, Y.; Son, Y.M. Impact of thinning intensity on
the diameter and height growth of Larix kaempferi stands in central Korea. For. Sci. Technol. 2016,12, 77–87.
55.
Song, C.H.; Lee, W.K.; Choi, H.A.; Kim, J.; Jeon, S.W.; Kim, J.S. Spatial assessment of ecosystem functions
and services for air purification of forests in South Korea. Environ. Sci. Policy 2016,63, 27–34. [CrossRef]
56.
Kim, G.S.; Lim, C.H.; Kim, S.J.; Lee, J.; Son, Y.; Lee, W.K. Effect of national-scale afforestation on forest water
supply and soil loss in South Korea, 1971–2010. Sustainability 2017,9, 1017. [CrossRef]
57.
Lee, J.; Lim, C.H.; Kim, G.S.; Markandya, A.; Chowdhury, S.; Kim, S.J.; Lee, W.K.; Son, Y. Economic viability
of the national-scale forestation program: The case of success in the Republic of Korea. Ecosyst. Serv.
2018
,
29, 40–46. [CrossRef]
58.
Correia, R.A.; Bugalho, M.N.; Franco, A.M.; Palmeirim, J.M. Contribution of spatially explicit models to
climate change adaptation and mitigation plans for a priority forest habitat. Mitig. Adapt. Strateg. Glob. Chang.
2018,23, 371–386. [CrossRef]
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... Rights reserved. distribution [27][28][29][30]. Although MaxEnt is a conventional SDM, it has received more attention than other SDMs in distribution modeling studies of various wildlife species [31][32][33]. ...
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Climate change has modified the structure and functions of ecosystems, affecting human well-being. Evergreen plants in the warm-temperate ecosystems will lose climatically suitable habitats under climate change but have not drawn much scholarly interest. Therefore, the present research aimed to predict the future climatic niches of eight coastal warm-adapted evergreen trees under climate change to provide information for an effective management practice. For this purpose, we used the ensemble species distribution models (SDMs) weighted by the TSS value in modelling the climatic niches of those evergreen trees and then ensembled their future distributions predicted under 20 future climate scenarios. Except for Neolitsea sericea (True Skill Statistic (TSS) = 0.79), all projections for the current climatic niches of evergreens showed excellent predictive powers (TSS > 0.85). The results showed that the climatic niches of the four evergreens—Castanopsis cuspidata, Pittosporum tobira, Raphiolepis indica var. umbellate, and Eurya emarginata—would expand to the northern part of the Korean Peninsula (KP) under climate change, but the ones of the remaining four—Kadsura japonica, Neolitsea sericea, Ilex integra, and Dendropanax morbiferus—would shrink. While the climatic niches of Pittosporum tobira showed the rapidest and greatest expansion under climate change, Dendropanax morbiferus was predicted to experience the greatest loss of habitat. On the other hand, regardless of whether the future distributions of climatically suitable habitats would expand or contract, the highly suitable habitats of all species were predicted to decline under climate change. This may indicate that further climate change will degrade habitat suitability for all species within the distribution boundary and restrict continuous habitat expansions of expanding species or accelerate habitat loss of shrinking species. In addition, the future distributions of most coastal evergreens were found to be confined to coastal areas; therefore, sea-level rise would accelerate their habitat loss under climate change. The present study provides primary and practical knowledge for understanding climate-related coastal vegetation changes for future conservation planning, particularly on the Korean Peninsula.
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Many studies about climate-related range shift of plants have focused on understanding the relationship between climatic factors and plant distributions. However, consideration of adaptation factors, such as dispersal and plant physiological processes, is necessary for a more accurate prediction. This study predicted the future distribution of marlberry (Ardisia japonica), a warm-adapted evergreen broadleaved shrub, under climate change in relation to the dispersal ability that is determined by elapsed time for the first seed production. We introduced climate change data under four representative concentration pathway (RCP 2.6, 4.5, 6.0, and 8.5) scenarios from five different global circulation models (GCMs) to simulate the future distributions (2041~2060) of marlberry. Using these 20 different climate data, ensemble forecasts were produced by averaging the future distributions of marlberry in order to minimize the model uncertainties. Then, a dispersal-limited function was applied to the ensemble forecast in order to exam the impact of dispersal capacity on future marlberry distributions. In the dispersal-limited function, elapsed time for the first seed production and possible dispersal distances define the dispersal capacity. The results showed that the current suitable habitats of marlberry expanded toward central coast and southern inland area from the current southern and mid-eastern coast area in Korea. However, given the dispersal-limited function, this experiment showed lower expansions to the central coast area and southern inland area. This study well explains the importance of dispersal capacity in the prediction of future marlberry distribution and can be used as basic information in understanding the climate change effects on the future distributions of Ardisia japonica.
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YIM, Yang-Jai (Dep. Biol., Coll. Lib. Arts & Sci., Chungang Univ., Seoul). 1977. Distribution of forest vegetation and climate in the Korean Peninsula. III. Distribution of tree species along the thermal gradient. Jap. J. Ecol., 27 : 177-189. The distribution of 50 woody plant species along the gradient of thermal climate was studied using existing records of their three-dimensional distribution in the Korean Peninsula. The range and frequency of occurrence of the species were examined in relation to KIRA's warmth index (WI). The frequency-WI curves obtained were found to be bell-shaped and more or less symmetrical with a single maximum. The range of thermal distribution of a species was 30-60 (mostly 40-50)℃ month in terms of the WI. The species were grouped into the following 4 groups according to the optimal ranges of their thermal distribution ; subalpine, cool-temperate, warm-temperate deciduous and warm-temperate evergreen group. Unlike other groups, the northern or upward limits of distribution of the species of the last group proved to be most closely correlated with the coldness of winter as expressed by KIRA's coldness index.
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As climatic and socio-economic changes progress, the decoupling between water retention and usage widens. In terms of water demand, agriculture and crop production are the sectors that have the highest than other industries. Also, agriculture is the most climate-dependent production sector; thus, it is necessary to accurately assess the impacts of climate change on the agriculture sector to achieve sustainability. i.e. for the sustainable agriculture in the era of climate change needs to find solutions for the retention and proper utilization of water. And also, agro-environment affected by environmental change, such as deforestation had an impact on cropland stability, water resource and productivity. In case of North Korea, lots of forest was degraded and converted to cropland, but it could be reason for decreasing food security. To contribute solving these problems, this book covered agriculture and water studies by climate change and environmental change using spatial modelling technique focus on Korean Peninsula.
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Predicting distributions of a pest species is an important part of Pest Risk Analysis, but it is not always an easy task. Expert knowledge may help, yet, validation with field data is essential. Pine Wilt Disease (hereafter PWD) caused by the presence of the Pine wood nematode (hereafter PWN), Bursaphelenchus xylophilus is one of the most severe forest diseases in the world. The symptom development of this disease only occurs in a warmer region while the nematode itself could be widely distributed in cooler regions in its native range. Isotherm approaches have been used to estimate the distribution of this disease, but these models have not been well tested with field data. A correlative species distribution model, MaxEnt, is used to evaluate the climatic suitability for PWD at a global scale, along with a simple thermal model. The MaxEnt analysis indicated that most of the current distribution of PWD is explained by the average monthly mean temperatures of the warmest three months and aridity. The thermal model suggested larger PWD risk zones, particularly in hot and arid areas. The current distribution of some susceptible host species (e.g., Pinus sylvestris) is largely outside the area suitable for the development of PWD, but results using projected future climate showed that half of those areas become at risk from PWD in future. Species distribution models such as MaxEnt are useful tools for the evaluation of not only the likely potential distributions of pests but also areas where symptom development could occur under various climatic conditions, even exotic pests not yet present in a region. Our results suggest that PWD will undoubtedly pose a major threat to European pine species if climate change proceeds as currently projected.
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The prediction of the climate change effect on plant distribution has become a primary research field for conservation practices and planning. The present research predicted future distributions of warm-adapted evergreen trees, Neolitsea sericea (Blume) Koidz. and Camellia japonica L., under climate change in the Korean Peninsula (KP) using an ensemble approach and quantified the predictive uncertainty. For these purposes, we used nine modeling algorithms and the pre-evaluation weighted ensemble method in modelling the current distributions of those evergreen trees; furthermore, we predicted their future distributions under 20 climate change scenarios and averaged the future predictions for ensemble forecasts. The results suggest that both species would expand to the northern part of KP under climate changes; however, the spatial pattern and rate of expansions would be different. C. japonica showed a faster expansion than N. sericea. While C. japonica showed both inland and northward expansions under climate change, N. sericea was mostly distributed in coastal areas. In addition, the highly suitable habitats of N. sericea and C. japonica will decline or shift to the north under climate change. This may indicate that climate change will degrade habitat suitability of those species within the distribution boundary and may restrict their continuous range expansions under further climate changes. Considering the lack of research on the climate-related range shifts of plants in Asia including KP, the present study provides fundamental and practical knowledge for a better understanding of climate-related vegetation changes in Asia as well as in KP.