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Article

Climate Change Alters Ecological Niches and Distribution of Two Major Forest Species in Korea, Accelerating the Pace of Forest Succession

by
Sang Kyoung Lee
1,
Dong-Ho Lee
2,
Yeo Bin Park
1,
Do Hun Ryu
1,
Jun Mo Kim
1,
Eui-Joo Kim
3,
Jae Hoon Park
3,
Ji Won Park
1,
Kyeong Mi Cho
1,
Ji Hyun Seo
1,
Sang Pil Lee
1,
Seung Jun Lee
1,
Ji Su Ko
1,
Hye Jeong Jang
1 and
Young Han You
1,*
1
Department of Biological Sciences, Kongju National University, Gongju 32588, Republic of Korea
2
Forest Management Research Division, National Institute of Forest Science, Seoul 02455, Republic of Korea
3
Biotechnology Research Institute, Kongju National University, Gongju 32588, Republic of Korea
*
Author to whom correspondence should be addressed.
Forests 2025, 16(8), 1331; https://doi.org/10.3390/f16081331
Submission received: 1 July 2025 / Revised: 8 August 2025 / Accepted: 13 August 2025 / Published: 15 August 2025
(This article belongs to the Section Forest Ecology and Management)
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Abstract

Temperate forest ecosystems in Korea are currently undergoing a successional transition from Pinus densiflora Siebold & Zucc. (evergreen conifer) communities to Quercus mongolica Fisch. ex Ledeb. (deciduous broadleaf) communities. This study aimed to assess interspecific differences in ecological responses to climate change [Representative Concentration Pathway (RCP) 4.5] by evaluating changes in ecological niche characteristics and species distribution. Controlled-environment experiments, principal component analysis (PCA), and MaxEnt species distribution modeling were employed to quantify and predict ecological shifts in the two dominant species under climate change scenarios. Both species exhibited increases in niche breadth and interspecific overlap under climate change conditions. However, Q. mongolica showed a more pronounced increase in niche breadth compared to P. densiflora, indicating greater ecological flexibility and adaptive potential to warming conditions. According to the MaxEnt model projections, climate change is expected to result in an approximate 30% reduction in suitable habitat for P. densiflora in lowland areas. In contrast, Q. mongolica is projected to expand its suitable habitat by over 80%, notably in both low-elevation (below 800 m) and high-elevation (above 1400 m) zones, without being restricted to any specific altitudinal range. Our findings suggest that climate change may increase ecological similarity between P. densiflora and Q. mongolica, thereby raising the potential for interspecific competition. This convergence in niche traits could contribute to an accelerated successional transition, although actual competitive interactions in natural ecosystems require further empirical validation. Consequently, Korean forests are likely to transform into predominantly deciduous forest ecosystems under future climate conditions.

1. Introduction

Climate change is acting as a major driver of structural change in ecosystems worldwide and has a significant impact on plant growth and ecological niche dynamics. Over the past decade (2011–2020), the global average temperature has risen by approximately 1.1 °C compared to the pre-industrial period (1850–1900), exerting widespread effects on forest ecosystems [1].
Climate change induces shifts in key phenological events such as flowering, fruiting, and leaf-out periods, potentially altering interspecific competition dynamics and ecological niches [2]. The ecological niche refers to the functional role a species plays within an ecosystem and its pattern of resource use [3]. It is typically classified into the fundamental niche and the realized niche [4].
Niche breadth is an indicator reflecting a species’ environmental adaptability and diversity of resource use. A broader niche implies greater resilience and adaptability to climate change [5,6,7], whereas species with narrower niches tend to depend more on specific environmental conditions, making them more vulnerable to climatic shifts [6,7].
Niche overlap refers to the degree to which two species share resources or space, with higher overlap potentially intensifying interspecific competition [8,9]. In contrast, low overlap may enhance the possibility of coexistence through resource partitioning or spatial separation [10]. These theoretical concepts offer a critical analytical framework for understanding changes in species distribution and interspecific interactions, and they were employed as quantitative metrics in the present study (Table 1).
Korea’s forest ecosystems have been shaped not only by natural succession but also by historical anthropogenic forest policies and management. During the Japanese occupation period, large-scale deforestation led to widespread forest degradation. Following liberation, the reforestation policy of the 1970s prioritized coniferous species, resulting in the establishment of extensive plantations dominated by P. densiflora and P. rigida [11]. This historical context suggests that the current dominance of pine forests is not merely a result of natural processes but rather reflects structural bias driven by anthropogenic influences. Pines tend to dominate in dry and nutrient-poor environments and are known for their pioneer traits, particularly their ability to establish rapidly after wildfires [12,13].
In contrast, Q. mongolica exhibits rapid growth and high adaptability in moist and nutrient-rich environments [14]. While P. densiflora tends to experience reduced survivability under rising temperatures and drought conditions, Q. mongolica may gain ecological competitiveness due to earlier leaf-out and flowering, which extend the photosynthetic period [15]. However, the current distributional area of Q. mongolica (11.6%) remains lower than that of P. densiflora (23.3%), not due to inferior ecological competitiveness, but rather as a result of historical conifer-oriented afforestation policies [16].
Previous studies have independently examined the distributional shifts or physiological responses of the two species; however, there is a lack of quantitative research directly comparing their ecological niches and trait responses through experimental approaches. For instance, the GARP model was employed to predict the distribution of P. densiflora, but experimental validation was not conducted [17]. Similarly, the suitable habitat for Q. mongolica under RCP scenarios was analyzed, yet a comparative analysis between the two species was not included [18].
Accordingly, this study conducted a closed greenhouse experiment simulating the RCP 4.5 scenario (+2.1 °C increase) to quantitatively measure the morphological and ecological traits of P. densiflora and Q. mongolica over a 180-day period. Based on data from 13 traits, ecological niche breadth (Levins’ index), niche overlap (Schoener’s index), and principal component analysis (PCA) were performed. In addition, MaxEnt modeling was used to compare the consistency between experimental results and predicted habitat suitability.
This approach has significant academic value as it provides a meaningful analytical framework for quantitatively evaluating interspecific differences in responses to climate change and potential future shifts in species dominance by integrating experimental trait-based analysis with model-based distribution projections.

2. Materials and Methods

2.1. Study Species

2.1.1. P. densiflora

P. densiflora is widely distributed in northeastern China, the Russian Far East, and across the Korean Peninsula [19]. In Korea, it inhabits most parts of the central and southern regions, primarily in forested areas [20]. As a dominant species in temperate deciduous forests, it plays a key role in forest ecosystems [21].
The root system stabilizes the soil and contributes to nutrient cycling [22], thereby enhancing soil stability and reducing erosion [23]. P. densiflora is generally recognized as a light-demanding pioneer species that establishes rapidly in disturbed areas and tends to dominate during the early stages of ecological succession [24]. It exhibits high growth rates in dry or high-light environments and is gradually replaced by deciduous broadleaf species over time. Additionally, it possesses fire-resistant needles and seeds, enabling rapid post-fire establishment and playing a key role in secondary succession processes [25].
The seeds of P. densiflora exhibit high germination and survival rates, making the species valuable for forest restoration and afforestation programs [26]. In particular, P. densiflora contributes to carbon storage within coniferous forest ecosystems and plays a significant role in climate change mitigation [27]. The pinecone and needle leaves of P. densiflora and the acorn and leaf morphology of Q. mongolica are presented in Figure 1.

2.1.2. Q. mongolica

Q. mongolica is distributed across northeastern China, the Russian Far East, and throughout the Korean Peninsula [19]. It is found across all regions of Korea, and typically inhabits the upper slopes of mountainous areas, including high elevations and climatically extreme sites, as well as exposed, rocky, dry, and nutrient-poor slopes, mountain ridges, and north-facing slopes [21,28]. It is a representative temperate deciduous tree species of the Korean Peninsula, widely distributed from the central inland to both the southern and northern regions, and is one of the major dominant species in Korean forests [29].
Its robust growth and drought and cold tolerance provide it with strong adaptive capacity under climate change. Q. mongolica also contributes to carbon sequestration, soil conservation, water retention, and biodiversity enhancement in forest ecosystems [30,31].
Q. mongolica is generally regarded as a dominant deciduous species in the mid-to-late stages of succession and exhibits semi-shade tolerance, allowing it to grow under low-light conditions [32]. Its seeds are dispersed primarily by gravity and rodents, and the species demonstrates high growth plasticity, enabling establishment across diverse environmental conditions [33]. Notably, Q. mongolica is distributed across a wide altitudinal range—from 300 m to over 1600 m—and is considered likely to expand its habitat into higher elevations under climate change scenarios [34].

2.1.3. Distribution of P. densiflora and Q. mongolica Communities

The National Forest Inventory (NFI) of Korea is a nationwide, sample-based forest survey conducted every five years to systematically monitor the structure, function, and temporal changes of forest ecosystems. The 6th NFI, carried out from 2016 to 2020, surveyed a total of 3938 permanent sample plots across the country [35]. Each plot was assessed for forest type, dominant species, stand structure, site conditions, and tree growth characteristics. The sampling design employed a systematic sampling approach to ensure statistical representativeness at the national scale. The NFI provides critical baseline data for forest policy development, climate change adaptation, carbon sink estimation, and ecosystem service evaluation.
The Republic of Korea is located in a temperate climate zone, with a mean annual temperature of approximately 13 °C and annual precipitation of about 1330 mm. To clarify the environmental conditions associated with the distribution of the studied species, Figure 2 presents a diagram integrating the geographical location of Korea, its climatic characteristics, and its relative position within the global biome system. The biome classification follows Whittaker’s climate-based framework [36].
According to the 6th National Forest Inventory (NFI), out of 3938 permanent sample plots, P. densiflora-dominant stands accounted for 39.1% (1538 plots), while Q. mongolica-dominant stands accounted for 14.7% (577 plots) (Figure 3). These data confirm that P. densiflora still dominates a larger area in Korea. Analysis of elevation data from the 6th National Forest Inventory (NFI) revealed that P. densiflora is primarily distributed at altitudes ranging from 30 to 971 m, while Q. mongolica occupies a higher elevation range of 147 to 1388 m. The overlapping altitudinal zone where both species coexist was identified as 147 to 941 m. Based on fixed sample plots from the 6th NFI, post-climate-change distributional shifts of P. densiflora and Q. mongolica communities were projected.

2.1.4. Environmental Conditions of P. densiflora and Q. mongolica

Climatic variables, including mean annual temperature and precipitation, were extracted from Automated Synoptic Observing System (ASOS) records covering the period from 1971 to 2014, provided by the Korea Meteorological Administration’s open data portal and interpolated using QGIS version 2.18 (QGIS Development Team, Madeira, Portugal). Physical variables, including elevation and slope, were derived from field measurements in the 6th National Forest Inventory (NFI).
In this study, four key environmental variables—mean annual temperature, mean annual precipitation, elevation, and slope—were selected as the final predictors, considering both ecological relevance and data availability. To examine potential multicollinearity among the variables, a correlation coefficient analysis was conducted. All pairwise correlation coefficients (|r|) were found to be ≤0.7, indicating that the variables were sufficiently independent.
Based on the analysis of environmental factors, P. densiflora was found to occur within the following ranges: mean annual temperature of 10.3–14.7 °C, annual precipitation of 909.4–1573.0 mm, elevation between 30 and 971 m, and slope ranging from 4° to 56°. In comparison, Q. mongolica was distributed across mean annual temperatures of 7.7–14.7 °C, annual precipitation of 1072.7–1780.9 mm, elevations of 147–1388 m, and slopes between 8° and 56°. The shared environmental range where both species co-occurred was defined as follows: mean annual temperature of 10.6–14.3 °C, annual precipitation of 1072.7–1573.0 mm, elevation between 147 and 941 m, and slope from 8° to 56° (Figure 4).

2.2. Seed Selection and Sowing

In this experiment, seeds of P. densiflora (a representative conifer) and Q. mongolica (a representative broadleaved species) were used, both of which are native to Korean forest ecosystems. Seeds were collected in October from naturally growing P. densiflora and Q. mongolica individuals in Singwan-dong, Gongju-si, Chungcheongnam-do, South Korea. To minimize maternal effects, seeds of each species were collected from a single parent tree. The collected seeds were stored under cold conditions (4 °C, 40% relative humidity) and sown the following March. From the germinated seedlings, 20 individuals of similar size were selected and cultivated for a period of 180 days.

2.3. Environmental Treatments

This study was conducted in a closed glasshouse under two conditions: a climate change treatment (T) and an ambient control (C). The ambient condition reflected current atmospheric temperatures, whereas the treatment condition simulated an average temperature increase of +2.1 °C based on the RCP 4.5 scenario. This scenario represents a moderate level of warming projected for the end of the 21st century in Korea and is widely used in ecological forecasting [37].
Temperature in the T treatment was actively controlled and maintained using an integrated environmental control system (LCS Environment Measure System, LCSEM S-002, Parus Co., Seoul, Republic of Korea). To monitor temperature fluctuations, data loggers (TR-71U, T&D, Nagano, Japan) were installed at the canopy level of both treatments, recording data every 10 min. The temperature in the T treatment was kept approximately 2.0 °C (±0.3 °C) higher than that in the control using ventilation systems.
Nutrient gradient treatments were established based on the average organic matter content of forest soil (5%) [38]. The substrate was composed of 100% sand, and organic matter (HyoSung ONB, Daejeon, Republic of Korea) was mixed at four levels: N1 (0%), N2 (5%), N3 (10%), and N4 (15%). Standard sand with a particle size of less than 2 mm (K.S.L 5100, Jumunjin Silica Sand, Jumunjin, Republic of Korea) was used (Table 2).

2.4. Harvest and Growth Trait Measurements

After 180 days of growth in a controlled glasshouse environment, 13 morphological and ecological traits were measured to assess the ecological niches of P. densiflora and Q. mongolica. Leaf-related traits included leaf width length (mm), leaf blade length (cm), and the width-to-length ratio. Leaf width was measured using a vernier caliper (CD-15CPX, Mitutoyo Co., Kanagawa, Japan), and blade length was measured with a 15 cm stainless steel ruler 15 cm stainless steel ruler (generic, Republic of Korea). Plant architecture traits included shoot length, root length, the shoot-to-root length ratio, and the leaf mass ratio. Biomass traits included leaf weight (g), leaves weight (g), stem weight (g), shoot weight (g), root weight (g), plant weight (g). All biomass measurements were performed using an electronic balance (UX400H, Shimadzu Co., Kyoto, Japan).

2.5. Niche Breadth, Overlap, and Rate of Change

Under climate change conditions, the niche breadth of P. densiflora and Q. mongolica in response to nutrient availability was calculated using the formula proposed by Levins [39]:
B = 1/∑(Pi2) · S,
where B is the niche breadth, Pi is the relative response of a species in gradient i, and S is the total number of gradients.
Niche overlap between the two species was assessed using proportional similarity (PS), following Schoener [40]:
PS = −(1/2) ∑ |Pij − Pih|,
where Pij and Pih are the relative responses of species j and h in the ith gradient, respectively.
The percentage change in ecological niche breadth and niche overlap for each trait was calculated using the formula: ((treatment—control)/control)/control value × 100. Positive values indicate an increase under climate change treatment, whereas negative values indicate a decrease.

2.6. Population Response Analysis

To compare the population-level responses of P. densiflora and Q. mongolica between ambient and climate change treatments, principal component analysis (PCA) was performed using 13 morphological and ecological traits [41]. In the resulting PCA biplots, the spatial distribution of individuals and the area of polygons formed by each species were analyzed to interpret changes in ecological niche under climate change [41]. All statistical analyses were conducted using Statistica 7 software (Statsoft Co., 2008, Tulsa, OK, USA).

2.7. Species Distribution Prediction Under Climate Change

2.7.1. Habitat Data Collection

To predict the distributional changes of P. densiflora and Q. mongolica under climate change, species occurrence points were obtained from fixed sample plots surveyed during the 6th National Forest Inventory (NFI), using TM coordinates (Transverse Mercator coordinate system). Among the environmental variables, elevation and slope were derived from field measurements, while mean annual temperature and precipitation were obtained from ASOS (Automated Synoptic Observing System) meteorological data recorded from 1971 to 2014. These climate variables were spatially interpolated to generate nationwide environmental distribution maps. The environmental variables, along with species occurrence data, were used to train the MaxEnt model to estimate the potential habitat suitability for the two species.

2.7.2. Application of Climate Change Scenario

To reflect habitat changes under climate change, the RCP 4.5 scenario was applied by simulating an average temperature increase of +2.1 °C by the year 2100. Based on ASOS (Automated Synoptic Observing System) temperature data collected from 1971 to 2014, a nationwide mean annual temperature map was constructed using QGIS version 2.18 (QGIS Development Team, Madeira, Portugal). The projected warming was then added to generate future climate raster layers representing the climate change scenario. Temperature values corresponding to each TM coordinate (Transverse Mercator coordinate system) were extracted from this map and used as input for the MaxEnt model to predict the impact of warming on habitat suitability.

2.7.3. Species Distribution Modeling

Species distribution under climate change was modeled using MaxEnt version 3.4.1 (American Museum of Natural History, New York, NY, USA), a widely used Maximum Entropy approach. Two representative tree species in Korean forests, P. densiflora and Q. mongolica, were selected as target species. Environmental predictors included mean annual temperature, annual precipitation, elevation, and slope, along with current occurrence data (TM coordinates). The model projected habitat suitability under the RCP 4.5 climate change scenario, incorporating a projected +2.1 °C increase by the year 2100, based on CMIP6 data. Model settings included Auto Features (e.g., Linear, Quadratic, Hinge), a Regularization Multiplier of 1.0, 10,000 background points, and up to 500 training iterations. The MaxEnt model was first trained using current environmental and spatial data. Subsequently, the climate change scenario was applied to forecast future habitat suitability for the two species.

2.7.4. Model Performance Evaluation Based on AUC

The predictive performance of the MaxEnt model was quantitatively assessed using the Area Under the Curve (AUC) of the Receiver Operating Characteristic (ROC) curve. AUC measures the model’s ability to distinguish between actual species occurrences and background (pseudo-absence) locations. Following the classification criteria by [42], AUC values were interpreted as follows: 0.5–0.69 = Poor, 0.7–0.79 = Reasonable, 0.8–0.89 = Excellent, and ≥0.9 = Exceptional.

2.7.5. Final Coordinate Extraction

Based on habitat suitability outputs predicted by the MaxEnt model, final TM coordinates reflecting habitat shift patterns were extracted using spatial analysis through Geographic Information Systems (GIS). Habitat shift modeling was implemented QGIS version 3.28.3 (QGIS Development Team, Madeira, Portugal) and R version 4.2.2 with packages sp, raster, dismo (R Foundation for Statistical Computing, Vienna, Austria) [43,44,45], while additional climate data analyses were conducted using ArcGIS Pro version 3.0 (Esri, Redlands, CA, USA). To account for potential natural dispersal under the RCP 4.5 scenario (+2.1 °C by 2100), a random shift algorithm was applied, resulting in the final set of species-specific TM coordinates.

3. Results

3.1. Niche Breadth of Two Species Under Climate Change Conditions

The ecological niche breadths, based on 13 morphological and ecological traits, were all above 0.9 for both species, indicating generally broad ecological adaptability. Under climate change conditions, the mean niche breadth of P. densiflora was 0.982 ± 0.025, showing a slight increase compared to 0.977 ± 0.022 under ambient conditions. In Q. mongolica, the niche breadth under the climate change treatment was 0.988 ± 0.010, which represented a relatively larger increase compared to 0.969 ± 0.034 in the control. Both species exhibited a tendency for expanded niche breadth under climate change, with Q. mongolica showing a more pronounced response than P. densiflora, suggesting greater sensitivity to environmental change (Table 3).

3.2. Niche Overlap Between the Two Species Under Climate Change Conditions

Under climate change conditions, the niche overlap between P. densiflora and Q. mongolica was measured at 0.939 ± 0.041 in the climate change treatment, which was approximately 5.3% higher than the value in the control treatment (0.892 ± 0.066) (Table 3). Among the 13 measured traits, plant architecture traits showed relatively low similarity in resource utilization between the two species, whereas biomass- and leaf-related traits exhibited high similarity. These results suggest that climate change is causing a partial convergence in resource use strategies between the two species. In particular, changes in biomass-related traits may indicate an increased potential for interspecific competition under future climate conditions.
Although Schoener’s niche overlap index is a measure of resource-use similarity, actual competitive intensity may vary depending on resource availability and environmental conditions [9]. Therefore, the observed increase in niche overlap in this study should be interpreted not as direct evidence of intensified competition, but rather as an ecological signal indicating increased similarity in resource utilization patterns. Additionally, the nutrient gradient treatments (N1–N4) used in this study were based on the average organic matter content of forest soils and represent simplified experimental conditions, which may differ from the complex resource environments found in natural forests.

3.3. Change Rates of Niche Breadth and Niche Overlap Under Climate Change

Under climate change conditions, the average ecological niche breadth increased by 0.52 ± 2.09% in P. densiflora and 2.06 ± 3.71% in Q. mongolica, with an overall trend of expansion across most traits. In P. densiflora, niche breadth increased for traits such as root length (+2.11%) and stem biomass (+1.22%), but decreased for leaf width (–5.13%) and shoot-to-root ratio (–1.20%). In Q. mongolica, substantial increases were observed in traits such as shoot length (+11.47%) and root length (+7.96%), with niche breadth expanding in most traits except shoot length, stem biomass, and aboveground biomass (Figure 5). Niche overlap between the two species increased by an average of 5.65 ± 3.71%, and resource-use similarity became more pronounced across most traits, except shoot length, shoot-to-root ratio, and leaf-to-total biomass ratio (Figure 5). Notably, Q. mongolica exhibited a relatively greater increase in niche breadth compared to P. densiflora, suggesting higher adaptive plasticity and ecological expansion potential under climate change.
However, given the relatively large standard deviations associated with the average change rates, these values should be interpreted as indicative trends at the level of individual traits rather than statistically significant shifts. These results suggest that climate change not only diversifies the ecological and morphological responses of the two species, but also increases the similarity in resource-use strategies, potentially intensifying interspecific competition. Ultimately, this may provide an ecological basis for structural succession from P. densiflora-dominated communities to Q. mongolica-dominated communities.

3.4. Correlation Between Climate Change Conditions and Morphological–Ecological Traits

Principal component analysis (PCA) of P. densiflora and Q. mongolica revealed clear distinctions in ecological and morphological responses between the control (C) and climate change treatment (T) groups. In P. densiflora, the PCA area under the treatment condition was 10.89, slightly reduced compared to (11.59) in the control. This may indicate reduced inter-individual trait variation under warming, though whether this reflects adaptive stabilization or constrained plasticity requires further investigation. In contrast, Q. mongolica showed a marked expansion in PCA area, with the treatment group reaching 41.97—approximately 3.16 times greater than the control group (13.26)—indicating a broader range of ecological and morphological responses induced by climate change (Figure 6). In this context, “Pd” and “Qm” refer to P. densiflora and Q. mongolica, respectively, while “C” and “T” denote the control and treatment groups. These results highlight differences in climate response and adaptation strategies between the two species and are consistent with trait-specific principal component loadings and correlation analyses (Table 4).
The principal component analysis revealed that PC1 accounted for 58.33% of the total variance and was primarily associated with biomass-related traits such as leaf width, leaf mass, and stem mass. This axis can be interpreted as reflecting differences in resource accumulation and growth strategies. In contrast, PC2 explained 16.67% of the variance, with major contributions from traits such as shoot-to-root ratio and shoot length, representing structural balance and morphological responsiveness. Notably, Q. mongolica demonstrated more flexible adaptive responses to climate change, suggesting a greater ecological advantage in dynamic environments. Such differences in adaptive capacity may, in the long term, enable Q. mongolica to attain a more favorable ecological position than P. densiflora, potentially driving shifts in forest community structure and species composition—particularly facilitating successional transition from P. densiflora-dominated to Q. mongolica-dominated communities.

3.5. Predicted Distribution of P. densiflora and Q. mongolica Under Climate Change

3.5.1. AUC Validation and Model Performance Evaluation

Using the MaxEnt model, we analyzed the impact of climate change on the potential habitat distributions of P. densiflora and Q. mongolica, and validated the predictive performance using AUC values and ROC curves. The prediction results demonstrated high reliability for both species, with AUC values of 0.84 for P. densiflora and 0.87 for Q. mongolica, indicating strong model performance even under the applied climate change scenario (Figure 4). The ROC curves were significantly higher than the random prediction threshold (AUC = 0.5), and Q. mongolica in particular showed a higher AUC value and superior ROC curve (Figure 7). This suggests that Q. mongolica may have a greater potential for habitat expansion and that its sensitive response to climate change was well captured by the model. Overall, the MaxEnt models were evaluated as reliable tools for predicting relative distribution changes and ecological sensitivity between the two species under climate change.

3.5.2. Predicted Distribution of the Two Species

Under the climate change scenario (RCP 4.5, +2.1 °C increase), the habitat suitability of the two species exhibited contrasting patterns. The suitable habitat for P. densiflora was projected to decline by approximately 30%, from 1588 to 1112 locations, with a particularly sharp reduction in lowland areas below 800 m in elevation. While a limited number of suitable sites remained between 800–1200 m, the overall trend indicated a range contraction. In contrast, Q. mongolica was projected to experience a substantial expansion in suitable habitat, increasing by approximately 82% from 577 to 1053 locations. This expansion included not only lowland areas—where P. densiflora suitability declined—but also high-altitude regions above 1400 m. These results suggest that climate change may create favorable conditions for Q. mongolica to occupy a broader ecological range, with important implications for interspecific competition (Figure 8). However, given the species’ current low occurrence rate (14.7%), actual range expansion may be limited by ecological constraints related to seed dispersal, seedling survival, and establishment processes in natural environments.

4. Discussion

This study quantitatively assessed the ecological and morphological responses of P. densiflora and Q. mongolica under climate change conditions by analyzing changes in niche breadth and niche overlap. The findings provided insights into potential shifts in species distribution and competitive dynamics between the two species. The results showed that Q. mongolica had a broader niche breadth and exhibited greater variation in morphological and ecological traits. Its distribution area is predicted to increase by approximately 82%. In contrast, P. densiflora is projected to experience a decline in population size, particularly in lowland regions, accompanied by a contraction in niche breadth and a 30% reduction in suitable habitat area. These contrasting trends indicate that climate change may significantly reshape the ecological competitiveness and distribution patterns of the two species. The results suggest that these differences may accelerate the successional shift from P. densiflora communities to Q. mongolica communities under climate change.
This study quantitatively evaluated changes in niche breadth and niche overlap of P. densiflora and Q. mongolica under climate change conditions (RCP 4.5) using principal component analysis (PCA) and independently constructed a MaxEnt model based on key environmental variables and species occurrence data to predict future habitat distributions. Both analyses consistently indicated an expansion of ecological responses and habitat range for Q. mongolica, along with a contraction in niche breadth and habitat reduction for P. densiflora under climate change. These convergent results enabled a coherent interpretation of potential interspecific competition and forest community shifts. By integrating experimental trait-based analysis with predictive modeling, this study provides a valid and practical framework for improving the reliability and realism of ecological forecasts under climate change [46]. Notably, the ecological response analysis using principal component analysis (PCA) in this study follows a similar approach to that presented in [46]. By applying PCA to experimentally derived trait data, this study effectively visualized species-specific niche shifts and ecological response patterns, offering clear insight into interspecific differences.
These findings are consistent with previous studies indicating a gradual successional shift from P. densiflora-dominated stands to Q. mongolica-dominated deciduous broadleaf forests in Korean forest ecosystems [47,48,49,50]. Repeated observations of P. densiflora forests declining and transitioning into deciduous forests across various forest regions have been reported [47]. Ground cover conditions such as the litter layer and shrub layer positively affect the survival of Q. mongolica seedlings [48] Disturbance was shown to cause structural instability and reduced resilience in P. densiflora stands [49], while warming significantly enhanced seed germination and seedling growth of Q. mongolica [50]. Taken together, qualitative and quantitative comparisons with prior studies support the conclusion that Q. mongolica may possess ecological advantages over P. densiflora in terms of survival, growth, and competitiveness under climate change.
Climate change is increasingly recognized as a key driver accelerating forest succession in Korean ecosystems. This shift typically manifests as the gradual replacement of P. densiflora by Q. mongolica, a species with superior physiological adaptability and competitive advantage. In particular, rising temperatures and precipitation imbalances contribute to growth suppression and increased mortality of P. densiflora, while simultaneously creating ecological conditions that favor the range expansion of Q. mongolica [51].
Moreover, external disturbances such as wildfires have been shown to facilitate the rapid establishment and increased dominance of Q. mongolica. This response is largely attributed to its ecological traits advantageous for post-disturbance resource acquisition, including fast initial growth and shade tolerance. Compared to P. densiflora, these traits confer a higher resilience to disturbance regimes. Consequently, such dynamics may represent an ecological tipping point, accelerating the long-term expansion of deciduous broad-leaved species [52].
Case studies from major forest regions such as Mt. Jumbongsan, Mt. Odaesan, and Mt. Gayasan have shown that the combined effects of multiple climate factors are contributing to the decline of P. densiflora forests and the expansion of deciduous broad-leaved forests [47]. In particular, urban forests and lowland pine stands have been reported to be more vulnerable to climatic and environmental stressors [53], while Q. mongolica has been identified as a species with high potential for habitat expansion under future climate scenarios [54].
According to the findings of this study, under the RCP 4.5 scenario (+2.1 °C increase), Q. mongolica is projected to expand its habitat range in both lowland and highland areas, whereas P. densiflora is expected to experience a significant reduction in habitat suitability, particularly below 800 m in elevation. Niche analysis revealed that Q. mongolica exhibited both an increase in niche breadth and greater niche overlap, indicating stronger ecological competitiveness than P. densiflora in terms of resource use and spatial occupancy. Furthermore, elevated temperatures were found to positively influence the survival and growth of Q. mongolica seedlings, supporting its potential for future habitat expansion [55].
However, the results of the MaxEnt model represent habitat suitability predictions based on climatic and topographic conditions, and do not directly indicate actual range expansion. Q. mongolica currently exhibits a low occurrence rate of 14.7%, and its seed dispersal relies on gravity and rodents, which may limit rapid expansion in the short term. Therefore, the projected increase in suitable habitat for Q. mongolica should be interpreted as a potential for gradual expansion over the long term, rather than immediate spread.
While this study predicted species distributions based primarily on climatic and topographic variables, it did not account for other influential environmental factors such as soil type, biotic interactions (e.g., competition, mutualism), or disturbance frequency. Moreover, the MaxEnt model estimates habitat suitability without incorporating absence data, and thus does not fully reflect the potential for population survival, reproduction, and establishment. Given these limitations, future research should pursue more refined predictions that integrate a broader range of environmental and biological factors, along with long-term field-based validation studies.
In recent years, large-scale wildfires have occurred repeatedly along Korea’s east coast and southern regions, including Gyeongsang Province. These affected areas are predominantly composed of P. densiflora-dominated monocultures, which exhibit high vulnerability to fire. The combination of high temperatures, dry conditions, and strong winds further increases wildfire risk and poses a serious threat to the long-term sustainability of pine forests. Vegetation shifts observed in disturbed environments—such as the ecological transition from P. densiflora to Q. mongolica identified in this study—are closely related to these disturbance regimes. To mitigate wildfire risks, forest management strategies should prioritize preventive measures such as thinning to reduce fuel loads, removal of deadwood, and establishment of firebreaks. In addition, introducing fire-tolerant species, implementing early detection systems, and improving rapid response capacity can help minimize wildfire damage and enhance the resilience of forest ecosystems under ongoing climate change.
The transition toward Q. mongolica-dominated broadleaf forests suggests not merely a species replacement, but a potential restructuring of forest ecosystems toward higher resilience and greater environmental adaptability. According to previous studies, Q. mongolica tends to establish rapidly and dominate after wildfires, whereas P. densiflora often exhibits low survival rates and slow recovery [45]. Although this study did not directly examine wildfire effects, the literature-based evidence supports the interpretation that Q. mongolica possesses ecological traits conducive to superior post-disturbance recovery. Accordingly, the potential expansion of Q. mongolica offers important implications for forest recovery strategies and ecological restoration planning in response to compound disturbances such as wildfires and climate change. Ultimately, climate change may amplify the ecological divergence between P. densiflora and Q. mongolica, accelerating shifts in species composition, dominance structure, and successional dynamics within forest communities.
As a result, climate change is likely to intensify the ecological response differences between P. densiflora and Q. mongolica, leading to shifts in species composition, dominance, and accelerated structural succession within forest communities. Such transformations are closely linked to the long-term reorganization of forest ecosystems in Korea, underscoring the urgent need for scientifically grounded management strategies. To enhance the sustainability of pine-dominated forests, practical field-based measures should be implemented, including the designation of high-altitude conservation zones, microclimate regulation to improve seedling survival, and the promotion of mixed-species stands. However, these conservation strategies should not be uniformly applied across all pine forests. Instead, a selective approach that considers ecological stability, biodiversity, and cultural value is essential. While it is important to accept the natural successional processes driven by climate change, it is equally critical to adopt strategic measures to preserve the unique ecological, scenic, and socio-cultural values of certain P. densiflora stands.

5. Conclusions

This study quantitatively compared the ecological responses of P. densiflora and Q. mongolica under climate change (RCP 4.5) conditions. Q. mongolica is projected to expand its niche breadth and increase its distribution area by approximately 82%, whereas P. densiflora is expected to decline, particularly in lowland regions, with about a 30% reduction in suitable habitat. These changes are likely to increase ecological overlap and competition between the two species, potentially accelerating the successional shift from P. densiflora- to Q. mongolica-dominated forests. Consequently, Korean forests may shift toward a predominance of deciduous broadleaf species under climate change, highlighting the need for targeted forest management strategies.

Author Contributions

Conceptualization, S.K.L. and Y.H.Y.; Investigation, D.H.R., J.M.K., J.S.K. and H.J.J.; Data curation, S.K.L., Y.B.P., K.M.C. and J.W.P.; formal analysis, S.K.L., S.P.L. and S.J.L.; Writing—original draft preparation, S.K.L. and Y.B.P.; Writing—review and editing, Y.H.Y., D.-H.L., E.-J.K., J.H.P. and J.H.S.; Supervision, Y.H.Y. All authors have read and agreed to the published version of the manuscript.

Funding

This research paper was supported by the research project, “The Sustainable Forest Management Model for Stable Wood Harvesting and Forest Ecosystem Services.” (Project No. FM0200-2022-01) from National Institute of Forest Science, Republic of Korea.

Data Availability Statement

Data are contained within the article.

Conflicts of Interest

The authors declare no conflicts of interest.

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Figure 1. The (left) panel illustrates the pinecone and needle leaves of P. densiflora, while the (right) panel shows the acorn and leaf morphology of Q. mongolica.
Figure 1. The (left) panel illustrates the pinecone and needle leaves of P. densiflora, while the (right) panel shows the acorn and leaf morphology of Q. mongolica.
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Figure 2. Biogeographic and climatic context of the Republic of Korea. (a) Geographical location of Korea. (b) Field photographs of representative forest stands: the upper photo shows a Q. mongolica stand, and the lower photo shows a P. densiflora stand. (c) Climatic diagram based on data from 1970 to 2024; the red line indicates monthly mean temperature, and the blue bars indicate monthly precipitation. (d) Location of Korea in the global biome classification chart based on temperature and precipitation.
Figure 2. Biogeographic and climatic context of the Republic of Korea. (a) Geographical location of Korea. (b) Field photographs of representative forest stands: the upper photo shows a Q. mongolica stand, and the lower photo shows a P. densiflora stand. (c) Climatic diagram based on data from 1970 to 2024; the red line indicates monthly mean temperature, and the blue bars indicate monthly precipitation. (d) Location of Korea in the global biome classification chart based on temperature and precipitation.
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Figure 3. Current distribution map of the P. densiflora community and the Q. mongolica community in South Korea.
Figure 3. Current distribution map of the P. densiflora community and the Q. mongolica community in South Korea.
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Figure 4. Environmental ranges of P. densiflora and Q. mongolica based on four key environmental variables: (a) annual mean temperature vs. annual mean precipitation; (b) altitude vs. slope. Rectangles represent the occupied environmental space of each species, and the overlapping shaded area indicates the shared environmental range.
Figure 4. Environmental ranges of P. densiflora and Q. mongolica based on four key environmental variables: (a) annual mean temperature vs. annual mean precipitation; (b) altitude vs. slope. Rectangles represent the occupied environmental space of each species, and the overlapping shaded area indicates the shared environmental range.
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Figure 5. Variation (%) in the ecological niche breadth and overlap of 13 variables of P. densiflora and Q. mongolica under climate change treatment. Negative numbers indicate a decrease and positive numbers indicate an increase compared to the control.
Figure 5. Variation (%) in the ecological niche breadth and overlap of 13 variables of P. densiflora and Q. mongolica under climate change treatment. Negative numbers indicate a decrease and positive numbers indicate an increase compared to the control.
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Figure 6. Principal component analysis (PCA) of P. densiflora (Pd) and Q. mongolica (Qm) across nutrient gradients using 13 morphological traits. Dashed lines represent responses under control conditions (Pd_C, Qm_C), and solid lines represent responses under climate change treatment (Pd_T, Qm_T).
Figure 6. Principal component analysis (PCA) of P. densiflora (Pd) and Q. mongolica (Qm) across nutrient gradients using 13 morphological traits. Dashed lines represent responses under control conditions (Pd_C, Qm_C), and solid lines represent responses under climate change treatment (Pd_T, Qm_T).
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Figure 7. ROC (Receiver Operation Characteristic) curves of P. densiflora and Q. mongolica under climate change projection scenarios with three AUC (Area Under the Curve) values.
Figure 7. ROC (Receiver Operation Characteristic) curves of P. densiflora and Q. mongolica under climate change projection scenarios with three AUC (Area Under the Curve) values.
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Figure 8. Predicted habitat suitability for P. densiflora and Q. mongolica under climate change. The left map shows the current habitat suitability based on present climatic and topographic variables, while the right map shows the predicted suitability under future climate conditions (RCP 4.5, +2.1 °C). These maps represent potential habitat suitability, not actual species distributions.
Figure 8. Predicted habitat suitability for P. densiflora and Q. mongolica under climate change. The left map shows the current habitat suitability based on present climatic and topographic variables, while the right map shows the predicted suitability under future climate conditions (RCP 4.5, +2.1 °C). These maps represent potential habitat suitability, not actual species distributions.
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Table 1. Ecological niche concepts and their potential influence on species distribution.
Table 1. Ecological niche concepts and their potential influence on species distribution.
ConceptDefinitionInfluence on Species Distribution
Fundamental nicheThe theoretical range of environmental conditions a species can occupy without competition or interference.Determines the potential distributional range and reflects the maximum response limits to environmental change.
Realized nicheThe actual habitat range limited by biotic interactions such as competition, predation, or disturbance.Corresponds to the actual distribution and is restricted by factors such as competition, predation, and resource constraints.
Niche breadthThe range of resources and environmental conditions under which a species can survive.A wider niche indicates adaptability to diverse environments and greater resilience to climate change.
Niche overlapThe extent to which two species share resources or space, closely related to potential interspecific competition.Greater overlap increases competition, while lesser overlap enhances the potential for resource partitioning and coexistence.
Table 2. Nutrient gradient treatments and organic matter levels.
Table 2. Nutrient gradient treatments and organic matter levels.
Treatment CodeOrganic Matter Content (%)Treatment Description
N10No organic matter (100% sand)
N25Low organic matter
N310Medium organic matter
N4 15 High organic matter
Table 3. Ecological niche breadth and overlap of P. densiflora and Q. mongolica under control and treatment (climate change).
Table 3. Ecological niche breadth and overlap of P. densiflora and Q. mongolica under control and treatment (climate change).
Trait GroupCharacterEcological Niche BreadthEcological Niche Overlap
P. densifloraQ. mongolica
ControlTreatmentControlTreatmentControlTreatment
Leaf traitsLeaf width length0.9990.9970.9960.9990.9590.987
Leaf lamina length0.9940.9990.9930.9950.9350.972
Leaf width/length ratio0.9950.9980.9960.9970.9600.963
Mean
(Stdev)		0.996
(0.003)		0.998
(0.001)		0.995
(0.002)		0.997
(0.002)		0.951
(0.003)		0.974
(0.003)
Plant architecture
traits		Shoot length0.9960.9970.9980.9970.9700.952
Root length0.9880.9880.9700.9980.8650.945
Shoot/Root length ratio0.9790.9290.9760.9920.8910.825
Leaf mass ratio0.9890.9900.9640.9900.9620.921
Mean
(Stdev)		0.988
(0.007)		0.976
(0.031)		0.977
(0.015)		0.994
(0.004)		0.922
(0.009)		0.911
(0.010)
Biomass traitsLeaf weight0.9550.9830.8860.9880.7970.941
Leaves weight0.9670.9880.9070.9790.8090.942
Stem weight0.9830.9940.9850.9650.9400.953
Shoot weight0.9810.9930.9760.9720.8780.930
Root weight0.9230.9230.9700.9850.8010.899
Plant weight0.9530.9880.9780.9820.8350.973
Mean
(stdev)		0.960
(0.023)		0.978
(0.025)		0.950
(0.034)		0.979
0.010)		0.843
(0.066)		0.940
(0.041)
Mean
(stdev)		0.977
(0.022)		0.982
(0.025)		0.969
(0.034)		0.988
(0.010)		0.892
(0.066)		0.939
(0.041)
Table 4. Correlation matrix of 13 traits with the PC1 and PC2 scores of the principal component analysis (statistically significant factors (|r| > 0.5) are shown in red.).
Table 4. Correlation matrix of 13 traits with the PC1 and PC2 scores of the principal component analysis (statistically significant factors (|r| > 0.5) are shown in red.).
Trait GroupCharacterPC1PC2
Leaf traitsLeaf width length0.950.19
Leaf lamina length0.35−0.16
Leaf width/length ratio0.890.31
Plant architecture traitsShoot length−0.61−0.71
Root length0.76−0.16
Shoot/Root length ratio−0.650.01
Leaf mass ratio0.750.36
Biomass traitsLeaf weight0.910.15
Leaves weight0.92−0.19
Stem weight−0.61−0.71
Shoot weight0.55−0.78
Root weight0.81−0.46
Plant weight0.78−0.58
Variance explained [%]58.3316.67
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Lee, S.K.; Lee, D.-H.; Park, Y.B.; Ryu, D.H.; Kim, J.M.; Kim, E.-J.; Park, J.H.; Park, J.W.; Cho, K.M.; Seo, J.H.; et al. Climate Change Alters Ecological Niches and Distribution of Two Major Forest Species in Korea, Accelerating the Pace of Forest Succession. Forests 2025, 16, 1331. https://doi.org/10.3390/f16081331

AMA Style

Lee SK, Lee D-H, Park YB, Ryu DH, Kim JM, Kim E-J, Park JH, Park JW, Cho KM, Seo JH, et al. Climate Change Alters Ecological Niches and Distribution of Two Major Forest Species in Korea, Accelerating the Pace of Forest Succession. Forests. 2025; 16(8):1331. https://doi.org/10.3390/f16081331

Chicago/Turabian Style

Lee, Sang Kyoung, Dong-Ho Lee, Yeo Bin Park, Do Hun Ryu, Jun Mo Kim, Eui-Joo Kim, Jae Hoon Park, Ji Won Park, Kyeong Mi Cho, Ji Hyun Seo, and et al. 2025. "Climate Change Alters Ecological Niches and Distribution of Two Major Forest Species in Korea, Accelerating the Pace of Forest Succession" Forests 16, no. 8: 1331. https://doi.org/10.3390/f16081331

APA Style

Lee, S. K., Lee, D.-H., Park, Y. B., Ryu, D. H., Kim, J. M., Kim, E.-J., Park, J. H., Park, J. W., Cho, K. M., Seo, J. H., Lee, S. P., Lee, S. J., Ko, J. S., Jang, H. J., & You, Y. H. (2025). Climate Change Alters Ecological Niches and Distribution of Two Major Forest Species in Korea, Accelerating the Pace of Forest Succession. Forests, 16(8), 1331. https://doi.org/10.3390/f16081331

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