Climate Refugia of Endangered Mammals in South Korea Under SSP Climate Scenarios: An Ensemble Species Distribution Modeling Approach

Abstract

Climate change is expected to alter the distribution of many threatened mammals, yet national-scale identification of climate refugia and conservation priorities remains limited for South Korea. This study aimed to map current hotspots and future refugia for 10 endangered mammal species and evaluate conservation implications under SSP climate scenarios. We compiled occurrence records from nationwide field surveys and protected-area monitoring and fitted ten species distribution models (GLM, GAM, GBM, CTA, ANN, SRE, FDA, MARS, RF, and MaxEnt) using biomod2 with climatic, topographic, and anthropogenic predictors at 1 km resolution. A weighted ensemble model achieved strong predictive performance (mean AUC = 0.840). Current richness hotspots were concentrated along the Baekdudaegan mountain range, and several national parks emerged as core multi-species areas. Variable-importance analysis indicated that topographic constraints (elevation and slope) dominated for most species, consistent with mountain-dependent habitat use. Future projections showed relatively stable richness patterns under SSP2–4.5 but pronounced contractions under SSP5–8.5 by the 2070s, with persistent high-suitability areas converging in the northern Baekdudaegan. The resulting suitability and richness layers provide spatial decision-support for protected-area strengthening, connectivity-oriented management, and targeted monitoring to support national climate-adaptation planning.

1. Introduction

The Earth is experiencing an unprecedented biodiversity crisis during the Anthropocene, often referred to as the “Sixth Mass Extinction” [1,2]. Unlike the previous five mass extinctions that were largely triggered by major natural catastrophes, the current crisis is driven by multiple interacting pressures. Human activities constitute a major accelerating force, while background natural variability and stochastic processes can also contribute to extinction dynamics [3,4]. Climate change acts as a key amplifier of biodiversity loss because it interacts with habitat loss, overexploitation, invasive species, and disease, thereby increasing extinction risk across taxa [5,6,7].

Mammals can be particularly sensitive to rapid environmental change due to their relatively slow life histories, large space requirements, and dependence on landscape connectivity for dispersal and range shifts [8,9,10,11]. Rare and range-restricted species may be especially vulnerable because limited population sizes and narrow distributions can reduce demographic resilience under rapidly changing conditions [12,13]. Moreover, land-use change and fragmentation can reduce landscape connectivity and may constrain dispersal or range shifts that facilitate responses to climate change, although the magnitude of such effects is species- and landscape-dependent [10,11].

Species distribution models (SDMs) relate occurrence data to environmental predictors to estimate habitat suitability and potential geographic distributions [14,15]. SDMs are widely used to assess climate-change impacts by projecting expected range shifts, contraction risks, and persistence areas under future scenarios, thereby supporting spatial prioritization and conservation planning [16,17,18,19]. However, projections can vary among algorithms and can be affected by sampling bias and spatial autocorrelation; therefore, ensemble approaches that integrate multiple SDMs are increasingly recommended to improve robustness and reduce method-specific uncertainty [20,21,22,23,24,25]. Recent studies in Asia have increasingly applied habitat-suitability modeling with climate projections to identify persistence areas and support conservation prioritization for threatened mammals, highlighting the need for comparable national-scale assessments in South Korea [26,27,28].

South Korea spans strong latitudinal and altitudinal gradients and contains a mountainous ecological network centered on the Baekdudaegan range, which functions as a major habitat backbone for mammalian biodiversity [29,30,31]. Rapid land-use change and habitat fragmentation have reduced and subdivided natural habitats, increasing extinction risk for many species [32]. The Korean Ministry of Environment legally designates endangered wildlife species (ME Classes I–II), including mammals, and provides national protection status [33,34]. Meanwhile, observed warming and scenario-based projections indicate continued temperature increases in the coming decades, raising concern for climate-sensitive mammals and emphasizing the need for quantitative, scenario-based assessments of habitat change and potential climate refugia in the country [35,36,37].

In this study, we focus on 10 endangered mammal species designated by the Korean Ministry of Environment (ME Classes I–II). These focal species were selected because they are of high conservation-policy priority and because sufficient nationwide georeferenced occurrence records were available from long-term surveys and monitoring programs to support consistent 1 km² modeling; their taxonomic status is summarized in

Table 1. Taxonomic classification and conservation status of the 10 endangered mammal species used in this study.

Order	Family	Species (Scientific Name)	ME Grade *	IUCN Status **
Artiodactyla	Bovidae	Naemorhedus caudatus	I	VU
	Cervidae	Cervus nippon hortulorum	I	LC
	Moschidae	Moschus moschiferus	I	VU
Carnivora	Felidae	Prionailurus bengalensis	II	LC
	Mustelidae	Lutra lutra	I	NT
	Mustelidae	Martes flavigula	II	LC
Chiroptera	Vespertilionidae	Murina ussuriensis	I	LC
	Vespertilionidae	Myotis rufoniger	I	LC
	Vespertilionidae	Plecotus ognevi	II	LC
Rodentia	Sciuridae	Pteromys volans aluco	II	LC

* ME grade: Endangered Wildlife Class I (I) or Class II (II) under the Korean Ministry of Environment classification (I indicates a higher protection level than II). ** IUCN status (global) abbreviations: LC = Least Concern; NT = Near Threatened; VU = Vulnerable.

. We aimed to (1) develop and evaluate AUC-weighted ensemble SDMs using 10 algorithms implemented in biomod2; (2) quantify key environmental determinants of habitat suitability across species; (3) map current habitat suitability and multi-species richness patterns and assess the conservation relevance of protected areas; and (4) project future suitability changes and identify potential climate refugia under SSP2-4.5 and SSP5-8.5 for the 2030s, 2050s, and 2070s. We tested three a priori hypotheses: (H1) habitat suitability and richness hotspots will contract and shift toward higher latitudes and/or elevations, with stronger contractions under SSP5-8.5, especially by the 2070s; (H2) climate refugia will be concentrated along the northern Baekdudaegan due to topographic complexity and climatic buffering; and (H3) predicted refugia/hotspots will not fully overlap with current protected areas, indicating spatial conservation gaps and the need for connectivity-oriented management.

2. Materials and Methods

2.1. Study Area

This study was conducted across the entire territory of South Korea, covering an area of approximately 100,210 km² (Figure 1). South Korea is located in the temperate monsoon climate zone of East Asia, with an average annual temperature ranging from 10 °C to 15 °C and annual precipitation between 1200 mm and 1500 mm, with over 60% of the precipitation occurring during the summer months (June to August) [32,38]. Geographically, the eastern and northern regions are characterized by the Baekdudaegan mountain range, with the highest peak, Daecheongbong of Mt. Seoraksan, reaching an altitude of 1708 m. The western and southern regions are dominated by plains and rolling hills [29]. Approximately 63% of the country is covered by forests, which are vertically and horizontally distributed depending on altitude and latitude, with coniferous forests, broadleaf forests, and mixed forests [30,39]. The Baekdudaegan mountain ecological corridor is a core area for biodiversity in South Korea, providing essential habitats for endangered mammals and other forest-dependent taxa [31,40]. However, in recent decades, habitat fragmentation has been accelerating due to industrialization, urbanization, and agricultural development, making climate change a major threat to biodiversity [32].

2.2. Species Occurrence Data

We modeled 10 mammal species designated as endangered wildlife species (Grades I and II) by the Korean Ministry of Environment [33,34]. The focal species and their taxonomic status (order and family) are summarized in Table 1, and the distribution of the focal taxa across orders and families is presented in

Table 2. Distribution of the 10 endangered mammal species by order and family.

Order	Family	No. of Species
Artiodactyla	Total	3
	Bovidae	1
	Cervidae	1
	Moschidae	1
Carnivora	Total	3
	Felidae	1
	Mustelidae	2
Chiroptera	Total	3
	Vespertilionidae	3
Rodentia	Total	1
	Sciuridae	1
	Total	10

. Overall, the study species comprise four orders (Artiodactyla, Carnivora, Chiroptera, Rodentia) and seven families. In South Korea, Grades I and II are national legal categories for endangered wildlife designated by the Ministry of Environment, where Grade I indicates the highest protection priority and Grade II indicates high priority. These national grades are not directly equivalent to IUCN Red List categories, which provide global conservation status and are reported for reference.

Species occurrence data were compiled from three nationwide and long-term monitoring sources: (1) the National Institute of Ecology (NIE)’s National Natural Environment Survey (1997–2021), (2) the National Park Service’s Natural Resource Survey (2003–2022), and (3) the NIE Endangered Species Restoration Center monitoring dataset (2001–2022). All occurrence records were georeferenced using GPS coordinates in the WGS84 coordinate system, and only records with positional accuracy < 1 km were retained for analysis.

To improve data quality and reduce spatial bias, we applied a multi-step filtering procedure [41,42]. Records with obvious spatial errors (e.g., locations in marine areas or outside South Korea) or uncertain species identification were excluded. To match the 1 km resolution of predictor variables and to minimize spatial autocorrelation, occurrences were aggregated to a 1 km² grid and duplicate records within the same grid cell were consolidated into a single representative presence point [41,43].

2.3. Environmental Variables

For species distribution modeling, a total of 10 environmental variables related to bioclimatic, topographic, and human activity factors were selected (

Table 3. Environmental Variables List and Characteristics.

Category	Variable Code	Description	Unit	Source	Resolution
Bioclimatic	BIO1	Annual mean temperature	°C	KMA	1 km
Bioclimatic	BIO2	Mean diurnal temperature range	°C	KMA	1 km
Bioclimatic	BIO3	Isothermality (BIO2/BIO7 × 100; BIO7 = annual temperature range (BIO5 − BIO6))	-	KMA	1 km
Bioclimatic	BIO12	Annual precipitation	mm	KMA	1 km
Bioclimatic	BIO13	Precipitation of wettest month	mm	KMA	1 km
Bioclimatic	BIO14	Precipitation of driest month	mm	KMA	1 km
Topographic	Elevation	Elevation above sea level	m	SRTM DEM	90 m (resampled to 1 km)
Topographic	Slope	Terrain slope	degree	SRTM DEM	90 m (resampled to 1 km)
Hydrological	Dist_water	Distance to nearest water body	m	WAMIS	1 km
Human activity	Dist_road	Distance to nearest road	m	NGII	1 km

Note: BIO7 denotes the annual temperature range (BIO5–BIO6) used to compute BIO3.

). The bioclimatic variables were based on high-resolution (1 km) climate data provided by the Korea Meteorological Administration (KMA). This data was produced by statistically ensembling five regional climate models (HadGEM3-RA, WRF, CCLM, GRIMs, RegCM4) based on the IPCC AR6’s Shared Socioeconomic Pathways (SSP) scenarios [44]. From the 19 available bioclimatic variables, the following were selected for analysis: annual mean temperature (BIO1), mean diurnal temperature range (BIO2), isothermality (BIO3), annual precipitation (BIO12), precipitation of the wettest month (BIO13), and precipitation of the driest month (BIO14) [45]. Topographic variables, including elevation and slope, were derived from the Shuttle Radar Topography Mission (SRTM) Digital Elevation Model (DEM) at a 90-m resolution. These variables were calculated using QGIS 3.28 software and then resampled to a 1 km² resolution [46]. To assess multicollinearity among the environmental variables, Pearson correlation coefficients were calculated, and pairs of variables with a correlation coefficient higher than 0.7 were evaluated. Based on ecological significance and prior studies, only one variable from each correlated pair was retained for analysis [47]. Human activity-related variables, such as the distance to the nearest water body and the distance to roads, were also included in the model. These variables are relevant for understanding the potential influence of human activities on species distributions. The distance to water bodies was derived from the WAMIS (Water Management Information System) database, while the distance to roads was calculated using spatial data from the National Geographic Information Institute of Korea (NGII).

2.4. Species Distribution Modeling (SDM)

Species distribution modeling was conducted using the biomod2 package (version 4.2-5) in R (version 4.2.3) [23,48]. A total of 10 SDMs were applied, including Generalized Linear Models (GLM), Generalized Additive Models (GAM), Generalized Boosted Models (GBM), Classification Tree Analysis (CTA), Artificial Neural Networks (ANN), Surface Range Envelope (SRE), Flexible Discriminant Analysis (FDA), Multivariate Adaptive Regression Splines (MARS), Random Forest (RF), and Maximum Entropy (MAXENT) [49,50,51]. Given the limited occurrence data for endangered species, 10,000 random background points (pseudo-absence) were generated for each species [52]. For model training and validation, a bootstrap cross-validation approach was employed. The data were randomly divided into 70% training data and 30% validation data, and this process was repeated 10 times [53]. The performance of each model was evaluated using three metrics: (1) Area Under the Curve (AUC), (2) True Skill Statistic (TSS), and (3) Kappa statistic [54,55]. AUC values range from 0.5 (random) to 1.0 (perfect prediction), with values above 0.7 being considered as acceptable models [56]. The predictions from individual models were integrated using ensemble techniques. Two ensemble methods were applied: (1) simple average ensemble (EM) and (2) weighted average ensemble (EMW). In the weighted average ensemble, each model’s weight was calculated based on its AUC value [22].

The weight $w_i$ for each model was calculated as follows (1):

$$w_i={\displaystyle \frac{{AUC}_i-0.5}{{\sum }_{j=1}^n{(AUC}_j-0.5)}}$$

(1)

Here, w_i is the weight of model i, AUC_i is the AUC value of model i, and n is the total number of models. The final ensemble prediction P_ensemble was calculated as a weighted sum of the individual model predictions (2):

$$P_{ensemble}=\sum _{i=1}^n{w}_i\times P_i$$

(2)

where P_ensemble is the ensemble prediction, and P_i is the prediction from model i.

The relative importance of environmental variables was assessed using the variable permutation method [51]. This method involves randomly reshuffling the values of each variable and measuring the resulting decrease in model performance, thereby quantifying the importance of each variable. The importance scores were then standardized on a scale from 0 (no influence) to 1 (maximum influence).

2.5. Climate Change Scenarios and Future Projections

To predict future habitat changes under climate change, the study applied two Shared Socioeconomic Pathways (SSP) scenarios from the IPCC AR6: SSP2-4.5 (moderate emissions scenario) and SSP5-8.5 (high emissions scenario) [57,58]. SSP2-4.5 represents a middle-of-the-road pathway with moderate climate mitigation efforts, while SSP5-8.5 assumes continued fossil fuel-driven development and the highest emissions trajectory [59]. Future climate data were obtained from the Korea Meteorological Administration (KMA) and are based on a statistical ensemble of five regional climate models (HadGEM3-RA, WRF, CCLM, GRIMs, RegCM4) at a 1 km² resolution [44]. These datasets were divided into three time periods: the 2030s (2021–2040), the 2050s (2041–2060), and the 2070s (2061–2080), with each period representing a 20-year average climate condition. The ensemble models, developed using baseline climate data derived from the 1981–2010 normals (hereafter, the ‘2010 baseline’), were projected onto future climate conditions to estimate habitat suitability changes for each species. For environmental variables such as elevation and slope, as well as human activity-related factors (e.g., distance to roads and water bodies), it was assumed that these variables would remain constant over time [18]. The projection was conducted with a clamping option to minimize extrapolation errors for environmental conditions that fall outside the range of training data [60].

2.6. Habitat Suitability Classification and Species Richness

Habitat suitability values for each species were calculated on a continuous scale ranging from 0 (very unsuitable) to 1 (very suitable). For binary classification (presence/absence), a suitability threshold was established to determine whether a species would be present in a given area. In this study, the threshold was set to maximize the sum of sensitivity and specificity (TSS-based threshold), which generally resulted in a threshold value around 0.5 for most species [54,55]. Species richness was calculated by summing the number of species predicted to be suitable for a given 1 km² grid cell. The resulting species richness maps were generated at the national level, by local government units (city/district), and by national parks to assess the spatial distribution of species across these different spatial scales. For the identification of climate refugia, regions with consistently high habitat suitability (≥0.7) across all time periods, from the present to the 2070s, were defined as potential refugia. These areas were considered key conservation zones for species survival under future climate change scenarios, as they are expected to provide long-term habitat suitability despite climate change [61,62].

3. Results

3.1. Model Performance Evaluation

Model performance across the 10 SDM algorithms and the two ensemble models is summarized in Figure 2. The corresponding numerical AUC values are provided in Table S1 (Supplementary Materials). Among the individual models, the Generalized Boosted Model (GBM) achieved the highest accuracy (AUC = 0.817), followed by Random Forest (RF) (AUC = 0.802) and Maximum Entropy (MAXENT) (AUC = 0.800). Models with lower performance included Surface Range Envelope (SRE) (AUC = 0.633) and Generalized Additive Model (GAM) (AUC = 0.671). Ensemble models outperformed individual models, with the weighted average ensemble (EMW) yielding the highest AUC of 0.840, followed by the simple average ensemble (EM) with an AUC of 0.835. The ensemble models showed more consistent performance, as indicated by lower standard deviations compared to individual models.

3.2. Species-Specific Model Performance

The accuracy of each species-specific prediction was evaluated using the Random Forest model, with performance varying across species (Figure 3). The corresponding numerical AUC summary statistics are provided in Table S2 (Supplementary Materials). The average AUC values ranged from 0.679 (L. lutra) to 0.956 (M. moschiferus), with an overall average of 0.827 across all species. M. moschiferus achieved the highest AUC of 0.956 (standard deviation = 0.037, range: 0.883–1.000), followed by P. ognevi with an average AUC of 0.925 (standard deviation = 0.022, range: 0.886–0.972), and N. caudatus with 0.924 (standard deviation = 0.013, range: 0.892–0.944). Medium-high performance species included M. flavigula (AUC = 0.860 ± 0.035), C. nippon hortulorum (AUC = 0.851 ± 0.048), M. ussuriensis (AUC = 0.838 ± 0.092), and P. volans aluco (AUC = 0.827 ± 0.030). These species all had an AUC greater than 0.8, indicating good model performance. Lower-performing species included Prionailurus bengalensis (AUC = 0.771 ± 0.036) and M. rufoniger (AUC = 0.764 ± 0.058), which both showed performance within the acceptable range but lower than the higher-performing species. L. lutra had the lowest AUC at 0.679 (standard deviation = 0.017, range: 0.636–0.702), although its low AUC was accompanied by low variability in predictions. The variability in performance was further assessed using the standard deviation. N. caudatus and P. ognevi exhibited very low variability (standard deviations of 0.013 and 0.022, respectively), indicating highly consistent predictions. In contrast, M. ussuriensis showed the highest variability (standard deviation = 0.092), likely due to a limited number of occurrence records. Overall, nine out of ten species had AUC values above 0.7, demonstrating the reliability of the model predictions for the majority of species.

3.3. Environmental Variables Importance

Species-specific rankings of predictor importance derived from the Random Forest model are summarized in

Table 4. Species-specific ranks of predictor importance derived from the Random Forest model. Note: 1 indicates the highest importance (rank), and larger numbers indicate lower importance.

Species	BIO1	BIO2	BIO3	BIO12	BIO13	BIO14	Elev	Slope	D_Road	D_Water
M. flavigula	3	9	8	7	10	6	1	2	4	5
C. nippon hortulorum	4	10	5	7	9	3	1	8	6	2
M. rufoniger	4	7	9	5	10	2	3	1	6	8
M. moschiferus	3	10	9	8	5	2	4	1	7	6
N. caudatus	2	5	9	8	7	3	4	1	10	6
P. bengalensis	1	10	8	6	9	2	7	4	3	5
L. lutra	2	9	6	8	7	4	5	10	3	1
M. ussuriensis	3	7	6	9	8	5	2	10	4	1
P. ognevi	2	9	10	7	3	6	8	5	1	4
P. volans aluco	1	3	10	8	2	5	6	4	7	9

Note: Numbers indicate the relative importance rank of each environmental variable for each species (1 = highest importance, 10 = lowest).

(rank 1 = highest importance). Topographic variables ranked first for five of the ten species: elevation was the top-ranked predictor for M. flavigula and C. nippon hortulorum, whereas slope ranked first for M. rufoniger, M. moschiferus, and N. caudatus. The climatic predictor BIO1 (annual mean temperature) ranked first for Prionailurus bengalensis and P. volans aluco. Among the landscape variables, distance to water bodies ranked first for L. lutra and M. ussuriensis, and distance to roads ranked first for P. ognevi.

3.4. Current Species Richness Distribution

Species richness was calculated by predicting habitat suitability for the 10 endangered mammal species using the weighted average ensemble model (EMW) under baseline climate conditions (1981–2010 normals; hereafter, the ‘2010 baseline’) (Figure 4). Species richness ranged from 0 to a maximum of 10 species per grid cell, showing distinct spatial patterns.

3.4.1. Overall Species Richness Distribution Pattern

The national-level species richness analysis revealed that areas with high species richness (7 or more species) were concentrated primarily in the mountainous regions of Gangwon Province and Gyeongsangbuk-do, particularly along the Baekdudaegan mountain range (Figure 4a). The highest species richness was observed along the Baekdudaegan ridge connecting Mt. Seoraksan, Mt. Odaesan, Mt. Taebaeksan, and Mt. Sobaeksan. Areas with moderate species richness (4–6 species) were distributed across forested regions in Chungcheongbuk-do, Jeollabuk-do, and Gyeongsangnam-do, while regions with low species richness (1–3 species) were predominantly found in the western plains and urban areas. The southern coastal areas, including Jeju Island, showed the lowest species richness, with most areas recording fewer than 3 species.

3.4.2. Current Species Richness by Local Government Units

Analysis of species richness by local government unit revealed that some areas in the mountainous regions of Gangwon-do showed high species richness (7–8 species) (Figure 4b). The northern parts of Gyeongsangbuk-do (e.g., Bonghwa, Uljin) and eastern Chungcheongbuk-do (e.g., Danyang) also exhibited moderate-to-high species richness (5–7 species). In contrast, local governments in the capital region (Seoul, Incheon, and most of Gyeonggi-do) and the southwestern plains (Jeollanam-do and western Chungcheongnam-do) recorded low species richness (1–3 species). Inland plain areas that are not adjacent to the Baekdudaegan also generally showed species richness ranging from 2 to 4 species.

3.4.3. Current Species Richness by National Parks

Species richness by national park showed that Mt. Odaesan National Park had the highest richness, with 9 species recorded, followed by Mt. Seoraksan and Mt. Sobaeksan National Parks, each with 8 species (Figure 4c). Mt. Deogyusan National Park recorded 6 species, while Mt. Jirisan National Park had 5 species. Inland mountainous national parks (Mt. Seoraksan, Mt. Odaesan, Mt. Sobaeksan, Mt. Deogyusan, and Mt. Jirisan) exhibited high species richness (5–9 species), whereas coastal and marine national parks (Hallyeohaesang, Taeanhaean) and lowland parks (Mt. Mudeungsan) recorded low species richness (2–3 species). Mt. Hallasan National Park showed moderate species richness, with 4 species. A clear difference in species richness was observed between inland mountainous national parks and coastal/lowland national parks, indicating that the habitats of endangered mammals are concentrated in higher-altitude mountainous areas. National parks located along the Baekdudaegan ecological corridor (Mt. Seoraksan, Mt. Odaesan, and Mt. Sobaeksan) maintained high species richness, with over 7 species present.

3.5. Future Habitat Changes Predictions

The changes in species richness under the SSP2-4.5 (moderate emissions scenario) and SSP5-8.5 (high emissions scenario) for the 2030s, 2050s, and 2070s were predicted. This analysis utilized species richness maps and local government and national park-level species richness maps to assess the impact of climate change on species richness and habitat suitability across different time points.

3.5.1. National-Level Species Richness Change

Under the SSP2-4.5 scenario, species richness patterns remained relatively stable from the 2030s to the 2070s. High species richness areas (5–7 species), centered around the Baekdudaegan mountain range, showed similar spatial distributions across all three time periods, while low species richness areas (0–2 species) in lowland and plain regions remained unchanged. A slight decrease in species richness was observed in some central inland regions by the 2070s, although the change was minor. In contrast, under the SSP5-8.5 scenario, species richness showed a marked decrease over time. In the 2030s, species richness patterns were similar to those in the SSP2-4.5 scenario; however, from the 2050s onwards, species richness decreased in the southern Baekdudaegan region and central mountainous areas. By the 2070s, high species richness areas were confined to a limited region in the northern Baekdudaegan, while central and southern mountainous areas saw a decline to medium species richness (3–4 species). Low species richness areas in lowland plains remained consistent in both scenarios (Figure 5).

3.5.2. Projected Species Richness by Local Government Units Under SSP Scenarios

Species richness at the local government level showed relative stability under the SSP2-4.5 scenario (Figure 6). Mountainous local governments in Gangwon-do were consistently classified in the highest richness class (6–8 species) from the 2030s to the 2070s, while several local governments in northern Gyeongsangbuk-do and eastern Chungcheongbuk-do remained in the moderate-to-high class (4–6 species) across the same periods. In contrast, many local governments in the capital region and western lowland/plains were consistently categorized in the lowest richness class (0–2 species). A modest decline in the richness class was observed in parts of northern Gyeonggi-do and the Chungcheong region by the 2070s.

Under the SSP5-8.5 scenario, a clearer downward shift in richness classes was evident over time (Figure 6). In the 2030s, local governments in Gangwon-do were largely classified as 6–8 species, but this pattern shifted toward 4–6 species by the 2050s, and several areas further declined to the 2–4 species class by the 2070s. In addition, some local governments in northern Gyeongsangbuk-do and eastern Chungcheongbuk-do that were classified as 4–6 species in the 2030s shifted to the 2–4 species class by the 2070s. Across both scenarios, low richness classes (0–2 species) persisted in many lowland/plain areas, including parts of the capital region.

3.5.3. Projected Species Richness by National Parks Under SSP Scenarios

Species richness by national park remained relatively stable under the SSP2-4.5 scenario. Mt. Seoraksan, Mt. Odaesan, and Mt. Sobaeksan National Parks maintained high species richness (6–8 species) from the 2030s to the 2070s. Mt. Jirisan and Mt. Deogyusan National Parks showed species richness in the 4–6 species range, with little change over time. Coastal and lowland national parks consistently showed low species richness (2–4 species) in all three time periods. Under the SSP5-8.5 scenario, different change patterns emerged across national parks. In the 2030s, most mountainous national parks maintained high species richness; however, from the 2050s onwards, species richness began to decrease in southern and central national parks (Mt. Jirisan, Mt. Deogyusan). By the 2070s, only Mt. Seoraksan and Mt. Odaesan National Parks retained high species richness (6–8 species), while Mt. Sobaeksan National Park decreased to 4–6 species, and Mt. Jirisan and Mt. Deogyusan National Parks dropped to 2–4 species. Coastal and lowland national parks showed little change in species richness in both scenarios (Figure 7).

3.5.4. Comparison Between Scenarios and Time Periods

Across spatial summaries (national 1 km grid, local government units, and national parks), projections under SSP2-4.5 and SSP5-8.5 were broadly similar in the 2030s, but differences increased in the 2050s and were most pronounced in the 2070s (Figure 5, Figure 6 and Figure 7). At the national scale, SSP2-4.5 maintained the main high-richness pattern along the Baekdudaegan corridor with only minor changes through the 2070s, whereas SSP5-8.5 showed a clearer downward shift in richness classes and a contraction of higher-richness areas toward the northern Baekdudaegan by the 2070s (Figure 5).

A consistent pattern was observed at finer administrative and protected-area scales. At the local government level, several mountainous jurisdictions retained relatively higher richness under SSP2-4.5 across time periods, while under SSP5-8.5, multiple jurisdictions shifted from moderate (e.g., 4–6) to lower (e.g., 2–4) richness classes by the 2070s (Figure 6). At the national park level, richness remained relatively stable under SSP2-4.5; under SSP5-8.5, reductions became clearer by the 2070s, with only Mt. Seoraksan and Mt. Odaesan remaining in the highest richness class (6–8 species), while Mt. Sobaeksan decreased to 4–6 species and Mt. Jirisan and Mt. Deogyusan declined to 2–4 species (Figure 7).

4. Discussion

4.1. Superiority of Ensemble Modeling

In this study, the ensemble models (EM, EMW) showed higher predictive performance than all individual models, with average AUC values of 0.835 and 0.840, respectively. These results align with previous studies indicating that ensemble approaches reduce the uncertainty of single models and improve prediction accuracy [21,22,24]. The weighted average ensemble (EMW) performed better than the simple average ensemble (EM), with the former achieving an AUC of 0.840. This indicates that assigning weights based on model performance is effective. Weighted ensembles can minimize the influence of weaker models while maximizing the contributions of stronger ones [22]. Among individual models, machine learning SDMs like GBM (0.817), RF (0.802), and MAXENT (0.800) showed the best performance, which can be attributed to their ability to effectively model complex, nonlinear species-environment relationships [48,49,50]. GBM’s high performance stems from its boosting technique, which sequentially minimizes errors during the learning process [63], while RF’s ensemble learning through decision trees helps prevent overfitting [64]. MAXENT, optimized for presence-only data, provides reliable results even with limited occurrence data [65].

On the other hand, simpler SDMs like SRE (0.633) and GAM (0.671) performed relatively poorly. SRE, which only considers the range of environmental variables, fails to capture the complexity of species-environment relationships [66]. However, even such simple models contribute useful information under ensemble modeling, enhancing the robustness of overall predictions [23]. The standard deviation of ensemble models (EMW 0.085, EM 0.087) was lower than most individual models, indicating higher consistency in predictions across species. This suggests that the ensemble approach offsets individual SDM biases and provides more stable predictions [67]. The minimum-maximum range for EMW (0.277) was narrower than the individual model average (0.312), effectively reducing prediction variance across species. These findings demonstrate that ensemble modeling can provide reliable predictions, even with limited occurrence data for endangered species. In particular, EMW proved to be a consistent and robust predictive tool, achieving an AUC greater than 0.7 for all species, which supports its use as a preferred approach in conservation planning [25,68].

4.2. Species-Specific Model Performance Differences

Recent SSP-based applications across Asia similarly use habitat-suitability modeling with climate projections to identify persistence areas and translate projections into conservation prioritization. For example, administrative-scale suitability outputs have been used to support decision-making based on field survey data [26], while regional assessments of mesocarnivores emphasize integrating projected suitability changes with protected-area networks and connectivity planning [27]. In addition, cervid-focused projections report climate-driven redistribution and contraction of suitable habitat under future warming, which is consistent with our scenario-based patterns and the identification of persistent high-suitability areas [28].

Species-specific accuracy, based on the Random Forest model, varied significantly, with AUC ranging from 0.956 (Moschus moschiferus) to 0.679 (Lutra lutra). These differences were influenced by ecological and methodological factors. First, habitat specialization affected model performance. Specialist species such as M. moschiferus (0.956) and Naemorhedus caudatus (0.924) showed higher accuracy due to clearer species-environment relationships. This aligns with studies suggesting that habitat specialists exhibit higher model performance [69,70]. Similarly, Plecotus ognevi (0.925) performed well due to its preference for specific habitats like caves [71]. Second, sample size and spatial distribution influenced model accuracy. The low accuracy of L. lutra (0.679) can be attributed to its linear distribution, which is difficult to capture with grid-based environmental variables [72]. Additionally, the wide-ranging and mobile nature of L. lutra means occurrence data may not reflect its true habitat preferences [73]. Despite this, the low standard deviation (0.017) suggests stable modeling even with lower accuracy. Third, limited sample sizes and uneven spatial distributions led to increased uncertainty, particularly for rare species. Murina ussuriensis had a high standard deviation (0.092) and a wide range (0.648–0.937), which increased model variability. This is consistent with studies showing that small sample sizes lead to higher prediction variability in rare species [71,74]. Lastly, species’ ecological traits and detectability influenced model performance. Bat species, such as Myotis rufoniger and P. ognevi, show strong sampling biases due to their nocturnal behavior and flight abilities [74,75]. Despite these challenges, the high performance (0.925) and low standard deviation (0.022) of P. ognevi suggest that clear habitat signals, such as caves, can enhance model accuracy.

In conclusion, these findings underscore the need for species-specific modeling approaches. For species like L. lutra, which rely on linear habitats, river network-based modeling may be useful [76]. Multi-layered models that distinguish between roosting and foraging ranges are recommended for bats [77]. Furthermore, increasing sample size through additional field surveys is essential for improving model performance in rare species [74].

4.3. Environmental Variables and Species Ecology

Table 4 (rank 1 = highest importance) shows clear species-specific structuring of habitat suitability by three main drivers: topography, climate, and landscape constraints. Topographic predictors dominated five species, with elevation ranked first for M. flavigula and C. nippon hortulorum, and slope ranked first for M. rufoniger, M. moschiferus, and N. caudatus. Temperature sensitivity was most evident for P. bengalensis and P. volans aluco, for which BIO1 (annual mean temperature) ranked first.

In contrast, landscape variables were primary for riparian- and disturbance-linked taxa: distance to water bodies ranked first for L. lutra and M. ussuriensis, whereas distance to roads ranked first for P. ognevi. These rankings are ecologically coherent and provide direct management implications. The strong role of elevation/slope supports the importance of conserving mountainous habitat structure and connectivity for terrain-associated mammals [78,79,80,81,82,83,84]. The prominence of BIO1 (and consistently high ranks of BIO14 for several species, e.g., M. moschiferus and M. rufoniger) suggests that warming and dry-season moisture constraints may jointly shape future suitability for climate-sensitive taxa [85,86,87]. Finally, the first-rank effects of D_water for L. lutra and M. ussuriensis highlight the need to maintain riparian habitat quality and continuity, while the first-rank effect of D_road for P. ognevi supports prioritizing road-impact mitigation (e.g., avoiding high-disturbance areas and improving safe movement routes) [73,88,89,90,91,92].

4.4. Species Richness Patterns and Conservation Implications

The current species richness analysis highlights the importance of the Baekdudaegan mountain range, particularly in Gangwon-do and Gyeongsangbuk-do, as a key habitat for endangered mammals in South Korea. High species richness areas (7+ species) were observed along the Baekdudaegan ridge, which connects Mt. Seoraksan, Mt. Odaesan, and Mt. Sobaeksan, indicating that these areas are critical for multi-species conservation.

4.4.1. Ecological Importance of the Baekdudaegan

The high species richness in the Baekdudaegan mountain range can be attributed to several ecological factors. First, Baekdudaegan provides essential ecological connectivity, facilitating species movement and gene flow across the region [29,31]. As an ecological corridor, it enhances species’ adaptive capacity to climate change and habitat disruption by preventing population isolation [93]. Second, the range encompasses diverse altitudes, from high mountain peaks above 1000 m to lowland areas below 500 m, supporting a wide variety of species with different habitat requirements [94]. Third, the region’s relatively low human impact and well-preserved forests provide suitable habitats for large mammals and bats, contributing to its ecological significance [95].

4.4.2. Role of National Parks in Conservation

Species richness analysis by national park shows that Mt. Odaesan (9 species), Mt. Seoraksan (8 species), and Mt. Sobaeksan (8 species) National Parks have the highest species richness, demonstrating that these protected areas play a crucial role as refugia for endangered species. National parks minimize habitat loss and disturbance through legal protection, while systematic monitoring and management contribute to maintaining biodiversity [96,97]. Inland mountainous national parks, such as Mt. Seoraksan, Mt. Odaesan, and Mt. Sobaeksan, support higher species richness (5–9 species) compared to coastal and lowland parks (2–3 species), reinforcing the dependency of endangered mammals on mountainous habitats. However, national parks alone are insufficient for the conservation of endangered species, as significant portions of high species richness areas lie outside park boundaries, indicating that current protected area networks do not fully encompass critical habitats [98,99]. Expanding existing national parks and establishing new protected areas, particularly to enhance connectivity along the Baekdudaegan corridor, are urgently needed [100].

4.4.3. Habitat Fragmentation and Decline in Lowland Species Richness

Low species richness in the capital region and western plains (1–3 species) results from habitat loss and fragmentation due to urbanization and agricultural expansion. South Korea has experienced rapid economic development and urban sprawl over the past 50 years, leading to a significant decline in lowland natural habitats [32,101]. Habitat fragmentation reduces population sizes, decreases genetic diversity, and increases the risk of local extinction [11,102]. Large mammals, in particular, require vast home ranges and face challenges to long-term survival in fragmented habitats [103]. The low species richness in regions with high road density further highlights the negative impact of roads on wildlife movement, increasing traffic-related mortality and acting as barriers to habitat connectivity [90]. Strategic placement of wildlife corridors is essential for restoring habitat connectivity [104].

4.4.4. Local Government-Level Conservation Priorities

The species richness analysis by the local government provides valuable insights for regional conservation planning. Mountainous local governments in Gangwon-do (7–8 species) should be prioritized for conservation, with strict development regulations and habitat restoration efforts. Local governments in northern Gyeongsangbuk-do and eastern Chungcheongbuk-do (5–7 species) are important as buffer zones for maintaining connectivity with the Baekdudaegan corridor, requiring sustainable land-use planning [105]. Even low-species richness areas can support species like Lutra lutra that depend on river ecosystems, highlighting the need for river ecosystem restoration and riparian zone conservation. Urban rivers, in particular, can serve as ecological corridors, contributing to biodiversity maintenance through proper management [106].

4.4.5. Need for an Integrated Conservation Strategy

This study underscores the necessity for multi-layered and integrated conservation strategies. First, the expansion and effective management of core protected areas, such as national parks, should be prioritized. Second, regions with high species richness outside protected areas should be designated as new protected areas or incentivized for private land conservation [107]. Third, strengthening habitat connectivity along the Baekdudaegan ecological corridor and installing wildlife crossings in key roads are essential for mitigating barriers to species movement. Fourth, local government-based conservation plans should be developed to apply region-specific management approaches. Finally, restoring river ecosystems in lowland and urban areas is crucial for improving habitats for aquatic species.

4.5. Climate Change Projections and Conservation Strategies

Our results indicate marked differences in projected future habitat changes between the SSP2-4.5 and SSP5-8.5 scenarios. These differences become especially pronounced by the 2050s and 2070s. The species richness patterns under these two scenarios showed distinct differences, emphasizing the importance of greenhouse gas mitigation efforts for biodiversity conservation.

4.5.1. Species Richness Change Patterns by Scenario

Under the SSP2-4.5 scenario, species richness remained relatively stable until the 2070s. High species richness areas (5–7 species) centered around the Baekdudaegan mountain range maintained similar spatial distribution across all three time periods, with key national parks such as Mt. Seoraksan, Mt. Odaesan, and Mt. Sobaeksan continuing to show high species richness (6–8 species). This suggests that under a moderate climate change scenario, the current protected area network remains effective for endangered species conservation. Local government-level analysis also indicated that mountainous regions of Gangwon-do maintained high species richness (6–8 species) through to the 2070s, demonstrating the region’s resilience to climate change [108]. In contrast, the SSP5-8.5 scenario showed a significant decline in species richness over time. While species richness patterns in the 2030s were similar to those under the SSP2-4.5 scenario, the 2050s saw a decline in species richness in southern Baekdudaegan and central mountainous areas. By the 2070s, high species richness areas were confined to a limited region in northern Baekdudaegan, and species richness in southern national parks (Mt. Jirisan, Mt. Deogyusan) dropped to 2–4 species. This highlights the insufficiency of current protected areas under the high emissions scenario, indicating the need for additional adaptation strategies [109].

4.5.2. Species-Specific Climate Change Impacts

Temperature rise is expected to cause elevational and latitudinal shifts in species distributions [8,110]. In this study, the northern Baekdudaegan region maintained relatively high species richness under both scenarios, suggesting its potential role as a climate refuge. Climate refugia maintain suitable environmental conditions despite climate change, playing a critical role in long-term species conservation [61,62]. Temperature-sensitive species, such as Pteromys volans aluco (0.827), are predicted to be most affected under the SSP5-8.5 scenario. This species, inhabiting cool-temperate mixed forests, is expected to shift to higher elevations due to temperature increases [111]. However, the limited height of South Korea’s highest peaks, such as Daecheongbong (1708 m) in Mt. Seoraksan, may restrict such upward shifts, putting these species at risk of “summit trap” effects [35,36]. Similarly, alpine species like Moschus moschiferus and Naemorhedus caudatus are vulnerable to climate change, as rising temperatures reduce the area of suitable habitat [84]. Reduced precipitation during dry seasons could also impact these herbivores by lowering food availability [112]. Intensive monitoring and habitat management are crucial to assess population trends and establish early response systems for these species.

4.5.3. Identification of Priority Conservation Areas

Under the SSP5-8.5 scenario, northern Baekdudaegan maintained high species richness through the 2070s. This region’s high elevation, complex terrain, and low human disturbance suggest it has a strong buffer capacity against climate change [113]. National parks such as Mt. Seoraksan and Mt. Odaesan, which retained 6–8 species in both scenarios, are predicted to remain key conservation areas in the future. Therefore, enhancing the protection levels of these areas and expanding adjacent buffer zones are necessary [114]. In contrast, southern mountainous regions (Mt. Jirisan, Mt. Deogyusan) showed a sharp decline in species richness after the 2050s under the SSP5-8.5 scenario, primarily due to lower elevations and higher temperature increases. While these regions remain important for biodiversity conservation, proactive adaptive management strategies are required. For example, creating a high-altitude stepping-stone habitat network could facilitate species migration [13].

4.5.4. Climate Adaptation Strategies

This study emphasizes the need for multi-layered climate adaptation strategies. First, priority should be given to protecting core habitats, such as northern Baekdudaegan, through stringent development regulations. Second, areas predicted to serve as climate refugia should be identified and designated as protected areas [115]. Third, strategic placement of wildlife corridors should facilitate species movement and enhance habitat connectivity [116]. Fourth, species monitoring programs should be established for different climate scenarios to track actual distribution changes and develop early warning systems [117]. Fifth, community-based conservation programs should incentivize private land conservation, and climate-adaptive land-use planning should be implemented [118]. Finally, greenhouse gas reduction policies are crucial, as the stark contrast between the SSP2-4.5 and SSP5-8.5 scenarios demonstrates the direct impact of climate change mitigation efforts on biodiversity conservation [33]. National carbon neutrality policies and international commitments like the Paris Agreement are essential for the long-term survival of endangered species [119].

4.6. Study Limitations and Future Research

This study primarily focused on climate and topographic variables, excluding biological factors such as vegetation structure, food availability, and interspecific interactions, which also significantly influence species distributions [120,121]. For instance, the distribution of Moschus moschiferus and Naemorhedus caudatus may have been influenced by the historical presence of predators like wolves or leopards [122]. Vegetation structure is a key determinant of habitat suitability for many mammal species, and integrating remote sensing-based vegetation indices (e.g., NDVI, EVI) and land cover data could improve model accuracy [123,124]. Including variables that reflect the spatial distribution of food resources would also enhance predictions for herbivores [125]. Additionally, this study relied solely on climate scenarios, without considering land-use changes. Habitat loss and fragmentation can significantly impact biodiversity, possibly more than climate change itself [126,127]. Given the ongoing urban expansion and infrastructure development in South Korea, integrating land-use change scenarios with climate models is crucial for more accurate predictions [128,129]. The study did not account for species dispersal capacity and adaptive potential. Species distribution models generally predict environmental suitability but do not assess whether species can actually move to new suitable habitats [130]. In areas with high habitat fragmentation, dispersal limitations can significantly constrain species’ ability to adapt to climate change [131]. Evolutionary adaptation is another factor, with some species capable of adapting quickly to environmental changes, while large mammals, with longer generation times, are likely to adapt more slowly [132]. Finally, uncertainty in climate models remains. This study used an ensemble of five regional climate models (RCMs), but predictions across global climate models (GCMs) still exhibit considerable variation [66,133]. Future research should aim to quantify the full range of uncertainties by incorporating various GCMs and RCMs into the predictive models [134].

5. Conclusions

This study used ensemble species distribution modeling to quantify current and future habitat suitability for 10 endangered mammal species in South Korea. The AUC-weighted ensemble achieved high performance (mean AUC = 0.840; 0.679–0.956 across species). Current richness hotspots were concentrated along the Baekdudaegan mountain range, with Mt. Odaesan, Mt. Seoraksan, and Mt. Sobaeksan National Parks identified as core areas. Topographic predictors (elevation and slope) dominated habitat suitability for most species, highlighting the importance of mountainous environments. Future projections diverged between scenarios: patterns remained relatively stable under SSP2-4.5, whereas SSP5-8.5 indicated substantial richness declines and a northward contraction of high-richness areas by the 2070s, suggesting that the northern Baekdudaegan may function as a key climate refugium.

The habitat suitability, richness, and refugia maps can be used as decision-support layers to (i) prioritize protected-area strengthening/expansion and buffer-zone designation around persistent hotspots and refugia, and (ii) target connectivity measures (corridors and wildlife crossings) and long-term monitoring in areas projected to persist or decline under SSP5-8.5. Together, these outputs support climate-adaptive, evidence-based conservation planning for endangered mammals in temperate East Asia.