Introduction
As global interest in biodiversity conservation and endemic species grows, the International Union for Conservation of Nature (IUCN) has been conducting Red List assessments to address the extinction crisis of endemic species (Kim et al., 2012). Moreover, with the entry into force of the Convention on Biological Diversity and the Nagoya Protocol, the fair and equitable sharing of benefits arising from the utilization of genetic resources has been emphasized (Chung et al., 2017), and the urgency of establishing national biosovereignty has emerged due to changing international circumstances (NIBR, 2020). Endemic species, as a core element of national biosovereignty, should be prioritized for conservation through continuous data acquisition, monitoring, and management to ensure their uniqueness and conservation (Coelho et al., 2020; NIBR, 2020). Formally declaring biosovereignty is an essential step in protecting endemic species and contributes to reducing their extinction risk. These international efforts highlight the need to increase attention and protection for endemic species. Research and monitoring of endemic species are essential for understanding these species and developing conservation strategies.
In particular, endemic plants, which are native to a specific geographic area, reflect the unique regional characteristics of their respective ecosystems (Chung et al., 2017). As a key species in need of conservation, it is crucial to clearly understand the relationship between habitat changes and environmental factors and, based on this understanding, develop species conservation and habitat restoration policies (Cho et al., 2020).
According to the Fifth Assessment Report (AR5) of the Intergovernmental Panel on Climate Change (IPCC), global average temperatures are projected to rise by 0.3 to 4.5°C by 2100 compared to 1986–2005 (Zhang et al., 2018). Recent global warming is expected to have various impacts on the growth environments of organisms adapted to natural environments (Kong et al., 2014; NGII, 2020). In particular, endemic plants, which are native to specific geographic areas, are expected to be dramatically affected, leading to changes in plant survival strategies and distribution (Kong et al., 2014). Additionally, the International Union for Conservation of Nature (IUCN) has warned that about 30% of the world’s resources will be at risk of extinction in the 21st century (IUCN, 2022). As extreme heat events and hydrological changes caused by climate change are expected to intensify, the risk to forest ecosystems is projected to increase (Yu et al., 2020). To effectively understand and respond to these changes, appropriate measures such as plant conservation and the development of alternative habitats are essential (Cho et al., 2020).
However, there is a lack of field studies, environmental variable construction, and research on long-term changes in suitable habitats for specific species (Kim et al., 2021). In Korea, habitat prediction studies have primarily focused on small-scale areas, and future prediction studies considering climate change remain insufficient (Kim et al., 2012). To develop conservation strategies for endemic plants with limited distributions, it is essential to fully understand the habitat environment, structure, and species characteristics of their native habitats (Jung et al., 2016). Additionally, it is necessary to identify the attributes of key biological populations and their correlations with relevant factors. Through such research, we can understand the ecological changes of endemic plants and develop conservation measures for ecosystems based on this understanding.
This study aims to contribute to the development of effective conservation policies by investigating the distributional changes of endemic plants in the Korean Peninsula due to climate change. We selected Eranthis byunsanensis as our target species due to its significance as an endemic plant in the Korean Peninsula, its classification as a Near Threatened species by the IUCN, and its designation as a flagship species of Byunsan Peninsula National Park. These factors underscore the high conservation value of this species, making it an ideal subject for studying the potential impacts of climate change on endemic plant distributions in the region. The specific objectives of this study are as follows. First, we aim to select appropriate environmental variables to understand the environmental characteristics of the regions where the target species are distributed. To this end, we selected key environmental variables that are expected to affect the habitat of Eranthis byunsanensis by referring to previous studies on Eranthis byunsanensis and similar plants. Second, we conducted modeling using an ensemble model that integrates individual models to provide more accurate predictions. Through this, we aimed to increase the reliability of the analysis results of the model. Third, we predicted the future habitat in the medium to long term by applying the SSP climate change scenario. By doing so, we analyzed the changes in the Korean endemic plants and explored alternative habitat securing measures to respond to such changes in the future.
The results of this study highlight the need to prepare for various future environmental changes by comparing the differences between habitat changes of Eranthis byunsanensis and climate change scenarios. This will provide important guidelines for assessing the stability of ecosystems and setting priorities for conservation activities. Furthermore, this study is expected to contribute to providing practical alternatives for the sustainable management and conservation of not only Eranthis byunsanensis but also other endemic plants in the Korean Peninsula.
Research Methods
Study Area and Target Species
The study region for this investigation encompasses the entire Korean Peninsula, excluding border regions with North Korea and insular areas due to logistical constraints and limitations in climate data resolution, which could introduce bias(Fig. 1). A comprehensive dataset of 69 occurrence points for Eranthis byunsanensis was compiled from multiple sources, including biological resource inventories from various national parks (e.g., Gyeongju, Naejangsan, Mudeungsan, and Byeonsanbando), the 3rd and 4th National Natural Environment Surveys, specimen locality data from the National Institute of Biological Resources, and a focused field study by Oh et al. (2011).
Eranthis byunsanensis, a perennial herb endemic to Korea, is classified within the Ranunculus acris. Primarily found in mountainous regions such as the Byeonsan Peninsula, Hallasan Mountain on Jeju Island, Mairisan Mountain, Jirisan Mountain, and Seoraksan Mountain, this species typically reaches a height of 10 cm and flowers between February and March. Its well-developed tubers facilitate growth in well-drained environments, and its growth is significantly influenced by light and moisture regimes (Oh et al., 2011). Habitat preferences include organic-rich environments and slightly acidic soil conditions, contrasting with the average Korean forest soil (Kim et al., 2012).
Data and Materials
Climate data were sourced from the WorldClim dataset, with both present and future climate data utilizing a spatial resolution of 1km2. The study’s temporal extent was partitioned into three periods: present (1981–2010), mid-term future (2041–2071), and long-term future (2071–2100). This temporal division facilitated the analysis of long-term climate patterns through the use of 30-year averages. By employing long-term climate data, the influence of short-term climate variability on potential habitats was mitigated, thereby addressing the limitations inherent in occurrence data collected during period A. The climate change scenarios underpinning this study were derived from the Shared Socioeconomic Pathways (SSPs) as outlined in the IPCC’s Sixth Assessment Report (AR6). To comprehensively evaluate changes in future potential habitat distribution under contrasting pathways, SSP1–2.6 and SSP5–8.5 were selected from the five available SSP scenarios.
A total of 32 environmental variables, closely linked to the ecological requirements of Eranthis byunsanensis as inferred from previous research, were selected and classified into climate, topography, land use, vegetation, and distance from rivers (Table 1). To mitigate the effects of multicollinearity among predictor variables, which can compromise model performance, Pearson correlation analysis was employed to identify and eliminate highly correlated variables (r ≥ 0.7). The final set of environmental variables was selected based on their relevance to the growth characteristics of Eranthis byunsanensis. All environmental data were processed at a spatial resolution of 1km2 using ArcMap 10.4.1.
Methodology
A species distribution modeling approach was adopted to predict the potential habitat of Eranthis byunsanensis and assess the impacts of climate change on its distribution. Initial data were gathered through a comprehensive literature review on the species’ ecological requirements. Occurrence data for Eranthis byunsanensis were compiled, and environmental variables were selected based on the literature review. Bioclimatic variables derived from SSP 1–2.6 and SSP 5–8.5 climate change scenarios (NIBR, 2022) were incorporated into the modeling process.
The R package biomod2 was employed to develop species distribution models. After evaluating model performance, ANN, CTA, FDA, GLM, MARS, and MAXENT were selected for ensemble modeling using the EMwmean algorithm. An ensemble model for period A was constructed, and future climate change scenarios were applied to predict the potential habitat of Eranthis byunsanensis. The resulting maps provided insights into the geographic shifts and changes in potential habitat under different climate change scenarios. Overlaid habitat maps were generated to identify potential alternative habitats for future conservation efforts (Fig. 2).
Model Development and Ensemble Forecasting
Species distribution models (SDMs) are quantitative methods that exploit the association between species occurrences and environmental conditions to model and predict species distributions. These models relate the presence or absence of a species to a suite of environmental variables to infer potential distributions across space and time (Srivastava et al., 2019). SDMs have found wide application in various fields, especially in assessing the impacts of climate change on biodiversity and informing conservation planning (Randin et al., 2009).
In this research, we employed the biomod2 package, a comprehensive R-based platform that provides a unified framework for modeling species-environment relationships using a variety of statistical and machine learning algorithms.
Individual species distribution models can produce varying results depending on the study area, environmental variables, and species selection. Given the challenges of generalizing the suitability of a single model and the uncertainties associated with predictions from a single model, there has been a growing trend towards ensemble modeling, which combines the results of multiple models (Hao et al., 2019).
To select models for ensemble modeling, we employed the receiver operating characteristic (ROC) curve and evaluated the area under the curve (AUC) value. Only models with an AUC value of 0.7 or higher were included in the ensemble. Generally, an AUC value between 0.5 and 1 is expected, with values greater than 0.8 indicating sufficient explanatory power (Franklin, 2009). Moreover, an AUC value greater than 0.9 implies a high degree of confidence in the model’s predictions (Thuiller et al., 2003; Araújo & New, 2005).
We used the Emwmean (Expectation-Maximization weighted mean) algorithm within the biomod2 package to combine the results of the ensemble models. This method optimizes the results of various models through weight adjustment, improving the performance of the ensemble model. By integrating diverse individual models, the ensemble model provides more robust predictive power and generalization ability, leading to more accurate and effective predictions.
We conducted future projections by inputting future climate data, expected to influence species distribution, into the ensemble model constructed based on the results of ensemble modeling. To compare the changes, we compared the current potential habitat predictions with the mid-term future (2041–2070) and long-term future (2071–2100) potential habitat distributions using SSP 1–2.6, a pathway towards carbon neutrality, and SSP 5–8.5, a pathway with similar or higher carbon dioxide emissions than the present. The prediction results were visualized as binary maps, clearly distinguishing between the presence and absence of the species, allowing for easy interpretation of the future distribution. To compare the analysis results, we focused on the area for each scenario, and by analyzing the area changes, we identified changes in the species’ habitat area according to each scenario. Through this, we can predict the species’ survival probability and habitat stability under climate change scenarios and understand the future trends of species distribution by identifying long-term trends under climate change.
Results and Discussion
Correlation Analysis of Environmental Variables
Through Pearson correlation analysis, a set of 14 environmental variables was identified as being significantly correlated with the growth characteristics of Eranthis byunsanensis. The selected variables included six climatic, four topographic, three soil, and one distance-from-rivers variable (Table 2).
Ensemble Modeling Results
To validate the predictive capacity of the ensemble model and ensure the accuracy and reliability of the predictions generated in this study, an ensemble was constructed using six models (ANN, CTA, FDA, GLM, MARS, and MAXENT) that were selected based on their robust performance and avoidance of overfitting in the Eranthis byunsanensis potential habitat prediction test. Each model, namely Artificial Neural Networks (ANN), Classification and Regression Trees (CART), Fisher’s Discriminant Analysis (FDA), Generalized Linear Models (GLM), Multivariate Adaptive Regression Splines (MARS), and Maximum Entropy (MAXENT), demonstrate distinct advantages in capturing nonlinear patterns, classification tasks, and regression analysis. The predicted potential distribution of Eranthis byunsanensis exhibited an AUC value of 0.975, suggesting a strong ability of the model to discriminate between presence and absence of the species.
Climate variables accounted for the largest proportion of the total variable contribution. Most climatic variables included in the model exhibited relatively high percentages, suggesting that the species is influenced by temperature and precipitation regimes. Notably, bio3 (Isothermality) demonstrated the highest contribution of 60.76%, indicating a preference for stable climatic conditions in Eranthis byunsanensis. Bio3 represents the ratio of mean diurnal temperature range to annual temperature range, serving as an indicator of climatic stability in a specific region. Eranthis byunsanensis is predominantly found in areas with high bio3 values, suggesting a preference for regions with large diurnal temperature ranges and relatively small annual temperature ranges. Considering the climate of the Korean Peninsula, while there is a significant temperature difference between summer and winter and distinct seasonal changes, coastal areas influenced by the ocean may experience smaller annual temperature variations compared to inland regions. Notably, bio3 values tend to be higher in the southern coastal regions. These regional characteristics provide stable growing conditions for Eranthis byunsanensis throughout the year. This aligns with previous research suggesting that a stable thermal environment benefits the growth cycle and flowering timing of perennial herbaceous plants like Eranthis byunsanensis (Chapman et al. 2022).
Other significant climatic variables were bio14 (Precipitation of Driest Month) and bio15 (Precipitation Seasonality), contributing 16.23% and 7.85%, respectively. These findings suggest that precipitation variability plays a crucial role in the growth of Eranthis byunsanensis, highlighting the species’ strong reliance on moisture conditions (Kim et al. 2012; Oh et al. 2011).
Topographic variables, including aspect, elevation, and slope, also exhibited notable contributions, indicating their correlation with factors such as altitude, drainage conditions, and light intensity in influencing the growth of Eranthis byunsanensis (Table 3).
Projected Changes in Potential Habitat
Based on the SSP scenarios, the potential habitat area of Eranthis byunsanensis exhibited varying trends. In period A, the estimated habitat area was 9,003km2. Under the SSP 1–2.6 scenario, the habitat area decreased in the mid-term but recovered somewhat in the long-term, reaching 8,095km2 by 2070. However, under the SSP 5–8.5 scenario, a continuous decline was observed, with the habitat area shrinking to 5,985km2 in the long-term. These findings are visually depicted in Fig. 3.
The potential habitat of Eranthis byunsanensis was primarily concentrated in coastal regions, particularly in Jeolla Province. Under the SSP 1–2.6 scenario, the southern coastal habitat showed a relatively stable trend, with minor fluctuations, while the East Coast experienced significant habitat expansion and inland migration. However, under the SSP 5–8.5 scenario, a continuous decline in overall ha-mid-termitat was observed, especially in inland areas. The spatial distribution of potential habitat under both scenarios is visualized in Fig. 4, clearly indicating the concentration of habitat in coastal regions and the occurrence of inland migration.
A comparative analysis of maps across different periods offers a more comprehensive understanding of the dynamic changes in the potential habitat of Eranthis byunsanensis (Fig. 5). The overlay of these maps highlights the distinct patterns of habitat reduction and expansion between periods mid-term and long-term, particularly in coastal regions and inland areas.
Our findings indicate that the potential distribution of Eranthis byunsanensis is susceptible to substantial shifts under different climate change scenarios and environmental conditions. The SSP 1–2.6 scenario, characterized by effective climate mitigation policies, suggests a relatively stable potential habitat, particularly in the southern coastal region, with the possibility of range expansion in the East Coast. Conversely, the SSP 5–8.5 scenario projects a significant decline in habitat, especially in inland regions of Jeollabuk-do. These results highlight the importance of climate mitigation policies in preserving the potential habitat of Eranthis byunsanensis.
Strategies for Habitat Selection and Conservation
Based on climate change scenarios, this study assessed the dynamic changes in the potential distribution of Eranthis byunsanensis and explored sustainable conservation strategies. A comparative analysis of potential habitats for mid-term and long-term periods revealed the significant impact of climate change on the species’ distribution (Fig. 6).
Under the mid-term projection for the SSP 1–2.6 scenario, the potential habitat of Eranthis byunsanensisis expected to remain relatively stable, with some expansion observed along the coastal regions of Gangwon-do. In contrast, the long-term projection under the SSP 5–8.5 scenario shows a drastic reduction in suitable habitat, particularly in the inland areas of Jeollabuk-do, along with a northeastward shift in distribution. This shift highlights a critical decline in inland areas, which could threaten the survival of the species in these regions.
To address these distributional changes, conservation strategies must be adapted to the different timeframes. In the mid-term, conservation efforts should focus on the relatively stable and expanding coastal habitats, particularly in Gangwon-do, to ensure the species’ survival under moderate climate change conditions. However, in the long-term, as more severe climate scenarios like SSP 5–8.5 predict significant habitat loss, it is essential to establish alternative habitats, especially in the inland areas of Jeollabuk-do and the northeastern regions, including the east coast of Gangwondo. This would involve not only preserving the current coastal habitats but also preparing inland regions as potential refuges for the species.
By tailoring conservation plans to the different distribution patterns projected for each period, we can better mitigate the risks posed by climate change. Prioritizing coastal regions for immediate conservation and planning for alternative inland habitats ensures a more resilient approach to protecting Eranthis byunsanensisin the face of shifting environmental conditions.
Conclusion
Using an ensemble modeling framework, this study assessed the projected changes in the potential distribution of Eranthis byunsanensis, a Korean endemic species, under various climate change scenarios. Results suggest that the potential habitat area under the SSP 1–2.6 scenario, characterized by effective climate mitigation, will be more extensive compared to the SSP 5–8.5 scenario. Although climate change is anticipated to decrease the extent of existing habitats, the study also revealed the emergence of novel habitats.
Despite the valuable insights gained, this study faced several limitations in projecting the potential habitat changes of Eranthis byunsanensis. As a flagship species of Byeonsanbando National Park with a Least Concern status according to the IUCN, the species exhibits low occurrence rates and a restricted distribution, hindering the acquisition of adequate data. Consequently, the model’s reliability was compromised due to data scarcity, particularly in capturing temporal dynamics. Furthermore, the limited availability of high-resolution environmental data restricted the accuracy of the results. Given the crucial role of data resolution in ecological modeling, the use of low-resolution data can impede detailed assessments and predictions of ecological changes. To address these limitations, future research should prioritize the acquisition of more occurrence data for endemic species and the utilization of high-resolution environmental data to enhance the precision of the study. This will not only bolster the reliability of habitat change prediction models for Eranthis byunsanensis but also facilitate the development of more effective conservation strategies.
This research offers a significant contribution to the field of studying changes in the distribution of endemic plant species. By employing ensemble modeling to project the potential habitat shifts of Eranthis byunsanensis, this study enhances our understanding of the ecological consequences of climate change. The integration of multiple climate scenarios and modeling approaches through ensemble modeling provides robust projections. The findings of this study provide invaluable baseline data for the conservation of endemic plant species and are expected to inform the development of effective management strategies. Notably, the proposed alternative habitats and conservation measures for Eranthis byunsanensis can serve as a practical framework for the formulation of conservation strategies. These alternatives are poised to play a pivotal role in safeguarding and restoring the habitats of Eranthis byunsanensis in the face of climate change, thereby facilitating the development of specific and actionable plans for the long-term conservation of this species.
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