From Ecological Niche to Conservation Planning; Climate‐Driven Range Dynamics of Ephedra intermedia in Central Asia

Abstract

ABSTRACT

Ephedra intermedia, a medicinally significant plant, is an important component of arid and semi‐arid ecosystems across Central and South Asia. This research sought to predict the present and future distribution of E. intermedia by applying ecological niche modeling (ENM) methods. The model incorporated comprehensive bioclimatic and edaphic variables to predict the species' habitat suitability. The results demonstrated high predictive accuracy, highlighting the importance of temperature seasonality, annual temperature range, soil pH, and nitrogen content as key species distribution determinants. The current habitat suitability map revealed core areas in Afghanistan, Pakistan, and Tajikistan mountain regions. Under future climate change scenarios (SSP2‐4.5 and SSP5‐8.5) for the 2050s and 2070s, the model projected a significant upward and northward shift in suitable habitats, coupled with a notable contraction in the extent of highly suitable areas, particularly under the high‐emission SSP5‐8.5 scenario. The predicted range shifts reflect the species' sensitivity to increasing temperatures and changing precipitation patterns. This suggests a potential loss of suitable habitats in low‐elevation and southern parts of its range. Including edaphic factors in the model provided novel insights, specifically highlighting the critical role of soil properties, such as soil pH and nitrogen content, in shaping the ecological niche of E. intermedia. These findings complement the observed upward and northward shifts in habitat suitability under future climate scenarios, emphasizing the species' reliance on high‐altitude refugia as climate conditions change. The results underscore important implications for conservation planning, suggesting that strategies should prioritize the protection of these refugial habitats while also considering measures such as habitat connectivity and assisted migration to support the species' adaptation to shifting environmental conditions.

Introduction

Climate change is one of the most pressing global environmental challenges, significantly affecting biodiversity and ecosystem stability (Abbass et al. 2022). The rapid alterations in temperature and precipitation patterns profoundly impact species distribution, particularly for those adapted to specific climatic and edaphic conditions (Dong et al. 2022). With increasing global temperatures and more erratic weather patterns, species must adapt, relocate, or risk local extinction (Pigot et al. 2023). These changes are expected to be more pronounced for plant species with narrow ecological niches, such as those adapted to arid and semi-arid environments (Zhao et al. 2024). Understanding how these species respond to changing climatic conditions is crucial for predicting future biodiversity dynamics and developing effective conservation strategies (Waldvogel et al. 2020; Trew and Maclean 2021). Edaphic variables are soil-related factors that influence the distribution, growth, and ecological interactions of plant species (Waheed et al. 2022; Sadia et al. 2024). These variables play a critical role in shaping ecological niches, particularly in environments where soil characteristics are primary determinants of habitat suitability (Arshad, Haq, et al. 2024; Arshad, Shoaib, et al. 2024).

Ecological niche modeling (ENM) has emerged as a powerful tool for predicting species distributions and assessing potential range shifts under future climate scenarios (Melo-Merino et al. 2020). ENM uses species occurrence data and environmental variables to estimate a species' niche and project its geographic distribution across different climatic conditions (Pshegusov et al. 2022). Among various modeling techniques, MaxEnt (Maximum Entropy) is widely recognized for its robustness and predictive accuracy, especially when dealing with presence-only data (Phillips et al. 2006). By integrating bioclimatic predictors, ENM provides insights into the factors that influence species distributions and helps forecast shifts in suitable habitats in response to climate change (Rather et al. 2022). Such predictive models are invaluable for identifying at-risk species and guiding conservation planning (Swan et al. 2021; Bai et al. 2024). Climate change is already causing observable range shifts across multiple taxa, with numerous species migrating towards higher altitudes or latitudes in search of suitable habitats (Spence and Tingley 2020). Such shifts may result in altered community dynamics, habitat loss, and intensified competition, posing significant threats to species that cannot relocate or adapt rapidly enough (Vitasse et al. 2021). For species with specialized habitat requirements, like many arid-adapted plants, predicting potential range contractions and identifying climate refugia are critical steps in mitigating the impacts of climate change (Buckner and Danforth 2022). ENM offers a strategic approach to anticipate these changes and inform adaptive conservation measures, ensuring the long-term preservation of vulnerable species and their habitats (Zurell et al. 2022).

Ephedra intermedia Schrenk & C.A. Mey is a plant species widely recognized for its adaptive traits, therapeutic properties, and significant role in traditional medicine systems (Guo, Gao, et al. 2023; Guo, He, et al. 2023). The species thrives in arid and semi-arid environments and contributes to ecosystem stability through various pathways. Although not explicitly identified as a keystone or foundation species, E. intermedia plays a critical ecological role by preventing soil erosion, stabilizing desert environments, and providing structural support that facilitates microhabitats for other species, such as soil-dwelling organisms and small fauna (Minami et al. 2021; Zhang et al. 2024). These roles are particularly vital in ecosystems that experience higher levels of abiotic stress, such as extreme temperatures, low precipitation, and nutrient-poor soils. Despite its ecological and medicinal importance, the current and future ecological niche of E. intermedia remains uncertain, particularly under the influence of climate change. The study aims to assess the current ecological suitability of E. intermedia in Central Asia, identify key environmental variables influencing its habitat, and project future distribution patterns under climate change scenarios for the 2050s and 2070s. Climate change significantly impacts species distribution, forcing plants to adapt, shift their ranges, or face local extinction, especially in environments with distinct climatic constraints (Hulme 2005; Spence and Tingley 2020). Studies have shown that many montane and drought-adapted species migrate towards higher elevations and latitudes in response to rising temperatures (Lenoir et al. 2008; Lindner et al. 2010; Zu et al. 2021), a trend observed in Ephedra sinica and Ephedra distachya (He et al. 2021; Zhang et al. 2022, 2024). It is hypothesized that E. intermedia will exhibit upward and northward range shifts in response to increasing temperatures and altered precipitation patterns, with core habitats in low-elevation and southern regions contracting and new suitable areas emerging at higher elevations and latitudes. The findings offer valuable understanding of the ecological requirements of E. intermedia, guiding the development of effective conservation strategies in the face of rapid environmental change.

Methodology

Ephedra intermedia

Ephedra intermedia is a dioecious shrub, typically reaching up to 1 m in height, characterized by its jointed, green stems and small, scale-like leaves (González-Juárez et al. 2020). The plant thrives in arid and semi-arid environments, commonly found in deserts, grasslands, floodplains, river valleys, slopes, cliffs, and sandy beaches at elevations ranging from 100 to 4000 m (Gul et al. 2017). Its distribution spans across Central and East Asia, including regions of China, Kazakhstan, Afghanistan, and Pakistan (Figure 1). Ephedra intermedia plays a vital role in stabilizing desert ecosystems and preventing soil erosion (Liu et al. 2020). However, overexploitation for its medicinal properties, particularly for ephedrine extraction, has led to concerns about population decline (Tokuda et al. 2024; Fan et al. 2024). Conservation efforts are essential to ensure the sustainability of this species, considering its ecological significance and medicinal value.

[IMAGE OMITTED. SEE PDF]

Data Collection and Screening

In this study, we gathered a detailed dataset to predict the distribution of E. intermedia. The occurrence dataset comprised 190 records: 170 data points were sourced from the GBIF (), whereas 20 additional records were obtained from published literature (Chaudhri 1957; Kakiuchi et al. 2007; Jan et al. 2009; Nowak et al. 2016; Khan et al. 2018; Hayashi et al. 2019; Basit et al. 2021; Li et al. 2024) (Table S1). To ensure data quality, duplicate records were removed, and occurrence points outside the known range of E. intermedia were excluded. To minimize spatial autocorrelation, we applied a spatial thinning process at a 1 km resolution. The cleaned occurrence dataset was then projected onto a 2.5 arc-minute grid for spatial consistency with the environmental predictors. We included 19 bioclimatic variables with a spatial resolution of 2.5 arc minutes, retrieved from WorldClim version 2.1 (, accessed March 20, 2022). Furthermore, we incorporated 10 edaphic variables from SoilGrids (, accessed March 20, 2022) to improve the model's ecological representation (Table S2). High-resolution elevation data (30 arc seconds) were processed using ArcGIS 10.5 to derive topographic features such as slope and aspect, which were subsequently included as predictive variables. To assess potential shifts in habitat suitability under future climate scenarios, we utilized two Shared Socioeconomic Pathways (SSPs): SSP2-4.5, indicating a moderate emissions pathway, and SSP5-8.5, representing a high-emissions scenario. These projections were based on climate data from the Coupled Model Intercomparison Project Phase 6 (CMIP6) for the time frames 2050s (2041–2060) and 2070s (2061–2080). We selected the BCC-CSM2-MR global climate model for its high spatial resolution and reliable performance in simulating bioclimatic variables.

Initial Processing of Environmental Variables

A two-step procedure was employed to ensure the selection of independent and ecologically relevant variables. First, Pearson correlation analysis was conducted to detect multicollinearity among the environmental variables. Variables with a correlation coefficient greater than 0.8 were excluded to minimize redundancy and multicollinearity issues (Graham 2003). Following this, a subset of nine variables was identified based on their ecological significance and minimal inter-correlation: temperature seasonality (bio04), maximum temperature of the warmest month (bio05), temperature annual range (bio07), precipitation seasonality (bio15), precipitation of the warmest quarter (bio18), precipitation of the coldest quarter (bio19), elevation, soil nitrogen, and soil pH (Figure 2).

[IMAGE OMITTED. SEE PDF]

Model Calibration and Fine-Tuning and Validation

MaxEnt software (version 3.4.4) was utilized for ENM of E. intermedia (Phillips et al. 2006). To enhance model performance and diminish the risk of overfitting, we tested diverse combinations of regularization multipliers (RM) and feature classes (FC) (Elith et al. 2006, 2011; Phillips et al. 2009). We specifically evaluated eight RM values, ranging from 0.5 to 4 in increments of 0.5, along with six FC (L, LQ, H, LQH, LQHP, and LQHPT) (Anderson and Gonzalez Jr. 2011; Bao et al. 2022; Fourcade et al. 2014; Merow et al. 2013). The ENMeval package in R was applied to optimize parameter selection and minimize overfitting (Kass et al. 2021). The model configurations included the use of a complementary log–log (clog–log) output format, 10-fold cross-validation, response curve analysis, and jackknife tests to appraise the status of each environmental predictor (Phillips and Dudík 2008; Peterson et al. 2007).

MaxEnt outputs were exported to ArcGIS 10.5 for spatial analysis. The continuous habitat suitability maps were classified into five distinct categories based on logistic output values: unsuitable (US), low suitability (LS), moderate suitability (MS), high suitability (HS), and very high suitability (VHS). This classification allowed for a consistent evaluation of habitat quality and facilitated the identification of core distribution zones for E. intermedia. The accuracy of the optimized ENM was determined using the area under the curve (AUC) of the receiver operating characteristic (ROC) curve (Elith et al. 2011). An AUC score of 0.9 or higher indicated excellent model accuracy (Summers et al. 2012; Yang et al. 2013). Additional evaluation metrics, including the 10% training omission rate (OR10) and the difference between training and testing AUC (AUC.DIFF), were used to assess the model's robustness and potential overfitting (Bai et al. 2018). The future climate projections were analyzed to predict range shifts and changes in habitat suitability for E. intermedia during the 2050s and 2070s.

Results

The ENM for E. intermedia demonstrated strong predictive performance, as evidenced by the Area Under the ROC Curve (AUC). The mean AUC value was 0.913, indicating a high level of model accuracy (Figure 3). The ROC curve shows a steep rise with minimal false positive rates, further emphasizing the model's robustness. The mean predicted omission closely aligns with the expected omission across different thresholds.

[IMAGE OMITTED. SEE PDF]

Jackknife tests for variable importance provided insights into the contribution of each predictor variable to the model (Figure 4). Among the environmental variables, soil pH exhibited the highest contribution, resulting in the highest training gain when used alone and a significant drop in training gain when omitted. This suggests that soil pH plays a critical role in determining the suitable habitat for E. intermedia. Other important predictors include nitrogen content, elevation, and bioclimatic variables such as bio07 (temperature annual range) and bio15 (precipitation seasonality), all of which contributed substantially to the model gain. The jackknife test for AUC further supports the significance of these variables, as phh2o, nitrogen, and elevation showed the highest AUC values when used in isolation.

[IMAGE OMITTED. SEE PDF]

Response of E. intermedia to Environmental Variables

The response curves for E. intermedia indicate the preferences and tolerances for a range of bioclimatic and edaphic factors. For bio04, the logistic output peaks at moderate seasonality values (~1000), suggesting that the species prefers environments with stable yet moderately fluctuating temperatures (Figure 5). The response to bio05 reveals an optimal range between 15°C and 25°C, beyond which the predicted suitability declines sharply, indicating sensitivity to extreme high temperatures. The response to bio07 displays a predilection for areas with an annual temperature range of 35°C to 45°C, highlighting the species' adaptation to regions with significant temperature variability. Similarly, bio15 demonstrates a peak in suitability at moderate precipitation seasonality values (40–60), suggesting that E. intermedia favors environments with balanced rainfall distribution throughout the year (Figure 5). Bio18 and bio19 both show a declining response at higher precipitation levels, indicating that E. intermedia prefers drier conditions during both the warmest and coldest periods of the year. Regarding edaphic and topographic factors, the response to elevation indicates a bimodal distribution, with optimal suitability observed at mid-elevations (2000–3500 m). The response to soil nitrogen content highlights an optimal range of 10–15 units, while the suitability decreases outside this range, indicating a preference for moderately fertile soils. The response to soil pH shows a peak at pH values between 5 and 15, suggesting that E. intermedia thrives in slightly alkaline to moderately acidic soils. the environmental response curves highlight the adaptability of E. intermedia to specific climatic and edaphic conditions, with clear preferences for moderate temperature seasonality, balanced precipitation, mid-elevation zones, and moderately fertile, slightly alkaline soils.

[IMAGE OMITTED. SEE PDF]

Spatial Distribution and Habitat Suitability

The predicted habitat suitability map for E. intermedia shows its potential distribution across Central and South Asia, categorized into five classes (Figure 6). The spatial analysis highlights several key findings: Regions of VHS are located in the mountainous areas of Afghanistan, Tajikistan, and northern Pakistan, particularly along the Hindu Kush and Karakoram ranges. These areas correspond to the known altitudinal preferences of E. intermedia, aligning with the mid-elevation zones identified as favorable in the response curves. The model also identifies significant areas of MS to HS extending into Uzbekistan, Turkmenistan, and parts of Kyrgyzstan. LS and US habitats are predominantly located in the arid lowlands of Turkmenistan and southern Uzbekistan, as well as the densely vegetated or high-rainfall areas in northern India and Nepal. The distribution map highlights the transboundary occurrence of E. intermedia, indicating potential cross-border conservation areas, particularly in the shared mountainous regions between Afghanistan, Pakistan, and Tajikistan. Ephedra intermedia exhibits HS in key regions of Central and South Asia. In Afghanistan, the Koh-i-Baba range and Hindu Kush mountains stand out, while in Pakistan, the Karakoram Range, Swat Valley, and Chitral region are critical. Tajikistan's Pamir-Alay and Gissar ranges, Kyrgyzstan's Tien Shan mountains, and Uzbekistan's Nuratau Mountains also host significant suitable habitats.

[IMAGE OMITTED. SEE PDF]

Projected Future Distribution

The future distribution of E. intermedia was modeled under two climate change scenarios: SSP2-4.5 (a moderate emissions pathway) and SSP5-8.5 (a high-emissions pathway) for both the 2050s and 2070s (Figure 7). The analysis indicates significant shifts in habitat suitability in response to changing climatic conditions, with notable differences between the two scenarios. Under the SSP2-4.5 scenario in the 2050s, the model predicts a moderate contraction of highly suitable habitats (VHS) in the current core distribution areas, particularly in Afghanistan and northern Pakistan. However, suitable habitats expand slightly towards the northeast into parts of China and Kyrgyzstan. This suggests a potential shift in the range due to increasing temperatures and changing precipitation patterns, with some areas in the lower elevation zones becoming less suitable (shift from MS/HS to LS/US). By the 2070s, the SSP2-4.5 scenario shows a more pronounced reduction in very HS areas, particularly in the western parts of the range (Afghanistan and western Pakistan). High and very HS zones are predicted to shrink, while areas of low to MS expand into previously US zones, especially towards the eastern parts of the distribution range (Tajikistan and China). This suggests a gradual eastward and upward migration of the species' suitable habitats. Under the more extreme SSP5-8.5 scenario in the 2050s, the model projects a substantial reduction in the extent of VHS habitats. The core habitats in the Hindu Kush and Karakoram regions show signs of fragmentation, with a noticeable decrease in highly suitable areas. Conversely, new suitable habitats emerge at higher altitudes and further north, indicating a potential shift in the species' range as it tracks cooler temperatures at higher elevations and latitudes. By the 2070s under the SSP5-8.5 scenario, the model predicts a drastic shift in the distribution of E. intermedia. VHS zones are largely confined to high-altitude regions in the Himalayas and northern ranges of Pakistan and China. The western and southern parts of the range experience a marked decline in suitability, likely due to increased temperature stress and altered precipitation patterns. The expansion of US and LS areas highlights the potential risk of habitat loss under this high-emissions scenario. Predictions under both SSP2-4.5 and SSP5-8.5 scenarios indicate a shift in the range of E. intermedia towards higher latitudes and elevations, accompanied by marked reductions in habitat suitability in the southern and western areas of its current range. The more severe SSP5-8.5 scenario forecasts greater habitat loss and increased fragmentation, underscoring the species' heightened susceptibility to intense climate change impacts.

[IMAGE OMITTED. SEE PDF]

Discussion

The ENM developed for E. intermedia demonstrated high predictive accuracy. The strong model fit can be attributed to the inclusion of both bioclimatic and edaphic factors, reflecting the multifaceted ecological preferences of E. intermedia. Comparatively, previous studies on closely related species, such as Ephedra sinica and Ephedra distachya, reported similar predictive performance when utilizing bioclimatic variables in ENMs (He et al. 2021; Zhang et al. 2022; Li et al. 2024; Xu et al. 2024). However, the inclusion of edaphic variables (e.g., soil pH and nitrogen content) in this study provided a more comprehensive understanding of habitat suitability, underscoring the importance of integrating soil-related factors in niche modeling for arid and semi-arid-adapted species like E. intermedia.

The analysis highlighted several key bioclimatic variables that significantly influenced the predicted distribution of E. intermedia. Notably, bio07 and bio04 emerged as critical predictors. The preference for moderate temperature seasonality and a specific annual temperature range suggests that E. intermedia is adapted to environments with marked seasonal temperature variations, which is consistent with its distribution in mountainous and semi-arid regions. Previous studies have reported similar findings, where temperature seasonality was a primary determinant of habitat suitability for other Ephedra species (Guo, Gao, et al. 2023; Guo, He, et al. 2023; Zhang et al. 2024). The strong response to temperature-related variables aligns with the physiological adaptations of E. intermedia to cope with temperature extremes (Tokuda et al. 2024). This suggests that the species' distribution is strongly constrained by temperature fluctuations, a pattern that is consistent across the other Gymnosperms (Subedi et al. 2020; Li et al. 2021). The response to bio15 further supports this interpretation. E. intermedia showed a higher suitability in regions with moderate precipitation seasonality, reflecting its adaptation to environments where water availability fluctuates seasonally. This finding is corroborated by previous niche modeling studies on desert and semi-desert plants, which emphasize the importance of seasonal precipitation in shaping the distribution of drought-tolerant species (Butterfield and Briggs 2011; Halmy et al. 2019; Herrando-Moraira et al. 2020). The response curves indicated a strong positive relationship between habitat suitability and moderately alkaline soils (pH 5–15). This aligns with field observations and ecological studies that have noted the prevalence of E. intermedia in regions with alkaline or calcareous soils (Asensi et al. 2007; Rabizadeh et al. 2018). Alkaline soils typically have higher nutrient availability, which may explain the observed preference for these soil types, as the species is adapted to exploit nutrient-poor but alkaline environments (Jobbagy and Jackson 2001; Adcock et al. 2007). The positive response to soil nitrogen content, a key environmental factor representing soil fertility, indicates that E. intermedia favors moderately fertile soils. Such soils may provide essential nutrients to support its growth, particularly in nutrient-limited arid environments. Positive biotic interactions, including facilitation, may enhance the establishment and survival of E. intermedia by improving resource availability and reducing environmental stress in challenging conditions (Jäschke et al. 2020). This finding is consistent with previous research on nitrogen uptake strategies in desert plants, where moderate nitrogen levels were found to enhance plant performance without exacerbating competition with co-occurring species (Zhuang et al. 2020). The response curve for elevation exhibited a bimodal distribution, indicating that E. intermedia is most suited to mid-elevations (2000–3500 m). This elevational range corresponds to montane ecosystems, where the species is commonly found (Li et al. 2024). The preference for mid-elevation habitats may be linked to favorable microclimatic conditions (Zhang et al. 2024; Xu et al. 2024).

The projected distribution of E. intermedia under future climate scenarios suggests significant range shifts, with a tendency for northward and upward migration of suitable habitats. Under both SSP2-4.5 and SSP5-8.5 scenarios, the model predicts a contraction of VHS areas, particularly in the southern and western parts of the current range. This finding aligns with broader trends observed across plant species in arid and mountainous regions, where warming temperatures and altered precipitation patterns drive species to track their climatic niches towards higher elevations and latitudes (Randin et al. 2009; Koo and Park 2022; Martínez et al. 2012). The upward and northward shift in the predicted range of Ephedra intermedia can be attributed to its sensitivity to temperature changes, as indicated by the strong response to bio04 and bio07. The species appears to be adapted to environments with moderate seasonal temperature fluctuations and a specific temperature range, conditions that are increasingly restricted to higher elevations under warming scenarios (Liu et al. 2009; La Sorte and Jetz 2010; Rumpf et al. 2018). As global temperatures rise, the suitable thermal niche of E. intermedia is expected to migrate upward, reflecting a broader trend observed in high-altitude flora (Shaheen et al. 2023; Lin et al. 2023). This pattern of range shifts is consistent with the “escalator to extinction” hypothesis, which suggests that montane species may face increased risk of habitat loss as they are forced into progressively smaller and fragmented high-elevation refugia (Zu et al. 2021; Waheed et al. 2024).

The SSP5-8.5 scenario projects a substantial reduction in the extent of highly suitable habitats for E. intermedia. This contraction is most pronounced in the low-elevation and southern parts of the range, where increased temperature stress and changes in precipitation regimes may exceed the physiological tolerances of the species (Marifatul et al. 2024; Waheed et al. 2024a). Similar findings have been reported for other arid-adapted plants, where niche contraction under high-emission scenarios is driven by the exacerbation of temperature extremes and reduced water availability (Waheed et al. 2023; Waheed and Arshad 2024; Arshad, Haq, et al. 2024; Arshad, Shoaib, et al. 2024). The potential for range contraction and habitat fragmentation under the SSP5-8.5 scenario highlights the vulnerability of E. intermedia to climate change. The species' preference for specific temperature and precipitation conditions suggests that its realized niche may be relatively narrow, making it more susceptible to changes in climatic conditions (Wallingford et al. 2020; Atwater and Barney 2021). As temperatures rise and precipitation patterns become more erratic, the ecological niche of E. intermedia may shift towards areas that can still offer the combination of moderate temperature seasonality and drier conditions preferred by the species.

The predicted range shifts for E. intermedia are consistent with findings from previous studies on range dynamics of arid and montane plants under climate change (Rather et al. 2021; Du et al. 2021). For example, research on alpine plant species in the European Alps and the Qilian Mountains, China, reported similar upward and northward shifts as species track their climatic niches (Dagnino et al. 2020; Du et al. 2021). The phenomenon of range shifts in response to climate warming has been well documented across taxa, highlighting a global trend of biotic responses to increasing temperatures (Peng et al. 2021; Tiwari et al. 2024). In the context of the Ephedra genus, studies on Ephedra sinica and Ephedra distachya also indicated a tendency for upward range shifts under future climate scenarios (He et al. 2021; Zhang et al. 2022; Li et al. 2024). However, the inclusion of edaphic variables in this study provided additional understanding, suggesting that soil characteristics may play a critical role in modulating the species' response to climate change. This highlights the importance of considering multiple environmental dimensions in ENM to capture the full complexity of species' habitat preferences (Pshegusov et al. 2022; Arshad et al. 2022).

Implications for Conservation

The results of this study carry significant implications for the conservation efforts of E. intermedia, a species of medicinal and ecological significance. The predicted contraction of suitable habitats under high-emission scenarios suggests that the species may face a substantial risk of population decline and local extirpation, particularly in the western parts of its range (Afghanistan and Pakistan). Conservation efforts should prioritize the protection of high-elevation refugia and consider the potential for assisted migration to facilitate the species' adaptation to shifting climate conditions. Furthermore, the strong influence of edaphic factors on habitat suitability underscores the need to integrate soil characteristics into conservation planning. Protecting areas with alkaline and moderate nitrogen levels could enhance the resilience of E. intermedia populations, as these sites may offer critical microhabitats for the species under changing environmental conditions.

Limitations and Future Research Directions

While the model demonstrated strong predictive performance, several limitations should be acknowledged. The reliance on current bioclimatic data may not fully capture the microhabitat variations that influence the distribution of E. intermedia, particularly in heterogeneous mountain landscapes. Additionally, the model does not account for biotic interactions, such as competition and herbivory, which could influence the realized niche of the species. Future studies could integrate fine-scale environmental data and biotic factors to enhance the predictive accuracy of ENMs for this and other similar species.

Conclusion

This study identifies the key ecological factors influencing the distribution of E. intermedia and predicts significant range shifts under future climate scenarios. The findings highlight the species' vulnerability to climate change and emphasize the importance of targeted conservation strategies. Protecting high-altitude refugia and maintaining habitat connectivity are crucial to mitigating the impacts of environmental changes on E. intermedia. By providing actionable insights, this research contributes to the development of evidence-based approaches for conserving arid and semi-arid ecosystems, ensuring the persistence of ecologically significant species like E. intermedia in the face of global change.

Author Contributions

Muhammad Waheed: conceptualization (equal), formal analysis (equal), investigation (equal), methodology (equal), validation (equal), visualization (equal), writing – original draft (equal), writing – review and editing (equal). Fahim Arshad: data curation (equal), resources (equal), supervision (equal), validation (equal), writing – review and editing (equal). Sehrish Sadia: data curation (equal), investigation (equal), writing – review and editing (equal). Beatrice Ambo Fonge: data curation (equal), validation (equal), writing – review and editing (equal). Abeer Al-Andal: funding acquisition (equal), resources (equal), writing – review and editing (equal). Asma Jabeen: investigation (equal), project administration (equal), writing – review and editing (equal). Shalom Dilshad: data curation (equal), visualization (equal), writing – review and editing (equal).

Acknowledgments

The authors extend their appreciation to the Deanship of Research and Graduate Studies at King Khalid University for funding this work through the Large Research Project under grant number RGP2/56/45.

Conflicts of Interest

The authors declare no conflicts of interest.

Data Availability Statement

The data that support the findings of this study are available as a Supporting Information to this manuscript.

References

Abbass, K., M. Z. Qasim, H. Song, M. Murshed, H. Mahmood, and I. Younis. 2022. “A Review of the Global Climate Change Impacts, Adaptation, and Sustainable Mitigation Measures.” Environmental Science and Pollution Research 29, no. 28: 42539–42559.

Adcock, D., A. M. McNeill, G. K. McDonald, and R. D. Armstrong. 2007. “Subsoil Constraints to Crop Production on Neutral and Alkaline Soils in South‐Eastern Australia: A Review of Current Knowledge and Management Strategies.” Australian Journal of Experimental Agriculture 47, no. 11: 1245–1261.

Anderson, R. P., and I. Gonzalez Jr. 2011. “Species‐Specific Tuning Increases Robustness to Sampling Bias in Models of Species Distributions: An Implementation With Maxent.” Ecological Modelling 222, no. 15: 2796–2811.

Arshad, F., S. M. Haq, M. Waheed, M. A. Jameel, and R. W. Bussmann. 2024. “Environmental Variables Drive Medicinal Plant Composition and Distribution in Various Forest Types of Subtropical Region in Pakistan.” Ecological Frontiers 44, no. 2: 234–246.

Arshad, F., M. Shoaib, M. A. Jameel, et al. 2024. “Vegetation Profile and Ecological Traits in Irrigated Plains of Semi‐Arid Regions: A Case Study of Okara, Punjab Pakistan.” Ecological Frontiers 44: 700–711.

Arshad, F., M. Waheed, K. Fatima, et al. 2022. “Predicting the Suitable Current and Future Potential Distribution of the Native Endangered Tree Tecomella Undulata (Sm.) Seem. In Pakistan.” Sustainability 14, no. 12: 7215.

Asensi, A., B. Díez‐Garretas, and P. Quézel. 2007. “Plant Communities of Juniperus Turbinata Guss. Subsp. Turbinata in the Mediterranean Region. A Biogeographical, Bioclimatical and Syntaxonomical Survey.” Phytocoenologia 37, no. 3: 599.

Atwater, Z. D., and J. N. Barney. 2021. “Climatic Niche Shifts in 815 Introduced Plant Species Affect Their Predicted Distributions.” Global Ecology and Biogeography 30, no. 8: 1671–1684.

Bai, D. F., P. J. Chen, L. Atzeni, L. Cering, Q. Li, and K. Shi. 2018. “Assessment of Habitat Suitability of the Snow Leopard (Panthera Uncia) in Qomolangma National Nature Reserve Based on MaxEnt Modeling.” Zoological Research 39, no. 6: 373.

Bai, J., H. Wang, and D. Yang. 2024. “Identification of Urban Trees at Risk due to Climate Change‐A Case Study of Tianjin City.” Ecological Indicators 167: 112611.

Bao, R., X. Li, and J. Zheng. 2022. “Feature Tuning Improves MAXENT Predictions of the Potential Distribution of Pedicularis Longiflora Rudolph and Its Variant.” PeerJ 10: e13337.

Basit, A., S. Saeed, A. Ahmed, N. Baloch, and A. Z. Ali. 2021. “Genetic Structuring of Ephedra an Important Medicinal Plant From Low to High Altitudinal Zones of Balochistan, Pakistan.” Biodiversitas Journal of Biological Diversity 22, no. 2: d220223.

Buckner, M. A., and B. N. Danforth. 2022. “Climate‐Driven Range Shifts of a Rare Specialist Bee, Macropis Nuda (Melittidae), and Its Host Plant, Lysimachia ciliata (Primulaceae).” Global Ecology and Conservation 37: e02180.

Butterfield, B. J., and J. M. Briggs. 2011. “Regeneration Niche Differentiates Functional Strategies of Desert Woody Plant Species.” Oecologia 165: 477–487.

Chaudhri, I. I. 1957. “Pakistan Ephedra.” Economic Botany: 11, no. 3: 257–262.

Dagnino, D., M. Guerrina, L. Minuto, M. G. Mariotti, F. Médail, and G. Casazza. 2020. “Climate Change and the Future of Endemic Flora in the South Western Alps: Relationships Between Niche Properties and Extinction Risk.” Regional Environmental Change 20, no. 4: 121.

Dong, Y. W., M. L. Liao, G. D. Han, and G. N. Somero. 2022. “An Integrated, Multi‐Level Analysis of Thermal Effects on Intertidal Molluscs for Understanding Species Distribution Patterns.” Biological Reviews 97, no. 2: 554–581.

Du, J., Z. He, L. Chen, P. Lin, X. Zhu, and Q. Tian. 2021. “Impact of Climate Change on Alpine Plant Community in Qilian Mountains of China.” International Journal of Biometeorology 65, no. 11: 1849–1858.

Elith, J., H. C. Graham, P. R. Anderson, et al. 2006. “Novel Methods Improve Prediction of Species' Distributions From Occurrence Data.” Ecography 29, no. 2: 129–151.

Elith, J., S. J. Phillips, T. Hastie, M. Dudík, Y. E. Chee, and C. J. Yates. 2011. “A Statistical Explanation of MaxEnt for Ecologists.” Diversity and Distributions 17, no. 1: 43–57.

Fan, X., Y. Cao, M. Zeng, Y. Ren, X. Zheng, and W. Feng. 2024. “Six New Compounds From the Herbaceous Stems of Ephedra intermedia Schrenket CA Meyer and Their Lung‐Protective Activity.” Molecules 29, no. 2: 432. https://doi.org/10.3390/molecules29020432.

Fourcade, Y., J. O. Engler, D. Rödder, and J. Secondi. 2014. “Mapping Species Distributions With MAXENT Using a Geographically Biased Sample of Presence Data: A Performance Assessment of Methods for Correcting Sampling Bias.” PLoS One 9, no. 5: e97122.

González‐Juárez, D. E., A. Escobedo‐Moratilla, J. Flores, et al. 2020. “A Review of the Ephedra Genus: Distribution, Ecology, Ethnobotany, Phytochemistry and Pharmacological Properties.” Molecules 25, no. 14: 3283.

Graham, M. H. 2003. “Confronting Multicollinearity in Ecological Multiple Regression.” Ecology 84, no. 11: 2809–2815.

Gul, R., S. U. Jan, S. Faridullah, S. Sherani, and N. Jahan. 2017. “Preliminary Phytochemical Screening, Quantitative Analysis of Alkaloids, and Antioxidant Activity of Crude Plant Extracts From Ephedra Intermedia Indigenous to Balochistan.” Scientific World Journal 2017, no. 1: 5873648.

Guo, L., Y. Gao, P. He, Y. He, and F. Meng. 2023. “Modeling for Predicting the Potential Geographical Distribution of Three Ephedra Herbs in China.” Plants 12, no. 4: 787.

Guo, L., P. He, Y. He, et al. 2023. “Predicting the Comprehensive Geospatial Pattern of Two Ephedrine‐Type Alkaloids for Ephedra sinica in Inner Mongolia.” PLoS One 18, no. 4: e0283967.

Halmy, M. W. A., M. Fawzy, D. A. Ahmed, N. M. Saeed, and M. A. Awad. 2019. “Monitoring and Predicting the Potential Distribution of Alien Plant Species in Arid Ecosystem Using Remotely‐Sensed Data.” Remote Sensing Applications: Society and Environment 13: 69–84.

Hayashi, H., M. Shukurova, S. Oikawa, et al. 2019. “Field Survey of Ephedra Plants in Central Asia (1). Characterization of Ephedra equisetina, Ephedra Intermedia, and Their Putative Hybrids Collected in the Zaravshan Mountains of Tajikistan.” Biological and Pharmaceutical Bulletin 42, no. 4: 552–560.

He, P., J. Li, Y. Li, et al. 2021. “Habitat Protection and Planning for Three Ephedra Using the MaxEnt and Marxan Models.” Ecological Indicators 133: 108399.

Herrando‐Moraira, S., D. Vitales, N. Nualart, et al. 2020. “Global Distribution Patterns and Niche Modelling of the Invasive Kalanchoe× Houghtonii (Crassulaceae).” Scientific Reports 10, no. 1: 3143.

Hulme, P. E. 2005. “Adapting to Climate Change: Is There Scope for Ecological Management in the Face of a Global Threat?” Journal of Applied ecology 42, no. 5: 784–794.

Jan, G., M. A. Khan, and F. Jan. 2009. “Traditional Medicinal and Economic Uses of Gymnosperms of Dir Kohistan Valleys, NWFP.” Pakistan. Ethnobotanical Leaflets 2009, no. 12: 9.

Jäschke, Y., G. Heberling, and K. Wesche. 2020. “Environmental Controls Override Grazing Effects on Plant Functional Traits in Tibetan Rangelands.” Functional Ecology 34, no. 3: 747–760.

Jobbagy, E. G., and R. B. Jackson. 2001. “The Distribution of Soil Nutrients With Depth: Global Patterns and the Imprint of Plants.” Biogeochemistry 53: 51–77.

Kakiuchi, N., K. Inoue, Y. Kurita, K. Ohkubo, Y. Tsuda, and M. Mikage. 2007. “Survey of Ephedra Resources in the Northern Areas of Pakistan and Their Genetic Diversity.” Journal of Natural Medicines 61: 357–365.

Kass, J. M., R. Muscarella, P. J. Galante, et al. 2021. “ENMeval 2.0: Redesigned for Customizable and Reproducible Modeling of Species' Niches and Distributions.” Methods in Ecology and Evolution 12, no. 9: 1602–1608.

Khan, R., S. Z. Ul Abidin, M. Ahmad, M. Zafar, J. Liu, and H. Amina. 2018. “Palyno‐Morphological Characteristics of Gymnosperm Flora of Pakistan and Its Taxonomic Implications With LM and SEM Methods.” Microscopy Research and Technique 81, no. 1: 74–87.

Koo, K. A., and S. U. Park. 2022. “The Effect of Interplays Among Climate Change, Land‐Use Change, and Dispersal Capacity on Plant Redistribution.” Ecological Indicators 142: 109192.

La Sorte, F. A., and W. Jetz. 2010. “Projected Range Contractions of Montane Biodiversity Under Global Warming.” Proceedings of the Royal Society B: Biological Sciences 277, no. 1699: 3401–3410.

Li, G., N. Xiao, Z. Luo, et al. 2021. “Identifying Conservation Priority Areas for Gymnosperm Species Under Climate Changes in China.” Biological Conservation 253: 108914.

Lenoir, J., J. C. Gégout, P. A. Marquet, P. de Ruffray,and H. Brisse. 2008. “A Significant Upward Shift in Plant Species Optimum Elevation During the 20th Century.” Science 320, no. 5884: 1768–1771.

Li, Y., Y. Wang, C. Zhao, X. Du, P. He, and F. Meng. 2024. “Predicting the Spatial Distribution of Three Ephedra Species Under Climate Change Using the MaxEnt Model.” Heliyon 10: e32696.

Lindner, M., M. Maroschek, S. Netherer, et al. 2010. “Climate Change Impacts, Adaptive Capacity, and Vulnerability of European forest Ecosystems.” Forest ecology and management 259, no. 4: 698–709.

Lin, N., Q. Liu, J. B. Landis, et al. 2023. “Staying In Situ or Shifting Range Under Ongoing Climate Change: A Case of an Endemic Herb in the Himalaya‐Hengduan Mountains Across Elevational Gradients.” Diversity and Distributions 29, no. 4: 524–542.

Liu, A., S. Li, N. Cen, et al. 2020. “Concentration of Fifteen Elements in Herbaceous Stems of Ephedra Intermedia and Influence of Its Growing Soil.” Scientific Reports 10, no. 1: 15077.

Liu, X., Z. Cheng, L. Yan, and Z. Y. Yin. 2009. “Elevation Dependency of Recent and Future Minimum Surface Air Temperature Trends in the Tibetan Plateau and Its Surroundings.” Global and Planetary Change 68, no. 3: 164–174.

Marifatul, H. S., D. Mohammed, W. Muhammad, K. Manoj, S. H. Manzer, and B. W. Rainer. 2024. “Predicting Potential Invasion Risks of Leucaena leucocephala (Lam.) de Wit in the Arid Area of Saudi Arabia.” Journal of Arid Land 16, no. 7: 983–999.

Martínez, I., F. González‐Taboada, T. Wiegand, J. J. Camarero, and E. Gutiérrez. 2012. “Dispersal Limitation and Spatial Scale Affect Model Based Projections of Pinus Uncinata Response to Climate Change in the P Yrenees.” Global Change Biology 18, no. 5: 1714–1724.

Melo‐Merino, S. M., H. Reyes‐Bonilla, and A. Lira‐Noriega. 2020. “Ecological Niche Models and Species Distribution Models in Marine Environments: A Literature Review and Spatial Analysis of Evidence.” Ecological Modelling 415: 108837.

Merow, C., M. J. Smith, and J. A. Silander Jr. 2013. “A Practical Guide to MaxEnt for Modeling Species' Distributions: What It Does, and Why Inputs and Settings Matter.” Ecography 36, no. 10: 1058–1069.

Minami, M., F. Taichi, Y. Honda, et al. 2021. “Environmental and Soil Characteristics in Ephedra Habitats of Uzbekistan.” Journal of Natural Medicines 75: 246–258.

Nowak, A., S. Nowak, M. Nobis, and A. Nobis. 2016. “Dwarf Shrub Vegetation of Rock Ledges and Clefts in the Pamir Alai Mountains (Middle Asia: Tajikistan).” Acta Botanica Croatica 75, no. 1: 109–120.

Peng, Q., R. Wang, Y. Jiang, and C. Li. 2021. “Contributions of Climate Change and Human Activities to Vegetation Dynamics in Qilian Mountain National Park, Northwest China.” Global Ecology and Conservation 32: e01947.

Peterson, T. A., M. Papeş, and M. Eaton. 2007. “Transferability and Model Evaluation in Ecological Niche Modeling: A Comparison of GARP and Maxent.” Ecography 30, no. 4: 550–560.

Phillips, S. J., R. P. Anderson, and R. E. Schapire. 2006. “Maximum Entropy Modeling of Species Geographic Distributions.” Ecological Modelling 190, no. 3–4: 231–259.

Phillips, S. J., and M. Dudík. 2008. “Modeling of Species Distributions With Maxent: New Extensions and a Comprehensive Evaluation.” Ecography 31, no. 2: 161–175.

Phillips, S. J., M. Dudík, J. Elith, et al. 2009. “Sample Selection Bias and Presence‐Only Distribution Models: Implications for Background and Pseudo‐Absence Data.” Ecological Applications 19, no. 1: 181–197.

Pigot, A. L., C. Merow, A. Wilson, and C. H. Trisos. 2023. “Abrupt Expansion of Climate Change Risks for Species Globally.” Nature Ecology & Evolution 7, no. 7: 1060–1071.

Pshegusov, R., F. Tembotova, V. Chadaeva, Y. Sablirova, M. Mollaeva, and A. Akhomgotov. 2022. “Ecological Niche Modeling of the Main Forest‐Forming Species in the Caucasus.” Forest Ecosystems 9: 100019.

Rabizadeh, F., H. Zare‐Maivan, and S. Kazempour. 2018. “Endemic Gypsophytes Composition Delimitated by Soil Properties and Altitude: From Calciphytes to Halophytes in the South–Central Alborz Ranges.” Nordic Journal of Botany 36, no. 8: e01568.

Randin, C. F., R. Engler, S. Normand, et al. 2009. “Climate Change and Plant Distribution: Local Models Predict High‐Elevation Persistence.” Global Change Biology 15, no. 6: 1557–1569.

Rather, Z. A., R. Ahmad, A. R. Dar, T. U. H. Dar, and A. A. Khuroo. 2021. “Predicting Shifts in Distribution Range and Niche Breadth of Plant Species in Contrasting Arid Environments Under Climate Change.” Environmental Monitoring and Assessment 193, no. 7: 427.

Rather, Z. A., R. Ahmad, and A. A. Khuroo. 2022. “Ensemble Modelling Enables Identification of Suitable Sites for Habitat Restoration of Threatened Biodiversity Under Climate Change: A Case Study of Himalayan Trillium.” Ecological Engineering 176: 106534.

Rumpf, S. B., K. Hülber, G. Klonner, et al. 2018. “Range Dynamics of Mountain Plants Decrease With Elevation.” Proceedings of the National Academy of Sciences 115, no. 8: 1848–1853.

Sadia, S., M. Waheed, S. Firdous, F. Arshad, B. A. Fonge, and A. Al‐Andal. 2024. “Ecological Analysis of Plant Community Structure and Soil Effects in Subtropical Forest Ecosystem.” BMC Plant Biology 24, no. 1: 1275.

Shaheen, H., S. Aziz, S. Nasar, et al. 2023. “Distribution Patterns of Alpine Flora for Long‐Term Monitoring of Global Change Along a Wide Elevational Gradient in the Western Himalayas.” Global Ecology and Conservation 48: e02702.

Spence, A. R., and M. W. Tingley. 2020. “The Challenge of Novel Abiotic Conditions for Species Undergoing Climate‐Induced Range Shifts.” Ecography 43, no. 11: 1571–1590.

Subedi, S. C., K. R. Bhattarai, T. M. Perez, and J. P. Sah. 2020. “Gymnosperm Species Richness Patterns Along the Elevational Gradient and Its Comparison With Other Plant Taxonomic Groups in the Himalayas.” Frontiers of Biogeography 12, no. 1: F5FBG44232.

Summers, D. M., B. A. Bryan, N. D. Crossman, and W. S. Meyer. 2012. “Species Vulnerability to Climate Change: Impacts on Spatial Conservation Priorities and Species Representation.” Global Change Biology 18, no. 7: 2335–2348.

Swan, M., M. Le Pla, J. Di Stefano, J. Pascoe, and T. D. Penman. 2021. “Species Distribution Models for Conservation Planning in Fire‐Prone Landscapes.” Biodiversity and Conservation 30, no. 4: 1119–1136.

Tiwari, R., D. K. Gupta, P. K. Chauhan, and A. K. Singh. 2024. “Long‐Term Spatio‐Temporal Vegetation Dynamics to Climate Change in Koppen Climatic Regions of India.” Earth Systems and Environment 504: 1–20.

Tokuda, H., M. Sekiguchi, H. Kaneda, et al. 2024. “Effects of Nutrient Conditions to Stem Photosynthesis and Growth in Ephedra intermedia (Ephedraceae).” Soil Science and Plant Nutrition 70: 1–8.

Trew, B. T., and I. M. Maclean. 2021. “Vulnerability of Global Biodiversity Hotspots to Climate Change.” Global Ecology and Biogeography 30, no. 4: 768–783.

Vitasse, Y., S. Ursenbacher, G. Klein, et al. 2021. “Phenological and Elevational Shifts of Plants, Animals and Fungi Under Climate Change in the E Uropean A Lps.” Biological Reviews 96, no. 5: 1816–1835.

Waheed, M., and F. Arshad. 2024. “Adaptive Convergence and Divergence Underpin the Diversity of Asteraceae in a Semi‐Arid Lowland Region.” Flora 317: 152554.

Waheed, M., F. Arshad, M. Majeed, et al. 2022. “Community Structure and Distribution Pattern of Woody Vegetation in Response to Soil Properties in Semi‐Arid Lowland District Kasur Punjab, Pakistan.” Landscape 11, no. 12: 2145.

Waheed, M., S. M. Haq, F. Arshad, et al. 2023. “Where Will Threatened Aegle Marmelos L., a Tree of the Semi‐Arid Region, Go Under Climate Change? Implications for the Reintroduction of the Species.” Landscape 12, no. 7: 1433.

Waheed, M., S. M. Haq, F. Arshad, et al. 2024. “Xanthium strumarium L., an Invasive Species in the Subtropics: Prediction of Potential Distribution Areas and Climate Adaptability in Pakistan.” BMC Ecology and Evolution 24, no. 1: 124.

Waheed, M., Ł. Walas, S. Alipour, et al. 2024a. “Global Climate Change Increases the Risk of Invasion and the Expansion of Paper Mulberry in the Subtropical Region.” Global Ecology and Conservation 54: e03088.

Waldvogel, A. M., B. Feldmeyer, G. Rolshausen, et al. 2020. “Evolutionary Genomics Can Improve Prediction of Species' Responses to Climate Change.” Evolution Letters 4, no. 1: 4–18.

Wallingford, P. D., T. L. Morelli, J. M. Allen, et al. 2020. “Adjusting the Lens of Invasion Biology to Focus on the Impacts of Climate‐Driven Range Shifts.” Nature Climate Change 10, no. 5: 398–405.

Xu, Y., X. Liu, L. Zhao, et al. 2024. “Prediction of Potential Suitability Areas for Ephedra Sinica in the Five Northwestern Provinces of China Under Climate Change.” Agriculture 14, no. 10: 1830.

Yang, X. Q., S. P. S. Kushwaha, S. Saran, J. Xu, and P. S. Roy. 2013. “Maxent Modeling for Predicting the Potential Distribution of Medicinal Plant, Justicia adhatoda L. in Lesser Himalayan Foothills.” Ecological Engineering 51: 83–87.

Zhang, H., J. Li, H. Zou, et al. 2024. “Distribution Pattern of Suitable Areas and Corridor Identification of Endangered Ephedra Species in China.” Plants 13, no. 6: 890.

Zhang, K., Z. Liu, N. Abdukeyum, and Y. Ling. 2022. “Potential Geographical Distribution of Medicinal Plant Ephedra Sinica Stapf Under Climate Change.” Forests 13, no. 12: 2149.

Zhao, Y., L. Wang, K. P. Chun, A. D. Ziegler, and J. Evaristo. 2024. “Dynamic Hydrological Niche Segregation: How Plants Compete for Water in a Semi‐Arid Ecosystem.” Journal of Hydrology 630: 130677.

Zhuang, W., J. Li, F. Yu, Z. Dong, and H. Guo. 2020. “Seasonal Nitrogen Uptake Strategies in a Temperate Desert Ecosystem Depends on N Form and Plant Species.” Plant Biology 22, no. 3: 386–393.

Zu, K., Z. Wang, X. Zhu, et al. 2021. “Upward Shift and Elevational Range Contractions of Subtropical Mountain Plants in Response to Climate Change.” Science of the Total Environment 783: 146896.

Zurell, D., C. König, A. K. Malchow, et al. 2022. “Spatially Explicit Models for Decision‐Making in Animal Conservation and Restoration.” Ecography 2022, no. 4: 54991.