Suitable habitat prediction and desertified landscape remediation potential of three medicinal Glycyrrhiza species in China

Suitable habitat prediction and desertified landscape remediation potential of three medicinal Glycyrrhiza species in China | Scientific Reports

Abstract

Glycyrrhiza uralensis, Glycyrrhiza glabra, and Glycyrrhiza inflata are significant medicinal plants in traditional Chinese medicine. They also contribute to ecological protection and restoration in the arid and semi-arid regions of Northwest China. This study employed the MaxEnt model to predict the potential habitats of these three species in China under climate change, and examined the relationship between their distribution and desert ecosystems. The results indicated that (1) G. uralensis has a broad distribution, primarily in Northeast, North, and Northwest China. Glycyrrhiza glabra and G. inflata are predominantly found in the northwest provinces. (2) Under current climate conditions, the distribution of the most suitable habitats for these species closely mirrors that of deserts. However, future climate change is expected to reduce the spatial range of all three species. (3) These three species are effective psammophytes, capable of thriving in desert margins and other highly suitable habitats on a large scale. This research offers a theoretical basis for the conservation of plant resources, the restoration and protection of desertified areas, and the advancement of deserticulture.

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Climate change has emerged as a critical issue, posing significant challenges to plant habitat distribution 1, 2 and the sustainable development of both economies and societies 3, 4. One of the major consequences of climate change is the decline in global plant cover, which not only worsens soil infertility but also accelerates desertification. China is one of the countries with the largest desert regions in the world 5, 6. As land resources become increasingly scarce, desertification stands out as a major threat to environmental security 7, 8, 9. In recent years, both natural climate change and human activities have further degraded the fragile soil environment, exacerbating desertification. Currently, desertification control primarily relies on engineering solutions, such as shelterbelts and sand barriers, as well as biological methods like planting sand - tolerant plants (psammophytes) to combat desertification 10, 11. The identification of salt - and drought - resistant psammophytes, which can also be used in traditional Chinese medicine, offers a promising strategy for combating desertification and developing the desert economy through the use of salinized and desertified land 12, 13. In the context of global climate change, studying how climate change affects plant habitats is crucial for the conservation, development, and sustainable use of plant resources. Currently, the MaxEnt model is the most widely used tool for predicting the potential habitats of plant species. The integration of MaxEnt with ArcGIS software has proven to produce reliable and accurate predictions, making it the primary method for delineating suitable plant habitats 14, 15, 16, 17.

Glycyrrhiza uralensis is a perennial plant from the Glycyrrhiza genus in the legume family and is classified as a Class II protected species in China 18. Worldwide, 29 species of Glycyrrhiza, including 6 variants, have been identified. In China, there are 18 species and 3 variants 19. The 2020 edition of the Chinese Pharmacopoeia lists Glycyrrhiza uralensis Fisch., Glycyrrhiza glabra L., and Glycyrrhiza inflata Bat. as medicinal herbs 20. Pharmacological research has demonstrated that Glycyrrhiza species possess anti-inflammatory, antioxidant, antibacterial, and anti-tumor properties, and are widely used in treating diseases such as liver disease, diabetes, and cardiovascular conditions 21, 22, 23. The roots of Glycyrrhiza plants are used in medicine, while the aerial parts serve as forage. Additionally, Glycyrrhiza species possesses strong stress resistance, including cold resistance, heat tolerance, drought resistance, salt and alkali tolerance, and wind and sand resistance 24, 25. The root system of Glycyrrhiza can stabilize the soil, prevent soil erosion, promote the accumulation of soil nutrients, and foster the reproduction of microorganisms, thus accelerating the recovery process of ecosystems and enhancing their stability and resilience 26, 27. Therefore, Glycyrrhiza is an important plant for wind proofing, sand fixation, and improving the desert ecological climate. It commonly grows in arid sandy areas and saline-alkali soils 28, 29. Compared to other plants, Glycyrrhiza has stronger survival ability in desertification areas and possesses certain advantages. However, due to climate change, environmental degradation, and over-exploitation, the distribution of Glycyrrhiza species has significantly declined 30, 31. Current research, as shown in the CiteSpace Knowledge Graph, primarily focuses on the cultivation, breeding, prescription combinations, clinical applications, and pharmacological mechanisms of Glycyrrhiza plants 32, 33.

Ecological models are important tools to aid the development and implementation of environmental policies and management programmes 34, 35, 36. These models are used for conservation planning 37, predicting the establishment and spread of invasive species 38, designing and implementing surveillance - and ecological management programmes 39, 40, and forecasting species’ responses to global chang, among other applications. Species distribution models (SDMs) are one example of ecological models that have become popular in recent years 41. SDMs typically take the form of correlative or mechanistic models, where correlative approaches correlate species presence/absences (or pseudo - absences) to environmental covariates to identify suitable climatic conditions for the study taxon 42. The Maximum Entropy SDM (hereafter MaxEnt) is among the most popular methods for climate modelling studies and has been shown to often outperform alternative modelling techniques 43, 44, 45, 46. The method uses maximum entropy to discriminate between environmental conditions where the focal taxon is present from environmental conditions at sites without confirmed presences 42. Based on environmental data such as climate, terrain, and soil, MaxEnt has also been applied to various species distribution predictions, such as Chinese Ophiocordyceps sinensis 47, Codonopsis pilosula 48, and Cunninghamia lanceolata 49.

This study employs the Maxent model and ArcGIS software to predict the spatial distribution of three medicinal Glycyrrhiza species in China and their association with desert areas. The study addresses the following key questions: (1) How will the spatial distribution of the three medicinal Glycyrrhiza species shift in response to various climate change scenarios? (2) What is the relationship between these species’ distributions and desert regions? (3) Can understanding this spatial distribution relationship support desertification prevention and foster the development of related industries? The findings will offer a scientific foundation for identifying priority planting regions for medicinal Glycyrrhiza species in China, conserving Glycyrrhiza resources, and advancing the development of these plants in relevant industries.

Materials and methods

Acquisition and filtering of species distribution data

The distribution data of the three medicinal Glycyrrhiza species were obtained from three sources: (1) The specific locations of distribution sites were collected from the plant databases of the Chinese Virtual Herbarium ( http://www.cvh.org.cn) and the National Plant Specimen Resource Center ( http://www.nsii.org.cn/), and the specific latitudes and longitudes were obtained by Google Maps. (2) The distribution data of Glycyrrhiza plants with latitudes and longitudes were downloaded from the Global Biodiversity Information Facility ( http://www.gbif.org). (3) Relevant literature were reviewed to obtain the distribution data of Glycyrrhiza plants in China. A total of 383 distribution sites across China were identified from the three sources listed above. To mitigate sampling deviation and overfitting caused by closely spaced distribution points, ENMTools.pl (V1.4.4) software was employed for data thinning, ensuring a single distribution point per 2.5 km grid (5 km × 5 km). After filtering, 314 valid sites were retained, including 250 sites for G. uralensis, 33 for G. glabra, and 33 for G. inflata. The distribution of the three medicinal plant species was mapped using ArcGIS 10.3 (Fig. 1).

Geographic distribution of medicinal licorice in China. The maps used in this figure are all based on the China map with approval number GS (2024) 0650, while the Chinese desert maps are sourced from the China 1:100,000 Desert (Sand Land) Distribution Dataset (CSTR:11738.11.ncdc.Westdc.2020.676 ). The software used for mapping is ArcGIS 10.3, unless otherwise specified.

Acquisition and filtering of climate and desert data

The environmental variable data were obtained from the WorldClim 2.1 climate database ( https://www.worldclim.org/), which provides 22 environmental variables, including 19 climate variables and 3 topographic data layers. Climate data from the contemporary period (1970–2000) and future projections, along with shared socio-economic pathways (SSPs) developed by the Intergovernmental Panel on Climate Change (IPCC), were used to model climate change scenarios. The SSPs, developed in alignment with global development frameworks, are widely used for predicting social and climate scenarios, and have proven effective in understanding future uncertainties 42, 50. To predict the future distribution of species under various climate scenarios, SSP126 and SSP585 were employed to simulate the suitable habitats of three medicinal Glycyrrhiza species for the 2050s (2041–2060) and 2070s (2061–2080). ArcGIS 10.3 was used to extract topographic features, such as slope and aspect, from the WorldClim version 2.1 data. Desert data were sourced from the National Cryosphere Desert Data Center ( http://www.ncdc.ac.cn).

The multicollinearity of environmental variables may lead to overfitting in species distribution models, thereby reducing the prediction accuracy of the MaxEnt model 51. Consequently, environmental variables that could result in overfitting must be excluded. In this study, correlation analysis of candidate variables was conducted using ENMTools (version 1.4.4), with variables exhibiting a Pearson correlation coefficient greater than 0.8 considered highly correlated 52. High - correlation environmental variables (|r| ≥ 0.8) were excluded, resulting in the final selection of 15 variables (Fig. 2), including annual temperature annual range (bio07), altitude (alt), variance of seasonal temperature change (bio04), Minimum temperature of coldest month (bio06), annual average temperature (bio01), monthly mean of the temperature difference between day and night (bio02), mean temperature of coldest quarte(bio11), annual average precipitation (bio12), precipitation in the coldest season (bio19), coefficient of seasonal precipitation change (bio15), precipitation in the warmest season (bio18), precipitation in the driest season (bio17), aspect (asp), average temperature in the warmest season (bio10), and slope (slo). Based on the selected variables, the maximum entropy model was constructed and combined with the desert distribution map.

Contribution of environmental variables in the simulation process of species distribution.

Construction and evaluation of species distribution models

In this study, we used MaxEnt 3.4.1 software to build species distribution models. Each model was configured with different regularization multipliers and parameter settings. For the complete dataset, 75% of the samples were allocated for model training, while the remaining 25% were used for validation. Each model was run 10 iterations, with a maximum of 5,000 iterations and up to 10,000 background points. The Jackknife method was employed to evaluate the relative contribution of each environmental variable 45, 53. The accuracy of the MaxEnt models was quantitatively assessed using the area under the receiver operating characteristic (ROC) curve (AUC). AUC values theoretically range from 0 to 1. A value closer to 1 indicates more accurate and reliable model predictions. A model is considered poor when the AUC is between 0.5 and 0.7, moderate when between 0.7 and 0.9, and excellent when it exceeds 0.9 54.

Suitable habitat zoning

The probability value of each grid output by the MaxEnt model was between 0 and 1, which was used to express relative suitability (P). The ArcGIS 10.3 software was used to visualize the model results, and the potential suitable habitats of the three medicinal Glycyrrhiza species were zoned according to the natural breaks method and the species distribution law. If P ≤ 0.1, it was classified as non-suitable habitat. If 0.1 < P ≤ 0.3, it was classified as lowly suitable habitat. If 0.3 < P ≤ 0.6, it was classified as moderately suitable habitat. If 0.6 < P ≤ 1, it was classified as highly suitable habitat 54.

Degree of niche overlap and geographical distribution overlap

The prediction results of MaxEnt model were imported into ENMTools (V 1.4.4) to calculate the degree of niche overlap and geographic distribution overlap. To analyze environmental variable data (in ASCII file format) from ArcGIS using ENMTools software, follow these steps: Import the environmental variable data sampled in ArcGIS into ENMTools. (1) Click on “ENM measurements and tools “Select “(2) Niche overlap (ASCII file)”. (3) Add the environmental variable data to the tool. (4) Calculate ecological niche overlap and geographic distribution overlap. This process allows for the assessment of niche overlap and geographic distribution overlap based on the imported environmental data. In this study, the degree of niche overlap was calculated using the D measurement method in ENMTools 55.

where \(\:{O}_{ij}\) represents the degree of niche overlap between species i and species j, \(\:{P}_{ia\:}\)and \(\:{P}_{ja}\) represent the number of species i and species j using resource \(\:a\), respectively. Theoretically, the degree of niche overlap is high when \(\:{O}_{ij}\) ≥ 0.5, moderate when 0.3 < \(\:{O}_{ij}\) < 0.5, and low when \(\:{O}_{ij}\) < 0.3.

Results

Evaluation of model prediction accuracy and environmental factors

The prediction of potential suitable habitats in China, based on 317 distribution data points and 15 environmental variables, yielded AUC values of 0.93 for the training set and 0.91 for the test set in the present period, demonstrating strong model performance. The model’s future predictions also showed AUC values consistently above 0.9, indicating reliable projections.

The Jackknife method (Fig. 3) and response curves (Fig. 4) for each environmental factor indicated that when the suitability index exceeded 0.5, the corresponding environmental variables were more conducive to the growth and distribution of the three species. The primary environmental factors influencing the geographical distribution of G. uralensis were annual average temperature change (bio07), altitude (alt), and the minimum temperature in the coldest month (bio06). These factors were critical in determining the suitability of G. uralensis in the current climate. Based on the response curves for key environmental variables (Fig.

Jackknife test of (a) Glycyrrhiza uralensis Fisch. (b) Glycyrrhiza glabra L. (c) Glycyrrhiza inflata Bat.by each environmental variable.

Response curve of dominant ecological factors for medicinal licorice.

The primary environmental factors influencing the geographical distribution of G. glabra were precipitation in the warmest season (bio18), the coefficient of seasonal precipitation change (bio15), and altitude (alt). These factors were crucial in determining the suitability of G. glabra in the current climate. Based on the response curves for key environmental variables (Fig. 4), the optimal habitat conditions for G. glabra were: precipitation in the warmest season (bio18): − 222.00 to 3.34 mm; coefficient of seasonal precipitation change (bio15): 21.45–80.15 (CV); and altitude (alt): 1139.61–2623.53 m (Fig.

Distribution of medicinal Glycyrrhiza species under contemporary climate scenarios

The total area of G. uralensis suitable habitats was about 2.7 × 106 km2, mainly distributed in northern, northwest, and northeast China, especially in Shaanxi, Ningxia, Gansu, Xinjiang, Shanxi, and Inner Mongolia provinces, of which 62% was distributed in deserts (Fig. 5 a). The suitable habitats expanded outward from the highly suitable habitats. The highly suitable habitats were concentrated in Shaanxi, Ningxia, Gansu, and Xinjiang provinces, covering an area of about 2.3 × 105 km2, accounting for 8.38% of the total suitable habitats. The highly suitable habitats accounted for about 13.29% of the total desert area of China. The moderately suitable habitats were widely distributed in Inner Mongolia, Xinjiang, and Gansu provinces, with an area of about 1.1 × 106 km2, accounting for 41.98% of the total suitable habitat. The moderately suitable habitats accounted for about 20.71% of the total desert area. The lowly suitable habitats were mainly distributed in Xinjiang, the northern edge of Inner Mongolia, and Shaanxi, with an area of about 1.3 × 106 km2, accounting for 14.28% of the total suitable habitat. The lowly suitable habitats accounted for about 28% of the total desert area.

The total suitable habitat of G. glabra was about 1.8 × 106 km2, mainly distributed in Xinjiang, Gansu, Inner Mongolia, Ningxia, and Qinghai provinces, of which 32.86% was distributed in deserts (Fig. 5 b). The highly suitable habitats were concentrated in Xinjiang and Gansu provinces, covering an area of about 1.2 × 105 km2, accounting for 6.74% of the total suitable habitat. The highly suitable habitats accounted for 2.29% of the total desert area of China. The moderately suitable habitats were mainly distributed in Xinjiang, Inner Mongolia, Gansu, and Qinghai provinces, with an area of about 4.6 × 105 km2, accounting for 25.38% of the total suitable habitat. The moderately suitable habitats accounted for 13% of the total desert area. The lowly suitable habitats were mainly distributed in Inner Mongolia, Xinjiang, Gansu, and central Qinghai, with an area of about 1.2 × 106 km2, accounting for 67.88% of the total suitable habitat. The lowly suitable habitats accounted for 17.57% of the total desert area.

The total suitable habitat of G. inflata was about 1.0 × 106 km2, mainly distributed in Xinjiang, Gansu, Inner Mongolia, Ningxia, and Qinghai provinces, of which 25.85% was distributed in deserts (Fig. 5 c). The highly suitable habitats were concentrated in Xinjiang and Gansu, covering an area of about 3.3 × 104 km2, accounting for 3.22% and 1.57% of the total suitable habitat and the total desert area of China, respectively. The moderately suitable habitats were mainly distributed in Xinjiang, Gansu, and Inner Mongolia, with an area of about 2.4 × 105 km2, accounting for 23.28% and 6.14% of the total suitable habitat and the total desert area, respectively. The lowly suitable habitats were mainly distributed in Xinjiang, Gansu, Inner Mongolia, and Qinghai, with an area of about 7.6 × 105 km2, accounting for 73.50% and 18.14% of the total suitable habitat and the total desert area, respectively. After the suitable habitats for the three species were superimposed on the desert distribution sites, almost all of the deserts were in the suitable habitats (Figs. 6).

Current distribution map of medicinal licorice and overlay map of desert areas. (a and d) G. uralensis, (b and e) G. glabra, (c and f G. inflata.

Overlay map of distribution of three medicinal licorice species with desert distribution.

Distribution of medicinal Glycyrrhiza species under future climate scenarios

The prediction results of the MaxEnt model showed that the suitable habitats for G. uralensis, G. glabra, and G. inflata all showed a shrinking trend in the future (Figs. 7 and 8). The moderately and highly suitable habitats of G. uralensis were mainly concentrated in the western edge of Xinjiang, specific areas in Gansu (e.g., Jiuquan, Zhangye, and Wuwei), and the Alxa League in Inner Mongolia. Under the SSP126 and SSP585 climate scenarios, the area of the suitable habitats of G. uralensis decreased significantly, and the area of the total suitable habitats decreased by 4.7 × 105 km2 and 6.1 × 105 km2 compared with that in 2041–2060, respectively. The suitable habitats of G. glabra also showed a shrinking trend, mainly distributed in the western edge of Xinjiang and some areas of Gansu (e.g., Jiuquan). Under the SSP126 climate scenario, the largest decrease in the area of the total suitable habitats of G. glabra was detected in 2061–2080, and the area of the suitable habitats decreased by 1.3 × 105 km2 compared with that in 2041–2060. Under the SSP585 climate scenario, the largest increase in the area of the total suitable habitats was detected in 2061–2080, and the area of the suitable habitats increased by 1.9 × 105 km2 compared with that in 2041–2060. The suitable habitats of G. inflata also showed a shrinking trend, mainly distributed in the southwestern edge and Hami of Xinjiang (e.g., the edge of the Tarim Basin) and specific areas of Gansu (e.g., Jiuquan). Under the SSP126 climate scenario, the largest decrease in the area of the total suitable habitats was detected in 2061–2080, and the total area decreased by 1.9 × 105 km2 compared with that in 2041–2060. Under the SSP585 climate scenario, the largest increase in the area of the total suitable habitats was detected in 2061–2080, and the total area of the suitable habitats increased by 2.0 × 105 km2 compared with that in 2041–2060.

Changes in suitable habitat area for three medicinal licorice species in future periods. (a) G. uralensis, (b) G. glabra, (c) G. inflata.

distribution of medicinal licorice under future climate scenarios. (a–f) G. uralensis, (g–l) G. glabra, (m–r) G. inflata.

Degree of niche overlap

Among the three species, G. uralensis and G. glabra showed moderate degree of niche overlap (D = 0.425), G. uralensis and G. inflata showed low degree of niche overlap (D = 0.307), and G. glabra and G. inflata showed high degree of niche overlap (D = 0.573) (Table 1). However, the overlap of the geographical distribution of G. uralensis and G. inflata was low, and the overlaps of geographical distribution among other species were high.

Discussion

Influence of environmental factors on spatial distribution of medicinal Glycyrrhiza species

The MaxEnt model has been widely applied in the study of potential species distribution areas. Traditional Chinese medicines such as Polygonatum 56 and desert plants like Hammada salicornica 57 have also used this model for suitability zoning studies, achieving promising prediction results. Climatic factors such as precipitation and temperature are important factors in determining the potential distribution areas of plant species, and the response of species to global climate change varies in different regions 58, 59. In this study, it was found that the annual mean temperature change, altitude, and minimum temperature in the coldest month were important environmental factors affecting the distribution of G. uralensis (Fig. 4 a–b). The annual mean temperature change had a great impact on the distribution of G. uralensis. Specifically, the suitable range of annual temperature change is 37.48–54.05 °C, and the distribution probability of licorice in the coldest month exceeding this range will decrease sharply with the increase of temperature. This may be closely related to the high adaptability of G.uralensis to high altitude semi-arid environment and the growth characteristics of G.uralensis seeds. Previous study has shown that G. uralensis has strong drought tolerance and mild salinity-alkali tolerance, and is suitable for growing in loose sandy soils 60. Different from G. uralensis, the distribution of suitable habitats of G. glabra was mainly affected by precipitation in the warmest season, coefficient of seasonal precipitation change, and altitude (Fig. 4 d–f). The suitable ranges of the warmest—season precipitation and coefficient of seasonal precipitation change were − 222.00–3.34 mm and 21.45–80.15 (CV), respectively. This indicates that G. glabra requires precipitation to be within a certain range. Therefore, G. glabra has stringent requirements for environmental conditions and shows better adaptability to neutral or slightly alkaline conditions, but a weak stress tolerance. The distribution of G.inflata is mainly restricted by the precipitation in the warmest season (4.25–254.69 mm), slope aspect and annual average precipitation (Fig. 61. In addition, studies on species such as Arctium lappa and Cordyceps sinensis 62, 63 have also found that these factors significantly influence their geographic distribution, which is related to the biological characteristics of medicinal and desert plants.

As different Glycyrrhiza species, G. uralensis, G. glabra, and G. inflata have differences in geographical distribution and growth habits (Table 1). These three medicinal licorice plants can usually grow in dry and semi-arid areas, adapt to sandy soil, and have strong drought tolerance. Based on the MaxEnt model and the distribution data of the three species, this study simulated the potential geographical distribution of the three species in the contemporary and future periods (2021–2040, 2041–2060, and 2061–2080). Compared with the suitable habitats under contemporary climate scenario, the total area of the suitable habitats for the three species will reduce to varying degrees under two different future climate scenarios. Under different future climate scenarios, the area of highly, moderately, and lowly suitable habitats showed obvious fluctuation trends. This reflects the complexity of the impact of climate change on species distribution. In summary, the distribution range of G. uralensis is wide, mainly distributed in Northeast China, North China, and Northwest China. Glycyrrhiza glabra and G. inflata were mostly distributed in the northwestern provinces of China (Fig. 5 a–c).

Conservation of medicinal Glycyrrhiza species resources

Accurate prediction of the distribution of plant suitable habitats and construction of wild germplasm resource banks and nature reserves are the main means of plant protection 7. For a long time in history, medicinal Glycyrrhiza plants were mainly collected from the wild. Since the 1980s, artificial cultivation of G. uralensis has been tried in Xinjiang, Inner Mongolia, Ningxia and other places. However, large-scale cultivation of Glycyrrhiza glabra and Glycyrrhiza inflata has yet to be implemented in China 64. Currently, Glycyrrhiza uralensis is cultivated extensively in Xinjiang, Inner Mongolia, Ningxia, and Gansu 65. The researches on the ecological adaptability and high-yield cultivation technology of Glycyrrhiza plants showed that the area of Glycyrrhiza plants grown in deserts in China was more than 146,666.67 hectare, and Glycyrrhiza plants could reduce the soluble salt of the surface soil by more than 80% compared with the bare land 66. Medicinal Glycyrrhiza plants are naturally distributed on the edge of the Taklamakan Desert and the Gurbantunggut Desert in Xinjiang, as well as Jiuquan in Gansu, Alxa League, and Bayan Zhuoer in Inner Mongolia. For wild Glycyrrhiza populations, in-situ conservation and construction of natural reserves should be carried out, while focusing on the protection of medicinal Glycyrrhiza plant habitats and the growth environment. Artificial cultivation can be carried out in suitable habitats to meet the market needs of medicinal Glycyrrhiza plants, to reduce the demand for wild resources. According to the highly overlap of the geographical distribution of G. glabra and G. inflata (Table 1), medicinal Glycyrrhiza variants can be developed in Xinjiang, China.

The role of medicinal licorice in desertification control and deserticulture

In this study, the distribution of highly suitable habitats of medicinal Glycyrrhiza plants was highly similar to that of deserts (Fig. 5 d–f), that is, about 98% of deserts was distributed within the highly suitable habitats of the three medicinal Glycyrrhiza species. Among them, the areas of the suitable habitats of G. uralensis, G. glabra, and G. inflata accounted for 62%, 32.86%, and 25.85% of the total desert area of China. Previous studies have shown that planting 4-year-old licorice plants in desert areas has improved soil quality and soil fertility, which provides data support for biological measures taken in desert management 67. According to the wide distribution of G. uralensis, it can be cultivated on a large scale in Northwest China, North China, and Northeast China, while G. glabra and G. inflata can be cultivated in Northwest China for related industries. Developing desertculture is an important approach to addressing both the ecological and economic challenges in desert regions. It represents the fundamental method for desert governance and is a necessary strategy under the current trend of increasing aridity 68. In general, it is necessary to achieve sustainable ecological and environmental governance, to effectively use the unique natural resources of the region and form comparative advantages, which also promotes the development of sustainable industries with regional characteristics. Optimizing the growth environment of Glycyrrhiza plants has the potential and feasibility to control desertification, providing a management strategy for sustainable development in desert areas.

Conclusion

In recent years, the global shortage of Glycyrrhiza plants has led to the implementation of protection measures for wild licorice species in many countries. Understanding the effects of climate change is essential for species conservation and the identification of suitable habitats. This study employed distribution data for three medicinal Glycyrrhiza species, along with climate and desert data from China, to predict the current and future spatial distribution of their suitable habitats using the MaxEnt model. The study also explored the relationship between these suitable habitats and desert areas. MaxEnt model predictions revealed that, under current climate conditions, the three species are primarily distributed in the arid and semi-arid regions of northern China. Precipitation and temperature were identified as the key factors limiting both the present and future distribution of the species. Future climate projections indicate that, with the ongoing rise in temperature, the area of suitable habitats for the three species is expected to shrink. The core distribution area is primarily concentrated in the northwest region of China. The suitable habitats for all three species closely align with desert areas, with the most suitable habitats found at the desert margins. Consequently, these three Glycyrrhiza species are valuable resources for desertification control efforts. This study offers important insights for the conservation and large-scale cultivation of medicinal Glycyrrhiza plants. It also highlights the potential of these plants in desertification control and the development of related industries.

Data availability

The data used to support the findings of this study are available from the corresponding author upon request.

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Acknowledgements

This research was funded by Corps Guiding Science and Technology Plan Project (Grant No. 2023ZD088), Tianchi Talent Program of Xinjiang Uygur Autonomous Region, and Key Technology Research, Evaluation, and Demonstration of Desertification Monitoring, Assessment, and Ecological Restoration in the Tarim Reclamation Area. We also thank our colleague Sulaiman shah for his contributions to the English grammar and spelling of this study.

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Ma, F., Huang, X., Yang, Z. et al. Suitable habitat prediction and desertified landscape remediation potential of three medicinal Glycyrrhiza species in China. Sci Rep 15, 11198 (2025). https://doi.org/10.1038/s41598-025-91874-4

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DOI: https://doi.org/10.1038/s41598-025-91874-4