Predicting how multiple scenarios of global climate change can influence the potential distribution pattern of plants: the case of the dominant herb Sophora alopecuroides (Fabaceae) in western China

Predicting how multiple scenarios of global climate change can influence the potential distribution pattern of plants: the case of the dominant herb Sophora alopecuroides (Fabaceae) in western China | Authorea try { document.documentElement.classList.add('js'); } catch (e) { } var _gaq = _gaq || []; _gaq.push(['_setAccount', 'G-8VDV14Y67G']); _gaq.push(['_trackPageview']); (function() { var ga = document.createElement('script'); ga.type = 'text/javascript'; ga.async = true; ga.src = ('https:' == document.location.protocol ? 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Data may be preliminary. 30 July 2025 V1 Latest version Share on Predicting how multiple scenarios of global climate change can influence the potential distribution pattern of plants: the case of the dominant herb Sophora alopecuroides (Fabaceae) in western China Authors : 扬 吕 0009-0000-3627-9566 , yinghui zheng , Xu Su [email protected] , Yuping Liu , Marcos Caraballo-Ortiz 0000-0003-4063-3657 , Ting Lv , cairang zhaxi , jieqiong lei , xuanlin gao , kaiyue wei , and xu feng Authors Info & Affiliations https://doi.org/10.22541/au.175387186.62039172/v1 304 views 257 downloads Contents Abstract Supplementary Material Information & Authors Metrics & Citations View Options References Figures Tables Media Share Abstract Sophora alopecuroides is a perennial drought-tolerant leguminous herb mainly distributed in the northwestern region of China with important medicinal and foraging economic values. We explored the geographical distribution pattern and dominant environmental variables in populations of S. alopecuroides under various climate change scenarios using the MaxEnt model and ArcGIS. Our dataset comprises geographic distribution from 137 sites combined with a temporal element comprising past (Last Interglacial, Last Glacial Maximum, Mid-Holocene), current, and future (2050s, 2070s) predicted environmental variables under four CO2 representative concentration pathways. Results indicate that (1) the result by the MaxEnt model showed that the average Area Under the Curve (AUC) values exceeded 0.9, suggesting that the performance of our model was optimal and reliable; (2) annual mean temperature (bio1), temperature annual range (bio7), mean temperature of driest quarter (bio9), and elevation were the most important variables explaining the model; (3) under current climatic conditions, suitable habitat for S. alopecuroides accounted for 28.9% of the total area of China, which is consistent with the actual distribution of the species; (4) from the Last Interglacial to current period, the center of gravity for the distribution of suitable areas for S. alopecuroides has gradually shifted southeastward by a small distance in response to the ongoing increase in temperature, and is expected to shift northwestwards from the present to the 2050s to the 2070s; and that (5) the Loess Plateau and the Inner Mongolia Plateau may be the origin and are the modern distribution centers of S. alopecuroides. 1 Introduction Climate change have been shown to modify the environment to the point that can cause the loss of suitable habitats for plants, which is one of the main reasons for species endangerment, extinction, and loss of genetic diversity (Thomas et al., 2004). Previous studies have shown that the geographical distribution of most plants would shift to high-elevation areas, reducing their suitable habitat while a few tolerant species might increase their range (Bertrand et al., 2011; Bellard et al., 2012). Therefore, to accurately predict the potential special arrangement of habitats, we need to explore historical species distributions, especially the response of potential geographical dispersal patterns to historical climate change and predicting the potential spreading areas across different periods (Bellard et al., 2012). Compiling this valuable historical information can be critical to design effective management plants for habitats and species (Chen et al., 2011). Evaluating habitat suitability for species based on distribution models has become a research hotspot after the recent development of niche models (Yang et al., 2013). The species distribution model takes the geographic information of existing populations and their environmental variables as constraints, using a specific algorithm to quantify the non-random relationship between species records and environmental responses (Guisan and Zimmermann, 2000; Guo et al., 2020). The model reflects the limiting variables of the distribution of species and their habitat preference (Zhu et al., 2013). At present, there are many environmental niche modeling based on biological data, including the Genetic Algorithm for the Rule Set Production (GARP), Maximum Entropy Model (MaxEnt), Environmental niche factor analysis (ENFA), and Bioclim model (Stockwell and Peters, 1999; Phillips et al., 2006; Dolgener et al., 2014; Semwal et al., 2021). Compared with GARP, ENFA, and Bioclim models, MaxEnt have the advantages of shorter calculative times, simple operation, and more stable results, which had become the preferred choice for predicting the potential habitats of species under climate change conditions (Merow et al., 2013). MaxEnt have been widely used in species habitat assessments, potential distribution predictions, alien species evaluations, pedigree geographical reconstructions, and other research fields (Phillips and Dudik, 2008; Kumar and Stohlgren, 2009; Chan et al., 2011; Verbruggen et al., 2013; Zhang et al., 2017). Sophora alopecuroides L. (Fabaceae: Papilionoideae) is a perennial herbaceous legume mainly distributed in the arid and semi-arid regions of the Asian continent, particularly in the northwestern regions of Xinjiang, Qinghai, Ningxia, and Tibet in China (Zha et al., 2020; Zhao et al., 2010). Due to its developed underground root system and its Nitrogen-fixation ability, S. alopecuroides plays a vital role in soil conservation and environmental protection across northwest China (Iinuma et al., 1995; Tanaka et al., 1998; Liang et al., 2012; Chang et al., 2014). Sophora alopecuroides has additional local economic value because it is considered an important medicinal plant in traditional Chinese medicine thanks to its pharmaceuticals and pesticidal properties, and is a valuable source of livestock forage, green manure, windbreak, and nectar for bees (Zhao et al., 2010; Duan et al., 2019; Wang et al., 2019). However, it is still unclear how distribution patterns of S. alopecuroides populations have responded to historical climate change. To answer this question, we simulated the geographical distribution pattern of S. alopecuroides under different climate scenarios based on the MaxEnt niche model. Thus, the aims of this study were (1) to predict the impact of climate change on the distribution of S. alopecuroides across time; (2) to trace the evolutionary history of populations and to identify which environmental variables affected the distribution of S. alopecuroides ; and (3) to predict geographical areas prone to be colonized by S. alopecuroides . Our results will contribute a theoretical basis for formulating conservation strategies to maintain and improve the genetic diversity of S. alopecuroides during the upcoming decades . 2 Materials and methods 2.1 Occurrence records and environmental variables Taking the arid and semi-arid areas of northwestern China as the study area, the geographical distribution data of S. alopecuroides were obtained mainly from the field data compiled over the years, as well as the China Virtual Herbarium (CVH, http://www.cvh.ac.cn/), the Global Biodiversity Information Facility (https://www.gbif.org), the National Specimen Information Infrastructure (http://www.nsii.org.cn) and the Teaching Specimen Resource Sharing Platform (http://mnh.scu.edu.cn). In total, after removing samples with identification errors, duplications, and unclear geographic information records, we obtained 137 effective distribution points, which met the sample size (≥ 30) required to ensure the stability and accuracy of the predictions (Table 1 and Figure 1). To predict the effects of Quaternary climatic oscillations on the geographic distributions of S. alopecuroides , we used 19 bioclimatic variables and one topographic variable (elevation) (Table 2). Bioclimatic variables were downloaded from the WorldClim dataset (http:// www.worldclim.org/) and included the Last Interglacial (LIG, approximately 120–140 ka ago), the Last Glacial Maximum (LGM, about 21 ka ago), and the Mid-Holocene (MH, between 7.5 and 2.5 ka ago), the present (1960–1990), and the future (2050s and 2070s) (Table 2). It should be noted that we selected the CCSM4 model with strong simulation capability and four CO 2 representative concentration pathways (RCPs) for future climate data, specifically RCP 2.6, RCP 4.5, RCP 6.0, and RCP 8.5. In addition, we extracted elevation data from topographic maps with a spatial resolution of 2.5 × 2.5 inches. 0pt 2.5ex plus 1ex minus .2ex 1.5ex plus .2ex 0pt 2ex plus .5ex minus .2ex 1ex plus .2ex 0pt 1.5ex plus .5ex minus .2ex 0.8ex plus .2ex 2.2 Screening and correlation analysis of environmental variables To minimize chances of having high associations between environmental variables causing an excessive fitting of the model, we assessed the correlation among environmental variables before building the MaxEnt model (Wang et al., 2020). First, we tested three time periods of model operations on all environmental variables using MaxEnt v3.4.4 and removed variables whose contribution rates were zero. Then, we performed the correlation analysis using SPSS and ENMTools v1.3. Environmental variables with correlation coefficients (r) below 0.8 were retained, while the remainder were excluded. This resulted in a final selection of Ten environmental variables for subsequent modeling. (Figure 2; Yan et al., 2020; Hu et al., 2022). 0pt 2.5ex plus 1ex minus .2ex 1.5ex plus .2ex 0pt 2ex plus .5ex minus .2ex 1ex plus .2ex 0pt 1.5ex plus .5ex minus .2ex 0.8ex plus .2ex 2.3 Maximum entropy modeling and model evaluation A MaxEnt approach was adopted to predict the potential distributions of S. alopecuroides across different periods in China (Phillips et al., 2008). To perform this analysis, we imported occurrence records of S. alopecuroides and ten environmental variables into the model. We divided occurrence records of S. alopecuroides in two groups: 25% of the data was used to test the validity of models while 75% was selected to train the model prediction (Yi et al., 2016; Jiang et al., 2019). We set the iteration number to 500, the convergence domain value to 0.00001, the maximum background point to 1000, the output data format to Logistic, and performed ten replicates. After the operation was completed, the prediction accuracy of this model was verified through the area under the curve (AUC) of the receiver operating characteristic (ROC) curve. The value of the AUC was typically between 0 and 1.0 when the AUC value was closer to 1.0, indicating a higher model prediction accuracy (Ounyambila et al., 2021) (Table 3). To assess the impact of environmental variables on the distribution of S. alopecuroides , we selected a jackknife approach to assess variable contributions (Johnson et al., 2016; Jiang et al., 2019). When the score values of only variables were high, contributing environmental variables had a greater influence over the potential distribution of species (Wang et al., 2017). 2.4 Analysis of model predictions To estimate the potential distribution area of S. alopecuroides during different time periods, we converted model outputs into raster format and reclassified habitat suitability categories into four levels according to the natural breakpoint method implemented in ArcGIS v.10.2. We classified the suitability maps into four categories: highly suitable habitat (0.89 ≤ P ≤ 1.00); moderately suitable habitat (0.53 ≤ P < 0.89); poorly suitable habitat (0.29 ≤ P < 0.53); and unsuitable habitat (0.10 ≤ P < 0.29). Next, we loaded the arithmetic results from MaxEnt into ArcGIS to generate visualizations displaying the potential distribution of S. alopecuroides . Furthermore, we counted the grid number of each rank to calculate the proportion of potential distribution area per category in each period of time (Zhang et al., 2020). 2.5 Calculating changes in the distribution of species After modeling the suitable habitat area of S. alopecuroides for different time periods, we calculated the changes in potential distribution areas using the tool “Distribution changes between binary SDMs” available in ArcGIS v.10.2. To further explore the dynamic migration paths of S. alopecuroides , we estimated the centroid of this species from its historical distribution to its future one with the centroid changes (lines) evaluator from the SDM toolbox of ArcGIS v.10.2 (Cong et al., 2020). The analysis of core distributional shifts was mainly done to focus the species distribution on an independent central point and created a vector file describing the magnitude and direction of changes over time (Hu et al., 2015; Cong et al., 2020). Therefore, we were able to investigate the migration trend of S. alopecuroides by observing centroid changes among different periods. 2.6 Origin and modern distribution center of S. alopecuroides As described in Vavilov (1926), geographic regions with both high genetic diversity and frequency of dominant genes usually represent the center of origin for source populations. In this study, we inferred the origin center of S. alopecuroides based on the results presented by Wang et al. (2019) on the genetic diversity and population structure of the species using microsatellite markers. We also speculated a modern distribution center of S. alopecuroides from the past to the present based on potential refugia, genetic barriers, and migration routes in arid areas of northwestern China reported in the literature along with the migration routes of potential distribution centers (Meng et al., 2015). 0pt 2.5ex plus 1ex minus .2ex 1.5ex plus .2ex 0pt 2ex plus .5ex minus .2ex 1ex plus .2ex 0pt 1.5ex plus .5ex minus .2ex 0.8ex plus .2ex 3 Results 3.1 Model accuracy and contribution of environmental variables The ROC curve generated by the MaxEnt model showed that the average AUC values obtained by 10 repetitions exceeded 0.9 (Figure 3). The average AUC values in the model testing different climate scenarios ranged from 0.91 to 0.93, and the standard deviation values after 10 repetitions were below 0.03, indicating that the model could be used to predict the potential distribution of S. alopecuroides because its performance was optimal. Finally, when we evaluated the relative contribution of ten environmental variables (bio1, bio3, bio7, bio8, bio9, bio12, bio15, bio17, bio18, and elevation) for model prediction, we found that the four most important ones were bio7 (28.2%), elevation (14.8%), bio18 (14.6%), and bio9 (14.5%) with a total cumulative contribution rate of up to 72.1% (Table 4). Unlike these, bio3, bio8, and bio17 contributed the least to the distribution of the species. Furthermore, when interrogating each environmental variable with the regularized training gain values against the MaxEnt model with the jackknife method, we found that the most important variables were bio1, bio7, bio9, and elevation, where estimates of bio1 and bio7 in the With only variable exceeded 0.7, while amounts of bio7 and bio9 without variable were the lowest (Figure 4). Our survey of ecological relationships among the distribution of S. alopecuroides and ten environmental variables pointed out that the annual mean temperature (bio1), temperature annual range (bio7), mean temperature of the driest quarter (bio9), and elevation were the most important climatic factor limiting distribution of the species. When distribution probability (P) greater than 0.5, indicating that their relative contributions were also relevant to sustain the species. Here, temperature annual range spanned from 42.4 to 48.5 ℃, the mean temperature of the driest quarter ranged from -9.9 to -4.1 ℃, and elevation ranged from 950 to 1948 m. In the case of annual mean temperature, its response curve reflected that when the P was greater than 0.5, with values ranging from 6.1 to 9.8 ℃ (Figure 5). 3.2 Potential distribution of S. alopecuroides under current climatic conditions The extant distribution of total suitable habitat for S. alopecuroides generated by the MaxEnt modeling exhibited a discontinuous spotted pattern across northwestern China, in agreement with its current range, spanning an area of 2.77 × 10 6 km 2 (Table 5). The largest area of highly suitable habitat was concentrated in the Badain Jaran Desert, Tengger Desert, Ordos Plateau, Hetao Plain, the southern Altai Mountains, the southeast Junggar Basin, and Ili River valley, covering an estimated area of 4.19 × 10 5 km 2 (Table 5 and Figure 6). Regions with moderately suitable habitat were concentrated in the north of Yinshan Mountains, the east of Taihang Mountains, the West Liaohe Plain, the Turpan-Hami Basin, and the central and eastern Junggar Basin, extending over an area of 7.21 × 10 5 km 2 (Table 5 and Figure 6). 3.3 Distribution changes of S. alopecuroides from the past to the future The areas of the total suitable habitat for S. alopecuroides produced the trend of contraction from the LIG to MH periods with an area of 2.80 × 10 6 km 2 , 2.79 × 10 6 km 2 , and 2.75 × 10 6 km 2 , respectively (Table 5). Although the areas of highly suitable habitat slightly contracted between the LIG and LGM periods, they marginally increased in size from LGM to MH (Table 5). The changing trend of moderately suitable habitat was opposite to that of highly suitable habitat, with the most obvious changes concentrated in the north of the Altun Mountains (Table 5 and Figure 7). The area of suitable habitat for S. alopecuroides in the current period was consistent with the distribution area of past epochs, of which the western Tarim Basin was the region with most notable changes (Table 5 and Figure 7). In the future, the distribution of S. alopecuroides is predicted to experience a loss of its original area and gain new areas (Table 5 and Figure 8-9). The suitable distribution area varied under different CO 2 RCPs such as in the 2050s-RCP 2.6 scenarios, where the highly suitable habitat reached 4.48 × 10 5 km 2 and diminished to as much as 4.19 × 10 5 km 2 in 2070 (Table 5 and Figure 9). The model predicted that under other RCP scenarios, the total suitable habitat in both the 2050s and 2070s will continue to decrease compared to the current period. The habitat losses in the immediate future compared to the current time occurred mainly in the north of the Altun Mountains, the western Tarim Basin, and the Horqin Sandy Land (Figure 8). In the RCP 4.5 scenarios, the total suitable habitat was reduced to 2.69 × 106 km 2 in 2050s-RCP 4.5 and 2.76 × 106 km 2 in 2070s-RCP 4.5 (Table 5 and Figure 9). Similarly, the RCP 6.0 scenarios showed a reduction in total suitable habitat predicted at 2.73 × 106 km 2 in 2050s-RCP 6.0 and 2.77 × 106 km 2 in 2070s-RCP 6.0 (Table 5 and Figure 9). Following the same pattern, the total suitable habitat for RCP 8.5 was reduced to 2.75 × 106 km 2 in 2050s-RCP 8.5 and 2070s-RCP 8.5 (Table 5 and Figure 9). 3.4 Migratory routes of the potential distribution centers of S. alopecuroides The centroid of the current distribution of S. alopecuroides was in eastern Jinta County within the Gansu province (40.34° N, 99.77° E; Figure 10). The range centers of this species in past periods lay between latitudes 40.34° N – 40.44° N and longitudes 99.57° E – 99.77° E, which are mainly located in the Jinta County (Figure 10). We calculated the range centers of suitable habitats for S. alopecuroides under the 2050s and 2070s scenarios and we noticed that the centers under RCP 2.6 were roughly similar, being located at 40.38° N, 99.54° E (2050s) and 40.38° N, 99.55°E (2070s) at elevations of about 1200 m (Figure 10). When analyzing the RCP 4.5 data from a 2050s to a 2070s scenario, the center for suitable distribution moved from the western Jinta County (40.49°N,99.11°E, 1479 m) to Alxa Youqi (40.20° N, 100.24° E, 1271 m). In addition, the migration directions of S. alopecuroides under the RCP 6.0 and RCP 8.5 situations would all likely migrate northwest, with its range centers located at 40.29° N, 99.92° E at 1140 m (2050s-RCP 6.0); 40.49° N, 99.11° E at 1363 m (2070s-RCP 6.0); 40.19° N, 100.38° E at 1311 m (2050s-RCP 8.5); and 40.34° N, 100.03° E at 1149 m (2070s-RCP 8.5) (Figure 10). Despite these shifts in range centers, the overall suitable distribution range of S. alopecuroides showed relatively few changes in latitude under several climatic scenarios (Figure 10). 3.5 Origin and modern distribution center of S. alopecuroides The results for Vavilov (1926) showed that the Shannon’s Information Index for seven populations located in the Tianshan Mountains was 0.57, while that of 16 populations located at the Loess Plateau and Inner Mongolia Plateau was 0.65. Meng et al. (2015) mentioned that the Loess Plateau and the Inner Mongolia Plateau were the flattest plateaus in China and that they did not represent impassable geographical barriers for the genetic exchange of this species. In fact, the Loess Plateau appeared to have provided an ecological corridor for the northward migration of S. alopecuroides under the interglacial phase. Therefore, we hypothesize that the Loess Plateau and the Inner Mongolia Plateau may be the origin centers of S. alopecuroides . Interestingly, the suitable distribution area of S. alopecuroides have not changed significantly from the past to the present, and the Loess Plateau and Inner Mongolia Plateau still represent the largest distribution areas for this herb (Figures 6 and 7). In fact, the predicted distribution centers of S. alopecuroides in the past and the present were in Jinta county, which comprise the Loess Plateau, supporting the idea that both the Loess Plateau and Inner Mongolia Plateau represent the modern distribution centers of S. alopecuroides (Figure 10). 4 Discussion 4.1 Model accuracy and contribution of environmental variables Ecological niche modeling is widely used to predict the potential range of species and to assess the impact of climate change on species distribution (Noedoost, et al., 2024). There are several models available for predicting species distributions such as ecological niche factor analysis (ENFA), genetic algorithm for rule- set production (GRAP), bioclimatic analysis system (BIOCLIM), and maximum entropy mode (MaxEnt) (Wang, et al., 2023). Among them, MaxEnt offers the highest modeling accuracy with stable predictions, simple operation, shorter run times, and smaller sample sizes (Allahverdyan, et al., 2020). A strength from our methodological approach is that analyses performed using MaxEnt and ArcGIS yielded an AUC value of 0.92, indicating that our predictions were reliable. Our study was complemented with a SPSS correlation analysis which directly analyze the actual distribution data and improves the prediction accuracy of the model because it ensures that the selected environmental variables are highly correlated with the actual distribution of the species to avoids model errors due to sample bias (Li and Ding, 2016). Another complimentary analysis was done using ENMTools, that perform a correlation not based on species distribution data, providing more flexibility to select environmental variables and can help reducing model errors due to sample and spatial bias (Warren et al., 2010). The congruence in results among the multiple methods here presented support our findings by reducing bias associated to the use of a single method, and at the same time allow a comprehensive screening to identify the most important bioclimatic variables to predict species distribution. Therefore, we consider that this study can be used as an appropriate reference to infer the potential geographic distribution of S. alopecuroides during the past, present, and future times. 4.2 Statistical analyses of environmental variables The results of this study show that the main environmental variables affecting the distribution of S. alopecuroides are temperature and elevation. Ranges of environment variables with a logistic output greater than 0.5 usually represent suitable conditions thresholds for a given species (Bai et al., 2024). Our variables predicted with values greater than 0.5 were bio7 with temperatures ranging from 42.4° C to 48.5° C, bio9 from -9.9° C to -4.1° C, and the elevation spanning from 950 to 1948 m above sea level, with a range of temperatures from 6.1 to 9.8° C based on the bio1 model. A study by Wang et al. (2011) mentioned that the suitable temperatures for S. alopecuroides include an annual range of ≥ 31℃, annual means of 7.7° C – 8.9° C, and an elevation of 1200 – 1700 m; remarkably, these values are in line with the findings described in this paper. Wang et al. (2011) also mentioned that the species Agriophyllum squarrosum (Amaranthaceae) is often associated to S. alopecuroides . Because both species are woody herbs sharing habitat, findings on environmental requirements on A. squarrosum can be relevant for S. alopecuroides . Sun et al. (2023) found that temperature was an important factor limiting the distribution of A. squarrosum , with bio1 contributing up to 42.1%, a value similar to the one we are reporting here. Beside temperature, elevation has been found to be an important ecological factor influencing the distribution of S. alopecuroides, as reported by Yao et al. (2023). 4.3 Distribution changes of S. alopecuroides from past to current times Previous studies have documented climate change-driven cycles of population contractions during cool glacial ages followed by expansions during interglacial periods (Reid et al., 2019). Our study is another example of this situation, where suitable habitats for S. alopecuroides experienced reductions from the LIG to our current time. The suitable distribution area of S. alopecuroides was wider during the LIG, comprising approximately 2.80 × 10 6 km 2 , but experienced a slight decline to 2.79 × 10 6 km 2 during the last glacial period after the LGM. It is worth noting that the high suitability zone has decreased from 4.4 × 10 5 km 2 to 4.2. × 10 5 km 2 . To explain these changes, we hypothesized that the LIG period was characterized by a warm and arid climate where semi-desertic plants like S. alopecuroides thrive , promoting an expansion in suitable habitat. Then, temperatures in China became colder during the LGM period, with an average cooling of 3.7 – 5.1° C (Jiang et al., 2022). This cooler climate is believed to be responsible for the shrinking of suitable habitat area of S. alopecuroides , a pattern previously observed in other species of plants from dry areas like Sinowilsonia henryi (Hamamelidaceae) (Zhou et al., 2015). The changes in distribution driven by climate experienced by all these species of plants, including S. alopecuroides , fit the well-documented pattern of “contraction during glacial periods and expansion during interglacial periods” (Xu et al., 2015). The MH period brought an increase of average temperatures in China of about 2 to 4° C higher than in modern times (Kuang et al., 2021). These climatic changes, which are supported by several lines of evidence from the geological record, caused another decrease in suitable area for S. alopecuroides. This situation is similar to global warming, where plants suffer from reductions of suitable habitat due to the rise in temperatures (Li et al., 2024). Interestingly, during the MH period, areas of the southern side of the Altai Mountains in Xinjiang were transformed into optimal habitat for S. alopecuroides when compared with the LGM period (Lai et al., 2023). Similar trends were observed in Trollius , supporting the idea that these patterns of climatic contractions and expansions were experienced by a variety of plant lineages (Fan et al., 2024). 0pt 2.5ex plus 1ex minus .2ex 1.5ex plus .2ex 0pt 2ex plus .5ex minus .2ex 1ex plus .2ex 0pt 1.5ex plus .5ex minus .2ex 0.8ex plus .2ex 4.4 Distribution changes of S. alopecuroides from current to future times Predicted global warming scenarios are often characterized by a reduction in the size of suitable areas of plant distributions. Given that China has warmed slightly faster than the global average over the past century, it is critical to predict changes in species’ habitats and researchers have started to assess ecological implications, especially at large biogeographical scales because they will serve as basis for biodiversity conservation efforts (Xiang et al., 2021). Our data for four CO 2 RCPs for future climates showed that suitable areas for S. alopecuroides will experience an overall shrinking when compared to the current total area, although the numbers also reflect slight increases in highly fitting habitats. Suitable areas will also decrease to varying degrees between the 2050s and 2070s with the increase in carbon concentration emissions during the 2050s, except under the RCP 2.6 scenarios. A previous study by Meehl et al. (2012) using the same model as us (CCSM4) have shown that the average global surface temperatures for the last 20 years of the 21 st century are +0.85° C for RCP 2.6 when compared to the reference period between 1986 to 2005. So under the RCP 2.6 scenarios, the suitable areas reduction of S. alopecuroides is minimal. It is possible that S. alopecuroides will experience a brief period with favorable climate for its growth during the upcoming rise in global surface temperatures. For example, Wang et al. (2023) showed that the distribution of suitable areas for Leonurus japonicus (Lamiaceae) increased under a SSP 126 scenario, which was like our RCP 2.6. This highlights the importance to follow sustainable development practices, because climate has a profound impact on the growth and reproduction of plants and in the conservation of ecological areas. This brief period of favorable climate will last until the 2070s period, when the suitable area showed a reduction in habitats when compared to the current period. An important point to highlight is that all important areas for S. alopecuroides in central China are experiencing unstable habitats: Tarim Basin has been under a constant state of contractions, while the Badan Jilin Desert, Ulan Buh Desert, and the Maowusu Sandy Land are in alternating states of habitat expansions and contractions. These instabilities threaten the long-term survival of S. alopecuroides species because our data shows that this herb is sensitive and unable to adapt to drastic environmental changes under future climatic conditions, which can reduce their development, reproduction, and fitness. 4.5 Migratory routes of potential distribution centers of S. alopecuroides Our findings indicate that the range center of suitable areas for S. alopecuroides has gradually shifted to the southeast, not too far from the past to the current period as the planet temperature increase. Future scenarios state that global warming is promoting range expansions to higher elevations and latitudes for many species of plants. For example, the distribution of forest plant species in western Europe have increased in elevation by an average of 29 meters per decade during the twentieth century (J. Lenoir et al., 2008). From the present time to the 2050s and then the 2070s, greenhouse gas emissions are predicted to gradually increase along with concentration pathways. This effect is expected to shift the range center of the habitable zone for S. alopecuroides to the northwest under the concentration pathways at RCP 2.6 and RCP 6.0, in agreement with previous studies describing that plants expand their distributions to high latitudes and elevations as global temperatures increase. Migrations of range centers in our model show that S. alopecuroides will experience minor contractions followed by small expansions, showing a dynamic population history. Unlike models RCP 2.6 and 6.0, concentration pathways in model RCP 8.5 showed that S. alopecuroides migrated to high elevations at 2050s, but then reversed its trajectory to the current latitude at 2070s, presumably because will experience high photosynthetic rates and water-use efficiency during flowering at high atmospheric CO 2 concentrations. We acknowledge some uncertainties in our method, which failed to consider the physiology and metabolism of S. alopecuroides as well as the impacts of natural and anthropogenic factors. In fact, our simulation of the species’ distribution model only presents the potential distribution ranges for healthy plants in ideal physiological and metabolic states. 4.6 Origin and modern distribution center of S. alopecuroides The results of this study place the modern distribution center of S. alopecuroides in Jinta County, Gansu Province. This region has a mean annual temperature (8 – 9.6 ° C) and elevation (921 –1924 m) consistent with the dominant environmental variables deduced for S. alopecuroides. Our inference for the modern distribution center partially agrees with the results presented by Vavilov (1926) and Meng et al. (2015), who speculated that the center of origin of S. alopecuroides is the Loess Plateau and the Inner Mongolia Plateau. These authors based their findings on karyotypic characters and evolutionary trends of six natural geographic populations and found that the species has an earlier origin and a lower degree of evolution in Ordos, Inner Mongolia (Hu et al., 2023). We recognize that our study design and results depict inferences which cannot pinpoint the exact origin of S. alopecuroides . To overcome this limitation, we encourage future research efforts to focus on gathering additional samples from the full distributional range of the species and examining them through a combination of population genetics and phylogenetic approaches. 4.7 Potential limitations Predictions through the MaxEnt model suffer from some uncertainties because not all variables can be accounted for in each analysis, limiting our knowledge on the growth characteristics of S. alopecuroides and the effects of natural and anthropogenic variables. Also, results of the species distribution model simulations only display the potential range of an ideal state given that data on factors such as land-use history and human activities are difficult to obtain. In future studies, we will enrich the distribution pattern with the above-described deficiencies to obtain better predictions on the past and future range of S. alopecuroides . 5 Conclusions In this study, the ROC curve generated by the MaxEnt model showed an optimal and reliable performance. Temperature and elevation were the main environmental variables affecting the distribution of the S. alopecuroides , and the expansion trend of the suitable area in the past climatic background follow the pattern of “contractions during glacial periods and expansions during interglacial periods” reported for other plant species. From the past to the future, the total suitable area for S. alopecuroides shrank, while from the past to the current time, the center of gravity for the distribution of suitable areas for the species has shifted in a north-western direction. Our work demonstrates that the history of S. alopecuroides has been dynamic, involving a series of minor contractions followed by small-scale expansions. Finally, our data suggest that both the Loess Plateau and the Inner Mongolia Plateau may be the places where S. alopecuroides first originated and where its modern distribution center is located. With the ongoing trend of global warming, the findings of this study could serve as a valuable reference to devise efficient schemes for adapting to climate change and conserving germplasm and biodiversity. Data availability statement Data availability statement All tables and figures supporting the results and conclusions were included in the article. The distribution point data sets analyzed in this study are available in Table 1, The environmental variables were obtained from the WorldClim-Global Climate dataset (http://www.worldclim2.0.org/) and EarthEnv (https://www.earthenv.org). Author contributions Yang Lv contributed to the collection and processing of data used in this study and drafted the manuscript. Yuping Liu provided valuable comments in writing the manuscript. Marcos A. Caraballo-Ortiz contributed to the revised work of the manuscript. Yinghui Zheng, Ting Lv, Zhaxi Cairang, Jieqiong Lei, Xuanlin Gao, Kaiyue Wei, and Xu Feng gave some ideas for the statistical analysis of this study and helped to collect the data. Xu Su proposed the idea of writing the manuscript and helped to carry out and check the manuscript revision work. Funding This work was financially supported by the Natural Science Foundation of Qinghai Province (Grant No. 2022-ZJ-913) and the National Natural Science Foundation of China (Grants No. 32160297 and 31960052). Conflicts of interest The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. Publisher’s note All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors, and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher. References Allahverdyan, A. E., Martirosyan, N. H. (2020). Maximum Entropy competes with Maximum Likelihood. arXiv preprint arXiv.201209430 Atta-Ur-Rahman, A., Choudhary, M. 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Keywords ecosystem plants population ecology statistical theoretical theory Authors Affiliations 扬 吕 0009-0000-3627-9566 Qinghai Normal University View all articles by this author yinghui zheng Qinghai Normal University View all articles by this author Xu Su [email protected] Qinghai Normal University View all articles by this author Yuping Liu Qinghai Normal University View all articles by this author Marcos Caraballo-Ortiz 0000-0003-4063-3657 Smithsonian Institution View all articles by this author Ting Lv Qinghai Normal University View all articles by this author cairang zhaxi Qinghai Normal University View all articles by this author jieqiong lei Qinghai Normal University View all articles by this author xuanlin gao Qinghai Normal University View all articles by this author kaiyue wei Qinghai Normal University View all articles by this author xu feng Qinghai Normal University View all articles by this author Metrics & Citations Metrics Article Usage 304 views 257 downloads .FvxKWukQNSOunydq8rnd { width: 100px; } Citations Download citation 扬 吕, yinghui zheng, Xu Su, et al. Predicting how multiple scenarios of global climate change can influence the potential distribution pattern of plants: the case of the dominant herb Sophora alopecuroides (Fabaceae) in western China. Authorea . 30 July 2025. DOI: https://doi.org/10.22541/au.175387186.62039172/v1 If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download. For more information or tips please see 'Downloading to a citation manager' in the Help menu . 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