Construction of an ecological model of Sambucus javanica blume in China under different climate scenarios based on maxent model

Sambucus javanica Blume is a Chinese native medicinal plant with high medicinal value. In this study, the MaxEnt model was used to explore the relationship between the geographical distribution of S. javanica and environmental factors, and to construct the distribution pattern of S. javanica under different climate scenarios. The results showed that the environmental conditions suitable for the distribution of S. javanica were as follows: precipitation in June ranged from 156.36 to 383.25 mm; solar radiation in December ranged from 6750.00 to 10,521.00 kJm−2day−1; isothermality ranged from 24.06 to 35.50; precipitation of warmest quarter ranged from 447.92 to 825.00 mm. Among them, precipitation and temperature were the key environmental factors affecting the distribution patterns of S. javanica. This plant could grow well mainly in two regions in China, covering a total area of 2.73 × 106 km2. The first region mainly consists of Guizhou, western Hubei, southeastern Chongqing, southwestern Hunan, northern Guangxi, and a small part of eastern Yunnan. The second region mainly consists of Zhejiang, southern Anhui, and northern Fujian. Under the future SSP126 and SSP585 scenarios, potentially suitable habitats in the eastern part of the potential distribution of S. javanica (Jiangxi, Fujian, Zhejiang, and Anhui) might be at risk of habitat fragmentation. Future climate change might have little effect on the distribution areas of S. javanica. But its suitable distribution has a tendency shift to higher altitude areas. Based on the result of this study, real-time monitoring of wild groups of S. javanica is now recommended to protect its genetic diversity. These findings are supposed to promote the effective conservation and utilization of S. javanica in the future.


Introduction
Sambucus javanica Blume, a perennial herb or subshrub, is a Chinese native medicinal plant mainly used for acute viral hepatitis, renal edema, rheumatism, traumatic injuries, fractures, and other diseases (Bingsheng 1988;Fang 2007;Putra and Rifa'I 2020). S. javanica extract could be used as an immunomodulatory agent to treat aplastic anemia by promoting the number of naive cytotoxic T-cells and regulatory T-cells (Putra and Rifa'I 2020). And the triterpenes of S. javanica has an outstanding anti-hepatitis effect according to modern pharmacological research (Yang et al. 2005;Chen et al. 2020). S. javanica also has essential nutrients such as trace elements and amino acids, which can be used in the development of health food (Fang 2007). The present S. javanica is gradually planted large scale as a traditional Chinese medicinal material with high medicinal value (Putra andRifa'I 2019, 2020). However, there are no literature reports on the environmental dominant factors and specific suitable distribution of S. javanica. In this study, the Maxent model was used to study the suitable distribution area of S. javanica, which provides a theoretical basis for large-scale cultivation and resource conservation of S. javanica.
As a product of the intersection of computer science and ecology, ecological niche models predict the survival probability of species by calculating the probability of survival based on existing distribution points and geographic layers (Merow et al. 2013;Andersen et al. 2022;Ferreira et al. 2022;Morente-Lopez et al. 2022;Sun et al. 2022). In recent years, with the development of computer science, an increasing number of ecological niche models have been developed to meet the needs of ecological research, such as MaxEnt model, DOMAIN model, CLIMEX model, BIOCLIM model, etc. (Carpenter et al. 1993;Pattison and Mack 2008;Liao et al. 2020;Semwal et al. 2021). Maxent model has been widely used in the field of species distribution research because of its advantages of simple operation, good prediction effect, and high accuracy (Merow et al. 2013;Yang et al. 2020;Zhao et al. 2021;Kong et al. 2021;Maruthadurai et al. 2022). Yang et al. (2020) used the Maxent model to predict the distribution of Astragalus membranaceus var. mongholicus medicinal plants in Inner Mongolia, China and combined with ecological factors to determine the appropriate distribution area of high-quality Astragalus. Zhao et al. (2021) used the Maxent model to simulate the potential distribution of the medicinal plant Coptis chinensis Franch. in China and predicted its distribution pattern and area change in different periods in the future. Based on 89 species distribution points and 30 environmental variables, Kong et al. (2021) constructed a Maxent model of the current and future suitable habitat of Osmanthus fragrans, and explained the most important environmental factors affecting the distribution of this species. In addition, the model constructed using the Maxent model can be transferred to different periods or geographic spaces, which can provide a reference for predicting the distribution of suitable habitats in other periods and assessing the risk of species invasion Zhang et al. 2018;Li et al. 2019).
Future climate change may have a dramatic impact on the current species distribution range and area, and even lead to complete extinction of some endangered species (Naudiyal et al. 2021;Wu et al. 2021;Xie et al. 2021). The 56th session of the Intergovernmental Panel on Climate Change (IPCC-56) noted that net anthropogenic greenhouse gas emissions (GHGs) from urban development have increased gradually since 2010 and are expected to rise after 2025 if without policy strengthening, leading to a global temperature increase of 3.2 °C by 2100. Understanding the impact of future climate scenarios on species distribution patterns and studying the potential distribution of species and dominant climate factors could guide the conservation of species diversity and the formulation of related ecological policies Zhang et al. 2022).
In this study, the suitable area of S. javanica was predicted by Maxent model. Based on the Percent contribution, Permutation importance and Jackknife test, the dominant climatic factors limiting the distribution of S. javanica suitable areas were comprehensively evaluated, in order to provide a basis for the scientific development and utilization of S. javanica. The potential distributions for four future periods (2040s, 2060s, 2080s and 2100s under SSP126/SSP585) were also predicted and compared with the modern distribution of suitable areas to understand the dominant climatic factors limiting the modern distribution of S. javanica.

Species distribution data
The geographic distribution data of S. javanica were extracted from the specimen database and records of literature. The herbarium records of S. javanica were downloaded from the Chinese Virtual Herbarium (CVH, http:// www. cvh. ac. cn/), Plant Photo Bank of China (PPBC, http:// ppbc. iplant. cn/), Global Biodiversity Information Facility (GBIF, http:// www. gbif. org), National Specimen Information Infrastructure (NSII, http:// www. nsii. org. cn/), Plants of the World Online (PWO, http:// powo. scien ce. kew. org/), and the Herbarium Kunming Institute of Botany (KUN, http:// www. kun. ac. cn/). Additional distribution records were obtained from Flora of China, local flora, and other relevant monographs, such as Flora of Jiangxi, Flora of Guizhou, and Flora of Guangxi. Through the coordinate transformation system, the distribution point addresses were converted to latitude and longitude information and saved in CSV format (https:// www. pilia ng. tech/ geoco ding). In addition, a detailed check was conducted in this work, the wrong distribution points were deleted and the duplicate records were removed. Spatial filtering was also performed so that only one point (2.5 min) appeared in each grid cell . Finally, 98 valid geographic distribution points were obtained for further analysis (Fig. 1).

Download of environmental variables
The current and future climate variables were downloaded from the WorldClim Database (version 2.1) (https:// www. world clim. org/). Altitude data were downloaded from Google Earth, and the spatial resolution of the environmental layer was 2.5 min (ca. 4.5 km 2 ) (Fick and Hijmans 2017). The SSP126 and SSP585 emission scenarios from the BBC-CSM2-MR Global Climate Models (GCMs) were adopted as the future climate change scenarios O Neill et al. 2017;Salawitch et al. 2017). The environmental layers were clipped using ArcMap 10.5 software to convert all layers to ASC format. The altitude and solar radiation variables remain unchanged in the simulation of future potential distributions to maintain the comparability of the model on the spatio-temporal series. The GCS_WGS_1984 projection coordinate system was applied as the map geographic coordinate system.  (Table 1), including temperature (Bio01 ~ 11, Tmin01 ~ 12, Tmax01 ~ 12, Tavg01 ~ 12), precipitation (Bio 12 ~ 19, Prec 01 ~ 12), solar radiation (Srad01 ~ 12), water vapor pressure (Vapr01 ~ 12), wind speed (Wind01 ~ 12), and altitude (Alt). Since the collinearity among variables will lead to overfitting of the niche model (Graham 2003), this study applied the Percent contribution and Pearson correlation analysis in order to select variables with a correlation less than 0.8 (Table 2), and to select the one with the largest contribution rate in those with a correlation greater than 0.8 (Qin et al. 2017;Gebrewahid et al. 2020;Liao et al. 2020). As a result, a total of thirteen environmental variables were reserved for model simulation: three temperature variables (Bio 03, Bio 08, Tmin 01), six precipitation variables (Bio 15, Bio 18, Prec 04, Prec 06, Prec 09, Prec 10), and three light variables (Srad 05, Srad06, Srad 12), and one altitude variable (Alt) ( Table 2).  The suitable climatic condition ranges of S. javanica were evaluated with the univariate response curve (Graham 2003;Qin et al. 2017). Figure 3a shows the omission rate and predicted region as a function of the cumulative threshold. It was calculated based on the training dataset records and the test dataset records (Phillips et al. 2021).

Evaluation of maxent model accuracy
The results showed that the model was well fitted and could reflect the actual distribution. The ROC curve was obtained by simulating the training dataset through the built-in function of MaxEnt software, and the area under the curve was the AUC value which is generally between 0 and 1 (Xie et al. 2021). If the AUC value is greater than 0.9, the model prediction effect is excellent, and the closer it comes to 1, the better the model prediction effect is. In this study, the average training AUC was 0.993 while the standard deviation was 0.001. According to the evaluation criteria, the overall prediction accuracy of the model has reached an excellent level, indicating that the model made an accurate prediction on the potential suitable distribution of S. javanica (Fig. 3b).

Assessment of environmental variables affecting the distribution of S. javanica
The analysis results of the relative contribution rate of environmental variables in the MaxEnt model are shown in Table 3. Specifically, the percent contribution values of Prec06, Bio18, and Bio03 were 37.90%, 25.50%, and 14.00%, respectively, and the cumulative value was 77.40%; the permutation importance values of Srad12 and Bio03 were 34.60% and 25.00%, respectively, and the cumulative value was 59.60% (Table 3). Among them, precipitation was the most important environmental variables with a cumulative contribution percent of 70.10%, followed by temperature (19.00%), solar radiation (10.00%), and altitude (1.00%). In addition, the jackknife test results of the environmental variables (Fig. 4) indicated that the regularized training gain, test gain, and AUC values of Prec06, Bio18, and Srad12 were respectively greater than 2.0, 0.95, and 0.95, which were significantly higher than those of the other environmental variables (see Fig. 4). Among them, the environmental variable with the highest gain is Prec06, which indicated that this environmental variable contained the most useful information on the distribution of S. javanica. When Bio03 or Srad12 was ignored to build the model, the regularized training gain was obviously decreased, showing that these two environmental variables contained a lot of information not found in the rest of the variables (Fig. 4). In summary, Prec06, Bio18, Bio03, and Srad12 were the key environmental limiting factors that significantly influence the distribution of S. javanica.

Assessment of the optimum environmental conditions
In order to conduct a further analysis of the influence of the environmental variables on the distribution of S. javanica, the 4 main environmental variables Prec06, Bio18, Bio03, and Srad12 were imported into MaxEnt models, respectively. A univariate model was established to fit the univariate response curve, which shows the relationship between the main environmental variables and the geographical distribution probability of S. javanica 1 3 Vol:. (1234567890) Fig. 2 Flowchart of processing methodology for niche model of S. javanica (Fig. 5). When the existence probability was greater than 50%, the corresponding environmental factor value was suitable for the growth of the species (Awade et al. 2012). According to the univariate response curve of environmental factors, the optimum environmental conditions for the growth and distribution of S. javanica were as follows: precipitation in June ranged from 156.36 to 383.25 mm; precipitation of warmest quarter ranged from 447.92 to 825.00 mm; isothermality ranged from 24.06 to 35.50; solar radiation in December ranged from 6750.00 to 10,521 kJ·m −2 ·day −1 (Fig. 5).

Prediction of distribution pattern under current climate condition
The results indicated that the suitable regions of S. javanica were widely distributed in southern China (Table 4 and Fig. 6a). From the perspective of administrative division, S. javanica is suitable for growing in 25 provinces and cities (Table 4). Among them, Guizhou was the most suitable area, followed by Zhejiang, Hunan, Chongqing, Fujian, Guangxi, Hubei, Jiangxi, Anhui, Guangdong, Shaanxixx, and eastern fringe of Yunnan. The current potential suitable areas of S. javanica in China were 2.73 × 10 6 km 2 , and among them, the highly suitable areas were 6.63 × 10 5 km 2 (24.27%); the moderately suitable areas were 7.93 × 10 5 km 2 (29.04%); the generally suitable areas were 6.86 × 10 5 km 2 (25.11%); the low suitable areas were 5.89 × 10 5 km 2 (21.58%). According to the current suitable distribution map of S. javanica (Fig. 6a), S. javanica is mainly suitable for growing in two regions in China. The first region, which was also the most important region (as shown in the blue oval in Fig. 6a), mainly consists of Guizhou, western Hubei, southeastern Chongqing, southwestern Hunan, northern Guangxi, and a small part of eastern Yunnan, covering a cumulative high suitable distribution area of 4.38 × 10 5 km 2 (66.16% of the total high suitable areas).  The second region mainly consists of Zhejiang, southern Anhui, and northern Fujian, covering a cumulative high suitable distribution area of 1.24 × 10 5 km 2 (18.67% of the total high suitable areas) (as shown in the black oval in Fig. 6a).
In general, in contrast to the climate scenario SSP126, the impact on the suitable distribution area of S. javanica for SSP585 was relatively minor. The Fig. 6 The potential suitable distribution of S. javanica in China under different climate scenarios (The blue oval highlights the first suitable distribution region, and the black oval highlights the second suitable distribution regions) distribution areas of S. javanica displayed an overall decreasing trend under the SSP126 scenario. However, under the SSP585 scenario, although the distribution area of S. javanica was reduced relative to the current distribution by the 2040s and 2060s, and the distribution area was steadily recovered by the 2080s and even gradually increases by the 2100s.

Response of suitable distribution to environmental variables
It is of great significance for ecological management to explore the changes in the geographical distribution  of species caused by climate change (Hamann and Wang 2006;Paź-Dyderska et al. 2021;Varol et al. 2021). O'Neill et al. (2017) predicted that the average global temperature would be more than 4 °C above pre-industrial levels by 2100s, which would alter the geographic distribution of many species (Lenoir et al. 2008;Bellard et al. 2012;Hundessa et al. 2018;Bouahmed et al. 2019). The profound impact on the distribution change of species might alter the function and structure of terrestrial ecosystems in turn . Therefore, assessing the impact of climate change on the spatial distribution of different species will help address issues related to changes in the distribution and range of various species. When constructing ecological models, predecessors often only considered the impact of climate factors on the distribution pattern of animals and plants (Sun et al., 2020;Xie et al. 2021). However, we that terrain factor also had a great impact on the distribution of plants (Liao et al. 2020). Therefore, in the MaxEnt model, 19 annual climate factors, 84 single-month climate data, and one altitude data were employed, and the environmental factors with high independent contribution and small correlation with each other were selected for modeling. From the phytogeographic point of view, S. javanica was mainly located in the subtropical region of China (south of the Qinling Mountains-Huaihe River Line and east of the Hengduan Mountains). The presence of mountain ranges affected the distribution of plants and animals (Elsen et al. 2018;Odland 2010). In this study, the Qinling-Huaihe line was found to be the northern boundary of S. javanica distribution, and the western boundary of S. javanica was the Hengduan Mountains. The Qinling Mountains-Huaihe River Line serves as the dividing line between the southern and northern regions of China, with significant differences in geography, climate, and environment (González-Prieto et al. 2016). The south region of the line has a humid, warm, and rainy climate, with an average January temperature above 0 °C and average annual precipitation above 800 mm ). This boundary is also the dividing line between the subtropical monsoon and temperate monsoon climates, and is considered to the main parameter affecting the distribution of many species (Zhou et al. 2011;Pan et al. 2020;Xie et al. 2021). Moreover, the warm and humid airflow from the Indian Ocean is blocked by two tall east-west mountain ranges (the Himalaya and the Gondola) and enters China along the north-south Hengduan mountain range, bringing abundant rain to the Tibetan Plateau Kramer et al. 2010;Xu et al. 2019). Therefore, the Qinling-Huaihe line and the Hengduan Mountains are the boundaries of the 800 mm equivalent precipitation area in China, which overlap with the modern distribution boundary of S. javanica (Fig. 6a). The cumulative contribution of two precipitation factors (Prec01 and Bio18) with a value of 63.4% (Table 3) was also further evidence that precipitation was critical to the distribution of S. javanica. This result suggested that the limiting effect of precipitation on the growth of S. javanica might be one of the main reasons why its modern distribution is mainly concentrated in the subtropical region of South China (Table 5).
In addition, the altitude-induced temperature change (decrease about 6 °C per 1000 m elevation) was also an important factor affecting the distribution of plants (Rangwala and Miller 2012). The Yunnan-Guizhou Plateau, bounded by the Wumeng Mountains, was divided into the Yunnan Plateau (altitude 3000-4000 m) and the Guizhou Plateau (altitude 2000-2400 m) (Zheng et al. 2020). In summer, the temperature of the Yunnan-Guizhou Plateau was lower than that of the same latitude region due to its higher altitude. In winter, the temperature of Yunnan-Guizhou Plateau was higher than that of the same latitude region due to the plateau is at the intersection of the Pacific and Indian Ocean monsoons (Yang 2008). The unique geographical location of the Yunnan-Guizhou Plateau results in low temperatures in summer and high temperatures in winter, also leading to its annual temperature difference smaller than that of the same geographical latitude (Yang 2008).
This might be the main reason why the Yunnan plateau has adequate precipitation (1000-1200 mm per year), but the distribution suitability class of S. javanica in the region is largely low suitability distribution area.

Changes of suitable habitat in future climate scenarios
Research into the impact of future climate changes on plants could be beneficial to develop strategies towards the challenges caused by climate change (Liao et al. 2020). The Climate Model Intercomparison Project phase 6 (CMIP6) has been widely applied to discuss the impact of future climate change on global ecology and economic development (Xu et al. 2022;Tan et al. 2022;Fan et al. 2022). The CMIP6 modeled future climatic conditions by combining possible future socio-economic conditions (Shared Socio-economic Pathways) and different greenhouse gas (GHG) emission scenarios (Representative Concentration Pathways, RCPs) (O'Neill et al. 2016). In this study, the climate scenarios SSP126 and SSP585 were used as a background to simulate the future distribution of S. javanica. The SSP126 scenario represented a sustainable development path. In this scenario, fossil energy dependence and global carbon dioxide emissions would be greatly reduced, and the radiative forcing level would approach 2.6Wm −2 . Carbon dioxide emissions are expected to be reduced to zero by around 2050s, and the global temperature would be about 1.8 °C higher than today by around 2100s (O'Neill et al. 2016). The SSP585 scenario represented the traditional path of economic development. In this scenario, people solve social and economic problems by emphasizing self-interest and rapid development. The radiative forcing level would approach 5.8Wm −2 . Carbon dioxide emissions are expected to double relative to current levels by around 2050s, and the global temperature would be about 4.4 °C higher than today by around 2100s (O'Neill et al. 2016).
In this study, the variation of the suitable distribution area of S. javanica are investigated under two different environmental strategies. The results showed that the suitable distribution area of S. javanica, under the SSP126 climate scenario, exhibited a shrinking trend relative to the current suitable distribution area. This is in agreement with the results by Xie et al. (2021) who reported potential reduce in areas suitable for Rhodomyrtus tomentosa (Aiton) Hassk. under RCP2.6 emission scenarios of greenhouse gases. However, under the SSP126-2080s scenario, the highly suitable distribution area of S. javanica showed a significant increase, caused by the conversion of low, generally, and moderately suitable distribution areas to highly suitable distribution areas. This suggested that there could be a risk of S. javanica suitable habitats loss and fragmentation under the SSP126 climate scenario. Previous research found that the geographic distribution of Fraxinus excelsior L. might narrow by 7.58% and 6.28% under SSP245-2100 and SSP585-2100 climate scenarios, respectively (Varol et al. 2021). However, some species ranges will increase, such as Paeonia veitchii Lynch (Zhang et al. 2019), Oxytenanthera abyssinica (A. Richard) Munro (Gebrewahid et al. 2020). Under the SSP585 scenario, there was little change in the suitable distribution area of S. javanica. Although the suitable distribution area of S. javanica showed a shrinking trend under the SSP585-2040s and SSP585-2060s, the distribution was gradually recovered and became stable after the SSP585-2080s. Meanwhile, in the context of future global warming, the low suitability distribution of S. javanica in the Yunnan Plateau showed a trend of conversion to moderate suitability distribution, probably caused by the relatively low temperature at high altitudes that provided a potential geographic barrier for S. javanica against future climate warming (Xie et al. 2021). Summary, although the distribution areas of S. javanica might be affected to some extent under future climate scenarios due to increased precipitation and temperature, the overall distribution pattern of S. javanica was unlikely to change. This suggested that S. javanica could cope with the effects of future global warming on its distribution pattern.

Prospects and Suggestions
The active substances (mostly secondary metabolites) produced by medicinal plants serve as direct indicators for judging its quality. Previous researches showed that the contents, types, and proportions of active substances are strongly related to environmental factors such as illumination, moisture, and temperature (Penuelas and Llusia 1997;Liu et al. 2016). However, a suitable growth environment does not necessarily lead to the accumulation of more active ingredients in the medicinal plants. They might also produce more active substances in response to environmental stress under unfavorable growth conditions Alhaithloul et al. 2019;Toivonen et al. 1992;Jochum et al. 2007). For example, the contents of myricetin-3-O-rhamnoside in the roots of Limonium bicolor (Bag.) Kuntze were significantly increased under salt stress ). In the medicinal plants Mentha piperita and Catharanthus roseus, the levels of tannins, terpenoids and alkaloids were significantly increased under the combined heat/ drought stress (Alhaithloul et al. 2019). At present, the relationship between the environmental factors and the active ingredients of S. javanica is currently not clear. Therefore, the influence of environmental factors on the medicinal active ingredients of S. javanica is a major research focus for the future.

Conclusion
This research indicated that S. javanica were mainly distributed in 25 provinces and cities in China (subtropical regions of China). Among them, Guizhou, Zhejiang, Hunan, Chongqing, Fujian, Hubei, and Jiangxi were the most suitable regions for S. javanica introduction and cultivation (highly suitable areas > 30%). Precipitation in June (Prec06), precipitation of warmest quarter (Bio18), isothermality (Bio03) and average solar radiation in December (Srad12) were mainly climatic factors that limit the distribution of S. javanica. Furthermore, future climate changes might have little impact on the distribution patterns of S. javanica. As a traditional Chinese herbal medicine, S. javanica has a rich variety of active ingredients (mainly including flavonoids, triterpenoids, steroidals, volatile oils, and phenylpropanioids), our study provides a theoretical basis for the exploitation of S. javanica, but further studies on the effects of environmental factors on the active ingredients of S. javanica are needed in order to produce high-quality S. javanica raw materials.
Author contributions QS: Conceptualization, writingreview and editing, supervision, visualization, project administration, funding acquisition; YH: Conceptualization, resources, writing-review and editing, supervision, project administration, funding acquisition; JL: Conceptualization, writingoriginal draft preparation, writing-review and editing, visualization; CY: investigation, writing-review and editing; QS: investigation, writing-review and editing. All authors have read and agreed to the published version of the manuscript.
Funding This work was supported by the Natural Science Foundation of Top Talent of SZTU [Grant No. 2019010801010, 2019010801009].
Data availability This raw data required to reproduce these findings cannot be shared at this time as the data also forms part of an ongoing study.

Declarations
Competing interests All authors declare that No conflict of interest exists.