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Article

The Distribution Range of Populus euphratica Oliv. (Salicaceae) Will Decrease Under Future Climate Change in Northwestern China

by
Xun Lei
1,2,
Mengjun Qu
1,2,
Jianming Wang
1,2,*,
Jihua Hou
1,
Yin Wang
2,3,
Guanjun Li
1,2,
Meiwen Luo
1,2,
Zhijun Li
4 and
Jingwen Li
1,2,*
1
School of Ecology and Nature Conservation, Beijing Forestry University, Beijing 100083, China
2
Ejina Institute of Populus euphratica, Beijing Forestry University, Ejin Banner 735400, China
3
Hebei Collaborative Innovation Center for Eco-Environment, College of Life Sciences, Hebei Normal University, Shijiazhuang 050024, China
4
College of Life Sciences and Technology, Tarim University, Aral 843300, China
*
Authors to whom correspondence should be addressed.
Forests 2024, 15(8), 1288; https://doi.org/10.3390/f15081288
Submission received: 31 May 2024 / Revised: 2 July 2024 / Accepted: 13 July 2024 / Published: 24 July 2024
(This article belongs to the Section Forest Ecology and Management)
Browse Figures
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Abstract

:
Climate change has been regarded as a primary threat to biodiversity at local, regional, and global scales. Populus euphratica Oliv. is one of the main constructive species in dryland regions and has a key role in regulating ecosystem processes and services. However, there is a knowledge gap regarding the spatial distribution of habitat suitability of P. euphratica and how it will be affected by future climate change. Based on the distribution records collected from an online database and specialized literature, we applied an optimized MaxEnt model to predict the distribution range of P. euphratica in China under four climate change scenarios (SSP126, SSP245, SSP370, and SSP585) for both current and future (2090s) conditions. We found that (1) future climate change would reduce the adaptability of P. euphratica, resulting in a significant decrease in its distribution area; (2) water availability had the most important effect on P. euphratica distribution; (3) the habitat for P. euphratica would shift northwestward and contract towards lower elevations closer to rivers in the future. These findings can provide a reference for developing long-term biodiversity conservation and management strategies in arid regions.

Graphical Abstract

1. Introduction

Climate change stands out as a primary determinant of biodiversity [1], concurrently serving as a key factor shaping species distribution [2]. The Sixth Assessment Report (AR6) of the Intergovernmental Panel on Climate Change (IPCC) has indicated that the global average surface temperature has increased by 1.1 °C from 2011 to 2020 compared to the preindustrial period [3]. Up to one-sixth of species have been threatened and some species have gone extinct under global warming. Climate change induces habitat changes, fragmentation, and loss by altering the growth, development, and phenology processes of species, resulting in a substantial variation in the structure and function of ecosystems [1,4]. Specifically, the impacts are more dramatic in areas with fragile ecosystems [5]. Therefore, predicting the potential distribution patterns of species in the future contributes to a better understanding of the impacts of climate change on biodiversity and ecosystems, as well as to the design of conservation and management strategies.
P. euphratica Oliv., belonging to the family Salicaceae and genus Populus, is both the oldest and most primitive woody plant in its taxonomic group, while also being a unique and valuable tree species in the desert riparian forests [6]. Due to its tolerance to tenacity, drought, salinity, and alkalinity, it is widely distributed in Central and West Asia, as well as in the Mediterranean arid areas, in elevations from 200 to 2400 m [7], serving as an important component of ecosystems in arid regions [8]. In addition, P. euphratica plays an irreplaceable role in resisting wind and sand, curbing desertification and vegetation degradation, protecting biodiversity, maintaining the ecological balance of arid regions, and regulating climate [9]. China boasts the largest distribution range of P. euphratica in the world that accounts for approximately 61% of the total global distribution area [6]. In recent decades, the distribution range of P. euphratica has drastically decreased under the combined influence of climate change and human activities, which has aroused widespread concerns in society [10]. P. euphratica was listed as a Chinese Near Threatened (NT) plant species by the Chinese government in 1984 and as vulnerable in the Flora of China project in 1999 [7]; it was also identified as a forest tree genetic resource in need of urgent priority protection in arid and semi-arid areas in 1993 [8,11]. Therefore, exploring the potential changes in the distribution of suitable habitats for P. euphratica under global warming is critical for biodiversity and genetic conservation.
Preserving species habitats is recognized as an effective measure to cope with climate change [12]. Therefore, predicting future species distribution is crucial for making management and conservation decisions to ensure optimal maintenance and provision of ecosystem services [13]. Thanks to the accumulation of species geographic distribution information and the rapid development of spatial analysis techniques, species distribution models (SDMs) have been widely employed to simulate species distribution ranges and assess species’ responses to climate change [14,15,16]. SDMs integrate species distribution data with environmental data to predict the potential suitable habitats for target species under past, present, and future scenarios [17]. Presently, various models have been established based on different algorithms, including Genetic Algorithm for Rule Set Production (GARP) [18], Maximum Entropy (MaxEnt) [19], Bioclim [20], Domain [21], and random forest (RF) [22]. Among them, the Maximum Entropy model (MaxEnt), which is known for its simple interface, high accuracy, requirement of minimal sample data [23,24], and short execution time, has been most extensively utilized in fields like conservation biology and ecology [25,26]. For instance, it can be used to investigate the impact of climate change on species distribution [27,28], conserve endangered species [16], establish species conservation areas [29], and analyze regional richness changes [30].
Previous research on P. euphratica has predominantly focused on physiological ecology [10,31], resulting in a limited understanding of the impact of climate change on its habitat distribution [32]. Meanwhile, given the increasing concerns of the Chinese government on biodiversity conservation, understanding the long-term impact of climate change on the distribution of P. euphratica can help improve biodiversity conservation and ecosystem function maintenance in dryland regions. Therefore, an optimized MaxEnt model was employed to predict the potential suitable habitats for P. euphratica under four different emission scenarios in 2090, based on substantial precise distribution data, selected environmental variables, and three general circulation models (GCMs). Our study aims to answer two questions: (1) whether and how climate change causes significant shifts in the range distributions of P. euphratica; and (2) do environmental factors related to water availability determine the distribution of P. euphratica?

2. Materials and Methods

2.1. Distribution Records of P. euphratica

The distribution records of P. euphratica were retrieved from (1) the Chinese National Plant Specimen Resource Center (NPSRC, http://www.cvh.ac.cn/, accessed on 4 November 2023), (2) the Global Biodiversity Information Facility (GBIF, https://www.GBIF.org/, accessed on 4 November 2023), and (3) specialized literature. To minimize the impact of data redundancy on the results, duplicate records were removed, retaining one record per grid (5 × 5 km). Overall, 143 distribution records were used to construct the MaxEnt model (Figure 1). All distribution records were saved in csv files in which each row corresponds to a single distribution message of P. euphratica individuals.

2.2. Environmental Variables

Based on previous studies and the specific growth environment of P. euphratica, 23 environmental variables were selected in this study [32,33] (Table 1). These environmental variables were categorized into the following three groups: climatic, topographic, and water source variables. Climatic variables reflect changes in temperature and precipitation over a certain period of time. Topographic factors are intimately connected to species growth and development, and can further alter species distribution by redistributing hydrothermal conditions [16,34]. Notably, relevant research has found that the distribution range of riparian vegetation in arid regions is also influenced by distance from rivers [11,35]. River runoff that acts as a replenishment source of groundwater in arid regions can meet the ecological water requirement of riparian vegetation [35]. Therefore, considering distance from rivers facilitates a more accurate understanding of the future potential distribution of P. euphratica. A total of 19 bioclimatic variables were downloaded from the WorldClim database (https://worldclim.org/, accessed on 5 November 2023) at a resolution of 2.5′ (ca. 4.5 km2) for current (1970–2000) and future (2081–2100) conditions. The future climate data are based on the Shared Socioeconomic Pathways (SSPs) provided by the Sixth Assessment Report (AR6) of the Intergovernmental Panel on Climate Change (IPCC), and four emission scenarios, including SSP126, SSP245, SSP370, and SSP585, have been selected (Table S1). Furthermore, this study regarded the average of three general circulation models (GCMs) as future climate data to predict the potential distribution of P. euphratica: BCC-CSM2-MR (China), CMCC-ESM2 (Italy), and MRI-ESM2-0 (Japan). Digital elevation model (DEM) data were obtained from the geographic spatial cloud ((https://www.gscloud.cn/, accessed on 6 November 2023), and geographic information system (GIS) techniques were employed to extract three terrain factors, namely, elevation, slope, and aspect, to support the research needs. River distribution data were obtained from the Resource and Environmental Science Data Center of the Chinese Academy of Sciences (https://www.resdc.cn/, accessed on 6 November 2023). The layer of distance from river was generated in ArcGIS software (version 10.8, Environmental Systems Research Institute, Redlands, CA, USA) through Euclidean distance calculation, resampling, projection, and clipping [36]. All environmental variables were standardized to a resolution of 2.5′, maintaining a consistent study area extent and coordinate system. Subsequently, these datasets were uniformly converted to .asc format for the construction of the MaxEnt model.
The large number of environmental variables and strong collinearity resulted in the overfitting of the model, which may negatively affect the prediction accuracy of the model [37,38]. Therefore, the Spearman’s rank correlation test was conducted on 22 environmental variables in R 4.2.3 (Figure S1). When the correlation coefficient between two environmental variables exceeded 0.8 (|r| > 0.8), it revealed a high correlation between the two variables [39,40]. In the case of two highly correlated environmental variables, the criterion for selection involved excluding variables with a low percentage contribution while retaining those with a high percentage contribution [41]. In the ultimate iteration, a total of 11 environmental variables, encompassing 7 climate variables (Bio2, Bio4, Bio5, Bio6, Bio13, Bio14, Bio15), 3 topographic variables (Elev, Slope, Asp), and water sources (Disriver), were selected for inclusion in the final MaxEnt model (Table 1, Figure S1).

2.3. Model Optimization and Accuracy Evaluation

MaxEnt models have been widely used in different research fields, and most researchers typically adopt default parameters when constructing their models [28]. However, there is evidence that the use of default parameters may lead to overfitting, which affects the portability of the model and the accuracy of the test results [23,42]. Key parameters that are closely correlated with model complexity include feature combination (FC) and regularization multiplier (RM), which are optimized to improve the accuracy of model predictions [14]. Therefore, it is important to adjust FC and RM parameters when constructing MaxEnt models.
In this study, we optimized the MaxEnt model using the “ENMeval” package in R 4.2.3 software [14,27], and adjusted the parameters of RM and FC to analyze the complexity of the model under different parameter conditions, aiming to select the most suitable parameter combination for predicting the distribution of P. euphratica. The MaxEnt model has five FCs: linear (L), quadratic (Q), hinge (H), product (P), and threshold (T). We combined six FCs (L, LQ, H, LQH, LQHP, and LQHPT) with ten RMs (0.5–5 at 0.5 intervals), obtaining a total of sixty parameter combinations. We assessed the model fitting on species distribution points based on the difference between training and testing AUC (AUCDIFF) and a 10% training omission rate (OR10), where lower values indicate poorer fit [43]. Moreover, the Akaike Information Criterion (AIC) was employed to assess the complexity and performance of the model [44], selecting the optimal model with the smallest AICc value (Delta.AICc = 0). Finally, the distribution of suitable habitats for P. euphratica was predicted using the optimal model parameters.
The default parameters for the model were RM = 1 and FC = LQHPT. The model performance was tested under various parameter combinations, and the best feature combination was selected based on the minimum AICc values. As shown in Figure S2, RM = 2 and FC = LQHPT were selected as the optimal model parameters (Delta.AICc = 0), and the delta AICc of the optimal model was significantly lower than that of the default parameter model (Delta.AICc = 85.60). Furthermore, the AUCDIFF (0.004) and OR10 (0.17) of the optimal model were both lower than those of the default parameter model (AUCDIFF = 0.006, OR10 = 0.19), with reductions of 29.45% and 7.13%, respectively. This result indicated that the optimal model had a substantially lower complexity and overfitting degree.
In this study, the MaxEnt software (version 3.4.1, American Museum of Natural History, New York, NY, USA) was used as a modeling platform, where distribution records of P. euphratica and environmental variable data were jointly input. As for model parameter configuration, the number of iterations was set to 1000, with 10,000 background points, and the process was repeated 10 times. To train and test the model, we adopted a conventional partitioning method, where 75% of the occurrence records were used for training, and the remaining one fourth were reserved for model testing [45]. The use of the area under the receiver operating characteristic curve (AUC) was unaffected by diagnostic thresholds, helping to reduce bias [46]. The choice of AUC was used as the metric to evaluate model performance, and the jackknife test was used to assess the relative importance of environmental variables in this study. The AUC value ranged from 0 to 1, with values closer to 1 indicating superior model performance [19]. Based on AUC categorization, model performance was classified into five categories: 0.5–0.6 (poor), 0.6–0.7 (fair), 0.7–0.8 (good), 0.8–0.9 (very good), and 0.9–1.0 (excellent) [47]. After running the MaxEnt software 10 times, we obtained an average AUC value of 0.971, significantly higher than 0.9 (Figure S3). This indicates that the model predictions are highly accurate and reliable, and they can provide a reasonable forecast for the suitable habitat distribution of P. euphratica in the arid region of northwestern China.

2.4. Analysis of the Potential Suitable Area

The probability values generated by the MaxEnt model ranged from 0 to 1, reflecting the presence probability of P. euphratica. The .asc files output by the MaxEnt model were converted into raster files in ArcGIS 10.8. According to previous research [48,49], we applied the natural break method to categorize probability values into four levels: highly suitable (1.0–0.6), moderately suitable (0.6–0.4), poorly suitable (0.4–0.2), and unsuitable (<0.2). We calculated the area of suitable habitats for each level and analyzed the percentage change in them. Additionally, we transformed the probability values into binary maps indicating presence or absence using the SDM Toolbox in ArcGIS. Areas with probability values greater than 0.2 were considered as potential suitable habitats for P. euphratica, while areas with probability values less than 0.2 were regarded as unsuitable habitats. Variations in P. euphratica distribution (expansion, contraction, no change) were quantitatively assessed by overlaying current and future binary maps. Finally, we analyzed the shifts in the geographical centroid and elevation of suitable habitats under future climate scenarios.

3. Results

3.1. Contributions of Environmental Variables

The jackknife test of variable contribution showed that precipitation of the wettest month (Bio13, 44.1%), distance from river (Disriver, 23.3%), temperature seasonality (Bio4, 13.9%), and max temperature of the warmest month (Bio5, 11.6%) were the major contributors to the potential suitable habitat for P. euphratica, the cumulative percentage contribution reached 92.9%, and the cumulative permutation importance was 92.6% (Table 2), indicating that precipitation had a significant impact on the distribution of P. euphratica, followed by water source factors. In comparison, temperature and topographical factors had a relatively weak impact. Overall, precipitation and water source factors were crucial environmental variables affecting the distribution of P. euphratica.
The relationship between presence probability of P. euphratica and environmental factors was analyzed, and probability values > 0.5 indicated a favorable effect on P. euphratica growth (Figure 2). The response curves for Bio13 and Disriver followed a similar decreasing trend, with an appropriate interval of 2–20 mm and 0–1.1 km, respectively. The presence probability of P. euphratica showed a unimodal relationship with Bio4 and Bio15, peaking at 1106.46 and 98.4, respectively. The habitat suitability of P. euphratica was relatively high when Bio5 was below 34.92 °C, whereas it became limited when Bio5 exceeded 42.35 °C.

3.2. Current Distribution of the Suitable Habitat for P. euphratica

The potential distribution of P. euphratica was consistent with its current distribution characteristics. Water sources limited the distribution of the suitable habitat for P. euphratica which was concentrated along riverbanks in the arid northwestern regions of China, such as the banks of the Tarim River and the Heihe River, showing a characteristic clustering pattern along river courses (Figure 3). P. euphratica was distributed within the latitude range of 75.2–108.0° E and the longitude range of 36.5–48.2° N, with a total distribution area of 49.14 × 104 km2, accounting for 5.12% of the total area of China. The suitable distribution zones extended outward along both riverbanks, and habitats close to rivers were more suitable for P. euphratica growth (Figure 3). Specifically, highly suitable habitat had the smallest area of 8.74 × 104 km2, moderately suitable habitat had an area of 19.16 × 104 km2, and poorly suitable habitat distributed on the outer edge of the suitable habitat had the largest area of 21.23 × 104 km2, accounting for 43.21% of the total area of the suitable habitat (Table 3).

3.3. Changes in the Potential Suitable Habitat for P. euphratica under Future Climate Change Scenarios

The suitable habitat area for P. euphratica will decrease in 2090 under all climate scenarios. Specifically, it would decrease by 18.93%, 27.78%, 46.22%, and 50.29% under SSP126, SSP245, SSP370, and SSP585 scenarios, respectively, compared to the current scenarios (Table 3). The magnitude of area reduction will increase with increasing radiative forcing. The most significant decline in the area of highly suitable habitats will be observed particularly under the scenario combining high development and large emissions (SSP585), where the highly suitable habitat will be almost entirely lost, with only a small part (0.04 × 104 km2) remaining. Besides, the contraction of suitable habitats will primarily occur on the outer edges of the current suitable habitat range, while the minor expansion areas will be mainly located in the Irtysh River Basin in northern Xinjiang (Figure 4 and Figure 5). The area of moderately and highly suitable habitats will show a similar contraction trend, with a positive correlation between radiative forcing and contraction area. Additionally, the contraction area of moderately suitable habitats will be lower than that of highly suitable habitats under all climate scenarios. The area of poorly suitable habitat will remain largely unchanged under the SSP585 scenario but will increase under the three other scenarios. Additionally, the distribution pattern of suitable habitats for P. euphratica will remain similar to the current pattern under future climatic scenarios. The suitable habitats will still be concentrated along the banks of the Tarim River and the Heihe River, but the range will contract towards the rivers, and the degradation will become more pronounced (Figure 4). Moreover, the suitable habitat for P. euphratica will experience various levels of degradation. These results indicated that future climate change would pose a threat to the current distribution of P. euphratica, especially in areas far away from rivers.
ArcGIS was also used to calculate the shifts in the geographical centroids of the suitable distribution range of P. euphratica under future climatic scenarios (Figure 6). The current centroid was located in Yuli County, Bayingol Mongolian Autonomous Prefecture, Xinjiang (86°27′56″ E, 41°35′41″ N). Under the SSP126 scenario, it will migrate 18.12 km northwestward to Korla City (86°17′44″ E, 41°41′50″ N). Under the SSP245 scenario, it will migrate 22.51 km northwestward to Bohu County (86°24′36″ E, 41°47′35″ N). Under both SSP370 and SSP585 scenarios, it will be located within Korla City. Specifically, it will migrate 24.38 km northwestward (86°11′42″ E, 41°40′47″ N) under the SSP370 scenario and migrate 56.83 km northwestward (85°57′59″ E, 41°56′43″ N) under the SSP585 scenario. In general, under the four climate scenarios, the suitable habitats for P. euphratica will migrate northwestward by the 2090s, and the migration distance will increase as radiative forcing intensifies.
Currently, the suitable habitat for P. euphratica is distributed at an average distance of 0 to 11 km from rivers, with an average elevation of 954 m (Table 4). However, it is expected to migrate to lower elevations and closer to rivers under the four climate scenarios, and such shifts will be more pronounced as radiative forcing increases. Under the most pessimistic scenario (SSP585), the suitable habitat for P. euphratica is projected to move closer to rivers (at an average distance of 6 km), with an average elevation of 896 m.

4. Discussion

4.1. Key Predictor Variables Affecting the Distribution of P. euphratica

Among the 11 environmental variables selected in this study, precipitation of the wettest month (Bio13) was identified as the most important variable affecting the suitable distribution of P. euphratica (Table 2), which is consistent with the findings of Zhang et al. [32]. In arid regions, water is essential for plant survival, and its temporal and spatial variations significantly impact plant growth and development [50]. Several studies have shown that hydrological conditions are the main factors that influence the distribution of P. euphratica [6,31,51]. In the arid regions of northwest China, precipitation is concentrated in summer months, with over 90% of the precipitation occurring during the growing season of P. euphratica (April–September) [52]. P. euphratica seedlings can effectively utilize precipitation, with utilization rates reaching up to 60% [53]. Meanwhile, precipitation directly replenishes soil moisture as well as groundwater, thereby improving the water conditions required for P. euphratica growth. In addition, frequent precipitation within a short period may trigger flood overflow events, which can help reduce the salinity–alkalinity stress in the habitat of P. euphratica [54], consequently affecting its population regeneration. Zhang et al. [31] have conducted river flooding experiments and found that when soil conductivity exceeds 7 ms/cm, it inhibits the germination of P. euphratica seeds.
Plant growth is closely related to water availability which is primarily determined by precipitation and groundwater [55,56]. In the arid regions of northwest China where precipitation is scarce and evaporation is intense, water resources are limited and groundwater is regarded as the primary water source for vegetation survival [57]. Meanwhile, it is also a key factor limiting the growth and development of P. euphratica [51,58]. As groundwater recharge in this region mainly derives from river leakage [59], the distribution of P. euphratica is closely related to Disriver (Table 2). Moreover, observations on seed germination and seedling growth of P. euphratica have found that the establishment of P. euphratica seedlings requires a highly moist flood plain environment [60], and flood overflow is a prerequisite for the regeneration of P. euphratica [54]. Therefore, in order to obtain sufficient water for growth and regeneration, P. euphratica tends to be distributed along riverbanks (Figure 4).
Despite not being a primary limiting factor for the distribution of P. euphratica, temperature exerts a certain degree of influence on it (Table 2). The process of photosynthesis in plants is highly sensitive to high temperatures, as elevated temperatures impact photosynthesis by affecting the membranes of plant cysts and electron transport [61]. Previous research has indicated that high temperatures can decrease the photosynthetic rate of P. euphratica leaves by reducing the aperture of their stomata [62]. Under severe heat stress, the chloroplast structure is disrupted, leading to irreversible inhibition of photosynthesis [63]. Hence, high temperatures can hinder the growth of P. euphratica, as excessive heat may potentially disrupt its growth and development, thereby affecting its distribution.

4.2. Prospective Changes in Suitable Habitat Distribution

Future climate change may alter the distribution patterns of species [27,64]. Studies have revealed that China’s annual average temperature may rise by 2.2–4.4 °C by the end of the 21st century, accompanied by an increase in precipitation in most regions. This climate change has a potential to disrupt the existing ecological balance and modify the distribution range of vegetation [65]. The response of different species to climate change varies considerably [64,66,67,68]. Specifically, species that benefit from global warming may expand their distribution range [67,69]. However, species constrained by climate change may contract their habitat range [16,41]. Previous studies carried out in the arid regions of northwestern China have pointed out that heat-tolerant and drought-resistant plants, such as Alhagi camelorum [70], Calligonum mongolicum [71], and Haloxylon ammodendron [72], are expected to expand their habitat ranges in the future. Our research indicated that future climate change would adversely affect the suitable habitats of P. euphratica, which would have a higher dependency on water resources. This result is consistent with previous research findings [32].
The area of suitable habitat will decrease to varying degrees under future emissions scenarios (Table 3). In the arid region of northwest China where P. euphratica thrives, evaporation significantly exceeds precipitation [58]. While future warming is expected to increase both evapotranspiration and precipitation in China, the intensity of the increase in evapotranspiration is significantly greater than that of precipitation in northwest China [73]. The dominance of evapotranspiration in the water balance further exacerbates aridity in the northwest arid zone. P. euphratica, whose growth and survival deeply depend on abundant water resources [74], will experience a dramatic reduction in its distribution area in an increasingly severe arid environment in the future. In addition, the effect of climate change on the distribution of P. euphratica will be more severe in areas far away from rivers than in those closer to rivers (Figure 4 and Figure 5). Because of increased drought stress and water scarcity in the future [73], water recharge will depend much on river leakage. Thus, as the vertical distance from river increases, the habitat degradation for P. euphratica will be more pronounced.
Some species will migrate to higher latitudes or elevations to cope with future climate change [27,28,69]. For example, Yin et al. have predicted that the potential distribution range of Alhagi camelorum will shift northward in the future [70]. In our study, the suitable habitats for P. euphratica would shift to higher latitudes under future climate change scenarios, which is consistent with previous findings (Figure 6) [32]. P. euphratica is expected to lose most of its habitat in high-altitude areas due to climate change, which will force its suitable habitat to shift from riverbanks to lower-elevation central river areas (Figure 4). However, our results contradicted the findings of Zhang et al. [32], possibly due to the inclusion of the environmental variable distance from river (Disriver) in this research, which directly influences the distribution of P. euphratica [75]. The prediction that future habitats of P. euphratica would shift to lower elevations may be attributed to the exacerbation of drought stress in the future. As a result, there may be inadequate groundwater replenishment in areas far away from rivers, and a declining groundwater level may reduce the adaptability of P. euphratica to the changing environment.

4.3. Recommendations for Conservation and Limitations of the Research

This study predicted that climate change would significantly affect the suitable habitat for P. euphratica in the future. As a barrier to desert expansion, P. euphratica plays a vital role in ecosystem protection in arid regions. Our suggestions for addressing the habitat degradation of P. euphratica in the future are as follows: (1) Improve the construction of nature reserves for P. euphratica forests on both sides of rivers. (2) Enhance the monitoring of P. euphratica forests located far away from rivers and establish nurseries to cultivate seedlings. (3) Scientifically allocate water resources and increase the irrigation supply for P. euphratica forests, particularly in areas near desert margins. Although an optimized MaxEnt model was used to simulate the suitable habitat distribution of Chinese P. euphratica currently and in the 2090s, this study had several limitations. Firstly, previous studies have indicated that human disturbances such as roads, ditches, farmlands, and population density have exerted substantial influences on the actual distribution of P. euphratica. Moreover, soil salinity and interspecific interactions also influence P. euphratica [31,76]. Because of the complexity and uncertainty of these environmental variables, they were not considered in our study. Secondly, given the inaccuracies in groundwater level data, we substituted distance from rivers for groundwater level in this study, which may introduce certain limitations to the research findings.

5. Conclusions

This study used an optimized MaxEnt model to predict the potential distribution of P. euphratica in northwestern China and obtained highly accurate results. Under the current climatic scenario, the suitable habitat for P. euphratica was primarily distributed along riverbanks in the arid regions of northwest China, such as the banks of the Tarim River and the Heihe River. The most critical environmental variable influencing its distribution was precipitation of the wettest month (Bio13), followed by distance from river (Disriver). Under four future climate scenarios, the suitable habitat area for P. euphratica will significantly decrease, with the distribution range contracting from bank to river center. Meanwhile, the suitable habitat will undergo internal transformation, leading to a reduced suitability. In response to climate change, the geographical centroid of suitable distribution range will shift northwestward, and suitable distribution areas will migrate to lower elevations closer to rivers. Overall, water availability is the most critical factor limiting the distribution of P. euphratica. These findings can provide further insight into predicting the future distribution of P. euphratica and offer a scientific basis for its conservation and management, which will contribute to combating desertification and maintaining the balance of desert ecosystems.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/f15081288/s1, Figure S1: Spearman correlation analysis of environmental variables. (Note: “Bio1–Bio11” are annual mean temperature, mean diurnal range, isothermality, temperature seasonality, max temperature of the warmest month, min temperature of the coldest month, temperature annual range, mean temperature of the wettest quarter, mean temperature of the driest quarter, mean temperature of the warmest quarter, and mean temperature of the coldest quarter, respectively. “Bio12–Bio19” are annual precipitation, precipitation of the wettest month, precipitation of the driest month, precipitation seasonality, precipitation of the wettest quarter, precipitation of the driest quarter, precipitation of the warmest quarter, and precipitation of the coldest quarter, respectively. “Asc” is aspect; “Ele” is elevation; “Slope” is slope; “Disriver” is distance from river.); Figure S2: The model significance (AUCDIFF), omission rate (OR10), and complexity (ΔAICc) of MaxEnt model with different parameter combinations. L = linear, Q = quadratic, H = hinge, P = product, and T = threshold; Figure S3: Reliability test of the distribution model created for P. euphratica; Table S1: Characteristics of four future emission scenarios.

Author Contributions

Conceptualization, J.L., J.W. and X.L.; methodology, software, and visualization, M.Q., J.H. and Z.L.; investigation and data curation, Y.W., G.L. and M.L.; writing—original draft preparation, X.L.; writing—review and editing, X.L. and J.W. All authors have read and agreed to the published version of the manuscript.

Funding

This work was supported by the National Natural Science Foundation of China (item identification no. 32271703) and Science and Technology Program of Inner Mongolia, China (2023KJHZ0022).

Data Availability Statement

The original contributions presented in the study are included in the article/Supplementary Materials, and further inquiries can be directed to the corresponding author/s.

Conflicts of Interest

The authors declare no conflicts of interest.

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Figure 1. Occurrence points distribution of P. euphratica Olivier.
Figure 1. Occurrence points distribution of P. euphratica Olivier.
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Figure 2. Relationship between presence probability of P. euphratica and dominant environmental factors when the particular factor is considered only. The probability value above the dotted line indicates a favorable effect on P. euphratica growth.
Figure 2. Relationship between presence probability of P. euphratica and dominant environmental factors when the particular factor is considered only. The probability value above the dotted line indicates a favorable effect on P. euphratica growth.
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Figure 3. Potential distribution pattern of P. euphratica under current climate (1971–2000).
Figure 3. Potential distribution pattern of P. euphratica under current climate (1971–2000).
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Figure 4. Potential suitable habitat distribution of P. euphratica under four future climate scenarios.
Figure 4. Potential suitable habitat distribution of P. euphratica under four future climate scenarios.
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Figure 5. Changes in potential suitable habitat for P. euphratica under four future climate scenarios.
Figure 5. Changes in potential suitable habitat for P. euphratica under four future climate scenarios.
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Figure 6. Shifts in geographical centroids of the potential suitable habitat for P. euphratica under the four future climate scenarios.
Figure 6. Shifts in geographical centroids of the potential suitable habitat for P. euphratica under the four future climate scenarios.
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Table 1. Environmental variables used for modeling the suitable habitat for P. euphratica.
Table 1. Environmental variables used for modeling the suitable habitat for P. euphratica.
TypesVariablesDescriptionUnits
Bioclimatic variablesBio1Annual mean temperature°C
Bio2Mean diurnal range°C
Bio3Isothermality ((BIO2/BIO7) (*100))/
Bio4Temperature seasonality/
Bio5Max temperature of the warmest month°C
Bio6Min temperature of the coldest month°C
Bio7Temperature annual range°C
Bio8Mean temperature of the wettest quarter°C
Bio9Mean temperature of the driest quarter°C
Bio10Mean temperature of the warmest quarter°C
Bio11Mean temperature of the coldest quarter°C
Bio12Annual precipitationmm
Bio13Precipitation of the wettest monthmm
Bio14Precipitation of the driest monthmm
Bio15Precipitation seasonality/
Bio16Precipitation of the wettest quartermm
Bio17Precipitation of the driest quartermm
Bio18Precipitation of the warmest quartermm
Bio19Precipitation of the coldest quartermm
Topography variablesElevElevationm
AspAspect°
SlopeSlope°
Water sourcesDisriverDistance from riverm
Note: The selected variables for the final model are shown in bold.
Table 2. Percentage contributions and permutation importance of the bioclimatic variables included in the MaxEnt models for P. euphratica.
Table 2. Percentage contributions and permutation importance of the bioclimatic variables included in the MaxEnt models for P. euphratica.
VariablePercent Contribution/%Permutation Importance/%
Bio1344.170.8
Disriver23.320.4
Bio413.90.8
Bio511.60.6
Bio153.10.9
Asp1.40.7
Slope1.20.7
Elev1.14.9
Bio20.20
Bio60.10.1
Bio1400
Table 3. Areas (104 km2) of suitable habitat for P. euphratica under current and future climate scenarios.
Table 3. Areas (104 km2) of suitable habitat for P. euphratica under current and future climate scenarios.
Suitable ClassesCurrent2090s
SSP126SSP245SSP370SSP585
Poorly suitable21.2323.9324.1222.5221.15
+12.7%+13.57%+6.05%−0.39%
Moderately suitable19.1613.9911.193.853.24
−27.01%−41.62%−79.91%−83.1%
Highly suitable8.741.920.430.060.04
−78.08%−95.04%−99.31%−99.56%
Total49.1439.8435.7426.4324.43
−18.93%−27.28%−46.22%−50.29%
Note: The area (10,000 km2) of suitable habitat was calculated under different climate scenarios. Bold values represent the percentage change in area compared to the current situation. The plus sign (+) represents an increase in the area of suitable habitat, and the minus sign (−) represents a decrease in the area of suitable habitat.
Table 4. Changes in suitable habitat for P. euphratica, distance from river, and elevation under current and future climate scenarios.
Table 4. Changes in suitable habitat for P. euphratica, distance from river, and elevation under current and future climate scenarios.
Current2090s
SSP126SSP245SSP370SSP585
Disriver10,973.289526.398604.127373.026282.92
Elev954.46936.79926.35910.46896.03
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Lei, X.; Qu, M.; Wang, J.; Hou, J.; Wang, Y.; Li, G.; Luo, M.; Li, Z.; Li, J. The Distribution Range of Populus euphratica Oliv. (Salicaceae) Will Decrease Under Future Climate Change in Northwestern China. Forests 2024, 15, 1288. https://doi.org/10.3390/f15081288

AMA Style

Lei X, Qu M, Wang J, Hou J, Wang Y, Li G, Luo M, Li Z, Li J. The Distribution Range of Populus euphratica Oliv. (Salicaceae) Will Decrease Under Future Climate Change in Northwestern China. Forests. 2024; 15(8):1288. https://doi.org/10.3390/f15081288

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Lei, Xun, Mengjun Qu, Jianming Wang, Jihua Hou, Yin Wang, Guanjun Li, Meiwen Luo, Zhijun Li, and Jingwen Li. 2024. "The Distribution Range of Populus euphratica Oliv. (Salicaceae) Will Decrease Under Future Climate Change in Northwestern China" Forests 15, no. 8: 1288. https://doi.org/10.3390/f15081288

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