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Article

Predicting the Effects of Future Climate Change on the Potential Distribution of Eolagurus luteus in Xinjiang

by
Qinghui An
1,
Jianghua Zheng
1,*,
Jingyun Guan
1,2,
Jianguo Wu
3,
Jun Lin
3,
Xifeng Ju
1 and
Rui Wu
1
1
College of Geography and Remote Sensing Sciences, Xinjiang University, Urumqi 830046, China
2
College of Tourism, Xinjiang University of Finance and Economics, Urumqi 830012, China
3
Locust and Rodent Control Headquarters of Xinjiang Uygur Autonomous Region, Urumqi 830001, China
*
Author to whom correspondence should be addressed.
Sustainability 2023, 15(10), 7916; https://doi.org/10.3390/su15107916
Submission received: 9 March 2023 / Revised: 28 April 2023 / Accepted: 10 May 2023 / Published: 11 May 2023
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Abstract

:
Eolagurus luteus (yellow steppe lemming Eolagurus luteus Eversmann, 1840) is a keystone species in the desert steppe of northern Xinjiang, one of the regions most affected by global climate change. Their behavior of eating grassland vegetation and digging holes has resulted in the reduction of grassland vegetation and soil erosion in northern Xinjiang, which has seriously affected the ecological balance of the grassland in northern Xinjiang, and pathogens carried by E. luteus pose a great threat to human health. Climate change exacerbates the uncertainty of the outbreak of E. luteus. Predicting the suitable habitat area of this species under climate change scenarios will help farmers and herders deal with the potential threat of an E. luteus outbreak. In this study, 117 actual occurrence points of E. luteus were used, and 24 climate models, 6 soil factors and 3 topographic factors from the Coupled Model Intercomparison Project (CMIP6) were taken into account. Combining the MaxEnt model and the overlay analysis function of ArcGIS software, the potential geographic distribution of E. luteus in 2030 and 2050 for the green development path (SSP126), the intermediate development path (SSP245), the regional competition path (SSP370), and the high development path (SSP585) was predicted. The change trend of the suitable area and distribution pattern of E. luteus in Xinjiang under future climate conditions was analyzed, and the main environmental factors affecting the distribution of E. luteus are discussed. The results show that the average area under curve (AUC) and true skill statistics (TSS) of the MaxEnt model are 0.993 and 0.8816, respectively, indicating that the model has a good prediction effect. The analysis of environmental factors showed that the main environmental factors affecting the potential geographical distribution of E. luteus are average annual temperature, isotherm, average temperature in the wettest quarter, average temperature in the driest quarter, and precipitation variation coefficient. With the increase of radiation intensity and time, the suitable areas of E. luteus will continue to decrease. Especially in the 2050s under the SSP585 scenario, the middle and high suitable areas will decrease by 2.58 × 104 km2 and 1.52 × 104 km2, respectively. Although the potential habitat area of E. luteus is shrinking, the future threat of E. luteus to grassland ecological security and human health should not be underestimated due to ecological adaptation of the community and the frequent occurrence of extreme weather. Therefore, studying changes in the potential geographic distribution of E. luteus under climate change scenarios and developing appropriate monitoring programs are of great importance for grassland ecological security and human health. This study fills in the gaps in the study of the potential geographical distribution of E. luteus and provides methodological and literature support for the study of the potential geographical distribution of other rodents.

1. Introduction

Rodent damage refers to the harm caused by a high number of rodents to the local ecosystem and human society [1]. Rodents have a very wide distribution, spanning polar mountains, desert, forest, and grassland, including rural and urban areas [2]. Owing to their rapid reproduction rate, the harm caused by rodents is widespread and severe [3]. First, rodents are hosts to various viruses and pathogens that can transmit diseases, which pose a threat to human health [4,5]. Second, rodents are mostly family cluster animals living in underground burrow systems, which involve complex networks of tunnels and rat holes. The depth of this system can reach several meters, resulting in a large amount of soil erosion, which can ultimately contribute to the overall degradation of an ecosystem [2,6,7]. Therefore, understanding the potential distribution and changes in rodent species is crucial for monitoring and controlling rodent pests, particularly in areas where the environment is already very fragile.
Biological species play a crucial role in an ecosystem, with their distribution range and quantity affecting the stability and sustainable development of the whole ecosystem [8,9,10]. Global biodiversity has been significantly affected by the climate change due to global warming, resulting in rapid changes to the number and spatial distribution of biological species, including rats [11,12,13]. Species distribution models (SDMs) are currently the most popular method for predicting the potential areas of suitability for species and can predict the impact of climate change on the geographical distribution of species [14]. Among them, the maximum entropy (MaxEnt) model is considered to have the highest stability, scalability, and accuracy among all SDM tools [15]. Mohammadi et al. [1] used the MaxEnt model to simulate the potential distribution of Blanford’s Jerboa Jaculus blanfordi and the Arabian Jerboa Jaculus loftusi (Mammalia: Ro-dentia) under current climate change scenarios, and how the distribution could change in the future. Similarly, Guan et al. [16] used the MaxEnt model to simulate and evaluate the habitat suitability of desert locusts (Schistocerca gregaria, 1775), and Qi et al. [17] analyzed the spatiotemporal dynamic changes in plateau rodents. Bizhanova et al. [18] combined the MaxEnt model with other environmental factors to simulate and evaluate the habitat of the Turkestan lynx (Lynx lynx isabellina Blyth, 1847). These studies provide a scientific reference basis for biodisaster prevention and management; however, as only a few typical climate models are considered in the research process, the uncertainty of the results is high.
The damage caused by E. luteus could lead to the death of a large volume of high-quality herbage, the reduction of grassland vegetation, and acceleration of the process of grassland degradation and desertification. In addition, E. luteus have a strong ability to dig burrows, creating a dense network of rat paths and holes in the grassland and causing a large area of soil and water loss. The grassland area of Xinjiang is 51.3 × 104 km2, accounting for 29% of Xinjiang’s land area. Studies have revealed that there are more than 70 species of rodent pests widely distributed in Xinjiang, among which the predominant species is E. luteus. The infestation area of E. luteus is approximately 3.3 × 104 km2, with approximately 1.5 × 104 km2 being severely infested. In addition, as an important part of the Xinjiang grassland ecosystem, E. luteus has special significance for maintaining grassland ecological balance and species diversification [19,20]. The dispersal potential of E. luteus depends on their climatic niche and ecological barriers, which may change with the warming of the climate and increasing frequency of storms. Studies have revealed that Xinjiang has a fragile environment and is extremely sensitive to climate change [21,22]. Therefore, climate change poses a great challenge to the monitoring and control of E. luteus in this area for preventing rodent epidemics. At present, most studies on E. luteus focus on ecological characteristics such as population number, distribution, habitat selection, and activity rules [20,23,24,25]. There have been few simulations of the potential habitat of E. luteus, particularly under future climate change scenarios. This study collected and screened geographical distribution information of E. luteus, and combined with relevant environmental variable data, the MaxEnt model was used to predict the potential geographical distribution of E. luteus in Xinjiang, under multiple environmental variables and based on different climate change scenarios. This study aimed to (1) determine the most important environmental variables affecting the potential geographical distribution of E. luteus in Xinjiang, (2) analyze the potential geographical distribution of E. luteus in Xinjiang, and (3) examine the changes in the potential geographical distribution of E. luteus in Xinjiang under different climate change scenarios in the future. The results of the study will help in identifying suitable habitats for E. luteus for now and in the future and provide a scientific reference basis for planning the spread of rodent control.

2. Materials and Methods

2.1. Study Area

Xinjiang is located in the hinterland of Eurasia (73°40′–96°23′ E, 34°25′–49°10′ N) with an area of 166 × 104 km2 (Figure 1). It borders eight countries, namely Mongolia, the Russian Federation, Kazakhstan, Kyrgyzstan, Tajikistan, Afghanistan, Pakistan, and India. In China, it adjoins Gansu, Qinghai, Tibet, and other provinces [19]. Xinjiang has a complex and diverse regional landform, owing to its location. From south to north, the Kunlun Mountains, Tarim Basin, Tianshan Mountains, Junggar Basin, and Altai Mountains form a unique geographical environment feature of “three−mountains−sandwich−two−basins” [26]. With this, it lies deep inland, which creates spatial differences in the distribution of climate within the region. Specifically, Northern Xinjiang, belonging to a temperate continental arid climate, receives more precipitation than that in Southern Xinjiang.

2.2. Species Occurrence Data of E. luteus

The species occurrence data, which includes the distribution range, number configuration, distribution characteristics of rat burrows, and GPS points of the spatial occurrence of E. luteus, were obtained based on the data collected by the Xinjiang Locust Control and Rodent Eradication Command Office. In total, 168 primary data points located in the Hami, Altay, and Tacheng districts were collected during the field survey. The influence of the sampling deviation used in the MaxEnt model was minimized by processing the data generation points during spatial filtering to better model the performance and obtain data accuracy [16]. SDMs tools were then used to build a 5 km × 5 km grid to extract the data from the 168 survey points, of which 117 species occurrence points were recorded (Figure 1).

2.3. Environment Variable Data

This study selected 28 environmental factor variables, of which three were topographic factors, nineteen were bioclimatic factors, and six were soil factors. The topographic variables (Table 1) had a spatial resolution of 2.5′; the bioclimatic factor variables were obtained from the WorldClim database (version 2.1, https://www.worldclim.org/, accessed on 10 April 2022) based on its detailed description [27], while the soil factor variables were obtained from the soil database of China (http://vdb3.soil.csdb.cn/, accessed on 13 April 2022). Furthermore, in this study, the Shared Socioeconomic Pathways (SSPs) tool proposed by the Intergovernmental Panel on Climate Change (IPCC) in 2010 was used, as it is a powerful tool for describing global socio-economic development scenarios. Specifically, SSPS126 (green development path), SSPS245 (intermediate development path), SSPS370 (regional competition path), and SSPS585 (high development path) were selected, in which 24 climate models for the base period (1970–2000) and future scenarios (2021–2040 and 2040–2060) were obtained. All of the variables were resampled to 5 km.
This study conducted Spearman decorrelation processing on all variables included in the prediction. According to Green, it is recommended that the correlation coefficient of r = 0.7 is selected as the optimal threshold [28]. Hence, environmental variables with a correlation coefficient of |r| < 0.7 were screened out, whereas variables with a lower percentage contribution to the MaxEnt model were discarded when the correlation coefficients was |r| > 0.7, whereas those with a higher percentage contribution were retained. With this, 16 variables were screened out for subsequent analysis. The variance inflation factor was calculated for the screening variables and the results were all less than 5. Finally, 16 variables were selected for model prediction.

2.4. Modeling Process

The MaxEnt3.4.4 model was used to build the potential E. luteus geographic distribution [29], in which 75% and 25% of the total occurrence point dataset were selected as the training and validation datasets of the model, respectively [30]. In addition, we set 20 sets of regularization multipliers (0.5–10, with an interval of 0.5), and six different feature combinations (L, LQ, H, LQH, LQHP, LQHPT; where L = linear, Q = quadratic, H = hinge, P = product, T = threshold) to optimize the MaxEnt model. The above process produced a total of 120 parameter combinations [31]. A bootstrap data segmentation method was then employed to repeat the modeling 10 times with 1000 iterations [32]. The output was in a logistic format, in which it was capable of estimating the probability value of species existence, using values ranging from 0 to 1 [33]. Finally, the natural breakpoint method was applied in Arc Gis10.2 and was used to reclassify the results, in which habitat areas were divided according to four suitability types: non-suitable, low suitable, medium suitable, and highly suitable areas [34]. At the same time, it is assumed that the soil and topographic variables used in the future scenario are consistent with those used in the current scenario, and the bioclimatic variables were derived from the CMIP model [16,35]. The probability of future potential suitable areas was derived from the average of 24 General Circulation Models (GCMs).

2.5. Model Evaluation

In this study, the average area under curve (AUC) and true skill statistics (TSS) were used to evaluate the modeling results. The former is a threshold-independent statistic used to distinguish classes and evaluate the performance of the model [36]. Its resulting value can be divided into five grades: fail (0–0.6), poor (0.6–0.7), fair (0.7–0.8), good (0.8–0.9), and excellent (0.9–1) [35]. However, AUC may sometimes result in unreliable outputs; hence, the latter may be simultaneously used [37]. In this study, the threshold recommended by the natural discontinuity method was adopted to calculate the TSS, where as the value approaches one, the higher the prediction accuracy. Meanwhile, prediction failure is indicated by values below 0 [38].
Majority voting was introduced, as different climate models have various effects on the prediction accuracy of suitable areas of E. luteus, to further reduce the uncertainty of the prediction results under various sharing economy paths [39]. The prediction results each have a corresponding scenario and period, and different modes were superimposed for analysis. Here, higher votes indicate the certainty and accuracy of the prediction results, and vice versa.

3. Results

3.1. Model Evaluation and Contribution of Variables

This study found that when the feature combination and regularization multiplier of the model were LQHPT and 0.5, respectively, the model ran 10 times, in which the AUC and TSS values were 0.993 and 0.882, respectively, indicating that the model obtained results with high accuracy and excellent performance.

3.2. Potential Geographical Distribution of E. luteus in Xinjiang under Current Climatic Conditions

According to the habitat suitability distribution map under the various climate scenarios (Figure 2), highly suitable areas covered 1.85 × 104 km2, or approximately 1.1% of the total Xinjiang area. These were mainly distributed in the northwestern portion of Hami Prefecture, eastern and central portions of Changji Hui Autonomous Prefecture, central portion of Tacheng Prefecture, northeastern portion of Tacheng Prefecture, and northwestern and eastern portions of Altay Prefecture. Meanwhile, moderately suitable areas covered 3.29 × 104 km2, accounting for approximately 2%, of the total area. These areas were distributed in the eastern and northern portions of the Altay region, in addition to the central portions of Tacheng Prefecture. Low-suitability areas covered 5.70 × 104 km2, accounting for 3.4%, with the central and northern portions of Tacheng Prefecture, western portions of Changji Hui Autonomous Prefecture, and central and southern portions of the Altay region as its main distribution areas. Lastly, 156.11 × 104 km2 were unsuitable areas, which accounted for approximately 93.4% of the total area.

3.3. Potential Suitable Areas for E. luteus in Xinjiang under Future Climate Scenarios

Figure 3 shows the potential suitable areas of E. luteus under the four climate change scenarios in two time periods. Compared with current climate models, low-suitability areas tend to expand in future climate scenarios, especially in the single-scenario mode of SSP126 in the 2030s. However, moderately and highly suitable areas showed continuous decreases by 4.14 × 104 km2 and 1.50 × 104 km2, respectively. In the 2050 time period, both the moderately and highly suitable areas showed consistent decreases to 4.81 × 104 km2 and 1.70 × 104 km2, respectively (Figure 3a,b and Figure 4). Moderately suitable areas were predicted to decrease to 4.51 × 104 km2 in the 2030s based on the SSP585 scenario, while highly suitable areas will decrease to 1.71 × 104 km2. The 2050 time period similarly showed decreasing trends in habitable areas, in which moderately suitable areas were predicted to decrease by 4.86 × 104 km2, whereas highly suitable areas were predicted to decrease by 1.78 × 104 km2. The areas of the two suitable areas showed a gradually decreasing trend (Figure 3g,h and Figure 4), which may be attributed to increases of solar radiation and time. This trend is also expected to become more apparent, which can affect the survival and distribution of E. luteus.

3.4. Acreage Change of Potential Habitat of E. luteus in Xinjiang under Future Climate Change Scenarios

For the 2030 and 2050 time periods, highly suitable areas were predicted to be reduced by 1.50 × 104 km2–1.71 × 104 km2 and 1.70 × 104 km2–1.78 × 104 km2, respectively. Specifically, these reductions will occur in the northern and northwestern Hami region, southern portions of Changji Hui Autonomous Prefecture, southern and northeastern portions of the Tacheng region, including its eastern border, and eastern and western portions of the Altay region (Figure 5q–x, Table 2). Meanwhile, highly suitable areas will increase by only 0.41 × 104 km2 –0.24 × 104 km2 and 0.50 × 104 km2–0.26 × 104 km2 in 2030 and 2050, respectively, which will mainly occur in the central portion of the Tacheng region and northern part of the Altay region.
Moderately suitable areas are expected to decrease by 4.14 × 104 km2–4.51 × 104 km2 and 4.81 × 104 km2–4.86 × 104 km2 in 2030 and 2050, respectively, in which varying degrees in reductions were observed in the central portion of the Hami region, southern portion of Changji Hui Autonomous Prefecture, central and southeastern portions of the Tacheng region, and southeastern and northwestern portions of the Altay region. Meanwhile, these areas will increase by 1.17 × 104 km2–1.27 × 104 km2 and 0.59 × 104 km2–0.73 × 104 km2 in the 2030s and the 2050s, respectively, under current climate change conditions, which may be attributed to the increases in highly suitable areas (Figure 5i–p, Table 2).
In the 2030s, low-suitability areas will increase by 4.07 × 104 km2–6.64 × 104 km2, reaching 4.28 × 104 km2–6.25 × 104 km2 in 2050, which may be attributed to their conversion from moderately and highly suitable areas. Decreases of these areas were also observed to be distributed areas with low suitability (Figure 5a–h, Table 2). It is inferred that the increasing intensity and duration of solar radiation will result in significant reductions of habitable areas of E. luteus in the middle of the 21st century.

4. Discussion

4.1. Effects of Environmental Factors on Potential Distribution of E. luteus in Xinjiang

Determining the contribution rates of the studied factors is essential, as it impacts the prediction results and accuracy (Table 3). Among all variables, five obtained the highest ranking based on their contribution rate, in the following order: isotherm (Bio_3, 32.7%), annual temperature (Bio_1, 10.5 °C), average temperature in the wettest quarter (Bio_8, 8.9 °C), cumulative contribution rate of the driest quarterly mean temperature (Bio_9, 5.4 °C), and precipitation variation coefficient (Bio_15, 5.3%), for a total of 62.8%. Based on the analysis of the environmental preferences of E. luteus in these five resulting variables, the results show that the optimum temperature range of Bio_1 was 0–9 °C, with a threshold range of 20–28% for Bio_3, 14–23 °C for Bio_8, −18–7 °C for Bio_9, and 24–77% for Bio_15 (Figure 6).
Rodent habitats are typically dynamic, and therefore, rodents need ideal climatic conditions at different stages of their growth cycle. Our results show that one of the most important factors affecting the survival of E. luteus is temperature [40]. Several studies have revealed that temperature directly affects the activity pattern, reproductive behavior, litter number, and survival rate of rodents [41]. Average air temperature also affects rodent density. When the average monthly temperature of the month did not exceed 21 °C, the population density of rodents increased with the increase in temperature. However, when the average temperature exceeded 21 °C, the population density was negatively correlated with temperature [42]. High temperature directly inhibits the ground reproduction rate of mice, as under high temperatures, females will abort. Extremely low temperature inhibits spermatogenesis in male mice [43]. When the temperature is between 10 and 25 °C, the reproduction rate of mice will reach a peak, with 17 °C being the optimum [44]. As Xinjiang has a typical temperate continental climate, the winter is cold, and therefore, E. luteus breeds during the summer [45]. Our results show that the average temperature of the wettest season suitable for the habitat of E. luteus is 14–23 °C. This result is consistent with existing conclusions and experience.
The coefficient of variation of precipitation is another important factor affecting the survival of E. luteus. Evidently, there was a significant negative correlation between rainfall and rodent population density [41]. When the rainfall was greater than 90 mm, the population density of rodents decreased with the increase in rainfall, and vice versa [46]. E. luteus is mostly distributed in the desert steppe area in the northern Tianshan Mountains of Xinjiang, where the annual precipitation is generally less than 90 mm [25]. The change in rainfall affected the population density of rodents primarily by affecting their habitat environment, reproductive rate, and food resource richness [41,46]. On the one hand, increases in rainfall will reduce the activity range of rat populations and lead to the flooding of rat holes, resulting in the death of a large number of young rats [42]. On the other hand, decreases in rainfall will lead to poor growth of vegetation, worsen the food conditions of rodents, and indirectly lead to increased mortality of rodents. Therefore, the above studies further explain that precipitation changes are correlated with the population distribution density of E. luteus.

4.2. Effects of Environmental Factors on Potential Habitat Areas of the E. luteus

Based on the current climate model conditions and the prediction of multiple GCMS in the CMIP6 model, the suitable habitat of E. luteus will significantly decrease in area with an increase in radiative forcing level and time. Xinjiang is one of the driest regions in the world, with an extremely fragile ecosystem that is vulnerable to extreme climatic events [47,48]. Numerous studies have revealed that the frequency and intensity of extreme climatic events in Xinjiang have been on the rise since the 1950s [49,50,51]. Moreover, Xinjiang is experiencing a climate transition from warm and dry to warm and wet, which is known as the climate “warming and wetting phenomenon” [22,52]. Previous studies have revealed that by the middle of this century, the temperature in Xinjiang will increase by 1.5−2 °C, with the hinterland of the Junggar Basin and Hami Basin exhibiting a relatively high increase in temperature. By the end of this century, the temperature in Xinjiang will increase between 4 °C and 6 °C. The regions where the temperature increased were consistent with the regions where the moderately suitable habitat decreased in spatial distribution. The increase in the precipitation is expected to be between 10% and 25% by the middle of the century and over 25% by its end [53]. The abovementioned areas with considerable climatic changes correspond to the areas where the potential habitat of E. luteus is reduced (Hami area, Changji Hui Autonomous Prefecture, Tacheng area, and Altay area). Continued warming and precipitation have destroyed E. luteus habitats, such as those used for breeding and foraging, resulting in a shrinking of medium- and high-suitability areas.
Climate change will reduce the potentially suitable habitat area of Xinjiang E. luteus. This may have the following two main effects: first, the reduction of the potentially suitable habitat area for E. luteus means that the damage range of E. luteus to the grassland ecology is shrinking. This change is beneficial to the monitoring and control work of E. luteus. Second, as an integral part of the grassland ecosystem, E. luteus is of great significance to maintaining the ecological balance. The potential threat of climate change to the habitat of E. luteus will aggravate its spatial distribution restriction, which may affect the ecological balance of regional grasslands. Although the potential habitat area of E. luteus is decreasing, the threat to the grassland ecological balance caused by the destruction of grassland vegetation by E. luteus and soil erosion caused by burrow excavation should not be underestimated [10,31,54].

4.3. Uncertainty and Prospects

In this study, the uncertainty regarding the future climate affected the prediction accuracy of potential suitable areas for E. luteus, and the prediction results might confuse decision makers. Therefore, the majority voting method was adopted in this study to further reduce the uncertainty and provide a better reference for the monitoring, early warning, and prevention of E. luteus. Figure 7 shows consensus maps of potential suitable areas for E. luteus in the 2030s and 2050s under 24 GCM projections. The higher the consensus number, the lower the uncertainty, and vice versa. In general, the uncertainty of low-suitability and high-suitability areas was lower, whereas that of moderate suitability areas was higher. In particular, under the SSP585 scenario, the uncertainty of low-suitability areas in the central Altay region and central Tacheng region is high in the 2050s (Figure 7h). Among the moderately suitable areas, the areas with high uncertainty primarily appeared south of the Altay region, south of the Tacheng region, west of the Hami region, and south of Changji Hui Autonomous Prefecture (Figure 7i–p). In the high-suitable area, the northwest of the Altay region, the north of the Tacheng region, and the south of the Tacheng region had high uncertainty (Figure 7q–x). In general, the uncertainty of the potential suitable area of E. luteus increased with the increase in time and radiative forcing (Figure 7a,h,i,p,q,x).
In this study, the potential habitats of E. luteus were found to be influenced by other factors in addition to climate, topography, and soil. However, this study has a limitation that the potential suitable areas were predicted rather than actual habitats that can be occupied in the future. Therefore, the forecast results might be overestimated.
The community composition and succession of E. luteus remain highly uncertain. Hence, future studies should combine migration patterns with climate change, biological factors, and anthropogenic factors to improve the prediction accuracy of suitable habitats for the species.

5. Conclusions

In this study, a species distribution model was successfully developed using the MaxEnt model, which was used to analyze the effects of current and future climate change scenarios on potentially suitable habitat areas of E. luteus, considering the climate, soil, and topographic factors. Its potential geographic distribution was predicted using the CMIP6 model under current and future scenarios and four paths to the sharing economy. The results indicate that climate change will affect its potential geographical distribution in the 2030s and 2050s. As solar radiation intensity and duration increases, moderately suitable areas are expected to decrease gradually in 2050 based on the SSP585 scenario model. Similarly, highly suitable areas also show continuous reduction. Although the potential habitat areas of E. luteus are predicted to decrease, it can still proliferate in population, owing to its ecological adaptation. In turn, it may continue to threaten grassland ecological security. Hence, this study may provide valuable insights on the monitoring and control of potential E. luteus infestation.

Author Contributions

Conceptualization, Q.A.; Data curation, R.W.; Formal analysis, J.L.; Methodology, J.G.; Software, X.J.; Supervision, J.W.; Visualization, Q.A.; Writing—original draft, Q.A.; Writing—review and editing, J.Z. All authors have read and agreed to the published version of the manuscript.

Funding

This study was jointly funded by the Xinjiang Tianshan Cedar Project (2020XS04) and the Xinjiang Grassland Biohazard Remote Sensing Monitoring Project (202105140008).

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

Not applicable.

Conflicts of Interest

The authors declare no conflict of interest.

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Figure 1. Overview of the study area.
Figure 1. Overview of the study area.
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Figure 2. Geographical distribution of E. luteus under current climatic conditions.
Figure 2. Geographical distribution of E. luteus under current climatic conditions.
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Figure 3. Geographical distribution of E. luteus under future climate conditions.
Figure 3. Geographical distribution of E. luteus under future climate conditions.
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Figure 4. Area of suitable habitat area of E. luteus under different scenarios in 2030 and 2050.
Figure 4. Area of suitable habitat area of E. luteus under different scenarios in 2030 and 2050.
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Figure 5. Changes of potential suitability zones in future and current suitability zones of E. luteus under different climate patterns. In Figure 5, (a–d) is the spatial distribution changes of low-suitability areas in four paths respectively in the 2030s. (e–h) is the spatial distribution changes of the low-suitability areas in the four paths respectively in the 2050s. (i–l) is the spatial distribution change of the middle suitable area in the four paths respectively in the 2030s. (m–p) is the spatial distribution change of the middle suitable area in the four paths respectively in the 2050s. (q–t) is the spatial distribution changes of the high suitability areas in the four paths respectively in the 2030s. (u–x) is the spatial distribution change of the high suitability area in the four paths respectively in the 2050s.
Figure 5. Changes of potential suitability zones in future and current suitability zones of E. luteus under different climate patterns. In Figure 5, (a–d) is the spatial distribution changes of low-suitability areas in four paths respectively in the 2030s. (e–h) is the spatial distribution changes of the low-suitability areas in the four paths respectively in the 2050s. (i–l) is the spatial distribution change of the middle suitable area in the four paths respectively in the 2030s. (m–p) is the spatial distribution change of the middle suitable area in the four paths respectively in the 2050s. (q–t) is the spatial distribution changes of the high suitability areas in the four paths respectively in the 2030s. (u–x) is the spatial distribution change of the high suitability area in the four paths respectively in the 2050s.
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Figure 6. Response curves for the five most important environmental variables. In Figure 6, (a) is the optimal annual mean temperature range for the survival of the E. luteus. (b) is the optimum isothermic range for the survival of E. luteus. (c) is the range of mean temperature variation in the wettest season in which the E. luteus lived. (d) is the variation range of the driest seasonal mean temperature of the E. luteus. (e) is the variation range of precipitation variation coefficient of survival of E. luteus.
Figure 6. Response curves for the five most important environmental variables. In Figure 6, (a) is the optimal annual mean temperature range for the survival of the E. luteus. (b) is the optimum isothermic range for the survival of E. luteus. (c) is the range of mean temperature variation in the wettest season in which the E. luteus lived. (d) is the variation range of the driest seasonal mean temperature of the E. luteus. (e) is the variation range of precipitation variation coefficient of survival of E. luteus.
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Figure 7. Voting consensus diagram of potential suitable areas of E. luteus in the 2030s and 2050s based on 24 GCMs climate data. The value (1–24) represents the number of the voting consensus. The larger the value, the higher the consistency of the simulation results. For example, a pixel value of twenty-four indicates that all simulation results agree that the pixel is a potential habitat for E. luteus. In Figure 7, (a–d) is the voting consensus diagram of the four paths in the low suitability area in the 2030s. (e–h) is the voting consensus map of low suitability areas in the next four paths in the 2050s. (i–l) is the voting consensus map of the four paths in the 2030s for the middle suitable area. (m–p) is the voting consensus map of the four paths in the mid 2050s. (q–t) is the voting consensus map in the four paths of high suitability areas under the 2030s. (u–x) is the voting consensus map of the four paths of high suitability areas in the 2050s.
Figure 7. Voting consensus diagram of potential suitable areas of E. luteus in the 2030s and 2050s based on 24 GCMs climate data. The value (1–24) represents the number of the voting consensus. The larger the value, the higher the consistency of the simulation results. For example, a pixel value of twenty-four indicates that all simulation results agree that the pixel is a potential habitat for E. luteus. In Figure 7, (a–d) is the voting consensus diagram of the four paths in the low suitability area in the 2030s. (e–h) is the voting consensus map of low suitability areas in the next four paths in the 2050s. (i–l) is the voting consensus map of the four paths in the 2030s for the middle suitable area. (m–p) is the voting consensus map of the four paths in the mid 2050s. (q–t) is the voting consensus map in the four paths of high suitability areas under the 2030s. (u–x) is the voting consensus map of the four paths of high suitability areas in the 2050s.
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Table
Table 1. Climate variables used in the study.
Table 1. Climate variables used in the study.
CategoryCodeDescription
Bioclimatic variablesBio_1Annual mean temperature
Bio_2Mean diurnal range (mean of monthly (max temp–min temp))
Bio_3Isothermality (bio02/bio07)
Bio_4Temperature seasonality (Standard deviation × 100)
Bio_5Max temperature of warmest month
Bio_6Min temperature of coldest month
Bio_7Temperature annual range (bio05-bio06)
Bio_8Mean temperature of wettest quarter
Bio_9Mean temperature of driest quarter
Bio_10Mean temperature of warmest quarter
Bio_11Mean temperature of coldest quarter
Bio_12Annual precipitation
Bio_13Precipitation of wettest month
Bio_14Precipitation of driest month
Bio_15Precipitation seasonality
Bio_16Precipitation of wettest quarter
Bio_17Precipitation of driest quarter
Bio_18Precipitation of warmest quarter
Bio_19Precipitation of coldest quarter
Topography variableAltitudeTopographic elevation
SlopeThe degree of steepness of the surface element
AspectThe direction the slope faces
Soil factors of variablesT_BSTopsoil basic saturation
T_ESPTopsoil exchangeable sodium salt
T_OCTopsoil organic carbon content
T_PH_H2OTopsoil pH value
T_SANDTopsoil sand fraction
T_TEXTURETopsoil texture
Table
Table 2. Comparison of future potential suitability areas and current suitability areas of E. luteus under different climate patterns.
Table 2. Comparison of future potential suitability areas and current suitability areas of E. luteus under different climate patterns.
ScenariosTimeLow Suitable Area
(×104 km2)		Moderately Suitable Area
(×104 km2)		Highly Suitable Area
(×104 km2)
GainLossUnchangedGainLossUnchangedGainLossUnchanged
SSP1262030s4.076.952.901.174.140.820.411.500.35
2050s4.287.801.220.594.810.140.501.700.15
SSP2452030s5.217.732.790.584.381.720.471.640.21
2050s6.404.730.980.564.830.130.501.730.12
SSP3702030s5.828.112.581.664.290.670.431.650.21
2050s7.165.631.740.934.810.150.371.750.11
SSP5852030s6.648.872.251.274.510.440.241.710.14
2050s6.256.090.950.734.860.100.261.780.08
Table
Table 3. Contribution and permutation importance of the 16 studied environmental variables.
Table 3. Contribution and permutation importance of the 16 studied environmental variables.
VariableDetailsPercent Contribution
Bio_1Annual mean temperature10.5
Bio_2Mean diurnal range (mean of monthly (max temp–min temp))4.8
Bio_3Isothermality (bio02/bio07)32.7
Bio_5Max temperature of warmest month4
Bio_7Temperature annual range (bio05-bio06)2.2
Bio_8Mean temperature of wettest quarter8.9
Bio_9Mean temperature of driest quarter5.4
Bio_13Precipitation of wettest month2.4
Bio_15Precipitation seasonality5.3
Bio_16Precipitation of wettest quarter2.5
Bio_17Precipitation of driest quarter3.3
AltitudeAltitude3.5
SlopeSlope5.5
AspectAspect3.7
T_OCOrganic carbon content1.7
T_PH_H2Oph3.5
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An, Q.; Zheng, J.; Guan, J.; Wu, J.; Lin, J.; Ju, X.; Wu, R. Predicting the Effects of Future Climate Change on the Potential Distribution of Eolagurus luteus in Xinjiang. Sustainability 2023, 15, 7916. https://doi.org/10.3390/su15107916

AMA Style

An Q, Zheng J, Guan J, Wu J, Lin J, Ju X, Wu R. Predicting the Effects of Future Climate Change on the Potential Distribution of Eolagurus luteus in Xinjiang. Sustainability. 2023; 15(10):7916. https://doi.org/10.3390/su15107916

Chicago/Turabian Style

An, Qinghui, Jianghua Zheng, Jingyun Guan, Jianguo Wu, Jun Lin, Xifeng Ju, and Rui Wu. 2023. "Predicting the Effects of Future Climate Change on the Potential Distribution of Eolagurus luteus in Xinjiang" Sustainability 15, no. 10: 7916. https://doi.org/10.3390/su15107916

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