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Article

MaxEnt Modeling and the Impact of Climate Change on Pistacia chinensis Bunge Habitat Suitability Variations in China

by
Chaohan Xu
,
Lei Zhang
,
Keliang Zhang
* and
Jun Tao
College of Horticulture and Landscape Architecture, Yangzhou University, Yangzhou 225009, China
*
Author to whom correspondence should be addressed.
Forests 2023, 14(8), 1579; https://doi.org/10.3390/f14081579
Submission received: 30 June 2023 / Revised: 24 July 2023 / Accepted: 28 July 2023 / Published: 2 August 2023
(This article belongs to the Section Forest Meteorology and Climate Change)
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Abstract

:
Climate change impacts the world’s biota, creating a critical issue for scientists, conservationists, and decision makers. Pistacia chinensis Bunge (Anacardiaceae) is an economical importantly species with strong drought resistance. Nevertheless, the characteristics of habitat distribution and the major eco-environmental variables affecting its suitability are poorly understood. By using 365 occurrence records along with 51 environmental factors, present and future suitable habitats were estimated using MaxEnt modeling, and the important environmental variables affecting its geographical distribution were analyzed. The results indicate that water vapor pressure, precipitation of wettest quarter, normalized difference vegetation index, and isothermality were the most influential environmental factors determining the existence of P. chinensis. In future climate change, MaxEnt predicted that inappropriate habitats of P. chinensis show a decreasing trend, whereas moderately and highly appropriate habitat areas exhibited an increasing trend. Furthermore, under this climate change scenario, the suitable habitat will geographically expand to higher latitude and altitude. Our results might be applied in a variety of contexts, including discovering previously unreported P. chinensis places where it may appear in the future, or possible areas where the species could be cultivated, thus contributing to the preservation and protection of this species.

1. Introduction

One of the largest challenges currently facing ecologists is knowing how to accurately predict the ways in which various types of ongoing environmental change that are occurring simultaneously will affect the geographical distribution of species, as well as the patterns of biodiversity on a global scale and the ecosystem services biodiversity provides to humans [1,2,3,4]. Many dramatic shifts in the distributions of species, as well as species extinctions, are occurring, especially in vulnerable or fragmented ecosystems [4,5]. Many studies have predicted changes in the environment, such as changing patterns of precipitation and fluctuations in temperature that will certainly affect habitat suitability in various ecosystems in the future. When addressing these potential effects, it is crucial to determine how various taxa will respond to future climate change to develop successful biodiversity management strategies [6]. This knowledge will not only help managers address any potential threats that might change the distribution of species [5,7], but can also help land managers to develop strategies related to the wise conservation of resources, and might include the preparation and storage of germplasm [8,9,10,11].
When distribution data are scarce, species distribution modeling (SDM) can be used to assess ecological needs and determine a species’ future biogeographic and regional ecological distribution range [12,13,14,15]. Various SDMs, such as the genetic algorithm for rule-set production (GARP) [4,16,17], CLIMEX [18], DOMIN [19], MaxEnt [4,20,21], ecological niche factor analyses (ENFA) [22], and the bioclimate envelope [23] have been used to predict the ecological needs and responses of species as well as future areas of distribution. Among those models, MaxEnt has proven to be one of the most reliable habitat-modeling methods in terms of predictive capability [24]. The fundamental idea behind MaxEnt is to select the probability distribution that maximizes entropy while satisfying the given constraints [12]. It seeks to strike a balance between fitting the available data and not making assumptions beyond what is necessary. One of the key advantages of MaxEnt is its ability to handle presence-only data, making it valuable in situations where absence data are not readily available or are difficult to obtain. It leverages the information from the presence data and the background (environmental) data to generate probability distributions that reflect the potential spatial distribution of the species [13].
Chinese pistache (Pistacia chinensis Bunge, Anacardiaceae) is a small-to-medium-sized tree that typically grows to about 20 m in height [25]. The species is distributed in central and eastern China, including provinces such as Anhui, Fujian, Guangdong, Guangxi, Guizhou, Shaanxi, and others. This broad distribution encompasses various ecological zones, including hill and mountain forests on rocky soil [25]. The tree can tolerate extreme weather and poor soil conditions [26,27]. Because it is drought-tolerant and can tolerate harsh circumstances, this species is a popular option for urban street trees [28,29]. Furthermore, the wood of this species is extensively used for furniture manufacture and generates a yellow dye, and the oil from the seeds is used to make biodiesel [26]. Given the ecological importance and economic relevance of P. chinensis, it becomes imperative to assess how changing climate conditions might influence the species’ suitable habitat area. Such information will not only provide valuable insights into its ecological response to varying environmental factors but also contribute to effective management planning and conservation strategies for this valuable tree species in the diverse landscapes of China.
Based on a vast array of presence data of P. chinensis and environmental data, we predicted the present and future suitable habitat of P. chinensis using MaxEnt modeling. The primary aims of this study include: (1) modeling the potential geographical location for this species under current and further climate change scenarios; (2) identifying important environmental factors that shape its distribution; and (3) using projected future climate conditions to quantify the changes in its geographical ranges in a way that will help researchers to create suitable habitats.

2. Materials and Methods

2.1. Species Occurrence Data

Data on the occurrence of P. chinensis were acquired from the following online herbaria databases: the Chinese Virtual Herbarium [30], Tropicos [31], and the GBIF [32]. Inaccurate locations without precise geo-coordinates in the occurrence records were excluded. For specimens with mere village locations documented in the Chinese Virtual Herbarium, we identified their longitude and latitude via Google Earth. Duplicate data points were eliminated, while the remaining points were subjected to spatial filtering. Hence, the number of points mapped per 1.0 × 1.0 km grid cell was only one. The number of unique geo-referenced occurrence records utilized totaled 365 (Figure 1).

2.2. Environmental Parameters

A total of 51 environmental factors that can potentially affect the distribution of P. chinensis were used. These include 19 bioclimatic variables along with 12 solar radiation and water vapor pressure variables [33] acquired from the World Climate Database. In addition, data related to three topographic variables of slope, aspect, and elevation were acquired from the RESDC website [34] covering 1984–1995. Furthermore, data for seven soil variables including soil organic carbon content, soil pH, soil buck density, and soil conductivity, along with soil sand, silt, and clay content were obtained from the Harmonized world soil database v1.2 [35]. Moreover, normalized difference vegetation index (NDVI) data were acquired from the China Meteorological Data Sharing Service System.
With regard to future climate scenarios, we adopted climate change modeling data from BCC-CSM2-MR of the Coupled Model Intercomparison Project Phase 6 (CMIP6) model from Shared Socio-economic Pathways (SSPs) 245 put forward by IPCC to predict distribution in 2041–2060 and 2081–2100. By selecting the “2041–2060” and “2081–2100” periods, we aimed to investigate two distinct future scenarios with sufficient temporal intervals, allowing us to observe potential changes and trends in species distributions over the medium and long term. BCC-CSM is recommended to be used to investigate short-time climate forecast and climate change in China (e.g., [4,36]). The SSPs 245 scenario indicates radiative forcing in 2100 + 4.5 W/m2 pre-industrial than the value of pre-industry [37]. Other variables, such as those related to soil and topography, should be included when modeling. However, the changes expected in those variables were not available for modeling with future climate change scenarios. Therefore, those variables were left unchanged for the current analysis of the future potential suitable habitat for P. chinensis. To ensure consistent results among diverse layers, we processed all environmental layers with identical cell size and spatial extent, while using WGS84 projection under the ArcGIS 10.0 scenario.
Pearson correlation analysis and principal component analysis (PCA) were used for minimizing model overfitting while decreasing high collinearity. Only one factor with a high correlation coefficient (|r| > 0.85) from every set was maintained in subsequent analysis. Factors enrolled into the eventual environmental dataset include aspect (ASPECT), BIO2–BIO5, BIO14, and BIO16 of world climate, soil buck density (BUCK), normalized difference vegetation index (NDVI), slope degree (SLOPET), solar radiation in April, June, August, September, October (kJ·m−2·day−1) (SRAD4, SRAD6, SRAD8, SRAD9, SRAD10, respectively), water vapor pressure (KPa) (VAP10) (Table 1).

2.3. Model Simulation

Models were established with MaxEnt version 3.3.3 k [20] based on species records together with environmental factors. We utilized 25% and 75% of occurrence records for model testing and training, separately. It is well known that sampling bias significantly affects presence–background distribution models, which was avoided by using one bias file layer in the present work [38]. The bias file layer was produced based on an occurrence point through the derivation of one Gaussian kernel density map (rescaled at 1–20) according to the description by [13]. We utilized a bias file option in MaxEnt to create maps. Previous studies had proved that nonoptimal models can be obtained when using default configuration. As a result, they are not necessarily suitable at all times, especially if just a few species’ occurrence records can be obtained. Consequently, we examined diverse regularization multiplier values, and discovered that the default option had the most favorable performance, i.e., the default option best displayed the P. chinensis known distribution without overfitting the model [14]. We restricted the background point number to 10,000 in sampling. However, a further increase in background point number (e.g., 100,000) made no difference to the model. The “fade by clamping” function in MaxEnt was utilized to modify areas with clamping-affected projections. The maximal iteration number was set to 1000, which provided sufficient time for model convergence, and the convergence threshold was selected at 1 × 10−6 [39]. Moreover, the default “autofeatures”, namely, linear, hinge, quadratic, threshold, and product features, were utilized [4].
Response curves were used to interpret MaxEnt output patterns. A Jackknife test was performed to analyze the relative importance of the environmental variables. The evaluation of robustness of the MaxEnt models was calibrated and then validated using tests of threshold-independent receiver operating characteristics (ROCs). To increase the precision of the analyses further, the area under the ROC curve (AUC), True Skill Statistic (TSS), and Kappa statistics were used. Poor performance is indicated by AUC 0.8; moderate performance is shown by AUC 0.9; good performance is indicated by AUC 0.95; and great performance is indicated by AUC 1 [40,41]. The TSS is a threshold-dependent statistic, with a range from −1 to 1 [42]. TSS values close to 0 or negative indicate that the distributions performed no better than a random pattern, whereas values equal to +1 indicate a consistent match between the observed and predicted distributions. The kappa statistic is a consistency test method that takes species distribution sensitivity and specificity into account. It is frequently employed in model evaluation. Kappa’s value ranges from −1 to 1. When kappa exceeds 0.75, it indicates good performance; when it falls below 0.4, it shows poor performance [43].
Four types of potential habitats were developed based on the final potential distribution map, which contained habitat suitability values ranging from 0 to 1. To classify the habitat suitability, we applied a threshold-based approach. Habitat suitability values greater than 0.75 were categorized as highly suitable, areas with values ranging from 0.50 to 0.75 were considered moderately suitable, and habitat areas with values between 0.25 and 0.50 were classified as poorly suitable. Areas with suitability values less than 0.25 were identified as not suitable. Moreover, by comparing these data with currently known suitable habitats, future potential distribution maps were re-categorized as (i) becoming suitable, (ii) becoming unsuitable, and (iii) remaining suitable; the geographical extents of areas in each category were calculated and are presented here.

3. Results

3.1. Evaluate the Model Performance of Pistacia chinensis

The average training AUC values of MaxEnt was 0.985, and the standard deviation was 0.001. The TSS and kappa values were 0.88 ± 0.02 and 0.90 ± 0.01, respectively. Therefore, the performance of the MaxEnt model was “excellent”, allowing accurate forecasting of the relationships between the geographical distribution of P. chinensis and the local climate.

3.2. Regions for the Potential Distribution of Pistacia chinensis

According to the MaxEnt model, the highly appropriate (p > 0.75) region spanned 1.07 × 105 km2, which is 1.12% of China’s land area. Those places were mostly found in eastern or central China, including eastern Hubei and Fujian, central Shanxi and Shandong, western Chongqing, and northern Taiwan (Figure 2). A total area of 2.59 × 105 km2 (2.70% of China’s land area) was identified as moderately appropriate habitats (0.50 ≤ p ≤ 0.75), primarily found in Hubei, Sichuan, and Gansu, southern Hunan, central and southern Shandong, northern Henan, and most of Yunnan. A total of 9.35% of China’s land area (0.25 ≤ p < 0.50), which is 8.98 × 105 km2, was designated as poorly suitable areas (Figure 2).

3.3. Critical Environmental Factors Influencing Geographical Location of Pistacia chinensis

Figure 3 displays Jackknife test analysis associated with factor contribution to the model. Of those 16 factors incorporated into the model, VAP10 (44.9% of variation), precipitation of wettest quarter (BIO16, 35.2% of variation), NDVI (8.7% of variation), and isothermality (BIO3, 5.2% of variation) made the largest contributions in separate use, yielding a cumulative contribution as high as 94% (Table 1). Additional factors, such as mean diurnal range, soil buck density, solar radiation, and precipitation seasonality had lesser contributions, indicating their restricted impact on appropriate P. chinensis habitat distribution (Table 1).
The variable response curves (marginal responses generated through keeping the remaining bioclimatic factors in the mean sample values) of VAP10 showed a logistic pattern, with their peaks occurring at 1.0 kPa, while BIO16, NDVI, and BIO3 showed an initial increasing and then decreasing pattern, with their peaks occurring at 500 mm, 0.3–0.6, and 25%–65%.

3.4. Future Alterations of Appropriate Habitat Area

In line with the SSP 245 climate change scenario, MaxEnt predicted an increase in appropriate habitat extent in south Jiangsu and Guangdong, north Hunan, west Chongqing, and east Sichuan in the 2060s, which increased to an area of ca. 1.14 × km5 (Figure 4A,B; Table 2). Relative to the present conditions, the total range-wide area percentage may elevate from 13.64% to 14.86%. In the 2100s, MaxEnt predicted an increase in appropriate habitat in north Sichuan, Guangxi, south Jiangsu, south Hunan, and north Shandong, which increased to an area of ca. 2.64 × km5 (Figure 4C,D; Table 2). On the whole, the inappropriate habitat area showed a decreasing trend, whereas the moderately and highly appropriate habitat areas exhibited an increasing trend (Table 2).

4. Discussion

Our study utilized MaxEnt species distribution modeling to predict the present and future suitable habitat of Pistacia chinensis across China. The model’s excellent performance ensured accurate forecasts of the species’ distribution in relation to local climate conditions. Critical environmental factors influencing distribution included water vapor pressure, precipitation of the wettest quarter, normalized difference vegetation index, and isothermality. Under the climate change scenario, the model predicted a potential expansion of suitable habitats by the 2060s and 2100s in various regions. These findings provide valuable insights for conservation and management strategies, ensuring the preservation of this ecologically and economically important species in the diverse landscapes of China.

4.1. Distribution and Prediction of Pistacia chinensis

P. chinensis has an extensive distribution. Provinces appropriate for the cultivation of this species include Guizhou, Fujian, Zhejiang, Gansu, Guangxi, Anhui, Hebei, Shanxi, Hubei, Huan, Guangdong, Jiangsu, Shaanxi, Henan, Sichuan, and Jiangxi. According to the results of our model, the moderately and highly appropriate habitat area of P. chinensis under present climatic conditions comprised around 3.66 × 105 km2. Our results are generally consistent with Flora of China [25]. In addition, some areas of Yunnan and eastern Xizang were also evaluated to have suitable habitats, which could provide areas for the future introduction of this species.

4.2. Environmental Factors Affecting Pistacia chinensis Distribution

Factors affecting species geographic distributions are the foremost concerns in ecology and evolution. Of the 16 environmental factors incorporated into our model, VAPR10, BIO16, NDVI, and BIO3 (Table 1), yielding cumulative contributions as high as 94%. However, it is worth emphasized that the response curve was generated by maintaining all other bio-climatic factors constant at their mean sample values. In actuality, however, variables will vary around their means. Interactions between variables exist, and these interactions alter habitat appropriateness in ways that marginal response curves do not. Nonetheless, this type of analysis allows us to simulate the correlations between the specific variables and the likelihood of conditions favorable to the existence of a species occurring in nature [4,44]. This could also possibly explain why, in the context of climate change, P. chinensis, a drought-tolerant species, might be moving towards higher altitudes (where more precipitation is likely to occur) (below Section 4.3).
Our results also indicate that water vapor pressure in October had a significant effect on the distribution of P. chinensis, with a suitable range occurring at ≥ 1.0 kPa. Water vapor pressure has been shown to play a major role in the relationship between plants and water resources on a global scale; an increase in water vapor pressure will cause the stomata of plants to close, minimizing water loss and avoiding adverse water tension conditions to exist within the xylem [45,46]. Nevertheless, little is known about how water vapor pressure affects the performance of P. chinensis, and future controlled quantitative studies could possibly solve this question.
BIO3 (isothermality) and BIO16 (precipitation of wettest quarter) also important environmental factors affect the presence of P. chinensis. Isothermality evaluates how much the daily temperature fluctuations differ from the annual fluctuations. Temperature variations may have a substantial influence on plant development, particularly respiration and metabolism, relating to carbohydrate accumulation [4,15]. Moreover, the temperature range had been reported to affect the differentiation of flower buds as well as seed dormancy/germination in P. chinensis as well as other plants [4,47]. Moreover, drought stress had been reported to result in a significant decrease in leaf area, number of branches, plant height, and photosynthesis of P. chinensis [26]. In addition, water supply has a significant effect on seedling recruit [48]. These hydrothermal elements shape P. chinensis’s ecological adaptation and have a significant impact on its distribution.
Vegetation indices, i.e., NDVI, also made a large contribution to conditions creating habitat for P. chinensis, indicating that NDVI can have a plausible effect on the distribution pattern of a species. This result may be a result of the fact that most populations of this species thrive in both degraded tropical evergreen and broad-leaved deciduous forests; it may also occur on cultivated and otherwise managed lands. Furthermore, previous studies have connected NDVI to various canopy attributes such as net primary production [42,49], percentage of absorbed photosynthetically active radiations [50], leaf area index [51], and evapotranspiration [52]. However, the habitat parameters with a need to be studied in detail to draw clear-cut conclusions.

4.3. Impact of Climate Change on Pistacia chinensis Distribution as Well as Related Forest Ecosystems

Some taxa may move to high elevations or latitude as a result of global warming [4,5,53,54], while others will adapt physiologically or phenologically to these changes [55,56]. Our study adopted the climate change scenario SSPs 245, and we found that, in the future, the sum of suitable habitats for P. chinensis shows an increased trend, indicating that P. chinensis may adapt to climatic changes physiologically or phenologically. Nevertheless, some regions, like south Guangdong, still show low suitability. Such results may indicate that P. chinensis was unable to tolerate the continuously increasing temperature. Further, changes in precipitation and temperature regimes may induce phenological shift of P. chinensis species, thus having indirect effects on dependent floral and faunal plants. Additionally, such changes harm a wide range of insects, rodents, birds, and mammals with indirect or direct dependence on P. chinensis seeds, flowers, and fruits.

4.4. Implications for Conservation Plans

According to the predictions made by our model, the potential suitable habitat of P. chinensis is projected to increase at high elevations in future climate scenarios. P. chinensis plantations in these potentially appropriate habitats could serve as a preservation strategy to respond to future climatic change. Furthermore, the insights garnered from our study can be effectively utilized in conservation planning to categorize the natural habitats of P. chinensis based on their vulnerability to climate change. By identifying areas of suitable habitat, conservation efforts can focus on introducing this species only in regions where it is most likely to thrive and adapt to changing climate conditions. In light of these findings, it is crucial to prioritize the maximization of natural regeneration in regions deemed to be at high risk under future climate scenarios. Preserving and conserving the natural regeneration process in such areas can safeguard the resilience and adaptability of P. chinensis populations in the face of climate-induced challenges. On the other hand, the areas of unchanged suitable habitat may serve as vital refugia during periods of climate change. These habitats can act as safe havens, providing a secure environment for P. chinensis to persist and maintain its genetic diversity.

4.5. Limitations of Modeling and Future Research Directions

Ecological niche modeling has been widely adopted and proved to be an effective method to help provide guidelines related to forest management during global climate change scenarios. However, uncertainties exist in the use of different climate change scenarios for projecting the possible distribution of various species. The present study used the BCC-CSM1.1 model, but it showed uncertainty in the nature of climate change, even though it has been recommended for studying climate change in China, thus leading to uncertainties in projected the distribution/suitability of habitat. Consequently, future studies must adopt diverse GCMs. Additionally, despite the fact that MaxEnt models have been frequently used, many drawbacks should be acknowledged [24]. The current study obtained presence-only data from a number of channels. As a result, a larger percentage of species presence records could have been included within those generally well-known places in contrast to other parts of the investigated species’ natural range [57]. However, the sampling bias layer utilized in this study merely approximates the true species distribution. In addition, some biologically important factors, such as dispersal capability, human activities, and competition, were not included in the model because robust data were not available. These factors should be included in future studies for analysis.

5. Conclusions

Our simulations revealed that P. chinensis was well suited to the temperate and subtropical regions of eastern China, where it had previously been described. Water vapor pressure (VAP10), wettest quarter precipitation (BIO16), normalized difference vegetation index (NDVI), and isothermality (BIO3) were significant environmental factors impacting the abundance of P. chinensis. Under climate change scenario SSPs 245, MaxEnt predicted that the inappropriate habitat of P. chinensis shows a decreasing trend, whereas the moderately and highly appropriate habitat areas exhibited an increasing trend. Furthermore, under this climate change scenario, the suitable habitat will geographically expand to higher latitude and altitude. The predicted geographical and temporal patterns of P. chinensis can be used to build forest management and preservation measures.

Author Contributions

K.Z. designed and supervised the study together with J.T.; C.X. and L.Z. performed statistical analyses; C.X. and L.Z. wrote the first draft of the manuscript. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by the Agricultural Scientific and Technological Independent Innovation Fund Project of Jiangsu Province (Grant No. CX(20)2030).

Data Availability Statement

All data generated or analyzed during this study are included in this published article.

Acknowledgments

The authors thank the two anonymous reviewers for their valuable suggestions on the manuscript.

Conflicts of Interest

The authors declare no conflict of interest.

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Figure 1. Distribution records of Pistacia chinensis Bunge in China.
Figure 1. Distribution records of Pistacia chinensis Bunge in China.
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Figure 2. Predicted potential distribution map of Pistacia chinensis Bunge under the current climate scenario. ➀ Gansu; ➁ Shaanxi; ➂ Henan; ➃ Jiangsu; ➄ Anhui; ➅ Hubei; ➆ Chongqing; ➇ Sichuan; ➈ Xizang; ➉ Yunnan; ⑪ Guizhou; ⑫ Hunan; ⑬ Jiangxi; ⑭ Zhejiang; ⑮ Fujian; ⑯ Taipei; ⑰ Guangdong; ⑱ Guangxi; ⑲ Ningxia Hui Autonomous Region; ⑳ Shanxi; ㉑ Hebei; ㉒ Beijing; ㉓ Tianjin; ㉔ Liaoning; ㉕ Shandong; ㉖ Shanghai; ㉗ Qinghai.
Figure 2. Predicted potential distribution map of Pistacia chinensis Bunge under the current climate scenario. ➀ Gansu; ➁ Shaanxi; ➂ Henan; ➃ Jiangsu; ➄ Anhui; ➅ Hubei; ➆ Chongqing; ➇ Sichuan; ➈ Xizang; ➉ Yunnan; ⑪ Guizhou; ⑫ Hunan; ⑬ Jiangxi; ⑭ Zhejiang; ⑮ Fujian; ⑯ Taipei; ⑰ Guangdong; ⑱ Guangxi; ⑲ Ningxia Hui Autonomous Region; ⑳ Shanxi; ㉑ Hebei; ㉒ Beijing; ㉓ Tianjin; ㉔ Liaoning; ㉕ Shandong; ㉖ Shanghai; ㉗ Qinghai.
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Figure 3. Response curves (red line) for (A) water vapor pressure in October, (B) precipitation of wettest quarter, (C) normalized difference vegetation index, and (D) isothermality in the species distribution model for Pistacia chinensis Bunge.
Figure 3. Response curves (red line) for (A) water vapor pressure in October, (B) precipitation of wettest quarter, (C) normalized difference vegetation index, and (D) isothermality in the species distribution model for Pistacia chinensis Bunge.
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Figure 4. Predicted potential distribution map of Pistacia chinensis Bunge under the Shared Socio-economic Pathways (SSPs) 245 climate change scenarios. (A) and (C) represent the distribution map during the periods 2041–2060 and 2081–2100, respectively. (B) and (D) represent the comparison between the current distribution map and the distribution map under the future climate scenarios of 2041–2060 and 2081–2100, respectively. ➀ Gansu; ➁ Shaanxi; ➂ Henan; ➃ Jiangsu; ➄ Anhui; ➅ Hubei; ➆ Chongqing; ➇ Sichuan; ➈ Xizang; ➉ Yunnan; ⑪ Guizhou; ⑫ Hunan; ⑬ Jiangxi; ⑭ Zhejiang; ⑮ Fujian; ⑯ Taipei; ⑰ Guangdong; ⑱ Guangxi; ⑲ Ningxia Hui Autonomous Region; ⑳ Shanxi; ㉑ Hebei; ㉒ Beijing; ㉓ Tianjin; ㉔ Liaoning; ㉕ Shandong; ㉖ Shanghai; ㉗ Qinghai.
Figure 4. Predicted potential distribution map of Pistacia chinensis Bunge under the Shared Socio-economic Pathways (SSPs) 245 climate change scenarios. (A) and (C) represent the distribution map during the periods 2041–2060 and 2081–2100, respectively. (B) and (D) represent the comparison between the current distribution map and the distribution map under the future climate scenarios of 2041–2060 and 2081–2100, respectively. ➀ Gansu; ➁ Shaanxi; ➂ Henan; ➃ Jiangsu; ➄ Anhui; ➅ Hubei; ➆ Chongqing; ➇ Sichuan; ➈ Xizang; ➉ Yunnan; ⑪ Guizhou; ⑫ Hunan; ⑬ Jiangxi; ⑭ Zhejiang; ⑮ Fujian; ⑯ Taipei; ⑰ Guangdong; ⑱ Guangxi; ⑲ Ningxia Hui Autonomous Region; ⑳ Shanxi; ㉑ Hebei; ㉒ Beijing; ㉓ Tianjin; ㉔ Liaoning; ㉕ Shandong; ㉖ Shanghai; ㉗ Qinghai.
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Table 1. Percentage contributions and permutation importance of the variables included in the MaxEnt models for Pistacia chinensis Bunge.
Table 1. Percentage contributions and permutation importance of the variables included in the MaxEnt models for Pistacia chinensis Bunge.
CodeEnvironmental VariablesUnit%ContributionPermutation Importance
VAP10Water vapor pressurekPa44.947.3
BIO16Precipitation of wettest quartermm35.229.1
NDVINormalized difference vegetation index 8.716.6
BIO3Isothermality×1005.22
ASPECTAspect°1.30.7
SRAD6Solar radiation of JunekJ m−2·day−10.90.4
SRAD9Solar radiation of SeptemberkJ m−2·day−10.71.2
SRAD4Solar radiation of AprilkJ m−2·day−10.70.2
SRAD8Solar radiation of AugustkJ m−2·day−10.60.8
SLOPESlope degree°0.50.4
BIO4Temperature seasonality× 1000.50.1
SRAD10Solar radiation in OctoberkJ m−2·day−10.40.1
BIO2Mean diurnal range°C × 100.30.1
BUCKSoil buck densityg/cm30.10.1
BIO14Precipitation of driest monthmm0.10.2
BIO5Max temperature of warmest month°C × 100.10.6
Table 2. Portions of the three suitability classes of areas of potential distribution of Pistacia chinensis Bunge under current and future climate scenarios/years. SSP2060s and SSP2100s represent climate conditions during the periods 2041–2060 and 2081–2100, respectively, based on the Coupled Model Intercomparison Project Phase 6 (CMIP6) models from the Shared Socio-economic Pathways (SSPs) 245 scenario.
Table 2. Portions of the three suitability classes of areas of potential distribution of Pistacia chinensis Bunge under current and future climate scenarios/years. SSP2060s and SSP2100s represent climate conditions during the periods 2041–2060 and 2081–2100, respectively, based on the Coupled Model Intercomparison Project Phase 6 (CMIP6) models from the Shared Socio-economic Pathways (SSPs) 245 scenario.
Area (×105 km2)Portion of Area (%)
Low SuitabilityModerate SuitabilityHigh
Suitability		Low
Suitability		Moderate SuitabilityHigh
Suitability
Current8.982.591.079.682.801.16
SSP2060s9.503.071.2110.253.311.30
SSP2100s11.132.921.2312.003.151.32
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Xu, C.; Zhang, L.; Zhang, K.; Tao, J. MaxEnt Modeling and the Impact of Climate Change on Pistacia chinensis Bunge Habitat Suitability Variations in China. Forests 2023, 14, 1579. https://doi.org/10.3390/f14081579

AMA Style

Xu C, Zhang L, Zhang K, Tao J. MaxEnt Modeling and the Impact of Climate Change on Pistacia chinensis Bunge Habitat Suitability Variations in China. Forests. 2023; 14(8):1579. https://doi.org/10.3390/f14081579

Chicago/Turabian Style

Xu, Chaohan, Lei Zhang, Keliang Zhang, and Jun Tao. 2023. "MaxEnt Modeling and the Impact of Climate Change on Pistacia chinensis Bunge Habitat Suitability Variations in China" Forests 14, no. 8: 1579. https://doi.org/10.3390/f14081579

APA Style

Xu, C., Zhang, L., Zhang, K., & Tao, J. (2023). MaxEnt Modeling and the Impact of Climate Change on Pistacia chinensis Bunge Habitat Suitability Variations in China. Forests, 14(8), 1579. https://doi.org/10.3390/f14081579

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