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Article

Impact of Climate Change on Distribution of Suitable Niches for Black Locust (Robinia pseudoacacia L.) Plantation in China

by
Shanchao Zhao
1,
Hesong Wang
2,3,* and
Yang Liu
2
1
Natural Forest Protection Center of Xinjiang Uygur Autonomous Region, Urumqi 830099, China
2
College of Ecology and Nature Conservation, Beijing Forestry University, Beijing 100083, China
3
College of Forestry and Landscape Architecture, Xinjiang Agricultural University, Urumqi 830052, China
*
Author to whom correspondence should be addressed.
Forests 2024, 15(9), 1616; https://doi.org/10.3390/f15091616
Submission received: 10 July 2024 / Revised: 9 September 2024 / Accepted: 10 September 2024 / Published: 13 September 2024
(This article belongs to the Section Forest Meteorology and Climate Change)
Browse Figures
Versions Notes

Abstract

:
Black locust (Robinia pseudoacacia L.), one of the major afforestation species adopted in vegetation restoration, is notable for its rapid root growth and drought resistance. It plays a vital role in improving the natural environment and soil fertility, contributing significantly to soil and water conservation and biodiversity protection. However, compared with natural forests, due to the low diversity, simple structure and poor stability, planted forests including Robinia pseudoacacia L. are more sensitive to the changing climate, especially in the aspects of growth trend and adaptive range. Studying the ecological characteristics and geographical boundaries of Robinia pseudoacacia L. is therefore important to explore the adaptation of suitable niches to climate change. Here, based on 162 effective distribution records in China and 22 environmental variables, the potential distribution of suitable niches for Robinia pseudoacacia L. plantations in past, present and future climates was simulated by using a Maximum Entropy (MaxEnt) model. The results showed that the accuracy of the MaxEnt model was excellent and the area under the curve (AUC) value reached 0.937. Key environmental factors constraining the distribution and suitable intervals were identified, and the geographical distribution and area changes of Robinia pseudoacacia L. plantations in future climate scenarios were also predicted. The results showed that the current suitable niches for Robinia pseudoacacia L. plantations covered 9.2 × 105 km2, mainly distributed in the Loess Plateau, Huai River Basin, Sichuan Basin, eastern part of the Yunnan–Guizhou Plateau, Shandong Peninsula, and Liaodong Peninsula. The main environmental variables constraining the distribution included the mean temperature of the driest quarter, precipitation of driest the quarter, temperature seasonality and altitude. Among them, the temperature of the driest quarter was the most important factor. Over the past 90 years, the suitable niches in the Sichuan Basin and Yunnan–Guizhou Plateau have not changed significantly, while the suitable niches north of the Qinling Mountains have expanded northward by 2° and the eastern area of Liaoning Province has expanded northward by 1.2°. In future climate scenarios, the potential suitable niches for Robinia pseudoacacia L. are expected to expand significantly in both the periods 2041–2060 and 2061–2080, with a notable increase in highly suitable niches, widely distributed in southern China. A warning was issued for the native vegetation in the above-mentioned areas. This work will be beneficial for developing reasonable afforestation strategies and understanding the adaptability of planted forests to climate change.

1. Introduction

Black locust (Robinia pseudoacacia L.), a key afforestation species in China, is characterized by its strong root system and potent nitrogen-fixing capacity and can improve soil fertility and texture. It acts as an important pioneer tree species for improving the ecological environment of the region [1,2]. The geographical distribution of Robinia pseudoacacia L. is determined by climatic factors such as temperature and precipitation. The change in climate will influence its growth trend and adaptive range [3]. In addition, compared with natural forests, planted forests are more sensitive to climate change because of their low biodiversity, simple forest structure, and poor stability [4,5]. Therefore, revealing the impact of climate change on the distribution of Robinia pseudoacacia L. plantations is of great significance for reasonable afforestation planning and achieving the carbon neutrality target. It is also an invasive species due to its ability to transform habitats [6,7]; thus, it is important to understand the habitat expansion area and habitat loss area of Robinia pseudoacacia L. in China in a changing climate.
Species distribution models are a powerful tool to predict the potential geographical distribution and ecological demand of species in different future climate scenarios. They analyze the relationship between species distribution and environmental factors, and the preference of a species for an environment is expressed as a form of probability [8,9]. By now, based on different algorithms, there are a large number of distribution models of suitable habitats, including Ecological Niche Factor Analysis (ENFA), Random Forest (RF), Maximum Entropy (MaxEnt) and the Biological Climatic Model (BIOCLIM) [10]. Specifically, the MaxEnt model is based on the actual distribution of species and the corresponding environmental variables. It describes the suitability of species in the ecological space through the maximum entropy principle and machine learning and projects them into the geographical space to predict the geographical region of species distribution [11]. Since its emergence, the MaxEnt model has been widely applied. For example, the suitable niches of mangroves in the Guangdong–Hong Kong–Macau region were studied to provide decision support for mangrove restoration and identify priority protected areas [12]. In different climate scenarios, to provide decision support for maintaining ecological balance in desert regions, the distribution of suitable niches of Populus euphratica and Tamarix chinensis was predicted [13]. Similarly, the potential distribution and influencing environmental factors of Pinus massoniana, Phellodendron and Acer monspessulanum L. were analyzed considering the background of climate change [14,15,16].
Studies on the growth of Robinia pseudoacacia L. have been conducted in China, Europe and globally [17,18,19,20]; other related research has mainly focused on the capacity of carbon sequestration [21,22], the mechanisms of drought response [23], and the impact on biodiversity as an invasive species [24,25]. Although the distribution and growth of Robinia pseudoacacia L. under the impacts of climate change were analyzed [17,18,19], they were all based on current and future scenarios; there is still a lack of studies showing how the spatial patterns of suitable niches changed with past climate change. The novelty of our approach lies in demonstrating future changes in areas suitable for the species within the context of historical changes that may have already occurred during the species’ presence in China.
In this study, the Maximum Entropy (MaxEnt) model was used to detect the main environmental factors restricting the distribution of Robinia pseudoacacia L. plantations. The distribution of suitable niches for Robinia pseudoacacia L. plantations since 1931 was simulated and analyzed. Meanwhile, potential suitable niches were also predicted for future climate scenarios. The main objectives were as follows: (1) to extract the main environmental factors affecting the distribution of Robinia pseudoacacia L. plantations, (2) to analyze the spatial distribution characteristics and distribution boundaries of Robinia pseudoacacia L. plantations under the current climate (1991–2020), and (3) to predict the changing trend of the suitable niches for Robinia pseudoacacia L. in a future climate scenario. This research provides theoretical guidance for ecological enhancement and afforestation planning.

2. Materials and Methods

2.1. Spatial Distribution Records of Robinia pseudoacacia L. Plantations

The geographical locations of Robinia pseudoacacia L. in China, including the longitude and latitude, were collected from the China Virtual Herbarium (www.cvh.ac.cn, accessed on 18 March 2023), CAS Earth Bioencyclopedia platform (http://www.especies.cn/, accessed on 18 March 2023), Global Biodiversity Information Facility (https://www.gbif.org, accessed on 18 March 2023), and National Specimen Information Infrastructure (http://www.nsii.org.cn, accessed on 1 April 2023). Based on journal search platforms such as CNKI and Web of Science, papers about “black locust” or “Robinia pseudoacacia L.” were searched. The specific distribution locations of Robinia pseudoacacia L. plantations in China were recorded and screened from the selected papers. Finally, after removing overlapped points within 1 km grids, a total of 162 geographic distribution records of Robinia pseudoacacia L. plantations were obtained (Figure 1).

2.2. Environmental Factors

Monthly average temperature, highest monthly temperature, lowest monthly temperature, and monthly precipitation data of China at 1 km resolution were downloaded from the National Tibetan Plateau Data Center (http://data.tpdc.ac.cn, accessed on 12 June 2022). Based on them, 19 variable climate factors (Table 1) were calculated to drive the MaxEnt model [26,27,28,29,30]. Future climate data were obtained from the WorldClim database, including two periods, 2041–2060 and 2061–2080, under three scenarios: RCP2.6, RCP4.5, and RCP8.5, representing low, medium, and high carbon emission intensities, respectively. Digital Elevation Models (DEMs) were downloaded from the Geospatial Data Cloud (http://www.gscloud.cn/home, accessed on 12 June 2022); the slope and aspect were calculated using spatial analysis tools with ArcGIS 10.8.
All the climate data (1931 to 2020) were resampled to a spatial resolution of 30″; among them, data from 1991 to 2020 were used to calculate the distribution of suitable niches under current climate conditions, while 1961–1990 and 1931–1960 were used to calculate the distribution of suitable niches in past climate conditions. The map of China was downloaded from the National Geomatics Center of China (https://www.ngcc.cn).

2.3. The MaxEnt Model

The MaxEnt software (Version 3.4.4k, https://www.gbif.org/tool/81279/maxent, American Museum of Natural History, New York, NY, USA) was adopted to calculate the suitable niches for Robinia pseudoacacia L. A random selection of 75% of the distribution points was used as the training set, with the remaining 25% for testing [31]. The Jackknife method was employed to evaluate the importance of environmental variables in the model. To minimize random errors, the replicate setting was adjusted to 10, allowing the model to run 10 times, and the results were averaged [32]. The outputs from the model were analyzed using ArcGIS 10.8.
The distribution of suitable niches calculated by the MaxEnt model was expressed as the distribution probability in the study area. Based on the probability, ArcGIS was used to classify the suitability of Robinia pseudoacacia L. in China. A probability value below 0.2 indicated that it was not suitable for Robinia pseudoacacia L. plantations and ranges of 0.2–0.4, 0.4–0.6, and 0.6–1 indicated niches with low, medium, and high suitability, respectively [33]. The importance of environmental factors was evaluated by using the MaxEnt model’s Jackknife test, model contribution rates, and Permutation importance value.

2.4. Accuracy Test of the Model

The accuracy of the model in this study was evaluated using the area under the curve (AUC) of the receiver operating characteristic (ROC) curve. The AUC, a widely recognized metric, reflected the model’s predictive performance. Generally, an AUC greater than 0.9 can be considered as “excellent” [34,35]. In this study, the AUC for the Robinia pseudoacacia L. plantation was 0.937, indicating a high level of reliability for the calculated results (Figure 2).

3. Results

The model accuracy analysis indicated excellent performance (Figure 2), with an AUC value of 0.937. A probability of presence above 0.5 suggested that the corresponding environmental factors were conducive to the growth of the plantations [36,37].

3.1. Key Factors Affecting the Distribution of Robinia pseudoacacia L. Plantations

It can be seen from Table 2 that the main environmental factors influencing the distribution of Robinia pseudoacacia L. plantations were the mean temperature of the driest quarter (with a contribution rate of 28.3%), precipitation of the driest quarter (19.5%), temperature seasonality (12.2%), altitude (8.9%), precipitation of the warmest quarter (7.9%), and precipitation of the driest month (7.2%). These four factors in total contributed 84%. The suitable range for Robinia pseudoacacia L. plantations included the mean temperature of the driest quarter, ranging from −1 °C to 9 °C, precipitation of the driest quarter, ranging from 25 mm to 150 mm; temperature seasonality, ranging from 50 to 125; and altitude, below 1500 m, which indicated that Robinia pseudoacacia L. plantations are suitable for growth at mid to low altitude areas with a warm temperate and semi-humid climate. The precipitation of the warmest quarter ranged from 280 mm to 510 mm and the precipitation of the driest month ranged from 5 mm to 50 mm (Figure 3).

3.2. Suitable Niches for Robinia pseudoacacia L. Plantations in the Current Climate

The potentially suitable niches for Robinia pseudoacacia L. plantations were mainly located in eastern China (Figure 4), including the Loess Plateau, Huai River Basin, Sichuan Basin, eastern part of the Yunnan–Guizhou Plateau, Shandong Peninsula and Liaodong Peninsula. The total of suitable niches covered about 134.48 × 104 km2, of which the highly suitable niches covered approximately 39.36 × 104 km2, accounting for 29.27% of the total area. These highly suitable niches were concentrated in central and eastern Shandong, southern Shanxi, southeastern Liaoning, Shaanxi, and Guizhou provinces. This is consistent with the current distribution of Robinia pseudoacacia L. The medium-suitable niches covered about 35.37 × 104 km2, accounting for 26.3% of the total. They were distributed around the highly suitable niches and concentrated in the Sichuan Basin. The less suitable niches were mainly distributed in the peripheral regions of the other niches, such as eastern Sichuan, western Hunan, northwestern Henan, and eastern Gansu, covering an area of approximately 59.75 × 104 km2 (Table 3).

3.3. Suitable Niches for Robinia pseudoacacia L. Plantations over Past Decades

From 1931 to 1960, with an area of 92 × 104 km2, the suitable niches for Robinia pseudoacacia L. were concentrated in mountainous areas between the northern part of Liupan Mountain and the southern part of Lvliang Mountain, as well as the northern part of the Qinling Mountains, parts of Taihang Mountain, Sichuan Basin, the eastern end of the Yunnan–Guizhou Plateau and the central and eastern part of Shandong. The highly suitable niches, covering areas of 52.26 × 104 km2, were mainly located in eastern Gansu, the northern part of Shaanxi, parts of Guizhou and the eastern part of Shandong (Figure 5).
From 1961 to 1990, the highly suitable area covered 29.44 × 104 km2, which was mainly concentrated in the middle- and low-altitude regions on the northern slope of the Qinling Mountains, the southern Taihang Mountains and the eastern part of the Yunnan–Guizhou Plateau (Figure 6). Compared to 1931–1960, the distribution center of highly suitable niches shifted northward and expanded by 1.5°, and the areas increased by 10 × 104 km2. The medium-suitable niches in the eastern Taihang Mountain, the northern part of the Lvliang Mountain and the northern part of the Liupan Mountains were upgraded to highly suitable niches. Meanwhile, suitable niches of Liaoning had extended northward and the less suitable niches of Shandong were upgraded to medium.
Compared to 1961–1990, the highly suitable niches of the current period (1991–2020) increased by 9.92 × 104 km2, and most of the niches with suitable with medium and low suitability adjacent to the northern part of the highly suitable niches in Shaanxi, Shanxi and the Yunnan–Guizhou Plateau transformed into highly suitable niches. Some unsuitable niches in Beijing and Hebei were upgraded to less suitable niches and the less suitable niches in the Sichuan Basin were upgraded to medium-suitable niches. Meanwhile, in East China, niches with medium and low suitability were downgraded to unsuitable niches. In the current period (1991–2020), the distribution center of the highly suitable niches significantly shifted northward, and the northern boundary of the suitability area had also expanded northward, with the boundary moving by 1° in a northward shift.

3.4. Suitable Niches for Robinia pseudoacacia L. Plantations in Future Climate Scenarios

In future climate scenarios, the potential suitable niches for Robinia pseudoacacia L. plantations significantly increased. It was suitable for growth in regions including south of the Loess Plateau and east of the Sichuan Basin. From 2041 to 2060, the potential suitable niches were concentrated in the east of the Sichuan Basin, extending to the Yunnan–Guizhou Plateau, the Loess Plateau and the southern side of the Qinling Mountains (Figure 7). In the RCP2.6, RCP4.5 and RCP8.5 scenarios, every potentially suitable niche showed a trend of expansion to the south, with southeastern hilly areas transitioning from unsuitable to medium or low suitability. In addition, the regions from the east of the Sichuan Basin to the Yunnan–Guizhou Plateau and from the north of the Qinling Mountains to the southern side of the Qinling Mountains shifted from low to high suitability. Meanwhile, the distribution boundary of suitable niches shifted northward gradually, indicating that with global climate warming, the northern boundary of its suitable distribution moves northward, expanding by 1.5°. The area of highly suitable niches remained relatively stable across the three scenarios, with the largest area in the RCP4.5 scenario, covering 124.78 × 104 km2.
From 2061 to 2080, for all three scenarios, the distribution center of highly suitable niches for Robinia pseudoacacia L. plantations tended to move northward, with an increase in the areas of high suitability. They were located in the north of the Qinling Mountains, the southern part of the Loess Plateau, the Taihangshan Mountains and the Lvliangshan Mountains (Figure 8). As carbon emissions increased, the highly suitable niches in the RCP8.5 scenario showed a trend of fragmentation, accompanied by a reduction in area. In the RCP8.5 scenario, the highly suitable niches in the southern regions were significantly reduced compared to the other two. In comparison to the period from 2041 to 2060, the area of high suitability in the RCP4.5 and RCP8.5 scenarios from 2061 to 2080 showed a decreasing trend, primarily in the regions surrounding the highly suitable niches, transitioning to medium suitability. The suitable niches in the Sichuan Basin and the Qinling Daba region were still stable.

4. Discussion

Robinia pseudoacacia L., a typical afforestation tree species in China, has its distribution significantly influenced by climate, one of the most crucial factors for plant distribution [18]. Changes in plant distribution are a clear response to climate change. The results showed that Robinia pseudoacacia L. mainly grows in regions with altitudes below 1500 m. The key environmental factors determining its suitable niches most strongly are the average temperature and precipitation in the driest quarter (bio9 and bio17). The suitable range for the temperature is from −1 °C to 9 °C, which indicates that the suitable niches for Robinia pseudoacacia L. are mainly concentrated in the southern part of China. Compared with worldwide research by Li et al. from 2021 [18], temperature (bio9) is still the most important factor affecting the distribution of Robinia pseudoacacia L., but precipitation (bio17) also contributes significantly. Bio17 was not adopted in their research [18], which may be a potential new index to improve the understanding of the water threshold for the distribution of Robinia pseudoacacia L. The suitable range for precipitation is from 28 mm to 150 mm, which indicates that the distribution of Robinia pseudoacacia L. has a wide range in moisture gradient and can be suitable for regions ranging from semi-arid to humid.
Based on future climate scenarios, the potential suitable niches for Robinia pseudoacacia L. are predicted to increase significantly. In the future in China, potential suitable niches will continue to expand, covering most parts of the eastern Sichuan Basin, northern Yunnan–Guizhou Plateau and southern Qinling, as well as most of the Shandong Peninsula. This study suggests that Robinia pseudoacacia L. has strong afforestation potential in China, with the possibility of expanded cultivation in regions like Shandong, Shaanxi, southern Shanxi, Liaodong Peninsula, Guizhou, Hubei and Henan [18,19]. However, by comparing the differences in suitable niches for Robinia pseudoacacia L. under future scenarios and past climate change, the central region of China including Hebei, Henan and Shaanxi will transform from previously unsuitable areas to highly suitable areas. For these areas with good vegetation coverage, there is a risk of invasion by Robinia pseudoacacia L. in the future. This indicates that while Robinia pseudoacacia L. expands its suitable area under the background of climate change, the risk of biological invasion also increases at the same time.
Robinia pseudoacacia L. is a fast-growing tree of high economic and cultural importance in China, causing changes in soil chemistry and consequently achieving notable ecological benefits in vegetation restoration [6,38,39]. With its role in windbreak and sand fixation, Robinia pseudoacacia L. has been widely planted in China, having a broad climate suitability and plant range. There is an agreement that planting Robinia pseudoacacia L. can be used as a soil improvement solution [7]. However, as an invasive species, care must be taken to comprehensively evaluate the negative effects of Robinia pseudoacacia L. planting. Robinia pseudoacacia L. can alter lighting, microclimate and soil conditions, leading to the disappearance of endangered light-demanding plants and invertebrates [40]. Due to the change of root exudate and the rhizosphere environment, soil PH can be changed and cause soil acidification [6,7]. Meanwhile, Robinia pseudoacacia L. has a relatively large specific leaf area, high plant height and high seed yield and quality, indicating its superior competitive strategy [41]. Furthermore, Robinia pseudoacacia L. increases soil nitrogen stores and soil organic matter. It will threaten the growth of native tree species such as Quercus acutissima, Pinus tabuliformis and Platycladus orientalis and be more favorable for non-native tree species [41,42,43,44]. In the highly suitable niches, due to the studied plant’s strong adaptability and rapid reproduction rate, attention should be paid to prevent altering the native plant communities [2]. Therefore, in valuable natural areas that maintain biodiversity, a cultivation ban should be performed within their scope and buffer zones [45]. The predicted suitable niches can be used to avoid potential afforestation failures caused by ongoing climate change. It is crucial to combine these predictions with accurate gridded meteorological data to fully assess the feasibility of the large-scale afforestation of Robinia pseudoacacia L. Introductions of Robinia pseudoacacia L. in new regions should refer to provenance tests.
The limitation of the model, on the other hand, should also be considered. The MaxEnt model assumed a suitable and stable state between species and the niches [46]. However, this does not mean that the species and the environment reached a defacto optimal state [47]. Due to poor migration ability, some plants are unable to reach suitable habitats, which may have impacted the results. In this study, only climatic and topographic factors were considered in predicting suitable niches, which may have resulted in a potential overestimation of its potential habitat [48]. It was concluded that soil moisture content significantly affects the growth and regional distribution of Robinia pseudoacacia L. [49,50]. Therefore, in order to make reasonable use of our results, it is necessary to consider the local soil conditions and vegetation types. Future studies incorporating additional environmental factors, such as soil moisture, will produce more accurate predictions.

5. Conclusions

In this study, the response of Robinia pseudoacacia L. to climate change in China was explored. The potential distribution of suitable niches for Robinia pseudoacacia L. plantations was predicted by using a MaxEnt model based on distribution records. We found that the main environmental variables constraining the distribution include mean temperature of the driest quarter, precipitation of the driest quarter and temperature seasonality. The current niches for Robinia pseudoacacia L. plantations are mainly distributed in eastern China and the highly suitable niches are mainly concentrated in the southern part of China. Over the past 90 years, the suitable niches have expanded. In future climate scenarios, the potential suitable niches are expected to expand significantly in both the periods 2041–2060 and 2061–2080, with a notable increase in highly suitable niches, widely distributed in southern China. A warning was issued for the native vegetation in the above-mentioned areas. This work will be beneficial for developing reasonable afforestation strategies and understanding the adaptability of planted forests to climate change.

Author Contributions

Conceptualization, S.Z. and H.W.; methodology, Y.L.; software, Y.L.; validation, S.Z. and Y.L.; formal analysis, S.Z.; investigation, Y.L.; resources, Y.L.; data curation, Y.L.; writing—original draft preparation, S.Z.; writing—review and editing, S.Z. and H.W.; visualization, Y.L.; supervision, H.W.; project administration, H.W.; funding acquisition, S.Z. and H.W. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by the National Key Research and Development Program of China (grant no. 2020YFA0608103) and the National Science Foundation of China (grant no. 42265012).

Data Availability Statement

Data are contained within the article.

Conflicts of Interest

The authors declare no conflicts of interest.

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Figure 1. The distribution records of Robinia pseudoacacia L. and the approximate range of the Loess Plateau and the Yunnan–Guizhou Plateau.
Figure 1. The distribution records of Robinia pseudoacacia L. and the approximate range of the Loess Plateau and the Yunnan–Guizhou Plateau.
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Figure 2. Receiver operating characteristic (ROC) curve of the MaxEnt model used in this study.
Figure 2. Receiver operating characteristic (ROC) curve of the MaxEnt model used in this study.
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Figure 3. Response curves of Robinia pseudoacacia L. plantations to the main environmental factors.
Figure 3. Response curves of Robinia pseudoacacia L. plantations to the main environmental factors.
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Figure 4. Potential distribution areas of Robinia pseudoacacia L. plantations in the current climate of China.
Figure 4. Potential distribution areas of Robinia pseudoacacia L. plantations in the current climate of China.
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Figure 5. Potential distribution areas of Robinia pseudoacacia L. plantations for the period from 1931 to 1960 in China.
Figure 5. Potential distribution areas of Robinia pseudoacacia L. plantations for the period from 1931 to 1960 in China.
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Figure 6. Potential distribution areas of Robinia pseudoacacia L. plantations in the period from 1961 to 1990 in China.
Figure 6. Potential distribution areas of Robinia pseudoacacia L. plantations in the period from 1961 to 1990 in China.
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Figure 7. Potential distribution areas of Robinia pseudoacacia L. plantations in future climate change scenarios (2041–2060).
Figure 7. Potential distribution areas of Robinia pseudoacacia L. plantations in future climate change scenarios (2041–2060).
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Figure 8. Potential distribution areas of Robinia pseudoacacia L. plantations in future climate change scenarios (2061–2080).
Figure 8. Potential distribution areas of Robinia pseudoacacia L. plantations in future climate change scenarios (2061–2080).
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Table 1. Environmental factors adopted in the MaxEnt model.
Table 1. Environmental factors adopted in the MaxEnt model.
SymbolEnvironment FactorsDescription
Bio1Annual mean temperature ∑ 1 12 T ¯ n 12
Bio2Mean monthly temperature range ∑ 1 12 Tmax − Tmin 12
Bio3Isothermality bio 2 bio 7 × 100
Bio4Temperature seasonality S t   d T 1 12 ¯ × 100
Bio5Max temperature of warmest monthMAX(Tmax1, Tmax2, Tmax3, …, Tmax12)
Bio6Min temperature of coldest monthMIN(Tmin1,Tmin2,Tmin3, …, Tmin12)
Bio7Temperature annual rangebio5–bio6
Bio8Mean temperature of wettest quarter   T ¯ M [PM = MAX(P123, P456, P789, P101,112)]
Bio9Mean temperature of driest quarter   T ¯ N [PN = MIN(P123, P456, P789, P101,112)]
Bio10Mean temperature of warmest quarterMAX(   T ¯ 123,   T ¯ 456,   T ¯ 789,   T ¯ 101,112)
Bio11Mean temperature of coldest quarterMIN(   T ¯ 123,   T ¯ 456,   T ¯ 789,   T ¯ 101,112)
Bio12Annual precipitation ∑ 1 12 P ¯ n
Bio13Precipitation of wettest monthMAX(P1, P2, P3, …, P12)
Bio14Precipitation of driest monthMIN(P1, P2, P3, …, P12)
Bio15Precipitation seasonality Std   P ¯ Mean   P ¯ × 100
Bio16Precipitation of wettest quarterMAX(P123, P456, P789, P101,112)
Bio17Precipitation of driest quarterMIN(P123, P456, P789, P101,112)
Bio18Precipitation of warmest quarterPW [TW = MAX(T123, T456, T789, T101,112)]
Bio19Precipitation of coldest quarterPC [TC = MIN(T123, T456, T789, T101,112)]
altitudeAltitudem
slopeSlope°
aspectAspect——
Tmax, highest monthly temperature; Tmin, lowest monthly temperature; T, average monthly temperature; std, standard deviation;   T ¯ 123,   T ¯ 456,   T ¯ 789 and   T ¯ 101,112 represent the average temperature of each quarter; P123, P456, P789 and   P ¯ 101,112 represent the precipitation of each quarter.
Table 2. Percent contribution of main environmental factors.
Table 2. Percent contribution of main environmental factors.
Environment FactorPercent
Contribution (%)		Cumulative
Contribution (%)
Mean temperature of driest quarter28.384
Precipitation of driest quarter19.5
Temperature seasonality12.2
Altitude8.9
Precipitation of warmest quarter7.9
Precipitation of driest month7.2
Table 3. Change of suitable niches for Robinia pseudoacacia L. plantations in different periods (×104 km2).
Table 3. Change of suitable niches for Robinia pseudoacacia L. plantations in different periods (×104 km2).
SuitabilityCurrent
(1991–2020)		1961–19901931–1960
High59.7574.3652.26
Medium35.3732.7820.30
Low39.3629.4419.44
Total area134.48136.5892.00
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Zhao, S.; Wang, H.; Liu, Y. Impact of Climate Change on Distribution of Suitable Niches for Black Locust (Robinia pseudoacacia L.) Plantation in China. Forests 2024, 15, 1616. https://doi.org/10.3390/f15091616

AMA Style

Zhao S, Wang H, Liu Y. Impact of Climate Change on Distribution of Suitable Niches for Black Locust (Robinia pseudoacacia L.) Plantation in China. Forests. 2024; 15(9):1616. https://doi.org/10.3390/f15091616

Chicago/Turabian Style

Zhao, Shanchao, Hesong Wang, and Yang Liu. 2024. "Impact of Climate Change on Distribution of Suitable Niches for Black Locust (Robinia pseudoacacia L.) Plantation in China" Forests 15, no. 9: 1616. https://doi.org/10.3390/f15091616

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