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Article

Assessing the Potential Distribution of a Vulnerable Tree under Climate Change: Perkinsiodendron macgregorii (Chun) P.W.Fritsch (Styracaceae)

by
Ming-Hui Yan
1,*,
Jie-Ying Si
1,
Nian-Ci Dong
1,
Bin-Wen Liu
1,
Bashir B. Tiamiyu
2,
Heng-Chang Wang
3 and
Hong-Yu Yuan
1,*
1
College of Life Science, Xinyang Normal University, Xinyang 464000, China
2
Department of Plant Biology, University of Ilorin, Ilorin 1515, Nigeria
3
CAS Key Laboratory of Plant Germplasm Enhancement and Specialty Agriculture, Wuhan Botanical Garden, Chinese Academy of Sciences, Wuhan 430074, China
*
Authors to whom correspondence should be addressed.
Sustainability 2023, 15(1), 666; https://doi.org/10.3390/su15010666
Submission received: 19 November 2022 / Revised: 24 December 2022 / Accepted: 27 December 2022 / Published: 30 December 2022
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Abstract

:
Species adaptation and their response to the warming climate offer understanding into the present geographical distribution and may assist in improving predictions regarding the expected response to future climate change. As a result, assessing the distribution and potentially suitable habitats is key for conserving important vulnerable species such as Perkinsiodendron macgregorii, a rare tree species of high ornamental value distributed only in the subtropical forests of China. In this study, 101 sampling points distributed in China and 11 climatic variables were selected and imported into the maximum entropy model (MaxEnt). We simulated the spatiotemporal dynamics of potential habitats under past, current, and future (2050s and 2070s) scenarios and found that the total suitable area for P. macgregorii is 1.67 × 106 km2 in recent times. This area is mainly located in seven provinces of southern China (Zhejiang, Anhui, Jiangxi, Fujian, Hunan, Guangdong, and Guangxi). The habitat centroid of P. macgregorii has been in Jiangxi province from the past to the 2070s. In both the lowest and the highest emission scenarios (RCP2.6 and RCP8.5), the potential distribution of P. macgregorii will slightly increase in the 2070s, indicating that climate change may have little effect on its distribution. The precipitation of the warmest quarter (bio_18) is the most important climatic factor, with an optimum range of 487.3–799.8 mm. Our work could help make scientific strategies for in situ and ex situ conservation of P. macgregorii.

1. Introduction

The earth has experienced a massive climate change since the Quaternary Period, which had changed the distribution of species worldwide [1]. Over the past century, the global climate has been warming [2,3], and the warming in the last half century is remarkably significant [4]. The degradation of habitat is taking place on a short timescale and the current extinction rates are higher than would be expected from the fossil record [5]. Therefore, it is important to probe into the distribution pattern change of vulnerable species in the future climate to avoid the risk of extinction and maintain ecological security.
Perkinsiodendron macgregorii (Chun) P.W.Fritsch, of the Styracaceae family, is a relic tree plant of the Tertiary period. It is narrowly scattered in southeastern China, including in the Hunan, Guizhou, Jiangxi, Fujian, Zhejiang, Guangxi, and Guangdong provinces, growing in damp, shaded areas on forested slopes or at forest edges, at an altitudinal range between 700 and 1200 m [6]. P. macgregorii is an elegant deciduous tree with high ornamental value. The flowers of P. macgregorii are white and graceful, produced in spring before the leaves appear, and are thus displayed to full effect (Figure 1). The leaves are attractive, with pink petioles and strongly pleated laminas. The fruits are four-winged, like those of H. carolina L. [7]. This species can be used as a landscape tree, planted in gardens and configured with other plants to enrich the colour of the garden. P. macgregorii has a poor natural regeneration ability due to its seeds being encased in hard episperm [8]. As a result of excessive deforestation and the construction of mountain roads, the forest range has been severely damaged and its habitat is deteriorating [9]. Predicting the potential habitat of P. macgregorii and figuring out the key climatic factors affecting its distribution is important for its conservation management, artificial introduction, and cultivation.
Species distribution models (SDMs) have been widely used to assess the suitable habitat and the shift of species distribution under climate change based on climatic factors [10]. As one of the SDMs, the maximum entropy model (MaxEnt) has the advantages of less requirement of sample size and category, flexible parameter settings, and high predictive accuracy [11]. Therefore, the MaxEnt model is suitable for studying the distribution pattern change of rare and endangered species under climate change [12,13].
To investigate the influence of climatic factors on the distribution of P. macgregorii populations and whether P. macgregorii can adapt to future climates in the context of climate change, we explored distribution models for P. macgregorii using records from the literature and public databases to simulate the spatiotemporal dynamics of potentially suitable habitats across past, current, and future (the 2050s and 2070s) periods by using the MaxEnt model. We also analyzed the main climatic factors affecting the distribution of P. macgregorii and predicted the changes of its potential habitat under future climate change scenarios to provide a scientific basis for resource conservation, natural population recovery, and introduction and cultivation of the species.

2. Materials and Methods

2.1. Species Occurrence Data

The current geographical distributions of P. macgregorii were collected from the published related literature [14,15,16] and public databases such as the Plant Photo Bank of China (PPBC; http://ppbc.iplant.cn/, last accessed on 15 April 2022), the Flora Reipublicae Popularis Sinicae (FRPS; http://www.iplant.cn/frps, last accessed on 15 April 2022), the Global Biodiversity Information Facility (GBIF; https://www.gbif.org/, last accessed on 15 April 2022), and the Chinese Virtual Herbarium (CVH; https://www.cvh.ac.cn/index.php, last accessed on 15 April 2022). A total of 140 coordinate points were obtained. To minimize spatial autocorrelation, only one of the multiple sites within a square kilometre was recorded [17,18,19]. Finally, 101 effective distribution sites for P. macgregorii were retained (Figure 2).

2.2. Bioclimatic Data

Nineteen bioclimatic variables (bio_1–bio_19) were downloaded from the World Climate Version 1.4 Database (http://www.worldclim.org/, last accessed on 15 April 2022) [20]. We adopted the climate data of the past, current, and future periods, with a spatial resolution of 30 arc seconds. Climate data from the Last Glacial Maximum (LGM) and the mid-Holocene (MH) were selected and downloaded. The future climate data for 2050 and 2070 were determined based on the Community Climate System Model 4 (CCSM4), which is one of the most efficient global climate projections that has been successfully tested [20,21,22]. The model has four emission scenarios from the Fifth Emission Report of the Intergovernmental Panel on Climate Change [23]. We chose the lowest and the highest emission scenarios, representative concentration pathways (RCP) 2.6 and 8.5, respectively [24,25]. All the climatic factors were transformed into the ASCII format using the ArcGIS conversion tool.
To avoid multicollinearity between climatic variables that might lead to model overfitting, Pearson correlation analysis of 19 bioclimatological variables was performed using SPSS 20.0 software (IBM Corp, Armonk, NY, USA) [26]. We selected the climatic variables with a greater contribution rate when the absolute value of the correlation coefficient between two or more bioclimate factors was greater than 0.8 (Table S1) [27]. Finally, 11 of the 19 variables were selected for predicting the potential habitat of P. macgregorii (Table S2).

2.3. Potential Habitat Evaluation

The modelling of the P. macgregorii distribution in southeastern China was performed using MaxEnt v.3.4.4 [28], based on the species distribution records and bioclimate variables. To make the probability of the occurrence of P. macgregorii close to normal distribution, 75% of the occurrence data were selected as training samples and the remaining 25% of the occurrence data was used for validation purposes [26]. The area under the curve (AUC) of receiver operating characteristic analysis (ROC) was used for calibrating the model and measuring model accuracy [29]. A jackknife test was implemented to assess the relative importance of each climatic variable in influencing the spatial distribution of P. macgregorii [30]. We also analyzed the influence of the dominant bioclimatic factors on the suitability of P. macgregorii and made a response curve for each variable.
The predicted results (binary maps) obtained from the MaxEnt software were then imported into ArcGIS 10.4 software (ESRI, Redlands, CA, USA) to calculate the suitable areas for P. macgregorii. The natural breakpoint classification method was used to divide the predicted results into four grades. The distribution areas with corresponding fitness indexes greater than 0.5 were defined as high-fitness areas. The regions with a fitness index between 0.3 and 0.5 and between 0.1 and 0.3 were classified as moderate and low suitability areas, respectively. Areas with a fitness index lower than 0.1 were defined as non-suitable areas [31]. Each of the four grades is shown in a specific colour. Finally, the predicted distribution map of the potential fitness areas for P. macgregorii in China was drawn. We pairwise compared the changes of the predicted potential distribution areas of adjacent periods by using the SDM toolbox v2.4 in ArcGIS 10.5. Also, we calculated the centroid changes between two binary SDMs and obtained the magnitude and direction of the distribution centroids of P. macgregorii.

3. Results

3.1. Assessment of Model Performance

The AUC value of the model produced to predict the distribution under current climate conditions obtained from 10 repetitions was 0.984 (Figure 3). Meanwhile, the AUC value of the model under the other periods and different climate change scenarios were all greater than 0.980. The high AUC values indicate that the predicted distribution models are accurate and reliable [32].

3.2. Climatic Factor Contribution

The results of the jackknife method show that among the 11 climatic factors, the precipitation in the warmest quarter (bio_18) was the most important climatic variable affecting the distribution of P. macgregorii; its contribution was 33% (Table 1, Figure 4). This was followed by the temperature seasonality (bio_4, 20.9%), the precipitation of the wettest month (bio_13, 17.6%), and the precipitation of driest month (bio_14, 16.1%), which also contributes to the distribution model for P. macgregorii. The four factors had a cumulative contribution of 87.6%. The precipitation seasonality (bio_15), minimum temperature of the coldest month (bio_6), and mean diurnal range (bio_2) contributed 7.8%, 1.7%, and 1.3%, respectively. The top four climatic variables with permutation importance: min temperature of coldest month (bio_6), isothermally (bio_3), precipitation seasonality (bio_15), and mean diurnal range (bio_2), had a cumulative contribution rate of 83.8% (Table 1, Figure 4).
From our results, the main climatic factors affecting the distribution of P. macgregorii in China were largely related to precipitation. The relationship between the main climatic factors and the fitness of P. macgregorii were represented by the response curves (Figure 5). Taking the probability of presence greater than 0.5 as an example, P. macgregorii occurs mainly in areas where the precipitation of warmest quarter is 487.3–799.8 mm and temperature seasonality is 6508.3–8092.2 °C. In addition, the precipitation of the wettest month should be 224.6–338.1 mm and the precipitation of driest month should be 39.2–55.2 mm (Figure 5). Indeed, P. macgregorii would be in the best condition under 689.9 mm of precipitation in the warmest quarter, a temperature seasonality of 7367.4, 285.1 mm precipitation in the wettest month, and 47.2 mm precipitation in the driest month.

3.3. Predicted Potential Distribution of P. macgregorii

In the LGM (Last Glacial Maximum), the suitable habitat of P. macgregorii was mainly between 19.5–37.7° N and 102.5–122.6° E in China (Figure 6), an area of 169.00 × 104 km2. The areas of high-, middle- and low-suitability were 49.71 × 104 km2, 56.80 × 104 km2, and 62.49 × 104 km2, respectively (Table 2).
In the MH (mid-Holocene) period, the habitat of P. macgregorii experienced a range contraction and the region between 19.2–37.7° N and 102.5–122.6° E (Figure 6) was the appropriate habitat for P. macgregorii, a total area of 163.61 × 104 km2.
In the current scenario, H. macgregorii is found growing in more than seven provinces in China and the population of P. macgregorii is mainly distributed between 19.2–37.5° N and 102.6–122.7° E (Figure 2). Anhui, Hunan, Jiangxi, Zhejiang, Fujian, Guangxi, and Guangdong provinces are highly suitable areas with a suitability of more than 50%.
Under the RCP2.6 scenario, the total suitable area in 2050 will be about 162.68 × 104 km2, mainly between 19.5–37.7° N and 102.6–122.6° E. In 2070, the total suitable area will be about 171.88 × 104 km2, including the high-, middle- and low-suitability areas covering about 57.35 × 104 km2, 46.37 × 104 km2 and 68.17 × 104 km2, respectively (Table 2). The latitude and longitude of the area are 19.0–37.0° N and 102.5–122.0° E in China.
Under the RCP8.5 scenario, the total suitable area for P. macgregorii will be about 170.17 × 104 km2 in 2050, mainly distributed between 19.2–37.6° N and 102.4–122.6° E. The total suitable area in the 2070s will be about 167.68 × 104 km2, with a distribution between 19.2–37.6° N and 102.5–122.6° E in China.

3.4. Suitable Growth Range Slight Expansion under Future Climate Change

In the LGM, MH, current period, and future scenarios, the most suitable area was mainly concentrated in the Hunan, Jiangxi, Guangxi, and Guangdong provinces (Figure 6). As time goes on, the suitable area shows a fluctuating trend, from LGM to the current period the total suitable growth area is slightly reduced by 1.77 × 104 km2 (1.05%). Therein, the high-suitability and low-suitability areas increased by 2.1 × 104 km2 and 8.94 × 104 km2, respectively, and the middle-suitability area decreased by 10.82 × 104 km2. From the MH to the current period, the total suitable area experienced a slight increase of 2.21% (3.62 × 104 km2), the expanded areas were mainly in Fujian, Anhui, and Zhejiang Provinces (Figure 7). Under the RCP2.6 scenario, the total suitable area is expected to be reduced to 162.68 × 104 km2 in the 2050s, 2.72% less than the current distribution area. By 2070, the suitable area increased to 171.88 × 104 km2, 2.78% more than the current distribution area, mainly changing in Hunan, Guangdong, Guangxi, and Fujian Provinces (Figure 7). Under the RCP8.5 scenario, a range expansion is expected and the total suitable area will increase in 2050 to 170.17 × 104 km2, 1.76% more than the current distribution area. In RCP8.5-2070, the suitable area is slightly reduced compared with RCP8.5-2050 (Figure 7) but is still more than the current distribution area (0.27%).
The current habitat centroid position of P. macgregorii was estimated to be in Yongfeng, the centre of Jiangxi Province (27.34° N, 115.34° E) (Figure 8). During two paleoclimate periods, the habitat centroid of the LGM was in Ji’an, Jiangxi (26.99° N, 115.00° E) and then shifted northwestward to Jishui, Jiangxi (27.36° N, 114.98° E) during the mid-Holocene. Under the low-concentration greenhouse gas emission scenarios, the centroid was predicted to migrate to Xiajiang, Jiangxi (27.60° N, 115.23° E) under RCP2.6-2050 and finally shifted to Jishui, Jiangxi (27.34° N, 115.20° E) in 2070. Under the high-concentration greenhouse gas emission scenarios, the centroid was in Jishui, Jiangxi (27.32° N, 115.20° E) under RCP6.0-2050, then shifted to Qingyuan, Jiangxi (27.06° N, 115.20° E) in 2070.

4. Discussion

A quarter of the planet’s species would face extinction as a result of accelerating climate change [33]. Saving extremely small populations is essential for ecological, economic, and human health reasons [34]. The P. macgregorii is a rare and vulnerable species endemic to China and has high ornamental value. Understanding how climate change affects the distribution of the silver bellflower and what are the key climatic factors affecting its distribution can provide a scientific basis for its conservation and artificial breeding.

4.1. Key Climatic Factors Determining the P. macgregorii Distribution

The 19 climatic factors from the WorldClim Global Climate Database are the key climatic factors used frequently in predicting suitable habitats [35,36]. Of the four most critical climatic factors affecting the growth of P. macgregorii, three (bio_18, bio_13, bio_14) are related to precipitation, indicating that precipitation is more important than temperature for P. macgregorii. Rainfall directly determines the moisture content and nutrient composition of soil, which has an important effect on plant growth [37]. Precipitation in warmest quarter reflects water sources utilized through the fast-growing season. The optimal bio_18 value of P. macgregorii was 475–793 mm, indicating greater demand for precipitation in summer. This result is consistent with the fact that P. macgregorii grows mainly in the subtropical damp forest and riversides [8]. It was predicted that precipitation will increase in most parts of China in the future climate scenario [38,39], which may be the potential reason for the expansion of the suitable area for P. macgregorii.

4.2. Geographical Distribution and Change of P. macgregorii

Based on our results, the whole suitable area experienced range contraction in the south, whereas the central and eastern parts of the suitable area expanded from the LGM to the MH (Figure 7). The total suitable area had little change in different periods, which means that the impact of climate change on this species in the glacial age is minimal. The high-suitability areas for P. macgregorii include the Nanling Mountain (Guangdong, Hunan, Guangxi), Dayao Mountain (Guangxi), and Wuyi Mountain (Fujian, Jiangxi) areas, which agrees with the opinion that mountains have provided refuge for species during the Neogene and Quaternary periods of global climate changes [40,41].
In the future, the suitable area of P. macgregorii shows an increasing trend, which may correlate with the rise of global temperature [38]. Compared with the previous prediction results of potential distribution changes under climate change in the same area, most plants will decrease in the future, such as Ormosia hosiei [42] and Lonicera oblata [43]. There are only a few species whose potential distribution areas were predicted to expand, such as Potaninia mongolica [44] and Areca catechu [45]. Similar to P. macgregorii, the precipitation in the hottest season was the climate factor with the largest contribution rate and influence for Areca catechu [45].

4.3. Implications for P. macgregorii Conservation

In addition to climate change, human activities, such as road construction and tourism development, may also seriously affect the distribution of P. macgregorii. For the most suitable areas, such as some areas in Jiangxi and Huna, anthropogenic intervention should be monitored and natural reserves established for in situ conservation. Global temperature and precipitation frequency change can also have an impact on seed germination [46,47]. With regards to the difficulty of seed germination for P. macgregorii in their natural state, manual measures for promoting seed germination should be considered [48]. Studies on the dormancy and germination of P. macgregorii seeds found that pretreatment of the seeds with concentrated sulphuric acid and gibberellin can increase the germination rate by 72%, greatly improving the possibility of seedling regeneration.

5. Conclusions

Our study predicted the potential distribution of P. macgregorii in the past, current, and future climate scenarios. The total suitable area for P. macgregorii fluctuates in different periods with a likely increase in the future under favourable conditions and the distribution area of the highly suitable area is relatively stable. Jiangxi province is the centroid for distribution and precipitation is the main climatic factor affecting its distribution.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/su15010666/s1, Table S1: Pearson correlation matrix; Table S2: Environmental data used in this study.

Author Contributions

Conceptualization, M.-H.Y.; methodology, J.-Y.S., N.-C.D. and B.-W.L.; formal analysis, N.-C.D. and B.B.T.; software: M.-H.Y. and J.-Y.S.; writing—original draft preparation, M.-H.Y. and J.-Y.S.; writing—review and editing, H.-C.W. and H.-Y.Y., B.-W.L. and B.B.T.; visualization, B.-W.L.; supervision, M.-H.Y.; project administration, M.-H.Y.; funding acquisition, M.-H.Y., H.-Y.Y. and H.-C.W. All authors have read and agreed to the published version of the manuscript.

Funding

This work was funded by the National Natural Science Foundation of China (31800276, 31370223), National Key Research and Development Program of China (2021YFD1601100), the Foundation of He-Nan Province Educational Committee (19A180029) and Nanhu Scholars Program for Young Scholars of XYNU granted to Ming-Hui Yan.

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. Photos of P. macgregorii. (a) Young leaves; (b) leaves with pink petioles; (c) the earliest flowers; (d) full-bloom flowers; (e) young fruits; (f) dried fruits.
Figure 1. Photos of P. macgregorii. (a) Young leaves; (b) leaves with pink petioles; (c) the earliest flowers; (d) full-bloom flowers; (e) young fruits; (f) dried fruits.
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Figure 2. Current distribution sites and potential distribution area of P. macgregorii in China. The black dots in the figure represent the existing distribution of P. macgregorii.
Figure 2. Current distribution sites and potential distribution area of P. macgregorii in China. The black dots in the figure represent the existing distribution of P. macgregorii.
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Figure 3. The receiver operating characteristic (ROC) curve of results obtained from the simulation performed ten times.
Figure 3. The receiver operating characteristic (ROC) curve of results obtained from the simulation performed ten times.
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Figure 4. Jackknife test of area under the curve (AUC) value.
Figure 4. Jackknife test of area under the curve (AUC) value.
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Figure 5. The response curve of the dominant climatic factors. Panel (a) indicates that of bio_18; (b) indicates that of bio_4; (c) indicates that of bio_13; (d) indicates that of bio_14.
Figure 5. The response curve of the dominant climatic factors. Panel (a) indicates that of bio_18; (b) indicates that of bio_4; (c) indicates that of bio_13; (d) indicates that of bio_14.
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Figure 6. Potential distribution of P. macgregorii in different periods. (a) Last Glacial Maximum (LGM); (b) mid-Holocene (MH); (c) future climate scenario RCP2.6-2050; (d) future climate scenario RCP2.6-2070; (e) future climate scenario RCP8.5-2050; (f) future climate scenario RCP8.5-2070.
Figure 6. Potential distribution of P. macgregorii in different periods. (a) Last Glacial Maximum (LGM); (b) mid-Holocene (MH); (c) future climate scenario RCP2.6-2050; (d) future climate scenario RCP2.6-2070; (e) future climate scenario RCP8.5-2050; (f) future climate scenario RCP8.5-2070.
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Figure 7. Changes of high suitability area of P. macgregorii in different periods. (a) Last Glacial Maximum (LGM) to mid-Holocene (MH); (b) mid-Holocene to current; (c) current to RCP2.6-2050; (d) RCP2.6-2050 to RCP2.6-2070; (e) current to RCP8.5-2050; (f) RCP8.5-2050 to RCP8.5-2070.
Figure 7. Changes of high suitability area of P. macgregorii in different periods. (a) Last Glacial Maximum (LGM) to mid-Holocene (MH); (b) mid-Holocene to current; (c) current to RCP2.6-2050; (d) RCP2.6-2050 to RCP2.6-2070; (e) current to RCP8.5-2050; (f) RCP8.5-2050 to RCP8.5-2070.
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Figure 8. Migration route of the habitat centroid of P. macgregorii in China under different climates. The blue arrows indicate migration direction of habitat centroid from the past to the future under the RCP2.6 scenario. The orange arrows indicate migration direction of habitat centroid from past to the future under the RCP2.8 scenario.
Figure 8. Migration route of the habitat centroid of P. macgregorii in China under different climates. The blue arrows indicate migration direction of habitat centroid from the past to the future under the RCP2.6 scenario. The orange arrows indicate migration direction of habitat centroid from past to the future under the RCP2.8 scenario.
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Table
Table 1. The contribution rate and permutation importance of the selected climatic factors.
Table 1. The contribution rate and permutation importance of the selected climatic factors.
OrderAbbreviationContribution (%)Permutation Importance (%)
1bio_1833.0 *0.9
2bio_420.9 *2.1
3bio_1317.6 *4.0
4bio_1416.1 *2.7
5bio_157.87.5
6bio_61.738.9 *
7bio_21.33.2
8bio_30.834.2 *
9bio_100.41.6
10bio_80.22.3
11bio_90.12.5
* Variable with highest contribution and importance.
Table
Table 2. The suitable areas of P. macgregorii under different period and climate scenarios (×104 km2).
Table 2. The suitable areas of P. macgregorii under different period and climate scenarios (×104 km2).
PeriodTotal Suitable AreaHigh-Suitability AreaMiddle-Suitability AreaLow-Suitability AreaNon-Suitability Area
PastLGM169.0049.7156.8062.49791.00
MH163.6154.8144.4664.35796.39
PresentCurrent167.2351.8143.9871.43792.77
FutureRCP2.6-2050162.6849.5145.2967.88797.32
RCP2.6-2070171.8857.3546.3768.17788.12
RCP8.5-2050170.1752.1849.3568.64789.83
RCP8.5-2070167.6849.9160.8656.91792.32
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Yan, M.-H.; Si, J.-Y.; Dong, N.-C.; Liu, B.-W.; Tiamiyu, B.B.; Wang, H.-C.; Yuan, H.-Y. Assessing the Potential Distribution of a Vulnerable Tree under Climate Change: Perkinsiodendron macgregorii (Chun) P.W.Fritsch (Styracaceae). Sustainability 2023, 15, 666. https://doi.org/10.3390/su15010666

AMA Style

Yan M-H, Si J-Y, Dong N-C, Liu B-W, Tiamiyu BB, Wang H-C, Yuan H-Y. Assessing the Potential Distribution of a Vulnerable Tree under Climate Change: Perkinsiodendron macgregorii (Chun) P.W.Fritsch (Styracaceae). Sustainability. 2023; 15(1):666. https://doi.org/10.3390/su15010666

Chicago/Turabian Style

Yan, Ming-Hui, Jie-Ying Si, Nian-Ci Dong, Bin-Wen Liu, Bashir B. Tiamiyu, Heng-Chang Wang, and Hong-Yu Yuan. 2023. "Assessing the Potential Distribution of a Vulnerable Tree under Climate Change: Perkinsiodendron macgregorii (Chun) P.W.Fritsch (Styracaceae)" Sustainability 15, no. 1: 666. https://doi.org/10.3390/su15010666

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