MaxEnt Modeling Based on CMIP6 Models to Project Potential Suitable Zones for Cunninghamia lanceolata in China

: Cunninghamia lanceolata (Lamb.) Hook. (Chinese ﬁr) is one of the main timber species in Southern China, which has a wide planting range that accounts for 25% of the overall afforested area. Moreover, it plays a critical role in soil and water conservation; however, its suitability is subject to climate change. For this study, the appropriate distribution area of C. lanceolata was analyzed using the MaxEnt model based on CMIP6 data, spanning 2041–2060. The results revealed that (1) the minimum temperature of the coldest month (bio6), and the mean diurnal range (bio2) were the most important environmental variables that affected the distribution of C. lanceolata ; (2) the currently suitable areas of C. lanceolata were primarily distributed along the southern coastal areas of China, of which 55% were moderately so, while only 18% were highly suitable; (3) the projected suitable area of C. lanceolata would likely expand based on the BCC-CSM2-MR, CanESM5, and MRI-ESM2-0 under different SSPs spanning 2041–2060. The increased area estimated for the future ranged from 0.18 to 0.29 million km 2 , where the total suitable area of C. lanceolata attained a maximum value of 2.50 million km 2 under the SSP3-7.0 scenario, with a lowest value of 2.39 million km 2 under the SSP5-8.5 scenario; (4) in combination with land use and farmland protection policies of China, it is estimated that more than 60% of suitable land area could be utilized for C. lanceolata planting from 2041–2060 under different SSP scenarios. Although climate change is having an increasing inﬂuence on species distribution, the deleterious impacts of anthropogenic activities cannot be ignored. In the future, further attention should be paid to the investigation of species distribution under the combined impacts of climate change and human activities.


Introduction
In recent years, climate change has adversely affected ecosystems and myriad biological species on a global scale [1][2][3][4]. Continuously intensifying deleterious changes in climate may lead to the extinction of nearly one-quarter of the world's species [5,6]. To prevent these global warming-induced losses, it is critical to develop the capacity to predict the potential distribution of species under global climate change, as well as formulate long term strategies for the protection of species to ensure the sustainability of the biosphere in the future [7].
To better analyze the past, present and future of climate change, the Coupled Model Intercomparison Project (CMIP) was implemented by the Intergovernmental Panel on Climate Change (IPCC) twenty years ago. The Representative Concentration Pathways (RCPs) adopted in the fifth assessment report (AR5) of IPCC were a series of integrated concentration and emission scenarios. The global climate models (GCMs) from different phases of the CMIP have been at the heart of climate change studies [8]. RCPs each contain In recent decades, land use/land cover change (LUCC) has become increasingly important for its possibilities to map and characterize land cover based on observation and remote sensing [39], and it has been employed to address the problem in various aspects, such as forest fragmentation [40], agricultural expansion [41,42], and suburbanization [43]. Along with a large number of forest land into farmland cases, the global forest area has decreased significantly in recent years [44].
Cunninghamia lanceolata (Lamb.) Hook. (Chinese fir) is a native species of China with a long planting history, which is widely distributed across the Yangtze River basin, as well as 16 southern provinces and autonomous regions. It is a subtropical tree species with a developed shallow lateral root system and strong regeneration capacity; C. lanceolata preferentially grows in moist, acidic, well-drained soils in partial shade; it tolerates full sun, but soils should not be allowed to dry out. This species is also known for its high level of wood quality with high yields [45]. As an important economic tree species in China, the area of C. lanceolata plantations has reached 11 million hectares, with a stock volume of 625 million cubic meters; accounting for 19.01% and 25.18% of the dominant trees in China, respectively [46].
In this study, the latest shared socioeconomic pathways (SSPs) from the CMIP6 were used to project the suitable area of C. lanceolata into the future under the changing global environment. To eliminate the influence of irrelevant variables on the result and enhance the credibility of the prediction results, the BCC-CSM2, CanESM5 and MRI-ESM2-0 GCMs were used in this paper. The projected results will be arithmetic averaged to find out the most likely changes in the suitable area of C. lanceolata in the future. Furthermore, considering the relationships between future suitable areas and current land use/land cover and agricultural policies, the prediction result would be combined with the land use/land cover change data to provide a guide for the distribution of plantations with the background of global warming.

Current Species Data
The current distribution data for C. lanceolata were collected from the Global Biodiversity Information Facility (GBIF, https//www.gbif.org, accessed on 6 December 2020) and the Chinese Virtual Herbarium (CVH, http://www.cvh.ac.cn, accessed on 6 December 2020). Any records without detailed geolocation information such as longitude or latitude, or repetitive records, were removed. A total of 406 distribution points of C. lanceolata were collected and employed to establish the MaxEnt model ( Figure 1).

Environmental Variables for Model Fitting
Twenty-one variables related to the distribution of C. lanceolata were collected, including 19 bioclimatic variables (bio01-bio19) and three topographic variables (ALT, ASP, and SLO) (Table A1). This was because previous studies indicated that these environmental variables were the most significant factors for modeling potential species distribution [47]. All of the layers were at the highest spatial resolution (30 arc-second (~1 km)). The bioclimatic variables and ALT variables were obtained from the WorldClim dataset. The aspect (ASP) and slope (SLO) were extracted from the altitude by using the ArcGIS 10.5.
To avoid the influence of the multicollinearity of these variables and overfitting of the MaxEnt [48], a Pearson correlation analysis of these twenty-one variables was conducted in SPSS 22.0. When the correlation coefficient of two variables was greater than 0.80 [49], the variables with lower ecological significance were removed. The selection principle was set according to the relevant literature and the habitat of C. lanceolata [50]. Finally, 12 environmental variables (bio2, bio3, bio4, bio5, bio6, bio8, bio13, bio14, bio15, ALT, ASP, and SLO) were retained in the process in MaxEnt (Table A2).

Environmental Variables for Model Forecasting
To predict the potential distribution of C. lanceolata under future climate conditions, projections of future bioclimatic variables, according to different global climatic models and emission scenarios from BCC-CSM2-MR (Beijing Climate Center, China), CanESM5 (Canadian Centre for Climate Modelling and Analysis, Canada) and MRI-ESM2-0 (Meteorological Research Institute, Japan) according to the shared socioeconomic pathways (SSPs) SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5 for 2041-2060 from the Coupled Model Intercomparison Project Phase 6 (CMIP6) were downloaded from the WorldClim dataset (http://www.worldclim.org, accessed on 8 December 2020).
As one set of scenarios in the CMIP6, the shared socioeconomic pathways (SSPs) were combined with the RCPs [51], which provides five different pathways of future socioeconomic development and contains possible trends in agriculture and land use. SSP1 represents a society that makes a shift to sustainable development. Conversely, the SSP2 depicts

Environmental Variables for Model Fitting
Twenty-one variables related to the distribution of C. lanceolata were collected, including 19 bioclimatic variables (bio01-bio19) and three topographic variables (ALT, ASP, and SLO) (Table A1). This was because previous studies indicated that these environmental variables were the most significant factors for modeling potential species distribution [47]. All of the layers were at the highest spatial resolution (30 arc-second (~1 km)). The bioclimatic variables and ALT variables were obtained from the WorldClim dataset. The aspect (ASP) and slope (SLO) were extracted from the altitude by using the ArcGIS 10.5.
To avoid the influence of the multicollinearity of these variables and overfitting of the MaxEnt [48], a Pearson correlation analysis of these twenty-one variables was conducted in SPSS 22.0. When the correlation coefficient of two variables was greater than 0.80 [49], the variables with lower ecological significance were removed. The selection principle was set according to the relevant literature and the habitat of C. lanceolata [50]. Finally, 12 environmental variables (bio2, bio3, bio4, bio5, bio6, bio8, bio13, bio14, bio15, ALT, ASP, and SLO) were retained in the process in MaxEnt (Table A2).

Environmental Variables for Model Forecasting
To predict the potential distribution of C. lanceolata under future climate conditions, projections of future bioclimatic variables, according to different global climatic models and emission scenarios from BCC-CSM2-MR (Beijing Climate Center, China), CanESM5 (Canadian Centre for Climate Modelling and Analysis, Canada) and MRI-ESM2-0 (Meteorological Research Institute, Japan) according to the shared socioeconomic pathways (SSPs) SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5 for 2041-2060 from the Coupled Model Intercomparison Project Phase 6 (CMIP6) were downloaded from the WorldClim dataset (http://www.worldclim.org, accessed on 8 December 2020).
As one set of scenarios in the CMIP6, the shared socioeconomic pathways (SSPs) were combined with the RCPs [51], which provides five different pathways of future socioeconomic development and contains possible trends in agriculture and land use. SSP1 represents a society that makes a shift to sustainable development. Conversely, the SSP2 depicts a society that develops following a historical pattern without substantial future deviations. SSP3 and 4 represent societies with rapidly growing populations and low investments in health or education. SSP5 assumes a social economy that is based on fossil fuels and intensive energy use. Each SSP achieves the same level of radiation forcing as the representative concentration pathways (RCPs) through the reduction of emissions and increased carbon absorption [52].
Considering the potential anthropogenic emissions and land use changes caused by energy structure in different SSPs, the scenario comparison plan (ScenarioMIP) became one of the most important sub plans of the CMIP6. The combination of RCPs and shared socioeconomic pathways (SSPs) makes the future scenarios more reasonable [53]. The RCP2.6, RCP4.5 and RCP8.5 representing scenarios in which the total radiative forcing in 2100 had reached 2.6 W/m 2 , 4.5 W/m 2 , and 8.5 W/m 2 .
In this study, SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5 were selected to predict the averaged suitable distribution areas for the expected climatic conditions from 2041-2060. Among these scenarios, SSP3-7.0 was the new scenario combinations, and the SSP1-2.6, SSP2-4.5 and SSP5-8.5 were the updated version of the RCP scenarios. The SSP1-2.6 scenario represented the low-end range of future scenarios measured by its radiative forcing pathway and predicted a waring inferior to 2 • C by 2100. The SSP2-4.5 scenario was considered as a medium stabilization scenario, while the SSP3-7.0 scenario corresponded to the medium-to high-end of the range of future forcing pathways. SSP5-8.5 was the only scenario that stabilized radiative forcing at 8.5 W/m 2 in 2100, which was considered to be a high radiative forcing scenario [54].

Land Use and Land Cover Data
For this study, the latest data set of land use types of China in 2015 was downloaded from the Chinese Academy of Sciences Resource and Environmental Science Data Center (http://www.resdc.cn/, accessed on 8 December 2020). Land use dataset in China , National Tibetan Plateau Data Center, 2019. The data classified the land into six basic categories based on land use types. To match the predicted MaxEnt results, while highlighting the purpose of this study, the data were reclassified into four categories: 1. farmland; 2. bare land; 3. Woodland; 4. grassland. These data were loaded to ArcGis, and all predictions of three models under four socioeconomic pathways were clipped by it. This revealed suitable areas for the practical available planting of C. lanceolata as predicted by MaxEnt, which provided this study with stronger realistic references.

MaxEnt Model Description and Modeling
For this study, the MaxEnt model was used to identify environmental variables that affected species distribution. Meanwhile, it can be employed to simulate current and project future potential suitable distribution areas. MaxEnt software for modeling species niches and distributions (Version 3.4.1). Available from url: http://biodiversityinformatics. amnh.org/open_source/maxent/, accessed on 2 December 2020. A 75% portion of the occurrence data was used for training, whereas the remaining 25% were used for testing.
The linear, quadratic, product, and hinge were set as automatic. The logistic output was used in MaxEnt, which generated a continuous map with an estimated probability of presence between 0 and 1. The current distribution data for C. lanceolata and twelve environmental variables were loaded into the MaxEnt model. The spatial autocorrelation in the model was reduced by a ten-fold cross-validation, because it can reduce model errors that may occur from the random splitting of data into test and training subsets [55]. The data were cross-validated with a random 25% of the presence points being withheld each time, after which the results were averaged.
The logistic output of the MaxEnt revealed a potential distribution map of the C. lanceolata in China. The results obtained from the MaxEnt were assembled in ArcGIS 10.5 to generate the output in raster format for further analysis. The Chinese administrative division vector map (1:4,000,000) was downloaded as the base map for analysis.

Evaluation of Model Results and Potential Habitat Classification
The performance of the model can be analyzed by the receiver operating characteristic curve (ROC) and the area under the ROC curve (AUC), where the AUC value ranges from 0 to 1. The performance of the model was classified as follows: failing (0.5-0.6), poor (0.6-0.7), fair (0.7-0.8), good (0.8-0.9), and excellent (0.9-1.0), with AUC values closer to 1, indicating more accurate prediction results.
The output in raster format was loaded into ArcGIS 10.5, which reclassified the distribution map into four classes of potential suitable zones.

Identification of Planting Area with LU Data
The LU data (2015) were loaded into ArcGis under four categories: 1. farmland; 2. bare land; 3. woodland; 4. grassland. As is the policy in China, farmland will be conserved; thus, these areas will not be available for the planting of C. lanceolata in the future. Consequently, the bare land, woodland, and grassland were assumed to have potential for the future planting of C. lanceolata. Calculations of the area of bare land, woodland, and grassland were based on the prediction of BCC-CSM2-MR, CanESM5, and MRI-ESM2-0 under SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5 socio-shared pathways, respectively.

Model Evaluations and Critical Environmental Variables
The Maximum Entropy model was employed to predict potential habitats with a mean AUC of 0.914 ( Figure 2). The mean AUC values for the MaxEnt models of C. lanceolata were significantly higher than the random prediction value (0.5). The prediction results were very accurate, which also meant that the results of the potential distribution area made by MaxEnt were reliable. generate the output in raster format for further analysis. The Chinese administrative division vector map (1:4,000,000) was downloaded as the base map for analysis.

Evaluation of Model Results and Potential Habitat Classification
The performance of the model can be analyzed by the receiver operating characteristic curve (ROC) and the area under the ROC curve (AUC), where the AUC value ranges from 0 to 1. The performance of the model was classified as follows: failing (0.5-0.6), poor (0.6-0.7), fair (0.7-0.8), good (0.8-0.9), and excellent (0.9-1.0), with AUC values closer to 1, indicating more accurate prediction results.
The output in raster format was loaded into ArcGIS 10.5, which reclassified the distribution map into four classes of potential suitable zones.

Identification of Planting Area with LU Data
The LU data (2015) were loaded into ArcGis under four categories: 1. farmland; 2. bare land; 3. woodland; 4. grassland. As is the policy in China, farmland will be conserved; thus, these areas will not be available for the planting of C. lanceolata in the future. Consequently, the bare land, woodland, and grassland were assumed to have potential for the future planting of C. lanceolata. Calculations of the area of bare land, woodland, and grassland were based on the prediction of BCC-CSM2-MR, CanESM5, and MRI-ESM2-0 under SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5 socio-shared pathways, respectively.

Model Evaluations and Critical Environmental Variables
The Maximum Entropy model was employed to predict potential habitats with a mean AUC of 0.914 ( Figure 2). The mean AUC values for the MaxEnt models of C. lanceolata were significantly higher than the random prediction value (0.5). The prediction results were very accurate, which also meant that the results of the potential distribution area made by MaxEnt were reliable.  MaxEnt corrects the adjustment of individual evaluation factors and coefficients in the model prediction using an iterative algorithm and calculates the contribution of environmental factors to the prediction.
The Table 1. showed that the precipitation of the driest month (bio14, 58%), minimum temperature of the coldest month (bio6, 24.4%), and mean diurnal range (bio2, 4.7%) were the top three environmental data used in the prediction of the MaxEnt model that affected the distribution of C. lanceolata, with a cumulative contribution of 87.1%. Min temperature of coldest month Temperature seasonality (standard deviation*100) Precipitation of wettest month mm 1.2 bio5 Max temperature of warmest month Mean temperature of wettest quarter Precipitation The jackknife results ( Figure 3) showed that the dominant environmental variables, which influenced the habitat of C. lanceolata, were the minimum temperature of the coldest month (bio6), mean diurnal range (bio2), precipitation of the wettest month (bio13), precipitation of the driest month (bio14), and temperature seasonality (bio4). In summary, temperature, precipitation, and temperature differences were the main factors that limited the selection of suitable areas for C. lanceolata.
The Table 1. showed that the precipitation of the driest month (bio14, 58%), minimum temperature of the coldest month (bio6, 24.4%), and mean diurnal range (bio2, 4.7%) were the top three environmental data used in the prediction of the MaxEnt model that affected the distribution of C. lanceolata, with a cumulative contribution of 87.1%. Mean temperature of wettest quarter °C 1 bio15 Precipitation The jackknife results ( Figure 3) showed that the dominant environmental variables, which influenced the habitat of C. lanceolata, were the minimum temperature of the coldest month (bio6), mean diurnal range (bio2), precipitation of the wettest month (bio13), precipitation of the driest month (bio14), and temperature seasonality (bio4). In summary, temperature, precipitation, and temperature differences were the main factors that limited the selection of suitable areas for C. lanceolata. According to the response curves for environmental variables in MaxEnt, the probabilities for the presence of C. lanceolata in China could be assessed. When the presence probability of C. lanceolata was greater than 0.1, the corresponding environmental variable value was the critical value for C. lanceolata. The mean diurnal range (mean of monthly (max temp-min temp)) (bio2) appropriate for the growth of C. lanceolata was found to range from 3.3 °C to 10.6 °C . When the mean diurnal range (mean of monthly (max temp- According to the response curves for environmental variables in MaxEnt, the probabilities for the presence of C. lanceolata in China could be assessed. When the presence probability of C. lanceolata was greater than 0.1, the corresponding environmental variable value was the critical value for C. lanceolata. The mean diurnal range (mean of monthly (max temp-min temp)) (bio2) appropriate for the growth of C. lanceolata was found to range from 3.3 • C to 10.6 • C. When the mean diurnal range (mean of monthly (max temp-min temp)) (bio2) was 5.77, the probability of the presence of C. lanceolata attained the maximum value. The seasonal temperature (standard deviation * 100) (bio4) that was suitable for the growth of C. lanceolata ranged from 1.4 to 9.5 • C. The maximum temperature of the warmest month (bio5) appropriate for the growth of C. lanceolata was found to range from 17.4 • C to 35.5 • C. The minimum temperature of the coldest month (bio6) > −5.4 • C was suitable for the growth of C. lanceolata. The threshold value of precipitation of wettest month (bio13) appropriate for the growth of C. lanceolata was 157.8 mm, whereas the threshold value of the precipitation of the driest month suitable for the growth of C. lanceolata was 5.5 mm (Figure 4). min temp)) (bio2) was 5.77, the probability of the presence of C. lanceolata attained the maximum value. The seasonal temperature (standard deviation * 100) (bio4) that was suitable for the growth of C. lanceolata ranged from 1.4 to 9.5 °C . The maximum temperature of the warmest month (bio5) appropriate for the growth of C. lanceolata was found to range from 17.4 °C to 35.5 °C . The minimum temperature of the coldest month (bio6) > −5.4 °C was suitable for the growth of C. lanceolata. The threshold value of precipitation of wettest month (bio13) appropriate for the growth of C. lanceolata was 157.8 mm, whereas the threshold value of the precipitation of the driest month suitable for the growth of C. lanceolata was 5.5 mm (Figure 4).
The highly suitable areas for C. lanceolata in China were found to be primarily located in Southeastern Tibet; Central and Western Chongqing; Southeastern Yunnan; Southeast-
The highly suitable areas for C. lanceolata in China were found to be primarily located in Southeastern Tibet; Central and Western Chongqing; Southeastern Yunnan; Southeastern Sichuan; southwestern Guizhou; Southern and Central Hunan; Southern, Northeastern Guangxi; Central and Northern Guangdong; Central Hong Kong; Central, Southern and Eastern Jiangxi; Central Fujian; as well as Central Zhejiang and Taiwan Provinces.
Moderately suitable areas for C. lanceolata in China were found to be mainly located in Western and Eastern Yunnan; Eastern and Southern Sichuan; Southern Shanxi; Chongqing; Central and Eastern Hubei; Western, Central and Eastern Anhui; Northeastern, Northwestern and Central Zhejiang; Central and Northern Hainan; Guizhou; Guangxi; Hunan; Guangdong; Hunan; Jiangxi; Fujian Provinces. ern Guangxi; Central and Northern Guangdong; Central Hong Kong; Central, Southern and Eastern Jiangxi; Central Fujian; as well as Central Zhejiang and Taiwan Provinces.
Moderately suitable areas for C. lanceolata in China were found to be mainly located in Western and Eastern Yunnan; Eastern and Southern Sichuan; Southern Shanxi; Chongqing; Central and Eastern Hubei; Western, Central and Eastern Anhui; Northeastern, Northwestern and Central Zhejiang; Central and Northern Hainan; Guizhou; Guangxi; Hunan; Guangdong; Hunan; Jiangxi; Fujian Provinces.
An analysis of the prediction of BCC-CSM2-MR under different SSPs revealed that the maximum threshold of suitable areas was attained under SSP3-7.0, with the minimum under SSP5-8.5. Compared to the prediction of SSP1-2.6, the highly suitable and moderately suitable areas increased slightly under SSP2-4.5 and SSP3-7.0. Under SSP2-4.5, the highly suitable and moderately suitable areas obtained the maximum value between four SSPs, after which they both decreased dramatically to 100.45 × 10 4 km 2 and 57.10 × 10 4 km 2 , respectively, under the SSP5-8.0. Meanwhile, the marginally suitable area under SSP3-7.0 increased to 15.11 × 10 4 km 2 and obtained the maximum value between the four SSPs. Through observations and comparisons ( Figure 6), it was found that the highly suitable zones in Guangxi, Tibet, and Anhui Provinces were degraded to moderately or marginally suitable zones. Furthermore, the moderately suitable zones in the Sichuan, Guangxi, and Anhui Provinces were degraded to marginally suitable zones under the high radiative forcing scenario (SSP5-8.5). This indicated that the highly radiative forcing environment had a negative effect on C. lanceolata.
An analysis of the prediction of BCC-CSM2-MR under different SSPs revealed that the maximum threshold of suitable areas was attained under SSP3-7.0, with the minimum under SSP5-8.5. Compared to the prediction of SSP1-2.6, the highly suitable and moderately suitable areas increased slightly under SSP2-4.5 and SSP3-7.0. Under SSP2-4.5, the highly suitable and moderately suitable areas obtained the maximum value between four SSPs, after which they both decreased dramatically to 100.45 × 10 4 km 2 and 57.10 × 10 4 km 2 , respectively, under the SSP5-8.0. Meanwhile, the marginally suitable area under SSP3-7.0 increased to 15.11 × 10 4 km 2 and obtained the maximum value between the four SSPs. Through observations and comparisons ( Figure 6), it was found that the highly suitable zones in Guangxi, Tibet, and Anhui Provinces were degraded to moderately or marginally suitable zones. Furthermore, the moderately suitable zones in the Sichuan, Guangxi, and Anhui Provinces were degraded to marginally suitable zones under the high radiative forcing scenario (SSP5-8.5). This indicated that the highly radiative forcing environment had a negative effect on C. lanceolata. Forests 2021, 12, x 10 of 21 Figure 6. Potential suitable area for C. lanceolata in the three models and MME under four SSPs.
Conversely, the prediction of the CanESM5 model under different SSPs revealed that the total of suitable areas attained a maximum under SSP3-7.0, and the minimum under SSP5-8.5 was the same as the prediction of the BCC-CSM2-MR model. The highly suitable area reached the maximum value under SSP1-2.6, whereas the total of moderately suitable and suitable areas simultaneously reached the maximum under SSP3-7.0. Under SSP5-8.5, the total suitable zone was the smallest, whereas the marginally suitable zone was the largest. Finally, according to the prediction of MRI-ESM2-0, the highly suitable area and sum of the suitable area attained their maximum under SSP2-4.5, and the marginally suitable area reached the minimum under SSP3-7.0.
An assessment of the prediction results of the three models under the four SSPs found that under SSP5-8.0, the prediction of the two models showed that the marginally suitable zone obtained its maximum, and there was no indication that the highly suitable zone obtained its maximum. Meanwhile, the average values of suitable and highly suitable areas in three models under SSP5-8.0 were the smallest between the four SSPs, which signified that the high radiative forcing had a negative effect on C. lanceolata. This initiated the degradation of highly or moderately suitable zones, predominantly located in Guangxi, Shanxi, Sichuan, and Jiangsu, to marginally suitable or unsuitable zones.
The arithmetic average values of highly suitable areas of the three models under SSP1-2.6 reached their maximum and their standard deviations were quite small, which further indicated that the prediction was very convincing. This showed that the environment with a low radiative forcing and a 2 °C increase in temperature would effectively Conversely, the prediction of the CanESM5 model under different SSPs revealed that the total of suitable areas attained a maximum under SSP3-7.0, and the minimum under SSP5-8.5 was the same as the prediction of the BCC-CSM2-MR model. The highly suitable area reached the maximum value under SSP1-2.6, whereas the total of moderately suitable and suitable areas simultaneously reached the maximum under SSP3-7.0. Under SSP5-8.5, the total suitable zone was the smallest, whereas the marginally suitable zone was the largest. Finally, according to the prediction of MRI-ESM2-0, the highly suitable area and sum of the suitable area attained their maximum under SSP2-4.5, and the marginally suitable area reached the minimum under SSP3-7.0.
An assessment of the prediction results of the three models under the four SSPs found that under SSP5-8.0, the prediction of the two models showed that the marginally suitable zone obtained its maximum, and there was no indication that the highly suitable zone obtained its maximum. Meanwhile, the average values of suitable and highly suitable areas in three models under SSP5-8.0 were the smallest between the four SSPs, which signified that the high radiative forcing had a negative effect on C. lanceolata. This initiated the degradation of highly or moderately suitable zones, predominantly located in Guangxi, Shanxi, Sichuan, and Jiangsu, to marginally suitable or unsuitable zones.
The arithmetic average values of highly suitable areas of the three models under SSP1-2.6 reached their maximum and their standard deviations were quite small, which further indicated that the prediction was very convincing. This showed that the environment with a low radiative forcing and a 2 • C increase in temperature would effectively enlarge the highly suitable zone for C. lanceolata. The arithmetic average of its total suitable area predicted by the three models under SSP1-2.6 was not the largest between the four SSPs; it was 15.79 × 10 4 km 2 less than the maximum value predicted under SSP3-7.0. The precipitation, temperature, and other environmental factors in the highly suitable zone had a positive impact on the growth of C. lanceolata, which indicated that the density, growth rate, or wood quality of C. lanceolata in highly suitable zones would be better than those in other suitable zones. Therefore, the low intensity of radiative forcing environment was best for the expansion of the suitable area for C. lanceolata. However, there was no significant difference between the moderately suitable area predicted under SSP2-4.5, and SSP3-7.0. Figure 7 has shown the future changes in suitable habitats in the future. From 2041-2060, the prediction of the BCC-CSM2-MR model under the SSP1-2.6 scenario indicated that the lost suitable habitat area of C. lanceolata would be 7.71 × 10 4 km 2 , whereas the gained area would be 25.60 × 10 4 km 2 . The lost area was primarily located in South Yunnan, where there was a marginally suitable area for C. lanceolata. The gained area was mainly concentrated in Southwestern and Eastern Shandong, as well as West and South Henan, which became a marginally suitable area for C. lanceolata. There were 0.39 × 10 4 km 2 of marginally suitable area gained, 20.07 × 10 4 km 2 of moderately suitable area lost, and 37.69 × 10 4 km 2 of highly suitable area gained ( Figure 8). The increased area of the highly suitable area was primarily located in Hunan and Guangxi Provinces.

Future Changes in Suitable Habitats
The prediction of BCC-CSM2-MR under SSP2-4.5 showed that there were 3.14 × 10 4 km 2 of suitable area lost and 32.26 × 10 4 km 2 gained. The lost area was mainly located in Eastern Jiangsu, where there was a marginally suitable area. The gained area was primarily located in Southern Shandong, Southern Shanxi, and Southern Yunnan, which became marginally suitable areas. There were 2.59 × 10 4 km 2 of marginally suitable area gained, 15.59 × 10 4 km 2 of moderately suitable area lost, and 42.34 × 10 4 km 2 of highly suitable area gained. The increased area of the highly suitable area was mainly located in Southern Anhui, as well as South and Central Guizhou, Fujian, Zhejiang, Hunan, and Guangxi Provinces.
The prediction of BCC-CSM2-MR under SSP3-7.0 revealed that there were 2.12 × 10 4 km 2 of suitable area lost, and 35.56 × 10 4 km 2 gained. In contrast to the other models, some of the gained area was located in Xinjiang Province. There were 11.37 × 10 4 km 2 of marginally suitable area gained, 20 × 10 4 km 2 of moderately suitable area lost, and 42.30 × 10 4 km 2 of highly suitable area gained. The marginally suitable area was located in Hubei, Southern Shanxi, whereas Southern Henan changed to a moderately suitable area, which caused a decrease in the marginally suitable area and increase in the moderately suitable area.
The prediction of BCC-CSM2-MR under SSP5-8.5 showed that there were 5.56 × 10 4 km 2 of suitable area lost and 24.72 × 10 4 km 2 gained. The lost area was mainly located in Southern Jiangsu and the gained area was mostly located in Southern Shandong, Southern Shanxi, and Southern Yunnan. There was a 26.48 × 10 4 km 2 increase of marginally suitable area, 24.75 × 10 4 km 2 decrease in moderately suitable area, and 17.61 × 10 4 km 2 increase of highly suitable area.
The following conclusions were drawn by analyzing the data: 1. The suitable distribution areas increased in the prediction of BCC-CSM2-MR model under four future sharing-economic scenarios, which were mainly located in Southern Gansu, Shanxi, Eastern Shandong, as well as Western and Southern Henan. Under the SSP3-7.0, the increased area attained a maximum of 33.44 × 10 4 km 2 . 2. The moderately suitable area decreased in all four SSP scenarios. Meanwhile, the highly suitable and marginally suitable areas increased. The growth value of the highly suitable area was far greater than that of the marginally suitable area, except under the SSP5-8.0 scenario.
The prediction of the CanESM5 model under SSP1-2.6 showed that there were 0.72 × 10 4 km 2 of suitable area lost and 41.95 × 10 4 km 2 gained. Except for this gained area, Southern Hebei became the marginally suitable area for C. lanceolata. The marginally suitable area decreased by 2.49 × 10 4 km 2 , whereas the moderately suitable area increased by 5.05 × 10 4 km 2 , and the highly suitable area increased by 38.93 × 10 4 km 2 . The newly increased highly suitable area was primarily located in the Eastern and Central Sichuan Province.
The prediction of the CanESM5 model under SSP2-4.5 revealed that there were 6.05 × 10 4 km 2 of suitable area lost and 32.92 × 10 4 km 2 gained. There was a 23.04 × 10 4 km 2 increase in marginally suitable area, a 15.40 × 10 4 km 2 decrease in moderately suitable area, and a 19.40 × 10 4 km 2 increase in highly suitable area.
The prediction of the CanESM5 model under SSP3-7.0 indicated a 6.34 × 10 4 km 2 loss in a suitable area, and 52.84 × 10 4 km 2 gained in suitable area compared with now. The marginally suitable area increased by~8.13 × 10 4 km 2 , moderately suitable area increased by 8.97 × 10 4 km 2 , and the highly suitable area increased by 29.67 × 10 4 km 2 . Compared with the predictions under SSP1-2.6 and SSP2-4.5, the marginally suitable area along the coast was upgraded to moderately suitable.
The prediction of the CanESM5 model under SSP5-8.5 showed that there were 7.06 × 10 4 km 2 in suitable area lost and 32.63 × 10 4 km 2 gained compared with now. There was an increase of 32.32 × 10 4 km 2 in marginally suitable area, a 32.00 × 10 4 km 2 decrease in moderately suitable area, and an increase of 25.42 × 10 4 km 2 in the highly suitable area.
An analysis of the prediction results of the CanESM5 under the four SSPs led to the following conclusions: 1. The suitable area increased in the future, which was similar to the prediction of BCC-CSM2-MR. 2. Unlike the prediction of BCC-CSM2-MR, the lost area predicted by the CanESM5 was principally located in Central Hubei, Northern Hunan, Southern Jiangsu, Central Anhui, and Southern Henan.
The predictions of the MRI-ESM2-0 model under SSP1-2.6 showed a suitable area loss of 4.10 × 10 4 km 2 and 11.33 × 10 4 km 2 gained compared with now. There was a decrease of 7.30 × 10 4 km 2 in marginally suitable area, a 12.36 × 10 4 km 2 increase in moderately suitable area, and a 27.11 × 10 4 km 2 increase in highly suitable area.
The prediction of the MRI-ESM2-0 model under the SSP2-4.5 showed that there were 14.24 × 10 4 km 2 of suitable area lost and 21.84 × 10 4 km 2 gained compared with now. There was an increase of 7.06 × 10 4 km 2 in marginally suitable area, a 27.24 × 10 4 km 2 decrease in the moderately suitable area, and an increase of 27.86 × 10 4 km 2 in highly suitable area.
The prediction of the MRI-ESM2-0 model under the SSP3-7.0 revealed that there were 12.02 × 10 4 km 2 of suitable area lost and 18.46 × 10 4 km 2 gained compared with now. There was an increase of 16.80 × 10 4 km 2 in marginally suitable area, a decrease of 14.05 × 10 4 km 2 in a moderately suitable area, and an increase of 3.81 × 10 4 km 2 in a highly suitable area.
The prediction of the MRI-ESM2-0 model under the SSP5-8.5 showed that there were 6.78 × 10 4 km 2 in suitable area lost and 17.27 × 10 4 km 2 gained compared with now. There was an increase of 8.39 × 10 4 km 2 in a marginally suitable area, a decrease of 19.48 × 10 4 km 2 in a moderately suitable area, and an increase of 21.76 × 10 4 km 2 in highly suitable area.

Practical Available Planting in the Predicted Suitable Area
The Table 2 shows the predicted average areas combined with land use types. Under SSP1-2.6, the average areas of farmland, woodland, grassland, and bare land for the three models were 78.52 × 10 4 km 2 , 119.39 × 10 4 km 2 , 26.93 × 10 4 km 2 , and 0.04 × 10 4 km 2 , respectively. Among the three models, the results of the CanESM5 model were generally higher. For example, the projected woodland, grassland, and bare land area were 125.57 × 10 4 km 2 , 30.81 × 10 4 km 2 , and 0.05 × 10 4 km 2 , respectively. The practical available area under SSP1-2.6 for the planting of C. lanceolata was 146.36 × 10 4 km 2 , which was 60.25% of the theoretical value.
The prediction under SSP5-8.5 indicated an average farmland area in the three models of 72.87 × 10 4 km 2 , an average woodland area of 121.98 × 10 4 km 2 , average grassland area of 28.16 × 10 4 km 2 , and average bare land area of 0.05 × 10 4 km 2 . The practical available area under SSP5-8.5 for the planting of C. lanceolata was 150.19 × 10 4 km 2 , which was 62.80% of the theoretical value.

Discussion
Global climate change is caused by both natural dynamics and anthropogenic activities. The increase of carbon dioxide and other greenhouse gases has been evident on a

Practical Available Planting in the Predicted Suitable Area
The Table 2 shows the predicted average areas combined with land use types. Under SSP1-2.6, the average areas of farmland, woodland, grassland, and bare land for the three models were 78.52 × 10 4 km 2 , 119.39 × 10 4 km 2 , 26.93 × 10 4 km 2 , and 0.04 × 10 4 km 2 , respectively. Among the three models, the results of the CanESM5 model were generally higher. For example, the projected woodland, grassland, and bare land area were 125.57 × 10 4 km 2 , 30.81 × 10 4 km 2 , and 0.05 × 10 4 km 2 , respectively. The practical available area under SSP1-2.6 for the planting of C. lanceolata was 146.36 × 10 4 km 2 , which was 60.25% of the theoretical value.
The prediction under SSP5-8.5 indicated an average farmland area in the three models of 72.87 × 10 4 km 2 , an average woodland area of 121.98 × 10 4 km 2 , average grassland area of 28.16 × 10 4 km 2 , and average bare land area of 0.05 × 10 4 km 2 . The practical available area under SSP5-8.5 for the planting of C. lanceolata was 150.19 × 10 4 km 2 , which was 62.80% of the theoretical value.

Discussion
Global climate change is caused by both natural dynamics and anthropogenic activities. The increase of carbon dioxide and other greenhouse gases has been evident on a global scale. The Chinese government has issued a series of policies to reduce greenhouse gas emissions and revert grain plots to forestry.
In recent years, plantation areas have increased, as afforestation has proven to be one of the most effective and ecologically compatible practices for the enhancement of carbon sequestration in terrestrial ecosystems. C. lanceolata is an excellent fast-growing conifer species that is unique to Southern China and is naturally distributed in the region between 101 • 13 and 121 • 53 E and 19 • 30 and 34 • 03 N, which has a long history in China [50]. Furthermore, C. lanceolata is not only important for timber production, but also for soil and water conservation, and land restoration. In addition to ecological functions, C. lanceolata is an ideal choice for roadside trees and the establishment of windbreaks, due to its strong adaptability, including robust wind and smoke resistance. The growth process of the C. lanceolata forest mainly includes seedling stage, quick-growing stage, mature stage and senescence stage. In 2-3 years after planting, C. lanceolata forest at the seedling stage and the quick-growing stage of C. lanceolata is from 3-4 years to 10-15 years after planting. In this stage, the height and diameter of C. lanceolata increase rapidly. From 10-15 to 25-30 years, the volume of C. lanceolata reached its maximum. C. lanceolata forests usually become mature after 30-40 years, and the growth of volume decreases slowly. After 60 years, the growth of C. lanceolata forests decreases sharply and enters the senescence stage.
It is critical to have a clear understanding of the suitable growth areas for C. lanceolata and to protect existing stands, while simultaneously increasing planted areas and economic income. However, there has been limited research focused on predicting suitable growth areas for C. lanceolata in China. Li et al. [31] employed the representative concentration pathways (RCPs) from the Beijing Climate Center Climate System model version 1.1 (BCC-CSM1-1) data to predict suitable distribution areas of C. lanceolata in China for three time periods (present day, 2041-2060, and 2061-2080). The MaxEnt parameters were optimized, and the prediction indicated that the suitable growth area for C. lanceolata exhibited a trend to move northward and became more fragmentated over time. However, the prediction based on the single global climate model was inevitably uncertain under certain extreme conditions. Furthermore, due to the lack of suitable data, the effects of anthropogenic activities on the species distribution were not considered in the research.
For this study, multiple global climate models and the latest CMIP6 data were used to predict the suitable growth areas of C. lanceolata. Furthermore, the LU data were combined with the prediction results to evaluate the effects of human activities on species distribution.
The most important bioclimatic variables affecting the presence of C. lanceolata were the precipitation of the driest month (bio14), and the minimum temperature of the coldest month (bio6). These results were similar to those of previous research [56,57]. The precipitation of the driest month at lower than 5.51 mm greatly decreased the probability of the presence of C. lanceolata.
Temperature is one of the most critical environmental factors that impacts the photosynthetic physiology and ecology of plants. Plants must reside within a certain temperature range to perform photosynthesis, and each possesses a lowest, most suitable, and highest temperature threshold [58]. A minimum temperature of the coldest month (bio6) higher than −5.4 • C greatly increases the probability of the presence of C. lanceolata. Temperature affects the photosynthetic mechanism, because many components of photosynthetic metabolism are highly temperature sensitive [59]. Low temperature can reduce the hydrolysis and transport of starch accumulated within the chloroplast, then decrease the rate of photosynthetic [60]. On the contrary, the rate of photosynthetic increases responses to the increase of temperature until reaching a thermal optimum, after which rates decline due to enzyme deactivation at increasingly high temperatures. Enzyme degradation at high temperatures can decrease electron transport rates and decrease chlorophyll content [61].
By analyzing the predictions of different models under the same scenario, it is found that prediction results are various. For example, in SSP1-2.6 scenario, the predicted results of the BCC-CSM2-MR and MRI-ESM2-0 model showed that the area of moderately suitable area of C. lanceolata will decrease in the future, but the predicted results of the CanESM5 model showed that it will increase slightly in the future. Conversely, in the SSP5-8.5 scenario, the prediction results of the three models are consistent in the change of the marginally suitable area, moderately suitable area and highly suitable area (increase or decrease at the same time). It is hard to find a regular trend or result based on the prediction of any single model. However, after averaging the prediction results of the three models, a unified trend can be found. It also proves that the prediction of multi-model can avoid the single model's uncertainty and show a trend that are more likely to happen in the future. In the SSP scenario with higher radiative forcing, the increase area of marginally suitable area is larger, while under the SSP scenario with lower radiative forcing, the increased area of highly suitable area is larger. The prediction of SSP5-8.5 lost the largest moderately suitable area and the SSP3-7.0 lost the smallest moderately suitable area. The reason caused the trend between different SSP scenario not only to be radiative forcing, but also the societies with different development pathways in the future. SSP1 represents a society that makes a shift to sustainable development. Conversely, the SSP2 depicts a society that develops following a historical pattern without substantial future deviations. SSP3 and 4 represent societies with rapidly growing populations and low investments in health or education. SSP5 assumes a social economy that is based on fossil fuels and intensive energy use. The more environmentally friendly a society is, the more increased a highly suitable area of C. lanceolata will be. On the contrary, the highly suitable area and moderately suitable area would degenerate, resulting in the increase of a marginally suitable area.
Although the predicted results of four scenarios showed that the suitable area of C. lanceolata will increase from 2041-2060, considering the longevity and life-cycle of C. lanceolata, the predicted results may have a lag period, because the C. lanceolata generally enters the stage of flowering and fruiting after 6-10 years of planting, and remains stable during 15-40 years after planting. However, most C. lanceolata are planted artificially in China, not naturally propagated. A variety of artificial cultivation techniques of C. lanceolata are mature and universal in China. This can greatly shorten the lag period of prediction.

Conclusions
For this study, the potential future suitable growth area of C. lanceolata was predicted using a MaxEnt model based on three GCM models under different scenarios from 2041-2060 in China. The major conclusions were summarized as follows: (1) The current potential suitable growth areas for C. lanceolata were mainly located in Southern China. The annual temperature range was the most important variable to affect the potential suitable area of C. lanceolata. The current potential highly suitable area of C. lanceolata was primarily located in areas with a low annual temperature range, which was in accordance with the existing distribution of C. lanceolata. When the annual temperature varied from 4.7-6.7 • C, the logistic output of MaxEnt was higher than 0.5, which indicated that the area was highly suitable for C. lanceolata. When the annual temperature varied from 3.30-10.60 • C, the logistic output of MaxEnt was higher than 0.1, which indicated that the area was suitable for C. lanceolata. (2) The suitable area of C. lanceolata was observed to increase in China under all SSPs scenarios from 2041-2060. The gained area was mainly located in Southwest and Eastern Shandong, Western and Southern Henan, Northern Anhui, Northern and Central Jiangsu, Southern Shanxi, and Southern Yunnan in most models under four SSPs, as the uncertainties were unavoidable for the prediction. However, the average of multiple models may effectively reduce the uncertainty and balance the prediction results. The average of multiple models revealed that the suitable area of C. lanceolata increased to 29.00 × 10 4 km 2 under SSP3-7.0, which was the largest between the four scenarios, whereas the increased area under SSP5-8.5 was the smallest between the four scenarios at 18.59 × 10 4 km 2 . This result revealed that the extremely high radiative forcing had a serious negative effect on C. lanceolata. Conversely, under SSP1-2.6, which had a low radiation intensity and effective control of global temperature growth at 2 • C, the growth area of the highly suitable area was the largest, increasing by 34.61 × 10 4 km 2 . The highly suitable area possessed various environmental factors that were more suitable for the growth of C. lanceolata, which cultivated higher quality trees, a faster growth rate, and higher timber yields. Consequently, the increased highly suitable area of C. lanceolata under SSP1-2.6 had important economic and ecological value, which could not be ignored. As described above, the suitable area and yield of C. lanceolata were greatly increased under the SSP1-2.6 and SSP3-7.0 in different ways. (3) The LUCC data were reclassified into four categories: farmland, bare land, forest land, and grassland. In view of China's arable land protection red line policy, the bare land, woodland, and grassland might be potentially available for the planting of C. lanceolata in the future. When the predictions were combined with the reclassified land use data, the result showed that the average practical available area for C. lanceolata would be approximately 61.76% of the predicted suitable area. However, with the further promotion of the policy of returning farmland to forests in China, progressively more grain plots would be returned to forestry and public awareness of forest protection would be enhanced. The practical available planting area and distribution of C. lanceolata in China will increase significantly in the future.

Data Availability Statement:
The data presented in this study are available on request from the corresponding author.

Conflicts of Interest:
The authors declare no conflict of interest. Mean diurnal range (mean of monthly (max temp-min temp)) WorldClim • C bio03