Patterns of tree species richness in Southwest China


As a region known for its high species richness, southwest China plays an important role in preserving global biodiversity and ensuring ecological security in the Yangtze, Mekong, and Salween river basins. However, relatively few studies focus on the response of tree species richness to climate change in this part of China. This study determined the main tree species in southwest China using the Vegetation Map of China and the Flora of China. From simulations of 1970 to 2000 and three forecasts of future benign, moderate, and extreme climate warming anticipated during 2061 to 2080, this study used a maximum entropy model (MaxEnt) to simulate main tree species richness in southwest China. Regions with a peak species richness at intermediate elevations were typically dominated by complex mountainous terrain, such as in the Hengduan Mountains. Likewise, regions with the smallest richness were low-elevation areas, including the Sichuan Basin, and the high-elevation Sichuan-Tibet region. Annual precipitation, minimum temperature of the coldest month, temperature seasonality, and elevation were the most critical factors in estimating tree species richness in southwest China. During future 2061 to 2080 climate scenarios, tree species tended to migrate towards higher elevations as mean temperatures increased. For climate change scenarios RCP2.6–2070 (benign) and RCP4.5–2070 (moderate), the main tree species richness in the study area changed little. During the RCP8.5–2070 extreme scenario, tree species richness decreased. This study provides useful guidance to plan and implement measures to conserve biodiversity.


According to the Fifth Assessment Report released by the Intergovernmental Panel on Climate Change (IPCC), global warming is expected to continue, with average temperatures across the globe rising from 0.3 °C to 4.8 °C from 2081 to 2100 compared with 1986 to 2005 (Stocker et al. 2013). Climate change poses a serious threat to global and regional biodiversity (O'Neill et al. 2017), including changes in phenology (Parmesan 2007), geographic range (Chen et al. 2011), ecological interactions (Kotter 2013), and primary productivity (Tavşanoğlu 2015). Some recent studies have shown that species habitat in different regions of China have contracted or expanded to different degrees under different future climate scenarios (Gao et al. 2016; Dakhil et al. 2019).

Forests play an essential role in global ecosystems by providing carbon storage (Bonan 2008; Buongiorno 2015), medicine, timber, wildlife habitat, and some regulation of the hydrological cycle (Anderegg et al. 2012) among other benefits. Importantly, all of these ecosystem services are sensitive to the effects of climate change (Frank et al. 2015). Global climate change affects two key habitat factors (heat and moisture) related to life-supporting processes in forest ecosystems. Changes in the quantity and spatial distribution of environmental variables dominated by heat and moisture may cause species to migrate to new areas. Because of these species migrations, organisms may become extinct and populations can become highly dispersed (Thomas et al. 2004).

The unique topographic and climatic conditions in southwest China offer a wide variety of forest habitat types. Indeed, southwest China is recognized as one of the 34 biodiversity hotspots in the world and has great significance in the global conservation of biodiversity (Mittermeier et al. 2011). As such, the region is important in the global appreciated Yangtze, Mekong, and Salween river basins. As the most important component of a forest ecosystem, trees provide habitat and resources for a multitude of other species (Feroz et al. 2016). Therefore, understanding tree diversity is essential for understanding the diversity of other guilds and taxonomic groups.

The patterns of species richness cannot only distinguish species richness in different regions, but can also reveal the relationship between species distributions and climate, topography, species migration, and geological history (Ricklefs 2004). As such, the richness patterns provide a basis for making informed decisions regarding biodiversity protection and reserve planning, and make better use of the human and economic resources that are available (Bojórquez-Tapia et al. 1995). However, evaluating the patterns of species richness areas that are under sampled remains highly challenging (Pineda and Lobo 2009). Existing studies on the patterns of species richness are based primarily on the sampling and line transect methods (Lin et al. 2018). Those results were mostly qualitative conclusions that lacked accurate information on spatial pattern and heterogeneity. In addition, traditional community survey methods cannot account for future climate scenarios, so clearly assessing changes in species richness at the regional scale is difficult under future climate scenarios.

Species distribution modeling is useful for simulating species ranges. Importantly, by stacking simulations from multiple individual distributions allows reliable, regional estimates of species richness and composition. In recent years, researchers have used species distribution models to identify hot spots of threatened species (Murray-Smith et al. 2009). The maximum entropy program (MaxEnt) is a particularly promising species distribution model because of the accurate simulations of current data (Pearson et al. 2006; Phillips and Dudík 2008).

To explore the impact of climate change on forest ecosystem biodiversity in southwest China, this study simulated the distribution of tree species to estimate the patterns of tree species richness, so as to formulate effective protection strategies. The chief objective of this study was to simulate the spatial patterns of tree species richness in southwest China using species distribution simulations. Specifically, this study determined the relationship between tree richness and selected environmental variables, and to forecast changes in richness in southwest China during future climate change scenarios. This paper provides insight into species richness patterns in southwest China for planning biodiversity conservation strategies and the implementation of conservation measures in the region.

Materials and methods

Research area

This study was conducted in southwest China (97° 21′ to 112° 04′ E and 20° 54′ to 34° 19’ N). The region has a total area of 1,137,600 km2 and includes Yunnan province, Guizhou province, Sichuan province, and Chongqing municipality. The region is topographically diverse, species richness increases from the east to west (Fig. 1). The unique geographical environment of the region provides diverse macro-climates and microclimates, as well as complex habitats.

Fig. 1

Location and topography of study area

Species occurrence

This study selected a total of 122 tree species from 51 genera and 28 families as target tree species. These primarily included species belong to Quercus, Castanopsis, Picea, and Abies. One hundred and four tree species were identified as the dominant species of the main forest types according to the Vegetation Map of China (Editorial Board of Vegetation Map of China 2001). The remaining 18 tree species were included, because these were recorded as the common tree species in the study area (Flora of China Editorial Committee 2018).

Occurrence records for the 122 common species were obtained from the following databases: (1) Chinese Virtual Herbarium (, (2) National Specimen Information Infrastructure (, and (3) Global Biodiversity Information Facility ( This investigation removed duplicated records and obviously misplaced records. In addition, this study removed species with fewer than five records of occurrence (Pearson et al. 2006). After, a total of 12,834 records remained for analysis. Where latitude and longitude were not recorded in the three databases, Google Earth was used to determine missing geographic coordinates.

Environmental variables

This study initially selected 28 environmental variables to describe tree species richness. These variables consisted of 5 datasets including 19 bioclimatic variables (labeled BIO1 to BIO19), soil variables (soil type, clay content, sand content, and silt content), terrain variables (slope, aspect, and elevation), landform, and the China land-use and land-cover change (LUCC) variable. The 19 bioclimatic variables were averaged from 1970 to 2000 and future (i.e., from 2061 to 2080) climatic conditions using data from the World Climate Database ( Information obtained from a digital elevation model with a 30-m spatial resolution ( was used to calculate slope and aspect using the Spatial Analyst and Surface Analyst tools in ArcGIS 10.1 (ESRI, Redlands, California, USA). The soil variables, the landform variable, and the China LUCC variables were downloaded from the Resource and Environmental Data Cloud Platform ( This study used the Beijing Climate Center Climate System Model version 1.1 (BCC-CSM1.1) to simulate climate change for three Representative Concentration Pathways (scenarios RCP2.6–2070, RCP4.5–2070, and RCP8.5–2070 represent benign, moderate, and extreme climate change) released with the Intergovernmental Panel on Climate Change, Fifth Assessment Report. The BCC-CSM1.1 is a global circulation model that is widely used for the Asian region (Xin et al. 2013). This study selected the 2018 China LUCC data to simulate future scenarios. This investigation specified all environmental data using a 30 arcsecond (1 km) spatial resolution.

Maximum entropy simulation of tree occurrence

The MaxEnt software is a machine learning program based on maximum entropy that estimates the probability distribution for species occurrence based on relevant environmental constraints (Phillips et al. 2006; This study used the MaxEnt program to simulate 1970 to 2000 and future (2061 to 2080) of tree species richness in southwest China. For model training or calibration, this study used 75% of the occurrences of a species; the remaining 25% were used to test the forecasting capability of the program (Moreno et al. 2011). This study specified all other model parameters as the default values of the MaxEnt program (Murray-Smith et al. 2009). Simulations were repeated a total of ten times. This investigation used the area under the receiving operator characteristic curve (AUC) to evaluate the accuracy of discriminating between true presences and absences (Allouche et al. 2006). The AUC ranges from 0 to 1 indicating no accuracy of a model simulation to perfect accuracy (Hanley and McNeil 1982). In addition, this study determined the percentage of the contribution that each variable made to a simulation.

Screening environmental variables

This study classified 28 environmental variables into 8 types, including temperature, temperature variations, precipitation, precipitation variations, soil, terrain, landform, and LUCC. These eight types of information were all specified for the MaxEnt simulations of 122 tree species. This study arbitrarily selected the variables with an average percentage contribution greater than 1.0% to be used in the final model. This investigation calculated the Pearson correlation coefficient (r) using ArcGIS (Fig. 2) (Dakhil et al. 2019). This study determined the variable with the greatest contribution arbitrarily based on r ≥ ±0.8 (Yang et al. 2013), thus selecting 11 environmental variables to determine species richness (Table 1).

Fig. 2

Multi-collinearity from cross-correlations among environmental variables

Table 1 Percentage contributions of environmental variables

Environmental impacts on species richness

Environmental suitability scores from the MaxEnt simulations were converted to presence or absence using the Maximum training sensitivity plus specificity threshold (Liu et al. 2013). Simulated range maps for the 122 species were then stacked to estimate species richness (Tang et al. 2018). To show the patterns more intuitively, this investigation divided the distribution map of species richness (S) for the main tree species in southwest China, into five groups (S = 0, 1 ≤ S < 23, 23 ≤ S < 47, 47 ≤ S < 71, S ≥ 71). Using ArcGIS Reclass with similar intervals, this study calculated the area proportion of the different groups of species richness then determined correlations between species richness and the 11 important environmental variables.

Future climate change effects on species richness

Change in species richness is the subtraction of the 1970 to 2000 species richness from the species richness forecast during the 2061 to 2080 RCP2.6–2070, RCP4.5–2070, and RCP8.5–2070 climate change scenarios. This study classified changes in species richness, ΔS, as (1) substantial increase: ΔS ≥ 15 species, (2) increase: 5 ≤ ΔS < 15, (3) no substantial change: -5 ≤ ΔS < 5, (4) decrease: -15 ≤ ΔS < −5, and (5) substantial decrease: ΔS < −15. For the three climate scenarios, this study calculated the landscape area covered by all five categories of species richness changes.


Spatial pattern of tree species richness in southwest China

Simulations for the 122 tree species distributions were significantly better than simulations based on random specifications of environmental variables. After training of the machine learning software, AUC ranged from 0.917 to 0.996, and for testing AUC ranged from 0.855 to 0.993, indicating good discrimination between true presence and absence locations. We can thus be relatively confident that our predictions are good representations of the tree species patterns.

Species richness was greatest in the boundary zone of the Hengduan Mountains and the Sichuan Basin, and the boundary zone of Yunnan and Guizhou. These two areas are all topographically complex in the extreme. In contrast, the southern Yunnan and eastern Guizhou provinces, and the Sichuan Basin all had lesser tree richness. The high-elevation Sichuan-Tibet region had the lowest species richness. Shiqu County, bordered on three sides by Qinghai province, is located in the northwest of the Ganzi Tibetan Autonomous Prefecture found in the larger Sichuan province, is dominated by alpine meadow that did not contain any tree species (Fig. 3).

Fig. 3

Tree species richness during 1970 to 2000

Correlations between species richness patterns and environmental variables

Table 2 presents the calculated Pearson correlation coefficients (r) that define any relationship between species richness and the 11 environmental variables. The minimum temperature of the coldest month (BIO6, r = 0.67), elevation (ELEV, r = −0.57), annual precipitation (BIO12, r = 0.45), temperature seasonality (BIO4, r = −0.35), and land-use and land-cover change (LUCC, r = −0.33) had a larger correlation with the species richness of key tree species in southwest China. The remaining six environmental variables had relatively little correlation with species richness, including (1) precipitation seasonality (BIO15, r = −0.27), (2) landform (LF, r = −0.27), (3) soil type (ST, r = −0.14), (4) isothermality (BIO3, r = −0.15), (5) slope (SLO, r = 0.00), and (6) aspect (ASP, r = 0.07).

Table 2 Cross-correlation in percent between tree species richness and the 11 important environmental variables

The four most important environmental variables to the MaxEnt simulation explained a total of 77.31% of the variation (Table 3). In the MaxEnt simulations, the following four environmental variables explained the most variation in the spatial patterns of species richness in southwest China: (1) annual precipitation (BIO12, 27. 36% of variation), (2) minimum temperature of the coldest month (BIO6, 20.36% of variation), (3) temperature seasonality (BIO4, 19.07% of variation), and (4) elevation (ELEV, 10.52% of variation).

Table 3 Percentage contribution and permutation importance of the environmental variables included in the MaxEnt simulations for species richness of main trees

Effect of climate change on tree species richness patterns in southwest China

As a result of the forecasting with the three climate change scenarios (Fig. 4), using the RCP8.5–2070 extreme scenario, the maximum tree species richness decreased from 95 to 71, and richness decreased precipitously from 53.98% to 14.62% in areas with more diverse species richness (≥47).

Fig. 4

(a) Tree species richness expected in 2070. (b) Changes from 1970 to 2000 tree species richness to tree richness expected to arise during the three 2061 to 2080 climate scenarios. Note: the panels correspond to future climate scenarios: 1 is RCP2.6 (benign), 2 is RCP4.5 (moderate), and 3 is RCP8.5 (extreme)

During the future 2061 to 2080 climate forecasts, the proportion of areas without tree species decreased from 0.52% to zero, indicating that alpine meadows in northwestern Sichuan were replaced by cold-temperature-tolerant coniferous forests. The proportional area changes of each richness range during climate change scenarios RCP2.6–2070 (benign) and RCP4.5–2070 (moderate) were less than 7.1%. During the RCP8.5–2070 extreme scenario, the richness proportion in the ranges of 1 < S < 23 and 23 < S < 47 increased by 39.87%, in 47 < S < 71 decreased by 32.58%, and in S ≥ 71 richness decreased to zero, compared to the 1970 to 2000 climate scenario (Fig. 5).

Fig. 5

(a) Proportional area of different ranges of tree species richness and (b) the proportional area of changes in species richness

This study determined changes in species richness by subtracting the 1970 to 2000 species richness (Fig. 3) from the species richness anticipated during the RCP2.6–2070, RCP4.5–2070, and RCP8.5–2070 scenarios (Fig. 4). Tree species richness in Yunnan province, Guizhou province, Sichuan province, and Chongqing municipality became less diverse and at low elevations in the southeast. On the contrary, species richness at the higher elevations in northwestern areas became more diverse.

Under the future climate scenarios, more areas declined in richness than increased. During scenarios RCP2.6–2070 and RCP4.5–2070, areas without substantial changes in tree species richness occurred over approximately half of the study area. During the RCP8.5–2070 scenario, areas that declined in richness were larger than areas with increases in richness. Compared to the 1970 to 2000 species richness, the RCP8.5–2070 scenario simulations showed that the areas with the decreasing number of tree species by more than 15 species accounted for 52.60% of the study area (Fig. 5).


The results of this study are consistent with the single-peak concept of tree species richness varying with elevation, which is consistent with the more holistic “mid-domain effect” of species diversity (Colwell and Lees 2000). This study showed that species richness peaks in mountainous regions at elevations from1000 m to 3000 m, including the mountains surrounding the Sichuan Basin, located in northwest Yunnan, and in the border zone between Yunnan and Guizhou provinces. In contrast, almost no trees grew at elevations above 4700 m in Shiqu county and northwest Sichuan province (Fig. 3). Mid-elevational mountainous regions, particularly the Hengduan Mountains, are where the most abundant number of tree species occur. The Hengduan Mountains, one of the 35 diversity hotspots worldwide, dominate Sichuan, Yunnan, and Guizhou provinces and consist of a series of parallel north-south mountain ranges and intervening alpine valleys, which have large spatial differentiation of thermal energy and moisture (Xing and Ree 2017).

The Sichuan Basin and Xishuangbanna Dai Autonomous Prefecture of southernmost Yunnan province typify the simulations of little tree diversity at lower elevations as shown in Fig. 3. With a mean elevation of 500 m, the Sichuan Basin famous for agriculture in China is typified by flat terrain, fertile soil, warm and humid climate, and abundant cultivated lands. Because of the flat terrain and extensive cultivation of monocultures, the Sichuan Basin does not provide diverse habitat for trees, which is why the Basin has relatively very little species diversity. In Fig. 3, the tree species richness in Xishuangbanna is moderate, which is inconsistent with the fact. Since there are various tropical, atypical tree species in Xishuangbanna includes some tree species that are rare in number and narrowly distributed.

Many tree species in the mountains of southwestern China can easily migrate generationally to more suitable habitats as these become available due to climate change (Loarie et al. 2009). As a result, these mid-elevational regions effectively act as refugia or shelters for a variety of species, especially for rare and endemic species (Sandel et al. 2011).

The scenarios RCP2.6, RCP4.5, and RCP8.5 represent benign, moderate, and extreme climate change. With all scenarios, overall tree species richness in southwest China declined. As expected, declines were greatest during the extreme RCP8.5 scenario, where this investigation observed severe losses in species richness (ΔS < −15, Fig. 4). Studies of European mountain plants found that many alpine plant species were forced to move to higher elevations in response to climate warming (Steinbauer et al. 2018). Indeed, parts of alpine meadows and steppes in southwest China have already been succeeded by shrublands and coniferous forests (Zhao et al. 2011; Gao et al. 2016). This simulation of the three climate scenarios (Fig. 4) showed more succession of alpine meadows by coniferous forests will continue to intensify as climate change intensifies. Mora et al. (2015) who also used scenarios RCP2.6–2070 and RCP8.5–2070 consistently found that tropical tree species richness decreased significantly due to climate warming changing suitable plant growing days.

Among the 11 environmental variables, annual precipitation (BIO12), minimum temperature of coldest month (BIO6), temperature seasonality (BIO4), and elevation (ELEV) were better correlated with tree species richness (Table 2). The percentage contribution of variables related to temperature (BIO6, BIO4, and BIO3) and precipitation (BIO12 and BIO15) was 74.14% and permutation importance reached 78.2% (Table 3), suggesting that thermal energy and moisture are vital underlying effects on tree species richness. Thus, this study supports the water-energy dynamic hypothesis (Wang et al. 2011).

Zhang et al. (2016a) found that the most important variable affecting the diversity of Chinese plants was consistent, large volumes of annual precipitation. Also important, Li et al. (2011) determined that colder temperature affects the existence of alpine plants. Primarily, climate warming enhances the photosynthesis and growth of these alpine plants.

This study found that BIO6 (minimum temperature of coldest month) was positively correlated with tree species richness, indicating that cold winter temperatures exclude species intolerant of freezing temperatures (Table 2). In the cold mountains of northwest Sichuan, winter temperatures and tree species richness gradually declined during the three simulated climate scenarios, further supporting the hypothesis that species richness is controlled by intolerance to freezing (Figs. 3 and 5). Similar to these findings, Li et al. (2016) found that the minimum temperature of the coldest month was the primary factor limiting the existence of Yunnan coniferous forests. Furthermore, Wang et al. (2017) found that the average temperature of the coldest season affected the vegetation of north and northeast China.

As with other studies using stacked species distribution simulations, the results of this study are influenced by differences in sample sizes among the 122 tree species of southwest China. Specifically, the number of occurrence records per species ranged from 16 to 650 out of 12,834. For these results, however, even for species with few records, the areas under the receiving operator characteristic curve (AUC) were still large. For example, Semecarpus reticulata had only 16 samples, for which the AUC is 0.988 (Elith et al. 2006). Populus davidiana had a large number of occurrence records in the study area, 412, but the AUC was 0.887. This contrast indicated that a wide range of species was difficult to establish high-precision species distribution simulations, so the AUC tended to be small, consistent with Yang et al. (2013). This study showed that the AUC of the simulations increased by 0.05 after selecting of the original 11 environmental variables, thus improving simulation accuracy.

Another important limitation is that the species occurrence records for this study were collected over many decades during which climate warming was occurring and human activities intensified, some tree species records have been replaced by successions of farmland and plantation. From 1970 to 2000, the simulations in this study likely overestimated the suitable habitat for some species, despite human activity in southwest China being relatively unchanged.

Southwest China is also a globally important diversity hotspot because of the two significant sources of moisture: the southeast monsoon from the Pacific Ocean and the southwest monsoon from the Indian Ocean. These monsoons and the complex topography contribute to biodiversity and significant species differentiation in form of numerous ecoregions within the study area. The area is not only diverse in species richness, but also has a greater than normal number of endemic species. In addition, southwest China is an important ecological security barrier protecting the headwaters of the Yangtze and Pearl rivers. Southwest China is also the most extensive karst region in China, has large population densities, is an underdeveloped economy, and has a fragile ecology (Zhang et al. 2016b). As such, the region is highly vulnerable to climate change and human disturbance. These impacts, in turn, have resulted in changes in ecological habits and species distributions, which has led to an overall reduction in biodiversity and ecosystem security (Ouyang et al. 2016). These findings holistically establish that this detailed analysis of species richness of common trees in southwest China from 1970 to 2000 and future 2061 to 2080 climate scenarios is important in formulating and applying a feasible conservation strategy.


Estimating how climate change will affect tree species richness is of vital importance for conservation. This study demonstrates that tree species richness in southwest China is consistent with the single-peak in richness over mountainous elevation gradients. Regions with the greatest species richness were located in the boundary zone of the Hengduan Mountains and the Sichuan Basin, and the boundary zone between Yunnan and Guizhou provinces. The high-elevation Sichuan-Tibet region had the lowest species richness. Annual precipitation, minimum temperature of the coldest month, temperature seasonality, and elevation were best correlated with species richness, explaining 77.31% of the total variation. During three different climate scenarios, although all the patterns of tree species richness in the study area varied, some species migrated to higher elevations. Low and moderate climate change scenarios (i.e., RCP2.6–2070 and RCP4.5–2070) had a relatively limited impact on tree species richness, while the extreme scenario (RCP8.5–2070) resulted in substantial declines in tree species richness. This study provides a scientific basis for the spatiotemporal changes in tree species richness in southwest China that will be useful for the formulation of forest management and protection strategies in the region and elsewhere.


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The authors thank Dr. Yuanjie Xu from Yunnan Academy of Biodiversity for helpful suggestions.


This work was supported by the National Natural Science Foundation [31700467], Agricultural joint general project of Yunnan Province [2018FG001–065], and Doctoral Research Launch Fund project of Southwest Forestry University [112003].

Author information




Shuangfei Lu participated in the design of the study, carried out data analysis, and wrote original draft. Xiaojie Yin conceived of the study, participated in the design, and provided project financial support. Siyi Zhou and Chao Zhang reviewed, rewrote, and edited this manuscript. Rongliang Li, Jiahui Chen, Dongxu Ma, Yi Wang, and Yuheng Chen carried out investigation and data curation. Zhexiu Yu was responsible for the operation of the software. All authors read and approved the final manuscript.

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Correspondence to Xiaojie Yin.

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The authors declare that they have no competing interests. The datasets analyzed during the current study are available from the corresponding author on request.

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Lu, S., Zhou, S., Yin, X. et al. Patterns of tree species richness in Southwest China. Environ Monit Assess 193, 97 (2021).

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  • Climate change
  • Biodiversity conservation
  • Maximum entropy model
  • Environmental variables
  • Tree species richness
  • Habitat evaluation