1. Introduction
The disruption caused by global climate change is leading to serious threats to the stability of global ecosystems and biodiversity [1], resulting in the loss of suitable habitats and extreme precipitation [2,3]. Habitat loss has a negative impact on the distribution ranges of species, jeopardizing the existence of regional populations [4]. The suitable distribution area of woody plants is highly sensitive to climate change [5].
In recent years, with the implementation of the “dual carbon” goal and increased emphasis on species diversity protection, predicting the response of species distribution patterns to climate change has emerged as a prominent research focus. Therefore, species distribution models are widely used to study relationships between species distribution and climate change [6]. Among them, the data-driven maximum entropy (MaxEnt) model has great significance in the habitat suitability assessment of endangered species [7]. The MaxEnt model can efficiently use the information of species distribution in association with the relevant environmental variables to predict the geographical distribution pattern of species [8]. Therefore, based on this model, many scholars have assessed the response of potential suitable areas of rare and endangered species to climate change, including gymnosperms [9], angiosperms [10], ferns [11], and bryophytes [12].
Ormosia microphylla Merr. & H. Y. Chen is a tall tree belonging to the genus Ormosia within the family Fabaceae. It remains in the local mountainous areas of Fujian, Guangdong, Hunan, Guangxi, and Guizhou provinces [13]. It holds the status of being a nationally protected plant of first-class importance. There are about 130 species of Ormosia plants in the world, mainly distributed in the Neotropics. The phylogenetic analysis shows that Ormosia is located in the branch of Papilionoideae, and is a sister group to the branch of Lupinus [14]. O. microphylla is primarily distributed in the subtropical monsoon climate zone in China. This zone is characterized by high temperature and abundant rain in summer and mild and little rain in winter. O. microphylla produces extremely precious timber known for its solid texture, straight grain, uniform structure, vibrant color, and exquisite grain patterns. Additionally, it shows a very high resistance to corrosion, making it a suitable material for high-grade furniture, musical instruments, and handicrafts [15]. It is called a meteorological tree because of its leaf color that changes with the change in weather. In addition, the Ormosia genus is relatively rich in alkaloids, flavonoids, triterpenes, and other chemical components, which have great medicinal values [16]. Therefore, O. microphylla has high economic and ecological values. However, the distribution area of O. microphylla in China is narrowing down due to habitat destruction. Human factors such as ecological fragmentation, land clearing for agriculture, and over-exploitation have led to serious damage to many wild populations of this species [17]. Hence, it is imperative to protect the natural habitats of O. microphylla. Some scholars have explored the changes in the potential distribution pattern of Ormosia in Guangdong Province under various climatic conditions. They found that the suitable area of 13 Ormosia including O. microphylla would be reduced due to climate warming [18]. In addition, Xiao’s [19] research shows that under the current climatic conditions, O. microphylla is highly suited to Fujian, Guangdong, Guangxi, Hunan, and Guizhou. However, its distribution range is affected by temperature and water conditions. Further clarification is needed to understand how the distribution pattern of O. microphylla in China has evolved under different climatic conditions in the past and is projected to change in the future. Additionally, elucidating how environmental factors influence the alternation of its geographical distribution pattern is necessary.
Therefore, this study uses the MaxEnt model based on kuenm packet optimization parameters, in conjunction with geographic information software (ArcGIS 10.4.1), to investigate the following objectives: (1) reconstructing the potential suitable geographical distribution of O. microphylla across various periods since the last interglacial period, and exploring the spatial distribution pattern changes over these periods; (2) analyzing the restriction mechanisms of the main environmental factors on the geographical distribution of O. microphylla; and (3) according to the results of the study, suggest tailored recommendations for various situations.
2. Materials and Methods
2.1. Data source and Processing
2.1.1. Collection and Processing of Geographic Distribution Data
The preliminary distribution data of O. microphylla were obtained from the Chinese Virtual Herbarium (CVH,
2.1.2. Source and Filter of Environment Variables
Climate data and elevation factors were obtained from the WorldClim Database (
To avoid the multicollinearity between climatic factors [21], Pearson correlation analysis was performed using SPSS 26.0 software. Among the two climatic variables with ∣r∣ > 0.7, one variable with a low contribution to species distribution was eliminated, while, At the same time, being combined with the physiological and ecological characteristics of O. microphylla. Finally, nine climatic factors were selected. These factors included mean diurnal range (bio2), temperature seasonality (bio4), min temperature of coldest month (bio6), mean temperature of wettest quarter (bio8), precipitation of wettest month (bio13), precipitation seasonality (bio15), precipitation of driest quarter (bio17), precipitation of warmest quarter (bio18), and elevation (elev).
2.2. Model Optimization and Construction
The kuenm data package related to the species distribution model was used to optimize the model [22]. The regularization multiplier (RM, 0.1–4, interval 0.1) was set, and 29 feature combinations (L, Q, P, T, H, LQ, LP, LT, LH, QP, QT, QH, PT, PH, TH, LQP, LQT, LQH, LPT, LPH, QPT, QPH, QTH, PTH, LQPT, LQPH, LQTH, LPTH, LQPTH) were used. Finally, the minimum delta.AICc value was selected from the 1160 model results as the best setting. The corresponding environmental variable data and the distribution information data of O. microphylla were imported into the MaxEnt model, and the optimized parameters were used to simulate the suitable habitat of O. microphylla in different periods. The data of 75% of species distribution points were used as the training of the model, and the remaining 25% of distribution points were used for testing the model. The output format was set to logistic. The model was repeated 10 times, and the average value served as the result for predicting the potential distribution of the species [23]. The accuracy of the model was evaluated by the area under the ROC curve (AUC) value under the receiver operating characteristic curve (ROC). The jackknife test was used to analyze the contribution of environmental factors. The AUC value is closely related to the prediction effect of the model. The larger the value, the higher the prediction ability of the model. When the AUC value exceeds 0.9, the prediction performance is considered excellent. An AUC value range of 0.7–0.9 indicates that the prediction accuracy is good, and 0.5–0.7 indicates a poor performance [24].
The results obtained from the MaxEnt model were imported into the ArcGIS 10.4.1 software. The reclassification tool was used to divide the suitable areas of O. microphylla into four grades: unsuitable habitats (0–0.2), low suitability habitats (0.2–0.4), middle suitability habitats (0.4–0.6), and highly suitable habitats (0.6–1) [25]. The potential distribution map of O. microphylla was created for different periods, and the area tabulation tool in ArcGIS 10.4.1 software was used to calculate the size of each suitable habitat.
2.3. Analysis of Changes in Spatial Pattern of Suitable Distribution Area of Species
The spatial units with the probability of species existence ≥0.4 were defined as potentially suitable areas and assigned the value of “1”. The spatial units with the probability of species existence <0.4 were defined as non-suitable areas and assigned the value of “0”. These numerical values indicate the existence/non-existence (0, 1) matrix of the potential geographic distribution of O. microphylla in each period. Based on this matrix, the migration pattern of the suitable area for O. microphylla was categorized into three types: increased area, lost area, and reserved area. Here, the matrix value of 0 → 1 was interpreted as an increase in area, 1 → 0 was considered a loss in area, and 1 → 1 was inferred as a reserved area [26]. Finally, the overall change pattern of the O. microphylla distribution area was drawn in ArcGIS 10.4.1 software. At the same time, the suitable area of O. microphylla was regarded as a whole and converted into a centroid in ArcGIS 10.4.1 software, and the migration trend of suitable habitat was evaluated by the change in centroid position in each period. Figure 2 summarizes the analysis process of this study.
3. Results
3.1. Model Optimization Results and Accuracy Test
Based on the current 40 distribution information data and nine climate variables of O. microphylla, the kuenm packet was used to optimize the MaxEnt model and to simulate the potential distribution area of O. microphylla. When the feature combination (FC) is product features (P), threshold features (T), and hinge features (H), and the RM = 1.7, delta.AICc = 0, indicating that the parameter combination is the best model. Under the parameter model, the accuracy is tested by the AUC value. The prediction results show that the training AUC values of the model simulation when tested 10 times were all greater than 0.9 (Table 1), and the current average training AUC value reached 0.982 (Figure 3), indicating that the prediction performance of the model was very high.
3.2. Potential Distribution Patterns of Species at Different Periods
According to Table 2 and Figure 4, it can be seen that the current potential suitable distribution area of O. microphylla is primarily concentrated in southern China, including southern Zhejiang, Fujian, Guangdong, Guangxi, Guizhou, southeastern Chongqing, southwestern Hubei, western Hunan, southern Jiangxi, and Taiwan. The total area of suitable distribution was about 83.61 × 104 km2, and the areas with highly and moderately suitable areas area covered about 13.81 × 104 km2 and 20.59 × 104 km2, respectively. These areas represent 16.52% and 24.63% of the total suitable distribution area, respectively. The area of low suitability habitat was the largest, encompassing approximately 49.20 × 104 km2, constituting 58.84% of the total suitable area. The highly suitable areas of O. microphylla under the current climatic conditions are mainly concentrated in Fujian Province, and there are also highly suitable areas in southern Zhejiang, Guangdong, Guangxi, southeastern Guizhou, southern Hunan, and southern Jiangxi. The area with moderately suitable distribution is larger than the highly suitable distribution area, which is distributed in Zhejiang, Fujian, Guangdong, Guangxi, Guizhou, Hunan, Jiangxi, and northern Taiwan.
Comparing the LIG, the LGM, and the MH periods, it is observed that the area of highly suitable distribution in the LIG is the largest, approximately 13.87 × 104 km2, which was about 2.46 × 104 km2 and 2.22 × 104 km2 higher than that in the LGM period and the MH. During the LIG, the highly suitable areas were mostly concentrated in Fujian, Guangdong, Guangxi, and southeastern Guizhou. The area of highly suitable distribution area decreased in the LGM, but there was a highly suitable area in the eastern part of Taiwan Province, China. The distribution area of highly suitable areas in the MH was larger than that in the LGM, mainly distributed in Fujian, the Nanling Mountains, and southeastern Guizhou.
Under different climate scenarios in the future, the total suitable distribution area of O. microphylla will be smaller than the current suitable total area, decreasing by about 0.21 × 104 km2, 5.27 × 104 km2, 12.09 × 104 km2, and 14.3 × 104 km2 depending on the scenario. From the current period to 2050s-SSP126 and then to 2090s-SSP126, the height suitable distribution area of O. microphylla shows a significant shrinking trend, approximately 0.82 × 104 km2 and 2.77 × 104 km2 smaller than the current distribution, respectively. Its highly suitable distribution range is still relatively concentrated in Fujian, the Nanling Mountains, and southeastern Guizhou. Under the SSP585 climate scenario, the highly suitable distribution area of 2050s-SSP585 and 2090s-SSP585 O. microphylla is smaller than that of the contemporary period. For example, the potential highly suitable distribution area at the junction of Zhejiang and Fujian will be more broken.
3.3. Spatial Variation of Suitable Area of O. microphylla in Different Periods
The changes in spatial migration patterns of the suitable areas of O. microphylla across the three historical periods and four future periods were compared and analyzed (Table 3 and Figure 5). The prediction results show that the new area in the LIG is much higher than that in the LGM and the MH, and the increase in area is 8.16 × 104 km2. During the LIG, the increase in area of O. microphylla mainly occurred in eastern Fujian, Guangdong, Guangxi, southeastern Guizhou, southern Jiangxi, northern Taiwan, and southern Hainan. During the LGM, the loss in area was as high as 12.91 × 104 km2, and the loss areas were mainly distributed in southern Zhejiang, Fujian, Guangdong, Guangxi, southern Hunan, and southern Jiangxi. In the MH, the newly increased area was the smallest, which was 1.98 × 104 km2. The newly increased areas are scattered in southern Zhejiang, Fujian, Guangdong, Guangxi, southeastern Guizhou, southwestern Hunan, southern Jiangxi, and Taiwan.
In different climate scenarios projected for the future, O. microphylla exhibits new areas of distribution. However, the extent of loss outweighs the degree of increase in these scenarios. Under the SSP126 climate scenario, the new area of 2050s-SSP126 is larger than that of 2090s-SSP126, which is mainly distributed in northern Fujian, Guangdong, Guangxi, southeastern Guizhou, western Hunan, and southern Jiangxi. The loss in area in the 2090s-SSP126 scenario was the largest and the new area was the smallest, which were 5.67 × 104 km2 and 1.61 × 104 km2, respectively. The loss area was mainly located in southern Zhejiang, Guangdong, Guangxi, southeastern Guizhou, southern Hunan, southern Jiangxi, and northern Taiwan. Under the SSP585 climate scenario, the area of the new area and the loss area of O. microphylla in the 2050s-SSP585 is less than that in the 2090s-SSP585. Under this climate scenario, there are new areas in Guangdong and Guangxi.
3.4. Major Climatic Factors Affecting Species Distribution
We analyzed the influence of climatic factors on the habitat suitability of O. microphylla using the contribution rate and permutation importance value (Table 4). The results showed that the top three climatic factors in terms of contribution rate to the MaxEnt model were bio17 (53%), bio4 (17.3%) and bio6 (11.3%). The cumulative contribution rate was 81.6%. Permutation importance values measure the degree of dependence of the model on environmental factors [27]. The top rankings were shown by bio17 (46.3%), bio6 (24.4%), and elev (13.4%). In the regularization training gain of the Jackknife test (Figure 6), when only one variable is used, the highest gain is observed with bio17. This indicates that bio17 contains the most useful information in the model construction. The environmental variable that exhibits the greatest decrease in gain when omitted is elev, suggesting that it possesses the most unique information not found in other environmental variables. Therefore, the dominant factors affecting the modern geographical distribution pattern of O. microphylla include bio17, bo4, bio6 and elev.
3.5. The Spatial Distribution Center of Species Changes and Migrations
Figure 7 shows that in the three historical periods (LIG, LGM, and MH), the centroids of the suitable areas of O. microphylla are located in Lianzhou City, Qingyuan City, Guangdong Province (112°21′26.95″ E, 24°49′7.82″ N), Mashi Town, Jianghua Yao Autonomous County, Yongzhou City, Hunan Province (111°58′0.78″ E, 24°57′6.58″ N), and Liangjiang Town, Lechang City, Shaoguan City, Guangdong Province (113°15′11.64″ E, 25°22′21.77″ N). The centroid of the current suitable distribution area is located in Wushan Town, Lechang City, Shaoguan City, Guangdong Province (113°24′39.88″ E, 25°14′26.23″ N). Under the SSP126 climate scenario, from the current era to the 2050s and then to the 2090s, the centroid of the suitable area of O. microphylla first migrated to the northwest by about 47.34 km to Wuling Town, Yizhang County, Chenzhou City, Hunan Province (113°2′51.05″ E, 25°30′42.54″ N), and then to the southeast by 57.52 km to Xiaoyuan Town, Rucheng County, Chenzhou City, Hunan Province (113°35′14.61″ E, 25°20′26.58″ N). At the same time, under the SSP585 climate scenario, from the current period to the 2050s and then to the 2090s, it is predicted that the centroid of the suitable area of O. microphylla will move to the southwest as a whole. It will first transfer about 50.01 km to Xietang Town, Yizhang County, Chenzhou City, Hunan Province (112°54′59.10″ E, 25°12′13.50″ N), and then transfer about 21.61 km to Dongfeng Township, Yizhang County, Chenzhou City, Hunan Province (112°44′58.19″ E, 25°4′53.09″ N).
4. Discussion
4.1. Evaluation of Model Prediction Results
In this study, the maxent model is optimized based on kuenm data packets. Our findings suggest that the model predictions are accurate and reliable when employing the parameter combination of FC = PTH and RM = 1.7, and achieving an AUC of 0.982. The main distribution areas of O. microphylla in China include Fujian, Guangdong, Guangxi, Guizhou, and Hunan. The current distribution pattern predicted in this study covers almost all the distribution points of O. microphylla. These findings are almost consistent with the actual distribution of O. microphylla in China [13].
4.2. Dynamic Changes in Potential Suitable Habitat in Different Periods of O. microphylla
The highly suitable habitats of O. microphylla in the LIG are primarily located in Fujian, Guangdong, Guangxi, and southeastern Guizhou. The existence of fossil pollen holds significant importance in reconstructing the distribution of paleovegetation and understanding the evolution of paleoclimate. Fossil data from Zhangzhou, Fujian Province in southeastern China reveal well-preserved Miocene fruit fossils of Ormosia [28]. Moreover, the presence of numerous complex and diverse mountain terrains in Central China, South China, and Southwest China has rendered these regions a refuge for many rare species during the Quaternary glacial periods [29]. According to phylogenetic genomics, the ancestor of Fabaceae originated 67 million years ago, slightly earlier than Cretaceous–Paleogene boundary [30]. Therefore, there may be a suitable distribution area for O. microphylla in the LIG.
The distribution area of the total suitable area and the highly suitable area of O. microphylla in the LGM was the smallest in the three periods of history. There are highly suitable areas in southwestern Fujian, northern Guangdong, Guangxi, southeastern Guizhou, and eastern Taiwan. During the last glacial maximum period, the sea level decreased 132–152 m in the shallow parts of the South China Sea, such as the Beibu Gulf and the Pearl River Estuary. This decrease caused the exposure of continental shelf to sea level [31]. The records of pollen such as Artemisia also reveal the existence of vegetation communities on the continental shelf of the South China Sea, which were exposed to sea level during the last glacial maximum period [32]. It is speculated that the decline in sea level in this period might have instigated the formation of land bridges to promote the exchange and diffusion of plant populations. Therefore, it is speculated that O. microphylla may have been distributed in Taiwan Province of China during the LGM.
In the MH, the distribution of potential suitable habitats of O. microphylla was mostly concentrated to the south of the Yangtze River, and the potential highly suitable habitats were distributed in Fujian, Guangdong, Guangxi, and southeastern Guizhou. Moreover, the fossil fruit of Ormosia cyclocarpa Li et Sun sp. nov. (new species), belonging to the late Miocene period, was also found in Tiantai County, Zhejiang Province [33]. The evidence of Ormosia pinnata wood fossils indicates the existence of Ormosia plants in Wuhan, Hubei Province as early as the late Tertiary period [34]. Therefore, the distribution of O. microphylla in the MH is more reliable in the above areas.
Under different climate change scenarios in the future, the suitable distribution range of O. microphylla showed a shrinking trend compared with the current distribution, and the potential loss area was greater than the new area. The centroid results show that under the SSP126 climate scenario, from the current era to the 2050s and then to the 2090s, the centroid of the suitable area of O. microphylla first shifts to the northwest and then to the southeast. In the SSP585 climate scenario, from the contemporary era to the 2050s and then to the 2090s, it is predicted that the centroid migration trend of the suitable area of O. microphylla will move as a whole to the southwest. In addition, the SSP585 climate scenario shows a greater reduction in the total potential suitable area than the SSP126 climate scenario. This phenomenon may be regulated by temperature factors. The physiological function of the species cannot quickly and effectively adapt to the prolonged and extensive warming climate [35]. Under the SSP126 and SSP585 scenarios, the recent (2021–2040), medium-term (2041–2060), and the long-term (2081–2100) warming in China will continue in the future, and the warming amplitude of the SSP585 climate scenario will be higher than that of the SSP126 climate scenario [36].
4.3. The Main Climatic Factors Restricting the Geographical Distribution Pattern of O. microphylla
Based on the comprehensive analysis of contribution rate, permutation importance value, and jackknife test, this study concludes that the precipitation of driest quarter (bio17), the temperature seasonality (bio4), the min temperature of coldest month (bio6), and elevation (elev) play a key role in shaping the geographical distribution area of O. microphylla. A number of studies have shown that temperature and water factors are often more sensitive to the impact of species distribution patterns. For example, the distribution range of Ormosia hosiei and Ormosia henryi, which belong to the genus Ormosia, are regulated by temperature conditions and precipitation factors [37,38]. Among them, the precipitation of driest quarter exhibits the highest contribution rate, and the jackknife test underscores that bio17 contains the most valuable information for model construction. The precipitation of driest quarter reflects water availability during the dry season and is significantly associated with plant richness patterns [39]. Some scholars have confirmed that the environmental pressure caused by drought has a negative impact on the growth rate of O. pinnata [40]. At the same time, the hard and thick seed coat is the main barrier affecting the absorption of water by the seeds of the Ormosia genus, resulting in the failure of seed germination [41]. Therefore, breaking the seed coat barrier and promoting seed vernalization to boost germination is of great significance to the propagation of the Ormosia genus. Pod cracking plays a key role in the propagation of Fabaceae seeds under natural conditions, and precipitation is a vital factor in regulating pod cracking [42].
The MaxEnt simulation results also show that the contribution rate of the temperature seasonality (bio4) ranks second. It reveals the spatial variation of temperature [43]. At high altitude, the temperature seasonality is stronger than that at low latitudes, indicating that the climate at low latitudes is more stable and favorable to the survival of rare species [44,45]. Some scholars have also found that the temperature seasonality significantly affects the geographical distribution of O. hosiei [46]. In addition, the min temperature of coldest month is also an important driving factor for shaping the geographical distribution pattern of O. microphylla. Low temperature stress has an inhibitory effect on plant growth and geographical distribution, and long-term exposure to cold environments below 0 °C causes cell membrane damage, leading to cell death [47]. The photosynthetic capacity of O. pinnata, a member of Fabaceae genus Ormosia, gradually diminishes when subjected to prolonged periods of low temperatures [48]. Most Ormosia plants generally thrive in mild and humid climates [49]. Therefore, it is speculated that a low temperature environment is not conducive to the growth of O. microphylla.
Elevation is also an important topographic factor for predicting species distribution. The distribution pattern of various legumes in the world is closely related to climatic factors and topographic factors. For example, the suitable habitat of Vachellia negrii (Pic.Serm.) Kyal. & Boatwr. in Ethiopia is affected by elevation, water, and temperature factors [50]. The effect of elevation on plant growth is often related to temperature and precipitation [51].
In summary, the geographical distribution pattern of O. microphylla is mainly restricted by temperature factors and water factors. Comparing and analyzing the winter temperature changes between South China and North China reveals the occurrence of winter cooling phenomena and extreme cold events primarily in North China. Moreover, the temperature change amplitude in North China is greater than that observed in South China [52]. In addition, China receives more precipitation in the southeast coastal areas than the northwest regions [53]. Therefore, it is speculated that the temperature seasonality and the min temperature of coldest month may be the key factors restricting the diffusion of O. microphylla across the Yangtze River to the north. The precipitation of driest quarter is the limiting factor that limits the westward expansion of O. microphylla across the Dalou Mountain–Wumeng Mountain.
4.4. Recommendations for the Protection of O. microphylla
In the reserved suitable area, in situ protection measures can be taken according to the distribution of the wild populations of O. microphylla, and corresponding protection policies can be formulated to strengthen the protection of rare and endangered species. It is worth mentioning that the highly suitable habitats in the east (Fujian Province) are mainly occupied by Ormosia microphylla var. tomentosa R. H. Chang, while the highly suitable habitats in the west (Guizhou, Guangxi, Guangdong, and Hunan) are predominantly occupied by O. microphylla. Ormosia microphylla var. tomentosa R. H. Chang is a variant of O. microphylla. The main difference between the two is the presence or absence of villi at the back of leaves. We speculate the presence of a diffusion corridor between the highly suitable areas in the east and west, which is possibly caused by geographical isolation and insufficient gene flow. Therefore, it is of great significance to safeguard the diffusion corridor between the highly suitable areas in the east and west, and connect the fragmented habitats to improve the migration ability of the population and maintain the genetic diversity of the germplasm resources. The native communities of O. microphylla should be protected, keeping in view the variable conditions in Fujian Junzifeng National Nature Reserve, Guangxi Sanpihu Autonomous Region Nature Reserve, and Hunan Nanshan National Park. Moreover, their distribution range should be increased through forest window opening and field return, letting them fully harness the ecological and economic benefits.
O. microphylla has the disadvantage of poor seed germination and weak self-renewal ability [17]. In the predicted expansion of suitable areas, the interference of human activities should be reduced, reasonable protection policies should be formulated, and the land-use space should be planned. In addition, it can be assisted by artificial migration to new suitable areas to help them spread and colonize.
In view of the lost area, the biological and ecological characteristics of the tree species should be fully considered, facilitating the smooth migration of O. microphylla to a suitable habitat for protection. O. microphylla has the issues of thick seed coat, diseases, and insect and pest attacks, and it is found that the community of O. microphylla is eroded by Cerambycidae [19]. Previous studies have found that environmental conditions such as temperature and rainfall can affect the survival of Cerambycidae [54,55]. Therefore, relevant prevention and control work can be carried out according to the characteristics of pests.
5. Conclusions
In this study, the MaxEnt model was used to simulate the response of the geographical distribution pattern of O. microphylla to climate change in different periods. It is concluded that since the last interglacial period, the suitable distribution of O. microphylla has been concentrating in Fujian, Guangdong, Guangxi, Guizhou, Hunan, and other provinces. Moreover, the distribution range is supposed to be shrinking under different climate scenarios in the future. Both temperature and precipitations collaboratively constrain the dynamic changes in the geographical distribution of O. microphylla. The precipitation of driest quarter (bio17), the temperature seasonality (bio4), the min temperature of coldest month (bio6), and the elevation (elev) are the dominant environmental factors that restrict the dynamic migration of O. microphylla from their potential distribution habitats. This study explores how climatic and topographic factors affect the geographical distribution pattern of O. microphylla. The results may have certain limitations, suggesting potential avenues for further research in the future. This could include more comprehensive investigations, such as soil factors, human factors, and other ecological factors, aimed at providing valuable insights and guidance for the protection and sustainable utilization of this precious tree species.
Conceptualization, B.L. and H.W.; methodology, B.L., H.W. and X.Y.; software, H.W., Z.Z., X.Y. and C.Z.; validation, B.L., H.W., X.Y. and Z.Z.; formal analysis, B.L., H.W. and X.Y.; investigation, B.L., H.W., X.Y., Z.Z., C.Z., Q.X., H.D. and Z.X.; resources, B.L., H.W., G.Z. and S.C.; data curation, B.L. and H.W.; writing—original draft preparation, B.L., H.W., X.Y., Z.Z., C.Z. and S.A; writing—review and editing, B.L., H.W. and S.A.; visualization, H.W. and Z.Z.; supervision, B.L.; project administration, B.L.; funding acquisition, B.L. All authors have read and agreed to the published version of the manuscript.
Data is contained within the article.
We thank the contributors to the databases used in this study. We also thank the Fujian JunziFeng National Nature Reserve Management Bureau for their assistance in the field investigation.
The authors declare no conflicts of interest.
Footnotes
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Enlarge this image.Figure 1. Photos and distribution points of Ormosia microphylla. (A) whole tree; (B) leaves; (C) seeds; (D) distribution points.
Enlarge this image.Figure 4. MaxEnt model predicted potential suitable habitats of O. microphylla in different periods. (A) Last interglacial; (B) last glacial maximum; (C) Middle Holocene; (D) current; (E) 2050s-SSP126; (F) 2050s-SSP585; (G) 2090s-SSP126; (H) 2090s-SSP585.
Enlarge this image.Figure 5. Spatial change patterns of potential suitable areas of O. microphylla in different periods. (A) Last interglacial; (B) last glacial maximum; (C) Middle Holocene; (D) current; (E) 2050s-SSP126; (F) 2050s-SSP585; (G) 2090s-SSP126; (H) 2090s-SSP585.
The AUC value of ten simulations.
| NO. | Training AUC | Test AUC |
|---|---|---|
| 1 | 0.9803 | 0.9773 |
| 2 | 0.9805 | 0.9831 |
| 3 | 0.9825 | 0.9739 |
| 4 | 0.9831 | 0.9893 |
| 5 | 0.9796 | 0.9671 |
| 6 | 0.9884 | 0.9532 |
| 7 | 0.9836 | 0.9695 |
| 8 | 0.9799 | 0.9898 |
| 9 | 0.9792 | 0.988 |
| 10 | 0.982 | 0.9804 |
Changes in the suitable area of Ormosia microphylla in different periods (unit: ×104 km2).
| Period | Area (×10⁴ km2) | |||
|---|---|---|---|---|
| Less Suitable Habitats | Middle Suitability Habitats | Highly Suitable Habitats | Total Suitable Habitats | |
| Last interglacial | 47.26 | 23.01 | 13.87 | 84.14 |
| Last glacial maximum | 42.71 | 15.04 | 11.41 | 69.16 |
| Middle Holocene | 43.69 | 19.13 | 11.65 | 74.47 |
| Current | 49.20 | 20.59 | 13.81 | 83.61 |
| 2050s-SSP126 | 49.64 | 20.76 | 12.99 | 83.40 |
| 2050s-SSP585 | 44.73 | 20.69 | 12.93 | 78.34 |
| 2090s-SSP126 | 41.15 | 19.33 | 11.04 | 71.52 |
| 2090s-SSP585 | 34.94 | 23.14 | 11.24 | 69.31 |
Spatial variation of suitable habitat of O. microphylla in different periods (unit: ×104 km2).
| Period | Area (×10⁴ km2) | Change (%) | ||||
|---|---|---|---|---|---|---|
| Reserved | Lost | Increase | Reserved | Lost | Increase | |
| Last interglacial | 28.68 | 5.72 | 8.16 | 83.37 | 16.63 | 23.71 |
| Last glacial maximum | 21.51 | 12.91 | 4.92 | 62.54 | 37.53 | 14.31 |
| Middle Holocene | 28.73 | 5.69 | 1.98 | 83.50 | 16.53 | 5.75 |
| 2050s-SSP126 | 29.84 | 4.61 | 3.87 | 86.74 | 13.39 | 11.26 |
| 2050s-SSP585 | 30.58 | 3.89 | 2.94 | 88.90 | 11.30 | 8.56 |
| 2090s-SSP126 | 28.68 | 5.67 | 1.61 | 83.37 | 16.48 | 4.69 |
| 2090s-SSP585 | 29.30 | 5.12 | 5.07 | 85.17 | 14.90 | 14.73 |
Various parameters of the environmental variables.
| Code | Environmental Variable | Contribution Rate/% | Permutation Importance/% |
|---|---|---|---|
| bio17 | Precipitation of driest quarter | 53 | 46.3 |
| bio4 | Temperature seasonality | 17.3 | 12.5 |
| bio6 | Min temperature of coldest month | 11.3 | 24.4 |
| elev | Elevation | 8.6 | 13.4 |
| Bio13 | Precipitation of wettest month | 5.9 | 0.1 |
| bio18 | Precipitation of warmest quarter | 1.9 | 0.4 |
| bio15 | Precipitation seasonality | 0.8 | 0.7 |
| bio2 | Mean diurnal range | 0.8 | 1.2 |
| bio8 | Mean temperature of wettest quarter | 0.3 | 1.1 |
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; Zhao, Zixin 1 ; Zhan, Chaoyu 1 ; Sagheer Ahmad 2
; Xu, Qingli 3 ; Ding, Hongfeng 3 ; Xiao, Zhi 3 ; Zhang, Guofang 1
; Chen, Shipin 1 1 Forestry College, Fujian Agriculture and Forestry University, Fuzhou 350002, China;
2 College of Landscape Architecture and Art, Fujian Agriculture and Forestry University, Fuzhou 350002, China;
3 Pushang State-Owned Forest Farm of Shunchang, Shunchang 353205, China;