Predicting the Distributions of Morus notabilis C. K. Schneid under Climate Change in China
Abstract
:1. Introduction
2. Materials and Methods
2.1. Species Data Sources and Processing
2.2. Environmental Factors
2.3. MaxEnt Modeling
2.4. Classification of Suitable Grades
3. Results
3.1. Model Optimization Results and Accuracy Evaluation
3.2. Model Performance and Key Environment Variables
3.3. Predicting the Current Distribution of Morus notabilis C. K. Schneid in China
3.4. Potential Distribution of Morus notabilis C. K. Schneid in the Future Period
3.5. Environmental Variable Analysis
4. Discussion
5. Conclusions
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
References
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Bio-Climatic Variables | Abbreviation | Percent Contribution/% | Permutation Importance/% |
---|---|---|---|
Annual Precipitation | Bio12 | 39.8 | 0.3 |
Min Temperature of Coldest Month | Bio6 | 18.1 | 0 |
Temperature Annual Range (bio5–bio6) | Bio7 | 13 | 3.3 |
Precipitation of Coldest Quarter | Bio19 | 10.5 | 11.4 |
Precipitation of Warmest Quarter | Bio18 | 8.1 | 1.8 |
Max Temperature of Warmest Month | Bio5 | 3.4 | 6 |
Precipitation Seasonality (Coefficient of Variation) | Bio15 | 2.7 | 1.5 |
Mean Temperature of Driest Quarter | Bio9 | 1.1 | 12.5 |
Precipitation of Driest Month | Bio14 | 1.1 | 2.3 |
Isothermality (bio 2/bio 7) (*100) | Bio3 | 0.8 | 1.7 |
Mean Temperature of Warmest Quarter | Bio10 | 0.5 | 4.1 |
Temperature Seasonality (SD *100) | Bio4 | 0.4 | 48.5 |
Precipitation of Wettest Quarter | Bio16 | 0.2 | 0 |
Precipitation of Wettest Month | Bio13 | 0.1 | 1.4 |
Mean Temperature of Wettest Quarter | Bio8 | 0.1 | 3.1 |
Annual Mean Temperature | Bio1 | 0.1 | 1.3 |
Mean Temperature of Coldest Quarter | Bio11 | 0.1 | 1 |
Mean Diurnal Range (Mean of monthly [max temp–min temp]) | Bio2 | 0 | 0 |
Precipitation of Driest Quarter | Bio17 | 0 | 0 |
Variable Classification | Environmental Variables | Unit | Abbreviation |
---|---|---|---|
Bio-climatic variables | Annual Precipitation | mm | Bio12 |
Min Temperature of Coldest Month | °C | Bio6 | |
Temperature Annual Range (bio5–bio6) | °C | Bio7 | |
Precipitation of Coldest Quarter | mm | Bio19 | |
Precipitation of Warmest Quarter | mm | Bio18 | |
Max Temperature of Warmest Month | °C | Bio5 | |
Precipitation Seasonality (Coefficient of Variation) | mm | Bio15 | |
Precipitation of Driest Month | mm | Bio14 | |
Isothermality (bio 2/bio 7) (×100) | ×100 | Bio3 | |
Soil variables | Soil reference depth | / | Ref-depth |
Soil acidity and alkalinity | / | pH | |
Upper soil sediment content | %wt. | T-sand | |
Organic carbon content | %wt. | TOC | |
Soil evaluation indicators | / | USDA | |
Terrain variables | The orientation of the terrain slope | Degree | Aspect |
The degree of steepness and gentleness of surface units | ° | Slope | |
Altitude | m | Alt | |
Chemical variables | Ultraviolet-B radiation | nm | UV-B |
Human variables | Human footprint | / | Hf |
Variable Classification | Abbreviation | Percent Contribution/% | Permutation Importance/% |
---|---|---|---|
Annual Precipitation | Bio12 | 42.6 | 0.4 |
Altitude | alt | 11 | 8.8 |
Min Temperature of Coldest Month | Bio6 | 9.2 | 26.3 |
Human footprint | hf | 8.5 | 6.1 |
Temperature Annual Range (bio5–bio6) | Bio7 | 7.5 | 19.4 |
Precipitation of Coldest Quarter | Bio19 | 5.7 | 15.2 |
Ultraviolet-B radiation | UV-B | 4.5 | 5.8 |
Precipitation of Driest Month | Bio14 | 3.8 | 7.2 |
Precipitation of Warmest Quarter | Bio18 | 3 | 3.2 |
Soil reference depth | Ref-depth | 1 | 0.7 |
Soil acidity and alkalinity | pH | 0.8 | 1.8 |
Precipitation Seasonality (Coefficient of Variation) | Bio15 | 0.5 | 0.2 |
The orientation of the terrain slope | Aspect | 0.4 | 1.1 |
Soil evaluation indicators | USDA | 0.4 | 0.1 |
Upper soil sediment content | T-sand | 0.3 | 2.1 |
Max Temperature of Warmest Month | Bio5 | 0.3 | 1 |
Isothermality (bio 2/bio 7) (×100) | Bio3 | 0.1 | 0.6 |
Organic carbon content | TOC | 0.1 | 0.1 |
The degree of steepness and gentleness of surface units | Slope | 0 | 0 |
Province | High Suitable Area (104 km2) | Medium Suitable Area (104 km2) | Low Suitable Area (104 km2) | Percentage of High Suitable Areas in Province (%) | Percentage of Suitable Areas in Province (%) |
---|---|---|---|---|---|
Sichuan | 9.07 | 10.40 | 7.36 | 19.93 | 58.94 |
Yunnan | 3.55 | 22.63 | 7.73 | 10.34 | 98.88 |
Guizhou | 2.79 | 9.32 | 3.80 | 17.47 | 99.66 |
Chongqing | 2.25 | 4.92 | 0.56 | 29.13 | 100.00 |
Hubei | 1.60 | 3.24 | 7.75 | 9.12 | 71.74 |
Xinjiang | 0.48 | 1.90 | 6.64 | 0.42 | 7.90 |
Guangxi | 0.13 | 2.43 | 10.08 | 0.62 | 60.42 |
Shanxi | 0.10 | 1.61 | 8.93 | 0.49 | 52.22 |
Taiwan | 0.04 | 0.45 | 1.53 | 1.27 | 64.02 |
Hunan | 0.02 | 0.66 | 5.11 | 0.13 | 29.91 |
Gansu | 0.02 | 0.36 | 3.66 | 0.05 | 9.74 |
Shandong | 0.01 | 0.34 | 6.43 | 0.07 | 44.24 |
Hainan | 0.01 | 0.11 | 2.40 | 0.19 | 92.89 |
Jiangsu | 0.00 | 0.17 | 5.38 | 0.04 | 57.60 |
Guangdong | 0.00 | 0.13 | 7.89 | 0.01 | 52.48 |
Henan | 0.00 | 0.29 | 9.97 | 0.01 | 63.63 |
Tianjin | 0.00 | 0.06 | 0.47 | 0.14 | 43.73 |
Anhui | 0.00 | 0.13 | 5.25 | 0.00 | 40.28 |
Beijing | 0.00 | 0.07 | 0.67 | 0.00 | 42.64 |
Fujian | 0.00 | 0.02 | 4.60 | 0.00 | 42.75 |
Hebei | 0.00 | 0.07 | 2.87 | 0.00 | 14.97 |
Heilongjiang | 0.00 | 0.00 | 0.00 | 0.00 | 0.01 |
Jilin | 0.00 | 0.00 | 0.05 | 0.00 | 0.24 |
Jiangxi | 0.00 | 0.00 | 0.27 | 0.00 | 1.80 |
Liaoning | 0.00 | 0.05 | 1.65 | 0.00 | 10.90 |
Inner Mongolia | 0.00 | 0.00 | 0.02 | 0.00 | 0.02 |
Ningxia | 0.00 | 0.00 | 0.06 | 0.00 | 1.09 |
Qinghai | 0.00 | 0.00 | 0.13 | 0.00 | 0.18 |
Shaanxi | 0.00 | 0.08 | 3.77 | 0.00 | 24.12 |
Shanghai | 0.00 | 0.00 | 0.26 | 0.00 | 44.48 |
Xizang | 0.00 | 0.00 | 0.01 | 0.00 | 0.00 |
Zhejiang | 0.00 | 0.00 | 1.01 | 0.00 | 10.88 |
China | 20.08 | 59.45 | 116.32 | 39.33 | 20.85 |
Predicted Area (104 km2) | Comparison with Current Distribution (%) | ||||||
---|---|---|---|---|---|---|---|
Decade | Scenarios | High Suitable | Medium Suitable | Low Suitable | High Suitable | Medium Suitable | Low Suitable |
Current | - | 20.08 | 59.45 | 116.32 | - | - | - |
2050s | SSP1-2.6 | 21.79 | 54.29 | 122.98 | 8.51 | −8.68 | 5.72 |
SSP2-4.5 | 21.33 | 56.83 | 133.35 | 6.21 | −4.40 | 14.63 | |
SSP5-8.5 | 25.21 | 53.19 | 123.66 | 25.52 | −10.53 | 6.30 | |
2090s | SSP1-2.6 | 22.00 | 54.08 | 127.50 | 9.53 | −9.04 | 9.60 |
SSP2-4.5 | 24.97 | 49.94 | 128.63 | 24.34 | −15.99 | 10.58 | |
SSP5-8.5 | 26.06 | 48.99 | 128.36 | 29.74 | −17.60 | 10.35 |
Environmental Variables | Unit | Suitable Range | Optimum Value |
---|---|---|---|
Annual Precipitation (bio12) | mm | 824.15–1682.20 | 1088.98 |
Precipitation of Driest Month (bio14) | Mm | 6.94–30.27 | 17.37 |
Min Temperature of Coldest Month (bio6) | °C | −6.05–12.05 | 2.58 |
Temperature Annual Range (bio5–bio6) (bio7) | °C | 10.00–31.02 | 26.50 |
Precipitation of Warmest Quarter (bio18) | Mm | 448.09–1955 | 761.12 |
Precipitation of Coldest Quarter (bio19) | mm | 27.53–124.09 | 56.68 |
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Gao, H.; Qian, Q.; Deng, X.; Peng, Y.; Xu, D. Predicting the Distributions of Morus notabilis C. K. Schneid under Climate Change in China. Forests 2024, 15, 352. https://doi.org/10.3390/f15020352
Gao H, Qian Q, Deng X, Peng Y, Xu D. Predicting the Distributions of Morus notabilis C. K. Schneid under Climate Change in China. Forests. 2024; 15(2):352. https://doi.org/10.3390/f15020352
Chicago/Turabian StyleGao, Hui, Qianqian Qian, Xinqi Deng, Yaqin Peng, and Danping Xu. 2024. "Predicting the Distributions of Morus notabilis C. K. Schneid under Climate Change in China" Forests 15, no. 2: 352. https://doi.org/10.3390/f15020352