Changes in Potentially Suitable Areas for Fruit Utilization of Acer truncatum in China under Climate Change
Abstract
:1. Introduction
2. Materials and Methods
2.1. Sample Point Data Collection and Processing
2.2. Source and Processing of Ecological Factor Data
2.3. Species Distribution Model
2.4. Classification of Suitable Areas
3. Results and Analysis
3.1. Spatial Distribution of Suitable Areas for A. truncatum Fruit Utilization under Current Climate Conditions
3.2. Spatial Distribution of Suitable Areas for A. truncatum Fruit Utilization under Future Climate Change Scenarios
3.3. Temporal and Spatial Changes in Suitable Areas for A. truncatum Fruit Utilization under Climate Change Scenarios
3.4. Centroid Migration in the Suitable Area for A. truncatum Fruit Utilization under Climate Change Scenarios
4. Discussion
5. Conclusions
Author Contributions
Funding
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
- Chen, I.C.; Jane, K.H.; Ralf, O.; David, B.R.; Chris, D.T. Rapid range shifts of species associated with high levels of climate warming. Science 2011, 333, 1024–1026. [Google Scholar] [CrossRef] [PubMed]
- Ruiz, L.; Nogues, B.; Ollero, H.S.; Schmitz, M.F.; Pineda, F.D. Forest composition in Mediterranean mountains is projected to shift along the entire elevational gradient under climate change. J. Biogeog. 2012, 39, 162–176. [Google Scholar] [CrossRef]
- Catherine, M.D.; Brian, A.B.; James, W.M.; Zoë, L. Climate change drives a shift in peatland ecosystem plant community: Implications for ecosystem function and stability. Glob. Chang. Biol. 2015, 21, 388–395. [Google Scholar]
- Toledo, M.; Poorter, L.; Peña-Claros, M.; Alarcón, A.; Balcázar, J.; Leaño, C.; Licona, J.C.; Llanque, O.; Vroomans, V.; Zuidema, P.; et al. Climate is a stronger driver of tree and forest growth rates than soil and disturbance RID C-8951-2009. J. Ecol. 2011, 99, 254–264. [Google Scholar] [CrossRef]
- Guisan, A.; Tingley, R.; Baumgartner, J.B.; Naujokaitis, I.L.; Sutcliffe, P.R.; Tulloch, A.; Regan, T.J.; Brotons, L.; Mcdonald, E.M.; Mantyka, C.P. Predicting species distributions for conservation decisions. Ecol. Lett. 2013, 16, 1424–1435. [Google Scholar] [CrossRef]
- Ghasemi, S.; Malekian, M.; Tarkesh, M. Climate change pushes an economic insect to the brink of extinction: A case study for Cyamophila astragalicola in Iran. J. Zool. Sys. Evol. Res. 2021, 59, 1632–1641. [Google Scholar] [CrossRef]
- Hughes, L. Biological consequences of global warming: Is the signal already apparent? Trends. Ecol. Evol. 2000, 15, 56–61. [Google Scholar] [CrossRef] [PubMed]
- Walther, G.R.; Post, E.; Convey, P.; Menzel, A.; Parmesan, C.; Beebee, T.J.C.; Fromentin, J.M.; Hoegh, O.G.; Bairlein, F. Ecological responses to recent climate change. Nature 2002, 416, 389–395. [Google Scholar] [CrossRef]
- Yang, X.Q.; Kushwaha, S.P.S.; Saran, S.; Xu, J.C.; Roy, P.S. Maxent modeling for predicting the potential distribution of medicinal plant, Justicia adhatoda L. in Lesser Himalayan foothills. Ecol. Eng. 2013, 22, 83–87. [Google Scholar] [CrossRef]
- Valjarević, A.; Djekić, T.; Stevanović, V.; Ivanović, R.; Jandziković, B. GIS-numerical and remote sensing analyses of forest changes in the Toplica region for the period of 1953–2013. Appl. Geogr. 2018, 92, 131–139. [Google Scholar] [CrossRef]
- Pilar, A.H.; Catherine, H.G.; Lawrence, L.M.; Deborah, L.A. The effect of sample size and species characteristics on performance of different species distribution modeling methods. Ecography 2006, 29, 773–785. [Google Scholar]
- Elith, J.; Graham, C.H.; Anderson, R.P.; Dudík, M.; Ferrier, S.; Guisan, A.; Hijmans, R.J.; Huettmann, F.; Leathwick, J.R.; Lehmann, A.; et al. Novel methods improve prediction of species’ distributions from occurrence data. Ecography 2006, 29, 129–151. [Google Scholar] [CrossRef]
- Han, Y.; Wang, Y.; Ye, J.R.; Li, Y.X.; Lin, S.X. Prediction of potential distribution of Xylella fastidiosa based on Maxent model in China. J. For. Eng. 2015, 29, 144–148. [Google Scholar]
- Zhang, W.H.; Ye, L.Q.; Chen, Q.H.; Ruan, S.N. Introduction and Cultivation Zoning of Acacia melanoxylon Based on Optimized MaxEnt Model. J. Northwest For. Univ. 2023, 38, 88–94+107. [Google Scholar]
- Dai, M.J.; Li, X.Y.; Wang, M.Q.; Wen, Y.F. Potentially Suitable Area of Cryptomeria japonica var. Sinensis and the Influence of Climate Changes on Its Distribution. J. Northwest For. Univ. 2022, 37, 26–33+128. [Google Scholar]
- Wang, X.Y.; Wang, S.Q. New resource food—Acer truncatum seed oil. China Oils Fats 2011, 36, 56–59. [Google Scholar]
- Ren, H.J.; Wang, C.X.; Du, X.Q.; Qiao, Q.; An, K.; Song, Z.K.; Feng, Z. Study on 3 Flavonoids in Acer truncatum Flowers. Chin. Agric. Sci. Bull. 2017, 33, 43–47. [Google Scholar]
- Jia, Z.M. Application of Acer truncatum products in industry. For. Shanxi 2015, 239, 28–29. [Google Scholar]
- Dang, Y.; Wang, W.; Yu, X.X.; Jia, G.D.; Fan, G.X. Eco-hydrological effects of litter layer in typical artificial forest stands in Xishan Mountain of Beijing. J. Beijing For. Univ. 2022, 44, 72–87. [Google Scholar]
- Wei, Y.; Yan, W.; Yang, R. Physiological characteristics of leaf color change during color change in four autumn leaf species. Contempo. Horti. 2014, 261, 17–19. [Google Scholar]
- Wang, L.J.; Jiang, P.; Xu, D.J. Analysis of Geographic Distribution Patterns of Lycium barbarum in the Context of Climate Oscillations. Acta Bot. Boreali-Occident. Sin. 2022, 42, 2133–2142. [Google Scholar]
- Gong, L.; Li, X.; Wu, S.; Jiang, L. Prediction of potential distribution of soybean in the frigid region in China with MaxEnt modeling. Ecol. Inform. 2022, 72, 101834. [Google Scholar] [CrossRef]
- Yu, X.; Tao, X.; Liao, J.; Liu, S.C.; Xu, L.; Yuan, S.; Zhang, Z.L.; Wang, F.; Deng, N.Y.; Huang, J.L.; et al. Predicting potential cultivation region and paddy area for ratoon rice production in China using Maxent model. Field Crops Res. 2022, 275, 108372. [Google Scholar] [CrossRef]
- Wu, Y.X.; YANG, Y.; Liu, C.; Hou, Y.X.; Yang, S.Z.; Wang, L.S.; Zhang, X.Q. Potential suitable habitat of two economically important forest trees (Acer truncatum and Xanthoceras sorbifolium) in east Asia under current and future climate scenarios. Forests 2021, 12, 1263. [Google Scholar] [CrossRef]
- Qian, Y.Y.; Mao, J.F.; Nie, J.L.; Pei, Y.; Lang, Y.J. Performance of Growth, Yield, Composition of an introduced Lycium ruthenicum Murr. under different Soil Conditions in Tianjin. Bull. Bot. Res. 2021, 41, 1023–1028. [Google Scholar]
- Wang, X.Y. Acer truncatum of China; Northwest A & F University Press: Xianyang, China, 2013; pp. 97–104. [Google Scholar]
- Eric, W.; Robert, J.H.; Peterson, A.T.; Árpád, S.N.; Susan, L.P.; Robert, P.G. Locating pleistocene refugia: Comparing phylogeographic and ecological niche model predictions. PLoS ONE 2007, 29, e563. [Google Scholar]
- Kong, W.Y.; Li, X.H.; Zou, H.F. Optimizing MaxEnt model in the prediction of species distribution. Chin. J. Appl. Ecol. 2019, 30, 2116–2128. [Google Scholar]
- Hijmans, R.J.; Cameron, S.E.; Parra, J.L.; Jones, P.G.; Jarvis, A. Very high resolution interpolated climate surfaces for global land areas. Int. J. Climatol. 2010, 25, 1965–1978. [Google Scholar] [CrossRef]
- Otto-bliesner, B.; Marshall, S.J.; Overpeck, J.; Miller, G.; Hu, A. Simulating arctic climate warmth and icefield retreat in the last interglaciation. Science 2006, 311, 1751–1753. [Google Scholar] [CrossRef]
- Shen, Y.P.; Wang, G.Y. Key Findings and assessment results of IPCC WGI fifth assessment report. J. Glaciol. Geocryol. 2013, 35, 1068–1076. [Google Scholar]
- Li, W.Q.; Xu, Z.F.; Shi, M.M.; Chen, J.H. Prediction of potential geographical distribution patterns of Salix tetrasperma Roxb. in Asia under different climate scenarios. Acta Ecol. Sin. 2019, 39, 3224–3234. [Google Scholar]
- Van, D.P.; Edmonds, J.; Kainuma, M.; Riahi, K.; Thomson, A.; Hibbard, K.; Hurtt, G.C.; Kram, T.; Krey, V.; Lamarque, J.F.; et al. The representative concentration pathways: An overview. Clim. Chang. 2011, 109, 5–31. [Google Scholar]
- Tu, Y. Based on Species Distribution Model Analysis suitable Distribution Area of Stipa and the Correlation with Climate Factors in China; Beijing Forestry University: Beijing, China, 2020. [Google Scholar]
- Hanley, J.A.; Mcnil, B.J. The meaning and use of the area under a receiver operating characteristic (ROC) curve. Radiology 1982, 143, 29–36. [Google Scholar] [CrossRef] [PubMed]
- Zhao, Z.F.; Guo, Y.L.; Zhu, F.X.; Jiang, Y. Prediction of the impact of climate change on fast-growing timber trees in China. For. Ecol. Manag. 2021, 501, 119653. [Google Scholar] [CrossRef]
- Wang, Y.C.; Mao, J.Y.; Chen, X.J.; Zhang, Z.Q.; Yang, X.Y. Predicting Potential Distribution of Pistacia chinensis in China Using MaxEnt Model. J. Northeast For. Univ. 2021, 49, 61–65. [Google Scholar]
- Zhang, L.; Sun, P.S.; Huettmann, F.; Liu, S.R. Where should China practice forestry in a warming world? Glob. Chang. Biol. 2021, 7, 28. [Google Scholar] [CrossRef] [PubMed]
- Qiu, H.J.; Song, J.J.; Xu, D.; Shen, A.H.; Jiang, B.; Yuan, W.G.; Li, S. MaxEnt model-based prediction of potential distribution of Liriodendron chinense in China. J. Zhejiang A F Univ. 2020, 37, 1–8. [Google Scholar]
- Pan, L.B.; Duan, W.; Huang, Y.J. Prediction on the potential planting area of Carya illinoinensis in China based on MaxEnt model. J. Zhejiang A F Univ. 2022, 39, 76–83. [Google Scholar]
- Wilson, R.J.; Gutiérrez, D.; Gutiérrez, J.; Monserrat, V. An elevational shift in butterfly species richness and composition accompanying recent climate change. Glob. Chang. Biol. 2007, 13, 1873–1887. [Google Scholar] [CrossRef]
- Leng, W.F.; He, H.S.; Bu, R.C.; Dai, L.M.; Hu, Y.M.; Wang, X.G. Predicting the distributions of suitable habitat for three larch species under climate warming in Northeastern China. For. Ecol. Manag. 2008, 254, 420–428. [Google Scholar] [CrossRef]
- Flower, A.; Murdock, T.Q.; Taylor, S.W.; Zwiers, F.W. Using an ensemble of downscaled climate model projections to assess impacts of climate change on the potential distribution of spruce and Douglas-fir forests in British Columbia. Environ. Sci. Policy 2013, 26, 63–74. [Google Scholar] [CrossRef]
- Zhang, M.; Lu, C.L.; Wei, X.X.; Ma, S.; Liu, W.F.; Song, J.; Pen, R.; Li, J.G. Potential suitable area forecast of jujube in Xinjiang based on MaxEnt model. Non-Wood For. Res. 2020, 38, 152–161. [Google Scholar]
- Midgley, G.F.; Hannah, L.; Millar, D.; Thuiller, W.; Booth, A. Developing regional and species-level assessments of climate change impacts on biodiversity in the Cape Floristic Region. Biol. Conserv. 2003, 112, 87–97. [Google Scholar] [CrossRef]
- Seto, K.C.; Güneralp, B.; Hutyra, L.R. Global forecasts of urban expansion to 2030 and direct impacts on biodiversity and carbon pools. Proc. Natl. Acad. Sci. USA 2012, 109, 16083–16088. [Google Scholar] [CrossRef] [PubMed]
Factor Type | Ecological Factor | Unit | Code |
---|---|---|---|
Climate factor | Mean annual air temperature | °C | bio1 |
Monthly mean of temperature difference between day and night | °C | bio2 | |
Max temperature of the warmest month | °C | bio5 | |
Mean temperature of the driest quarter | °C | bio9 | |
Mean temperature of the warmest quarter | °C | bio10 | |
Precipitation of the wettest quarter | mm | bio16 | |
Precipitation of the driest quarter | mm | bio17 | |
Terrain factor | Elevation | m | elev |
Slope | ° | slope | |
Aspect | - | aspect | |
Soil factor | Topsoil sand fraction | % | t_sand |
Topsoil organic carbon content | % | t_oc | |
Topsoil cation exchange capacity | cmol/kg | t_cec | |
Topsoil pH value | - | t_ph | |
Topsoil available water content | % | t_awc |
Climate Scenarios | Highly Suitable Areas | Moderately Suitable Areas | Lowly Suitable Areas | Unsuitable Areas | ||||
---|---|---|---|---|---|---|---|---|
Area (105 km2) | Increase Rate (%) | Area (105 km2) | Increase Rate (%) | Area (105 km2) | Increase Rate (%) | Area (105 km2) | Increase Rate (%) | |
Current | 2.78 | - | 5.35 | - | 8.29 | - | 16.42 | - |
RCP2.6—2050 | 44.99 | 61.95 | 7.91 | 47.78 | 10.02 | 20.75 | 22.42 | 36.52 |
RCP4.5—2050 | 3.4.8 | 25.23 | 7.25 | 35.57 | 9.54 | 15.00 | 20.27 | 23.43 |
RCP6.0—2050 | 3.29 | 18.50 | 7.74 | 44.65 | 10.72 | 29.29 | 21.75 | 32.47 |
RCP8.5—2050 | 4.21 | 51.44 | 7.93 | 48.15 | 9.56 | 15.30 | 21.70 | 32.12 |
RCP2.6—2070 | 3.39 | 22.17 | 6.56 | 22.60 | 9.29 | 12.02 | 19.24 | 17.18 |
RCP4.5—2070 | 4.17 | 50.04 | 7.98 | 49.21 | 9.98 | 20.33 | 22.13 | 34.76 |
RCP6.0—2070 | 4.08 | 46.87 | 8.60 | 60.71 | 11.12 | 34.06 | 23.80 | 44.91 |
RCP8.5—2070 | 6.59 | 137.08 | 8.32 | 55.44 | 9.04 | 9.02 | 23.94 | 45.80 |
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content. |
© 2024 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
Share and Cite
Liu, Y.; Wang, Y.; Guo, H.; Wu, D.; Wu, S.; Xin, X.; Pei, S. Changes in Potentially Suitable Areas for Fruit Utilization of Acer truncatum in China under Climate Change. Forests 2024, 15, 713. https://doi.org/10.3390/f15040713
Liu Y, Wang Y, Guo H, Wu D, Wu S, Xin X, Pei S. Changes in Potentially Suitable Areas for Fruit Utilization of Acer truncatum in China under Climate Change. Forests. 2024; 15(4):713. https://doi.org/10.3390/f15040713
Chicago/Turabian StyleLiu, Yitong, Yuqing Wang, Hui Guo, Di Wu, Sha Wu, Xuebin Xin, and Shunxiang Pei. 2024. "Changes in Potentially Suitable Areas for Fruit Utilization of Acer truncatum in China under Climate Change" Forests 15, no. 4: 713. https://doi.org/10.3390/f15040713
APA StyleLiu, Y., Wang, Y., Guo, H., Wu, D., Wu, S., Xin, X., & Pei, S. (2024). Changes in Potentially Suitable Areas for Fruit Utilization of Acer truncatum in China under Climate Change. Forests, 15(4), 713. https://doi.org/10.3390/f15040713