Prediction of the potential distribution of Curcuma in China under current and future climate scenarios

doi:10.11686/cyxb2023435

Abstract

Abstract:

Curcuma species are important medicinal herbs in China， and are crucial for the sustainable development of Chinese herbal medicine resources. However， the potential distribution pattern of Cucurma species in China under climate change is still unclear. To study the potential spatial distribution characteristics of Curcuma species under climate change in China， we selected the three most representative species， namely Curcuma phaeocaulis， Curcuma longa， and Curcuma aromatica， for detailed analyses. The MaxEnt model （maximum entropy） and geographic information system （ArcGIS） methods were applied， and along with records of the present distribution of these three Curcuma species and environmental factors in their distribution area such as climate， topography， and soil， were used predict the potential habitat of the three species under current and future climate scenarios. The results showed that the area under receiver operating characteristic curve （AUC） values of the three Curcuma species predicted by the MaxEnt model were all greater than 0.95， indicating that the prediction results had high accuracy. At present， the three species are mainly distributed in the provinces of Yunnan， Guangxi， Guangdong， Fujian， Taiwan， and Hainan， with C. longa having the largest area of suitable habitat and C. aromatica having the smallest. During the 2090s， under the SSP245 climate scenario， there is a trend for all three species to expand their distribution towards the northeast in China with the maximum area of suitable habitat. Under the SSP370 and SSP585 climate scenarios， only small areas of the three species will undergo contraction and expansion of the fitness zone. The mean temperature of the driest season was identified as the key factor affecting the potential distribution of C. phaeocaulis， while the mean annual precipitation was identified as the key factor affecting the potential distribution of C. longa and C. aromatica. In conclusion， the three species of Curcuma are mainly distributed in southern China and will tend to expand northward under climate change. On the basis of these results， the Conservation Departments in some identified provinces can carry out planting to expand the growing area of Curcuma， which will help to enhance the economic and ecological benefits of this genus.

Key words: Curcuma plants, MaxEnt model, environmental factors, climate change, potential distribution

Peng WANG, Zheng JIN, Ting YU, Kang-qiang QIN, Xin-ya SANG, Jian-ping TAO, Wei-xue LUO. Prediction of the potential distribution of Curcuma in China under current and future climate scenarios[J]. Acta Prataculturae Sinica, 2024, 33(10): 14-27.

Figures/Tables 10

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代号 Code	中文名称 Name	单位 Unit	代号 Code	中文名称 Name	单位 Unit
bio1	年平均气温值 Annual mean temperature	10^-1 ℃	bio15	降水季节性 Precipitation seasonality	mm
bio2	昼夜温差月均值 Mean diurnal range	10^-1 ℃	bio16	最湿季度降水量 Precipitation of wettest quarter	mm
bio3	等温性Isothermality ［（bio2/bio7）×100］	—	bio17	最干季度降水量 Precipitation of driest quarter	mm
bio4	季节性温度 Temperature seasonality	—	bio18	最暖季度降水量 Precipitation of warmest quarter	mm
bio5	最暖月最高温 Max temperature of warmest month	10^-1 ℃	bio19	最冷季度降水量 Precipitation of coldest quarter	mm
bio6	最冷月最低温 Min temperature of coldest month	10^-1 ℃	topo_aspect	坡向 Aspect	—
bio7	温度年较差 Temperature annual range	10^-1 ℃	topo_elev	海拔 Elevation	m
bio8	最湿季度均温 Mean temperature of wettest quarter	10^-1 ℃	topo_slope	坡度 Slope	—
bio9	最干季度均温 Mean temperature of driest quarter	10^-1 ℃	soil_BD	土壤容重 Soil bulk density	kg·dm^-3
bio10	最暖季度均温 Mean temperature of warmest quarter	10^-1 ℃	soil_sand	土壤沙粒含量 Soil sand content	%
bio11	最冷季度均温 Mean temperature of coldest quarter	10^-1 ℃	soil_TN	土壤全氮 Soil total nitrogen	g·kg^-1
bio12	年平均降水量 Annual precipitation	mm	soil_pH	土壤pH Soil pH	—
bio13	最湿月降水量 Precipitation of wettest month	mm	soil_ocd	土壤有机碳密度 Soil organic carbon density	kg·m^-3
bio14	最干月降水量 Precipitation of driest month	mm	soil_soc	土壤有机碳含量 Soil organic carbon content	g·kg^-1

代号 Code	中文名称 Name	单位 Unit	代号 Code	中文名称 Name	单位 Unit
bio1	年平均气温值 Annual mean temperature	10^-1 ℃	bio15	降水季节性 Precipitation seasonality	mm
bio2	昼夜温差月均值 Mean diurnal range	10^-1 ℃	bio16	最湿季度降水量 Precipitation of wettest quarter	mm
bio3	等温性Isothermality ［（bio2/bio7）×100］	—	bio17	最干季度降水量 Precipitation of driest quarter	mm
bio4	季节性温度 Temperature seasonality	—	bio18	最暖季度降水量 Precipitation of warmest quarter	mm
bio5	最暖月最高温 Max temperature of warmest month	10^-1 ℃	bio19	最冷季度降水量 Precipitation of coldest quarter	mm
bio6	最冷月最低温 Min temperature of coldest month	10^-1 ℃	topo_aspect	坡向 Aspect	—
bio7	温度年较差 Temperature annual range	10^-1 ℃	topo_elev	海拔 Elevation	m
bio8	最湿季度均温 Mean temperature of wettest quarter	10^-1 ℃	topo_slope	坡度 Slope	—
bio9	最干季度均温 Mean temperature of driest quarter	10^-1 ℃	soil_BD	土壤容重 Soil bulk density	kg·dm^-3
bio10	最暖季度均温 Mean temperature of warmest quarter	10^-1 ℃	soil_sand	土壤沙粒含量 Soil sand content	%
bio11	最冷季度均温 Mean temperature of coldest quarter	10^-1 ℃	soil_TN	土壤全氮 Soil total nitrogen	g·kg^-1
bio12	年平均降水量 Annual precipitation	mm	soil_pH	土壤pH Soil pH	—
bio13	最湿月降水量 Precipitation of wettest month	mm	soil_ocd	土壤有机碳密度 Soil organic carbon density	kg·m^-3
bio14	最干月降水量 Precipitation of driest month	mm	soil_soc	土壤有机碳含量 Soil organic carbon content	g·kg^-1

物种Species	保留的环境因子Retained environmental factors
莪术C. phaeocaulis	bio3、bio4、bio9、bio12、bio15、soil_BD、soil_sand、soil_TN、topo_aspect、topo_slope
姜黄C. longa	bio3、bio6、bio8、bio12、bio15、soil_BD、soil_pH、soil_sand、topo_aspect、topo_slope
郁金C. aromatica	bio3、bio9、bio12、bio15、soil_pH、soil_TN、topo_aspect、topo_elev、topo_slope

物种Species	保留的环境因子Retained environmental factors
莪术C. phaeocaulis	bio3、bio4、bio9、bio12、bio15、soil_BD、soil_sand、soil_TN、topo_aspect、topo_slope
姜黄C. longa	bio3、bio6、bio8、bio12、bio15、soil_BD、soil_pH、soil_sand、topo_aspect、topo_slope
郁金C. aromatica	bio3、bio9、bio12、bio15、soil_pH、soil_TN、topo_aspect、topo_elev、topo_slope

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