基于优化MaxEnt模型及土地利用类型的中国薄壳山核桃适生区预测评价

doi:10.3864/j.issn.0578-1752.2026.14.012

Abstract

Abstract:

【Objective】Carya illinoinensis (C. illinoinensis) (Pecan), as a characteristic agricultural and economic nut tree species, has been widely cultivated in multiple provinces across China in recent years. This study aimed to provide a scientific basis for the scientific layout of the C. illinoinensis industry by investigating and predicting its suitable habitats and land resources in China.【Method】First, based on the optimized Maximum Entropy (MaxEnt) model, 29 environmental variables, including climate, topography, and soil factors from 167 C. illinoinensis cultivation sites in China were analyzed. Then the dominant environmental factors were screened out, and the potential suitable habitats of C. illinoinensis in China under the current climate scenario were predicted. Second, the available cultivation area for C. illinoinensis was extracted and predicted by overlaying the forest distribution data of China.【Result】The MaxEnt model performed optimally with Feature Class (FC)=Linear-Quadratic (LQ) and Regularization Multiplier (RM) = 2. The AUC value of the ROC curve was 0.93, and the mean TSS of the optimized model was 0.7655, demonstrating that the optimized MaxEnt model exhibits exceptionally high reliability and accuracy. The suitable habitats of C. illinoinensis were mainly influenced by temperature and altitude. The primary dominant environmental factor was the minimum temperature of the coldest month (bio6), followed by altitude, isothermality (bio3), and temperature seasonality (bio4). The corresponding suitable threshold ranges were as follows: -5 ℃ to 5 ℃ for the minimum temperature of the coldest month (bio6), below 1 000 m for altitude, 15-30 and 50-57 for isothermality (bio3), and 400-1 000 for temperature seasonality (bio4). Ten provinces in China were identified as moderately to highly suitable habitats for C. illinoinensis, with a medium-suitable area of 9 208.69×10⁴ hm²_,accounting for 10% of China's total land area, and the high suitable area of 6 411.22×10⁴ hm², accounting for 7% of China's total land area. By overlaying with the forest distribution data of China, the actual available cultivation area for C. illinoinensis in China was 1 360.53×10⁴ hm².【Conclusion】In China, C. illinoinensis has potentially suitable habitats in Anhui, Hubei, Henan, Jiangsu, Hunan, Jiangxi, Zhejiang, the central part of Yunnan, south-central Shaanxi, and southwestern Shandong. After overlaying the suitable habitat map with China's forest land distribution data, the actual usable area was significantly reduced. At the provincial scale, Hubei Province had the largest area of highly suitable forest land, reaching 343.35×10⁴ hm², followed by Hunan Province with 305.57×10⁴ hm². Jiangxi and Anhui ranked third and fourth, with 219.81×10⁴ and 161.83×10⁴ hm², respectively, making them important potential main producing areas for pecans. Combining the optimized MaxEnt model with land use types can effectively predict and scientifically evaluate the suitable areas for pecans in China, thereby providing a scientific basis for the development of the pecan industry in China.

Key words: Carya illinoinensis, MaxEnt model, suitable habitat prediction, environmental variables

ZHAO Ning, ZHANG JiYu, WANG Rui, YUAN ShuangJin, CHEN JinHui, LI ShaSha, CHEN ZhiKun. Prediction and Evaluation of Suitable Habitats for Carya illinoinensis in China Based on an Optimized MaxEnt Model and Land Use Types[J].Scientia Agricultura Sinica, 2026, 59(14): 3147-3161.

Figures/Tables 9

Fig. 1

Table 1

Environmental impact factors"

环境变量 Environment variable	代号 Code	环境因子名称 Name of environmental factor	单位 Unit	贡献率 Rate of contribution (%)
气候变量 Weather variable	Bio1	年均温 Mean annual temperature	℃	4.7
	Bio2	平均气温日较差 Mean diurnal temperature range	℃	2.9
	Bio3*	等温性(bio2 /bioc7)*100 Isothermal property ratio	-	1.6
	Bio4*	温度季节性*100 Standard deviation of seasonal variation of temperature	-	0.8
	Bio5	最暖月最高温 Max temperature of the warmest month	℃	0.5
	Bio6*	最冷月最低温 Minimum temperature of the coldest month	℃	20.7
	Bio7	气温年较差(bio5-bio6) Temperature annual range	℃	0.4
	Bio8	最湿季均温 Mean temperature of the wettest quarter	℃	1.0
	Bio9	最干季均温 Mean temperature of the direst quarter	℃	0.9
	Bio10	最热季均温 Mean temperature of the warmest quarter	℃	0.7
	Bio11	最冷季均温 Mean temperature of the coldest quarter	℃	0.4
	Bio12	年降水量 Annual precipitation	mm	2.1
	Bio13	最湿月降水量 Precipitation of the wettest month	mm	0.3
	Bio14*	最干月降水量 Precipitation of the driest month	mm	43.7
	Bio15*	降水季节性变动 Precipitation seasonality	—	1.5
	Bio16	最湿季降水量 Precipitation of the wettest quarter	mm	0.1
	Bio17	最干季降水量 Precipitation of the driest quarter	mm	2.2
	Bio18	最暖季降水量 Precipitation of the warmest quarter	mm	0.2
	Bio19	最冷季降水量 Precipitation of the coldest quarter	mm	0.5
地形变量 Landform variable	Dem*	海拔Altitude	m	3.1
	Slope*	坡度Slope	—	4.0
	Aspect*	坡向Aspect	—	1.3
土壤变量 Soil variable	T_Clay*	顶层黏土含量Topsoil clay content	%	1.3
	T_Gravel*	顶层碎石体积百分比Topsoil gravel content (% by volume)	%	1.2
	T_PH*	顶层酸碱度（-log（H⁺））Topsoil pH（-log（H⁺））	—	0.8
	T_Sand*	顶层沙含量Topsoil sand content	%	0.3
	T_Silt*	顶层淤泥含量Topsoil silt content	%	2.4
	T_Oc*	顶层有机碳含量Topsoil organic carbon content	%	0.2
	T_Ref_Bulk	顶层土壤容重Topsoil bulk density	kg·dm^-3	0.1

Table 1

Fig. 2

Table 2

Fig. 3

Fig. 4

Fig. 5

Fig. 6

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Prediction and Evaluation of Suitable Habitats for Carya illinoinensis in China Based on an Optimized MaxEnt Model and Land Use Types

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环境因子 Climate variable	贡献率 Percent contribution (%)	随机分布重要性 Permutation importance (%)
Bio6	82.0	74.0
Dem	7.1	10.5
T_Silt	2.9	0.5
Bio3	2.2	4.8
Bio15	1.6	2.2
Bio4	1.3	6.2
Slope	1.2	0.2
Aspect	0.4	0.1
T_Clay	0.4	0.2
T_PH	0.3	0.3
T_Gravel	0.3	0.3
T_Sand	0.3	0.7
Bio14	0	0
T_Oc	0	0

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