基于MaxEnt模型的中国山楂潜在适生区

doi:10.11707/j.1001-7488.20220705

Abstract

Abstract:

Objective: This study was designed to comprehensively analyze the relationship between Hawthorn distribution and environmental factors, and to provide a theoretical basis for the protection and utilization of Hawthorn resources. Method: Based on the data of 192 geographical distribution points and 20 environmental variables downloaded from Worldclim from 1970 to 2000, the potential suitable growth area of Hawthorn in 2020 was predicted using the MaxEnt model, and the suitability grades was classified. At the same time, the key environmental factors affecting the distribution were analyzed. Result: The analysis showed that the prediction of MaxEnt is accurate, and the AUC values of both test data set and training data set are more than 0.9. Nine environmental factors such as precipitation of warmest quarter (28.6% contribution), temperature seasonality standard deviation (27.2%), mean temperature of driest quarter (13%), maximum temperature of warmest month (11.8%), annual precipitation (10%), altitude (7.7%), mean diurnal range (1.2%), precipitation seasonality coefficient of variation(0.4%)and isothermality(0.2%) are the key environmental factors that affect the distribution of Hawthorn. The total suitable area of Hawthorn in China has reached 2 487 002 km², which is mainly distributed in North, Northeast and East China. The top 10 provinces account for 82.38% of the total suitable area. Shandong, Hebei, Liaoning, Henan, and Shanxi Provinces have the largest area, with a total of 481 519 km², accounting for 82.56% of all highly suitable areas. Jiangsu, Shaanxi, Beijing, Tianjin, Heilongjiang, Jilin, and Inner Mongolia have great potential for development. Conclusion: The suitable growing area of Hawthorn is highly concentrated, and the suitability decreases gradually from the Bohai Rim to the inland provinces, among which Shandong, Hebei, Liaoning, Henan, and Shanxi are the core areas suitable for Hawthorn cultivation.

Key words: Crataegus pinnatifida, potential suitable areas, MaxEnt, environment variables

CLC Number:

S718.45

Dongsheng Wang,Wei Zhao,Beibei Cheng,Jijun Zhang. Potential Suitable Areas of Crataegus pinnatifida in China based on MaxEnt Modeling[J]. Scientia Silvae Sinicae, 2022, 58(7): 43-50.

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环境因子 Environment variables	初次运算贡献率 Contribution rate of initial operation (%)	环境因子 Environment variables	初次运算贡献率 Contribution rate of initial operation (%)
温度季节变化标准差 Temperature seasonality standard deviation	25.6	最冷季均温Mean temperature of coldest quarter/℃	2.7
最热季降水量Precipitation of warmest quarter/mm	20.0	最冷月最低温Minimum temperature of coldest month/℃	1.6
海拔Altitude/m	7.8	最暖季度均温Mean temperature of warmest quarter/℃	1.3
最湿月降水量Precipitation of wettest month/mm	7.1	年均温Annual mean temperature/℃	1.3
最热月最高温Maximum temperature of warmest month/℃	7.1	最干季降水量Precipitation of driest quarter/mm	1.2
最干季均温Mean temperature of driest quarter/℃	5.9	年温度变化范围Temperature annual range/℃	1.2
年降水量Annual precipitation/mm	4.9	平均日较差Mean diurnal range/℃	1.0
最干月降水量Precipitation of driest month/mm	3.8	最冷季降水量Precipitation of coldest quarter/mm	0.3
最湿季均温Mean temperature of wettest quarter/℃	3.7	等温性Isothermality	0.2
最湿季降水量Precipitation of wettest quarter/mm	3.1	降水量季节变异系数 Precipitation seasonality coefficient of variation	0.1

环境因子 Environment variables	二次运算贡献率 Contribution rate of secondary operation(%)	适宜范围 Suitable range	最适值 Optimum value
最热季降水量Precipitation of warmest quarter/mm	28.6	354.94~477.89	372.51
温度季节变化标准差Temperature seasonality standard deviation	27.2	93.93~107.31	99.42
最干季均温Mean temperature of driest quarter/℃	13	-3.44~2.04	0.96
最热月最高温Maximum temperature of warmest month/℃	11.8	27.17~32.66	27.90
年降水量Annual precipitation/mm	10	522.45~869.04	728.65
海拔Altitude/m	7.7	2.23~243.02	25.53
平均日较差Mean diurnal range/℃	1.2	9.25~12.98	10.85
降水量季节变化变异系数Precipitation seasonality coefficient of variation	0.4	< 0.19	0.15
等温性Isothermality	0.2	90.75~166.7	153.22
合计Total	100

省级行政区 Provincial administrative region	低适生区 Low suitable area/km²	中适生区 Medium suitable area/km²	高适生区 High suitable area/km²	适生区面积 Total suitable area/km²	占适生区总面积比例 Proportion of the total suitable area(%)
山东Shandong	313	4 080	149 214	153 607	6.176 4
河北Hebei	21 353	40 883	100 167	162 403	6.530 1
辽宁Liaoning	5 144	45 267	97 355	147 766	5.941 5
河南Henang	40 864	42 467	77 161	160 492	6.453 2
山西Shanxi	47 907	44 418	57 622	149 947	6.029 2
陕西Shaanxi	59 008	76 944	36 524	172 476	6.935 1
吉林Jilin	59 711	106 416	19 564	185 691	7.466 5
江苏Jiangsu	61 706	14 112	13 135	88 953	3.576 7
北京Beijing	827	4 230	11 262	16 319	0.656 2
内蒙古Inner Mongolia	238 849	115 642	8 424	362 915	14.592 5
天津Tianjin	0	6 773	5 127	11 900	0.478 5
黑龙江Heilongjiang	345 947	78 803	4 531	429 281	17.261 0
安徽Anhui	100 285	20 715	3 172	124 172	4.992 8
湖北Hubei	117 860	3 119	11	120 990	4.864 9
甘肃Gansu	45 234	15 076	0	60 310	2.425 0
江西Jiangxi	36 901	533	0	37 434	1.505 2
宁夏Ningxia	5 337	51	0	5 388	0.216 6
青海Qinghai	4 858	2	0	4 860	0.195 4
广西Guangxi	3	0	0	3	0.000 1
湖南Hunan	59 485	0	0	59 485	2.391 8
上海Shanghai	5 282	0	0	5 282	0.212 4
四川Sichuan	28	0	0	28	0.001 1
新疆Xinjiang	30	0	0	30	0.001 2
浙江Zhejiang	26 927	0	0	26 927	1.082 7
重庆Chongqin	343	0	0	343	0.013 8
合计Total	1 284 202	619 531	583 269	2 487 002	100.000 0

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Potential Suitable Areas of Crataegus pinnatifida in China based on MaxEnt Modeling

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