基于MaxEnt模型的土耳其扁谷盗在中国的潜在地理分布预测

doi:10.3969/j.issn.1004-1524.20240522

浙江农业学报 ›› 2025, Vol. 37 ›› Issue (5): 1107-1120.DOI: 10.3969/j.issn.1004-1524.20240522

基于MaxEnt模型的土耳其扁谷盗在中国的潜在地理分布预测

李涛¹^,²(), 江海艳¹, 张远鹏¹, 赵芹¹, 吴芳¹^,^*(), 朱延光³, 李丹丹³, 何洋⁴

1.宜宾学院质量管理与检验检测学部,四川宜宾 644000
2.四川农业大学食品学院,四川雅安 625014
3.中储粮成都储藏研究院有限公司,四川成都 610091
4.中央储备粮成都直属库有限公司,四川成都 611400

收稿日期:2024-06-18 出版日期:2025-05-25 发布日期:2025-06-11
作者简介:李涛(2001—),男,四川眉山人,硕士研究生,研究方向为食品质量与安全。E-mail:3069430643@qq.com
通讯作者: *吴芳,E-mail:77307697@qq.com
基金资助:
第二次青藏高原综合科学考察研究项目(2019QZKK0303);宜宾学院启航项目(2019QD14)

Potential geographical distribution of Cryptolestes turcicus (Grouville) in China based on MaxEnt model

LI Tao¹^,²(), JIANG Haiyan¹, ZHANG Yuanpeng¹, ZHAO Qin¹, WU Fang¹^,^*(), ZHU Yanguang³, LI Dandan³, HE Yang⁴

1. Faculty of Quality Management and Inspection & Quarantine, Yibin University, Yibin 644000, Sichuan, China
2. College of Food Science, Sichuan Agricultural University, Ya'an 625014, Sichuan, China
3. Sinograin Chengdu Storage Research Institute Co., Ltd., Chengdu 610091, China
4. Sinograin Chengdu Depot Co., Ltd., Chengdu 611400, China

Received:2024-06-18 Online:2025-05-25 Published:2025-06-11

摘要/Abstract

摘要： 为预测和分析土耳其扁谷盗[Cryptolestes turcicus(Grouville)]的潜在地理分布,研究不同时期气候环境对其分布的影响,基于土耳其扁谷盗的分布点和环境因子变量,采用MaxEnt生态位模型,预测当前时期(1950—2000年)、未来时期(2021—2040年和2041—2060年)、4种气候模式(BCC_CSM2_MR、IPSL_CM6A_LR、MIROC6、MRI_ESM2_0)、2种变化情景(ssp245和ssp585)下土耳其扁谷盗在中国的潜在地理分布和环境影响因素。结果表明:当前土耳其扁谷盗的最佳适生区面积为66.35×10⁴ km²,主要分布于河北省、山东省、河南省、安徽省、江苏省和湖北省的大部分地区,以及广西壮族自治区等沿海区域的部分地区;高适生区面积为116.53×10⁴ km²,主要分布于黑龙江省西部的少部分区域、吉林省、辽宁省和内蒙古自治区接壤区域、北京市南部、河北省北部、山西省南部、陕西省中部南部、河南省西南部和四川省东北部。刀切法分析(Jackknife)结果表明,8月平均最低温(Tmin8)、海拔、8月平均降水量(Prec8)和11月平均降水量(Prec11)是影响土耳其扁谷盗分布的主要因素,海拔 0~300 m、Tmin8 20~29 ℃、Prec8 100~550 mm、Prec11 15~108 mm条件下土耳其扁谷盗的发生概率高(>0.5)。在未来不同气候情景下,高适生区和最佳适生区面积总体上均呈上升趋势。与当前时期相比,在ssp245和ssp585 2种不同共享社会经济路径下适生区总面积的平均增幅分别为8.5%和4.1%;湖南省、江西省、广西壮族自治区、香港特别行政区、澳门特别行政区、广东省、福建省的大部分地区,内蒙古自治区西部、四川省东部和重庆市西部的少部分地区各等级适生区面积增加,适生区等级上升。随着未来气候变化,土耳其扁谷盗最佳适生区有从河南省、安徽省和江苏省向南扩散至湖南省、江西省和上海市等地的趋势。

关键词: 土耳其扁谷盗, MaxEnt模型, 地理分布, 气候变化

Abstract:

To predict and analyze the potential geographical distribution of Cryptolestes turcicus(Grouville) and investigate the impact of climatic environments on its distribution across different periods, based on the actual distribution data of C. turcicus and the environmental variables of the current period (1950—2000) and the future period (2021—2040 and 2041—2060) with 4 climate models (BCC_CSM2_MR, IPSL_CM6A_LR, MIROC6, MRI_ESM2_0) and two shared socioeconomic pathways (ssp245 and ssp585), this study used the MaxEnt model to predict the potential distribution area of C. turcicus in China. The results indicated that the current most suitable area for C. turcicus reached to 66.35×10⁴ km², primarily covering most regions of Hebei, Shandong, Henan, Anhui, Jiangsu, and Hubei provinces, as well as coastal areas of Guangxi Zhuang Autonomous Region. The highly suitable area (116.53×10⁴ km²) was mainly distributed in western Heilongjiang, border regions of Jilin, Liaoning, and Inner Mongolia Autonomous Region, southern Beijing, northern Hebei, southern Shanxi, central-southern Shaanxi, southwestern Henan, and northeastern Sichuan. Jackknife analysis identified the average minimum temperature in August (Tmin8), altitude, average precipitation in August (Prec8), and average precipitation in November (Prec11) as key factors influencing the C. turcicus distribution. High occurrence probability (>0.5) was observed under conditions of altitude 0-300 m, Tmin8 20-29 ℃, Prec8 100-550 mm, and Prec11 15-108 mm. Under different future climate scenarios, both highly suitable area and most suitable area are projected to expand.Compared with the current period, the average increases in total suitable area under the two shared socioeconomic pathways (ssp245 and ssp585) were 8.5% and 4.1%, respectively. Regions such as Hunan, Jiangxi, Guangxi, Hong Kong SAR, Macao SAR, Guangdong, Fujian, western Inner Mongolia, eastern Sichuan, and western Chongqing are expected to show increased suitability levels. Additionally, the most suitable area may shift southward from Henan, Anhui, and Jiangsu toward Hunan, Jiangxi, and Shanghai under future climate changes.

Key words: Cryptolestes turcicus, MaxEnt model, geographical distribution, climate change

中图分类号:

S433.5

李涛, 江海艳, 张远鹏, 赵芹, 吴芳, 朱延光, 李丹丹, 何洋. 基于MaxEnt模型的土耳其扁谷盗在中国的潜在地理分布预测[J]. 浙江农业学报, 2025, 37(5): 1107-1120.

LI Tao, JIANG Haiyan, ZHANG Yuanpeng, ZHAO Qin, WU Fang, ZHU Yanguang, LI Dandan, HE Yang. Potential geographical distribution of Cryptolestes turcicus (Grouville) in China based on MaxEnt model[J]. Acta Agriculturae Zhejiangensis, 2025, 37(5): 1107-1120.

图/表 9

参考文献 31

[1]	赵慧龙. 土耳其扁谷盗生态学特性及其防治技术研究[D]. 广州: 华南农业大学, 2016.
	ZHAO H L. Study on the ecological characteristics of Cryptolestes trucicus Grouvilleandits control[D]. Guangzhou: South China Agricultural University, 2016. (in Chinese with English abstract)
[2]	孙田依. 锈赤扁谷盗和土耳其扁谷盗线粒体基因组特征及系统发育研究[D]. 南昌: 南昌大学, 2016.
	SUN T Y. Characterization of the complete mitochondrial genomes of Cryptolestes ferrugineus and Cryptolestes turcicus and their phylogenetic analysis[D]. Nanchang: Nanchang University, 2016. (in Chinese with English abstract)
[3]	李占鳌. 粮食安全视角下的粮食储备治理研究: 以H市为例[D]. 广州: 广东财经大学, 2022.
	LI Z A. Research on grain reserve management based on the view of food security[D]. Guangzhou: Guangdong University of Finance & Economics, 2022. (in Chinese with English abstract)
[4]	罗屹, 黄东, 武拉平, 等. 储粮损失与农户储粮决策: 基于23省1199户玉米种植户的实证分析[J]. 中国农业大学学报, 2022, 27(9): 30-42.
	LUO Y, HUANG D, WU L P, et al. Storage losses and households' storage decision in China: based on the survey data of 1199 corn growers from 23 provinces[J]. Journal of China Agricultural University, 2022, 27(9): 30-42. (in Chinese with English abstract)
[5]	赵霞, 陶亚萍, 曹宝明. 中国粮食产后损失评估分析[J]. 干旱区资源与环境, 2022, 36(6): 1-7.
	ZHAO X, TAO Y P, CAO B M. Assessment on post-harvest losses of grains in China[J]. Journal of Arid Land Resources and Environment, 2022, 36(6): 1-7. (in Chinese with English abstract)
[6]	程开禄, 黄富, 潘学贤, 等. 酒曲害虫的发生及危害规律研究[J]. 西南农业学报, 2001, 14(1): 74-77.
	CHENG K L, HUANG F, PAN X X, et al. Studies on the occurrence and harm regularities of the pests of distillers yeast[J]. Southwest China Journal of Agricultural Sciences, 2001, 14(1): 74-77. (in Chinese with English abstract)
[7]	王培生, 刘传奎, 吴健, 等. 利用土耳其扁谷盗控制桑梢小蠹虫的调查研究[J]. 江苏蚕业, 1991, 13(2): 11-15.
	WANG P S, LIU C K, WU J, et al. Investigation and study on the control of mulberry bark beetle by Turkish flat-valley thief[J]. Jiangsu Sericulture, 1991, 13(2): 11-15. (in Chinese)
[8]	武万里, 刘垚, 马菁, 等. 基于MaxEnt模型的宁夏枸杞瘿螨适生性分析[J]. 山地农业生物学报, 2022, 41(2): 66-70.
	WU W L, LIU Y, MA J, et al. Adaptability analysis of Aceria macrodonis keifer in Ningxia based on MaxEnt model[J]. Journal of Mountain Agriculture and Biology, 2022, 41(2): 66-70. (in Chinese with English abstract)
[9]	刘学琴, 白明, 贺达汉, 等. 基于MaxEnt模型的草原步甲属潜在地理分布研究[J]. 生态学报, 2022, 42(10): 4217-4224.
	LIU X Q, BAI M, HE D H, et al. Simulating potential distribution of Carabus beetle in the steppe based on MaxEnt model[J]. Acta Ecologica Sinica, 2022, 42(10): 4217-4224. (in Chinese with English abstract)
[10]	PHILLIPS S J, ANDERSON R P, SCHAPIRE R E. Maximum entropy modeling of species geographic distributions[J]. Ecological Modelling, 2006, 190(3/4): 231-259.
[11]	PHILLIPS S J, DUDÍK M. Modeling of species distributions with maxent: new extensions and a comprehensive evaluation[J]. Ecography, 2008, 31(2): 161-175.
[12]	LI Y X, SHAO W H, JIANG J M. Predicting the potential global distribution of Sapindus mukorossi under climate change based on MaxEnt modelling[J]. Environmental Science and Pollution Research, 2022, 29(15): 21751-21768.
[13]	唐杨欣, 皮杰, 刘新华, 等. 基于最大熵模型预测气候变化下河蚬在中国的潜在分布[J]. 生态学报, 2023, 43(10): 4250-4259.
	TANG Y X, PI J, LIU X H, et al. Predicting potential distribution of Corbicula fluminea under climate change scenarios using MaxEnt model[J]. Acta Ecologica Sinica, 2023, 43(10): 4250-4259. (in Chinese with English abstract)
[14]	祝梓杰, 王桂瑶, 乔飞, 等. 基于MaxEnt模型的两种捕食性盲蝽潜在分布区及其适生性分析[J]. 昆虫学报, 2017, 60(3): 335-346.
	ZHU Z J, WANG G Y, QIAO F, et al. Potential geographical distribution and habitat suitability analysis for two mirid predators Tytthus chinensis and Cyrtorhinus lividipennis(Hemiptera: Miridae) in rice fields based on MaxEnt model[J]. Acta Entomologica Sinica, 2017, 60(3): 335-346. (in Chinese with English abstract)
[15]	LI A N, WANG J W, WANG R L, et al. MaxEnt modeling to predict current and future distributions of Batocera lineolata(Coleoptera: Cerambycidae) under climate change in China[J]. Écoscience, 2020, 27(1): 23-31.
[16]	徐养诚, 刘孝贤, 王婷, 等. 基于MaxEnt模型的菜豆象全球潜在适生区预测[J]. 生物安全学报, 2021, 30(3): 213-219.
	XU Y C, LIU X X, WANG T, et al. Potential suitable distribution of Acanthoscelides obtectus(Say) based on the MaxEnt model[J]. Journal of Biosafety, 2021, 30(3): 213-219. (in Chinese with English abstract)
[17]	易山青, 彭硕, 贾涛, 等. 基于MaxEnt模型的菜豆象和蚕豆象在中国的适生区预测[J]. 植物保护学报, 2023, 50(6): 1480-1490.
	YI S Q, PENG S, JIA T, et al. Prediction of the potential suitable areas for bean weevil Acanthoscelides obtectus and the broad bean weevil Bruchus rufimanus in China based on the MaxEnt model[J]. Journal of Plant Protection, 2023, 50(6): 1480-1490. (in Chinese with English abstract)
[18]	马菁, 刘垚, 张学俭. 基于MaxEnt模型的宁夏枸杞蚜虫适生分布预测研究[J]. 地理与地理信息科学, 2019, 35(5): 40-45.
	MA J, LIU Y, ZHANG X J. Prediction on potential establishment area of Aphis sp. in Ningxia based on MaxEnt model[J]. Geography and Geo-Information Science, 2019, 35(5): 40-45. (in Chinese with English abstract)
[19]	MA W D, JIA W, ZHOU Y T, et al. Prediction of suitable future natural areas for highland barley on the Qinghai-Tibet Plateau under representative concentration pathways (RCPs)[J]. Sustainability, 2022, 14(11): 6617.
[20]	刘小燕. 基于最大熵模型的白鹤栖息地适宜性评价研究[D]. 南京: 南京林业大学, 2023.
	LIU X Y. Study on habitat suitability assessment of Grus leucogeranus based on MaxEnt model[D]. Nanjing: Nanjing Forestry University, 2023. (in Chinese with English abstract)
[21]	刘明明, 刘丹丹, 芦星, 等. 基于MaxEnt模型的新疆地区钝缘蜱适生区分布研究[J]. 中国媒介生物学及控制杂志, 2023, 34(5): 671-678.
	LIU M M, LIU D D, LU X, et al. MaxEnt model-based analysis of distribution of suitable habitats of Ornithodoros ticks in Xinjiang Uygur Autonomous Region, China[J]. Chinese Journal of Vector Biology and Control, 2023, 34(5): 671-678. (in Chinese with English abstract)
[22]	聂守明, 曾庆柏. 湖南储粮昆虫在不同地理环境的分布分析[J]. 粮食科技与经济, 1995, 20(4): 19-21.
	NIE S M, ZENG Q B. Distribution analysis of stored grain insects in different geographical environments in Hunan Province[J]. Grain Tecnnology and Economy, 1995, 20(4): 19-21. (in Chinese)
[23]	云昌杰, 顾文毅, 贺明强, 等. 湖北省储粮害虫调查研究[J]. 粮油仓储科技通讯, 1992, 8(6): 40-45.
	YUN C J, GU W Y, HE M Q, et al. Investigation on stored grain pests in Hubei Province[J]. Grain and Oil Storage Technology and Communication, 1992, 8(6): 40-45. (in Chinese with English abstract)
[24]	李娜, 周国磊, 贺培欢, 等. 河北省储粮昆虫与螨类分布调查[J]. 粮油食品科技, 2018, 26(6): 84-89.
	LI N, ZHOU G L, HE P H, et al. Investigation on species and distribution of insects and mites in stored grain in Hebei Province[J]. Science and Technology of Cereals, Oils and Foods, 2018, 26(6): 84-89. (in Chinese with English abstract)
[25]	李慧, 王殿轩, 洪习文, 等. 山东省储粮昆虫种类分布调查研究[J]. 中国粮油学报, 2022, 37(11): 32-37.
	LI H, WANG D X, HONG X W, et al. Investigation on the species and distribution of stored product insect species in Shandong Province[J]. Journal of the Chinese Cereals and Oils Association, 2022, 37(11): 32-37. (in Chinese with English abstract)
[26]	崔淼, 伍祎, 曹阳, 等. 安徽省涉粮场所近期储粮虫螨调查[J]. 粮油食品科技, 2021, 29(3): 215-221.
	CUI M, WU Y, CAO Y, et al. Recent investigation of stored-grain insects and mites in grain-related sites in Anhui Province[J]. Science and Technology of Cereals, Oils and Foods, 2021, 29(3): 215-221. (in Chinese with English abstract)
[27]	贺培欢, 曹阳, 林丽莎, 等. 江西省储粮昆虫调查[J]. 粮油食品科技, 2017, 25(4): 76-81.
	HE P H, CAO Y, LIN L S, et al. Research on stored grain insects in Jiangxi Province[J]. Science and Technology of Cereals, Oils and Foods, 2017, 25(4): 76-81. (in Chinese with English abstract)
[28]	ENGLER R, GUISAN A, RECHSTEINER L. An improved approach for predicting the distribution of rare and endangered species from occurrence and pseudo-absence data[J]. Journal of Applied Ecology, 2004, 41(2): 263-274.
[29]	HERNANDEZ P A, GRAHAM C H, MASTER L L, et al. The effect of sample size and species characteristics on performance of different species distribution modeling methods[J]. Ecography, 2006, 29(5): 773-785.
[30]	陈李林, 钟文玉, 胡海琴, 等. 气候变化情景下茶丽纹象甲在中国的潜在适生区预测[J]. 环境昆虫学报, 2024, 46(6): 1445-1456.
	CHEN L L, ZHONG W Y, HU H Q, et al. Prediction of potential geographical distribution of Myllocerinus aurolineatus in China under climate change[J]. Journal of Environmental Entomology, 2024, 46(6): 1445-1456. (in Chinese with English abstract)
[31]	程雪蓉, 任立良, 杨肖丽, 等. CMIP5多模式对中国及各分区气温和降水时空特征的预估[J]. 水文, 2016, 36(4): 37-43.
	CHENG X R, REN L L, YANG X L, et al. A CMIP5 multi-model estimation of spatio-temporal characteristics of temperature, precipitation in 7 regions of China[J]. Journal of China Hydrology, 2016, 36(4): 37-43. (in Chinese with English abstract)

环境因子代号 Code of environmrntal factor	环境因子含义 Description for environment factor	单位 Unit
Bio1	年平均气温Annual average temperature	℃
Bio2	平均气温日较差Mean daily temperature range	℃
Bio3	等温性Isothermality
Bio4	气温季节性变动系数Seasonal variation coefficient of temperature
Bio5	最热月份最高温度Maximum temperature in the hottest month	℃
Bio6	最冷月份最低温度Minimum temperature in the coldest month	℃
Bio7	最冷月份最低温度差The minimum temperature difference in the coldest month	℃
Bio8	最湿季度平均温度Average temperature of the wettest quarter	℃
Bio9	最干季度平均温度Average temperature of the driest quarter	℃
Bio10	最暖季度平均温度Average temperature of the warmest quarter	℃
Bio11	最冷季度平均温度Average temperature of the coldest quarter	℃
Bio12	年降水量Annual precipitation	mm
Bio13	最湿月份降水量Precipitation in the wettest month	mm
Bio14	最干月份降水量Precipitation in the driest month	mm
Bio15	降水量季节性变化Seasonal variation of precipitation	mm
Bio16	最干季度降水量Precipitation in the driest quarter	mm
Bio17	最湿季度降水量Precipitation in the wettest quarter	mm
Bio18	最暖季度降水量Precipitation in the warmest quarter	mm
Bio19	最冷季度降水量Precipitation in the coldest quarter	mm
Prec1-12	1~12月平均降水量 Average precipitation from January to December	mm
Tmin1-12	1~12月平均最低温度 Average minimum temperature from January to December	℃
Tmax1-12	1~12月平均最高温度Average maximum temperature from January to December	℃
Altitude	海拔Altitude	m

环境因子代号 Code of environmrntal factor	环境因子含义 Description for environment factor	单位 Unit
Bio1	年平均气温Annual average temperature	℃
Bio2	平均气温日较差Mean daily temperature range	℃
Bio3	等温性Isothermality
Bio4	气温季节性变动系数Seasonal variation coefficient of temperature
Bio5	最热月份最高温度Maximum temperature in the hottest month	℃
Bio6	最冷月份最低温度Minimum temperature in the coldest month	℃
Bio7	最冷月份最低温度差The minimum temperature difference in the coldest month	℃
Bio8	最湿季度平均温度Average temperature of the wettest quarter	℃
Bio9	最干季度平均温度Average temperature of the driest quarter	℃
Bio10	最暖季度平均温度Average temperature of the warmest quarter	℃
Bio11	最冷季度平均温度Average temperature of the coldest quarter	℃
Bio12	年降水量Annual precipitation	mm
Bio13	最湿月份降水量Precipitation in the wettest month	mm
Bio14	最干月份降水量Precipitation in the driest month	mm
Bio15	降水量季节性变化Seasonal variation of precipitation	mm
Bio16	最干季度降水量Precipitation in the driest quarter	mm
Bio17	最湿季度降水量Precipitation in the wettest quarter	mm
Bio18	最暖季度降水量Precipitation in the warmest quarter	mm
Bio19	最冷季度降水量Precipitation in the coldest quarter	mm
Prec1-12	1~12月平均降水量 Average precipitation from January to December	mm
Tmin1-12	1~12月平均最低温度 Average minimum temperature from January to December	℃
Tmax1-12	1~12月平均最高温度Average maximum temperature from January to December	℃
Altitude	海拔Altitude	m

省级 Provincial level(number)	地市级 Municipal level	经度 Longitude	纬度 Latitude	海拔 Altitude	省级 Provincial level(number)	地市级 Municipal level	经度 Longitude	纬度 Latitude	海拔 Altitude
安徽 Anhui(5)	安庆Anqing	116.51	30.55	30	吉林Jilin(5)	白城Baicheng	122.80	45.66	160
	滁州Chuzhou	118.10	32.88	30		四平Siping	124.34	43.19	301
	阜阳Fuyang	116.26	32.65	20		松源Songyuan	124.82	45.15	180
	黄山Huangshan	118.60	29.74	122		延边Yanbian	129.14	43.17	190
	六安Lu'an	116.52	31.56	60		长春Changchun	125.31	43.86	276
福建 Fujian(5)	福州Fuzhou	119.30	26.14	10	湖南Hunan(2)	益阳Yiyang	112.37	28.58	81
	龙岩Longyan	117.02	25.08	329	湖南Hunan(2)	岳阳Yueyang	113.13	29.39	37
	南平Nanping	117.33	27.35	6	江西Jiangxi(3)	高安Gaoan	115.38	28.42	47
	厦门Xiamen	118.08	24.49	2		南昌Nanchang	115.84	28.72	16
	漳州Zhangzhou	117.65	24.54	19		萍乡Pingxiang	113.85	27.65	96
甘肃 Gansu(2)	陇南Longnan	105.27	33.25	1 688	江苏 Jiangsu(2)	连云港Lianyungang	119.12	34.84	20
甘肃 Gansu(2)	武威Wuwei	102.56	38.09	1 632	江苏 Jiangsu(2)	南通Nantong	119.12	32.07	20
广东 Guangdong(6)	东莞Dongguan	113.76	23.04	6	湖北 Hubei(6)	恩施Enshi	109.49	30.30	490
	惠州Huizhou	114.42	23.12	21		黄冈Huanggang	114.87	30.46	49
	茂名Maoming	110.93	21.70	30		荆门Jingmen	112.19	31.08	87
	梅州Meizhou	116.12	24.32	86		荆州Jingzhou	112.21	30.39	34
	汕头Shantou	116.68	23.37	8		武汉Wuhan	114.29	30.63	16
	阳江Yangjiang	111.98	21.91	4		襄阳Xiangyang	112.10	32.04	70
广西 Guangxi(2)	北海Beihai	109.13	21.49	21	四川 Sichuan(2)	成都Chengdu	104.08	30.66	500
广西 Guangxi(2)	南宁Nanning	108.37	22.82	79	四川 Sichuan(2)	西昌Xichang	102.27	27.90	1 590
贵州 Guizhou(3)	贵阳Guiyang	106.41	26.33	1 758	陕西 Shannxi(3)	宝鸡Baoji	107.24	34.37	618
	六盘水Liupanshui	104.84	26.60	1 797		商洛Shangluo	110.07	33.81	673
	遵义Zunyi	106.93	27.73	1 100		咸阳Xianyang	108.72	34.34	383
河北 Hebei(5)	承德Chengde	117.93	40.98	325	宁夏 Ningxia(3)	固原Guyuan	106.57	36.19	1 219
	邯郸Handan	114.51	36.65	55		吴忠Wuzhong	106.54	36.17	1 173
	秦皇岛Qinghuangdao	119.59	39.95	4		银川Yinchuan	106.24	38.49	1 200
	石家庄Shijiazhuang	114.47	38.07	82	新疆 Xijiang(2)	喀什Kashi	76.00	39.48	1 289
	张家口Zhangjiakou	114.86	40.79	716	新疆 Xijiang(2)	伊宁Yining	81.54	43.99	836
黑龙江 Heilongjiang(6)	哈尔滨Harbin	126.52	45.83	118	山东 Shandong(4)	济南Jinan	117.11	36.70	149
	黑河Heihe	127.54	50.25	122		聊城Liaocheng	115.99	36.50	37
	牡丹江Mudanjiang	129.63	44.56	234		青岛Qingdao	120.36	36.09	6
	齐齐哈尔Qiqihar	123.92	47.39	149		日照Rizhao	119.52	35.45	37
	双鸭山Shuangyashan	131.17	46.66	179	云南 Yunnan(2)	昆明Kunming	102.84	24.91	1930
	绥化Suihua	126.95	46.69	175	云南 Yunnan(2)	曲靖Qujing	103.78	25.54	1 874
北京Beijing(1)	北京Beijing	115.98	40.46	484	内蒙古 Inner Mongolia(4)	巴彦淖尔Bayannur	107.36	40.79	1 042
海南Hainan(1)	琼海Qionghai	110.20	20.04	18		鄂尔多斯Ordos	109.79	39.64	1 311
重庆Chongqing(1)	重庆Chongqing	106.06	29.85	444		呼和浩特Hohhot	111.76	40.89	1 072
浙江Zhejiang(1)	宁波Ningbo	121.56	29.89	10		锡林郭勒Xilin Gol League	116.12	43.93	987

省级 Provincial level(number)	地市级 Municipal level	经度 Longitude	纬度 Latitude	海拔 Altitude	省级 Provincial level(number)	地市级 Municipal level	经度 Longitude	纬度 Latitude	海拔 Altitude
安徽 Anhui(5)	安庆Anqing	116.51	30.55	30	吉林Jilin(5)	白城Baicheng	122.80	45.66	160
	滁州Chuzhou	118.10	32.88	30		四平Siping	124.34	43.19	301
	阜阳Fuyang	116.26	32.65	20		松源Songyuan	124.82	45.15	180
	黄山Huangshan	118.60	29.74	122		延边Yanbian	129.14	43.17	190
	六安Lu'an	116.52	31.56	60		长春Changchun	125.31	43.86	276
福建 Fujian(5)	福州Fuzhou	119.30	26.14	10	湖南Hunan(2)	益阳Yiyang	112.37	28.58	81
	龙岩Longyan	117.02	25.08	329	湖南Hunan(2)	岳阳Yueyang	113.13	29.39	37
	南平Nanping	117.33	27.35	6	江西Jiangxi(3)	高安Gaoan	115.38	28.42	47
	厦门Xiamen	118.08	24.49	2		南昌Nanchang	115.84	28.72	16
	漳州Zhangzhou	117.65	24.54	19		萍乡Pingxiang	113.85	27.65	96
甘肃 Gansu(2)	陇南Longnan	105.27	33.25	1 688	江苏 Jiangsu(2)	连云港Lianyungang	119.12	34.84	20
甘肃 Gansu(2)	武威Wuwei	102.56	38.09	1 632	江苏 Jiangsu(2)	南通Nantong	119.12	32.07	20
广东 Guangdong(6)	东莞Dongguan	113.76	23.04	6	湖北 Hubei(6)	恩施Enshi	109.49	30.30	490
	惠州Huizhou	114.42	23.12	21		黄冈Huanggang	114.87	30.46	49
	茂名Maoming	110.93	21.70	30		荆门Jingmen	112.19	31.08	87
	梅州Meizhou	116.12	24.32	86		荆州Jingzhou	112.21	30.39	34
	汕头Shantou	116.68	23.37	8		武汉Wuhan	114.29	30.63	16
	阳江Yangjiang	111.98	21.91	4		襄阳Xiangyang	112.10	32.04	70
广西 Guangxi(2)	北海Beihai	109.13	21.49	21	四川 Sichuan(2)	成都Chengdu	104.08	30.66	500
广西 Guangxi(2)	南宁Nanning	108.37	22.82	79	四川 Sichuan(2)	西昌Xichang	102.27	27.90	1 590
贵州 Guizhou(3)	贵阳Guiyang	106.41	26.33	1 758	陕西 Shannxi(3)	宝鸡Baoji	107.24	34.37	618
	六盘水Liupanshui	104.84	26.60	1 797		商洛Shangluo	110.07	33.81	673
	遵义Zunyi	106.93	27.73	1 100		咸阳Xianyang	108.72	34.34	383
河北 Hebei(5)	承德Chengde	117.93	40.98	325	宁夏 Ningxia(3)	固原Guyuan	106.57	36.19	1 219
	邯郸Handan	114.51	36.65	55		吴忠Wuzhong	106.54	36.17	1 173
	秦皇岛Qinghuangdao	119.59	39.95	4		银川Yinchuan	106.24	38.49	1 200
	石家庄Shijiazhuang	114.47	38.07	82	新疆 Xijiang(2)	喀什Kashi	76.00	39.48	1 289
	张家口Zhangjiakou	114.86	40.79	716	新疆 Xijiang(2)	伊宁Yining	81.54	43.99	836
黑龙江 Heilongjiang(6)	哈尔滨Harbin	126.52	45.83	118	山东 Shandong(4)	济南Jinan	117.11	36.70	149
	黑河Heihe	127.54	50.25	122		聊城Liaocheng	115.99	36.50	37
	牡丹江Mudanjiang	129.63	44.56	234		青岛Qingdao	120.36	36.09	6
	齐齐哈尔Qiqihar	123.92	47.39	149		日照Rizhao	119.52	35.45	37
	双鸭山Shuangyashan	131.17	46.66	179	云南 Yunnan(2)	昆明Kunming	102.84	24.91	1930
	绥化Suihua	126.95	46.69	175	云南 Yunnan(2)	曲靖Qujing	103.78	25.54	1 874
北京Beijing(1)	北京Beijing	115.98	40.46	484	内蒙古 Inner Mongolia(4)	巴彦淖尔Bayannur	107.36	40.79	1 042
海南Hainan(1)	琼海Qionghai	110.20	20.04	18		鄂尔多斯Ordos	109.79	39.64	1 311
重庆Chongqing(1)	重庆Chongqing	106.06	29.85	444		呼和浩特Hohhot	111.76	40.89	1 072
浙江Zhejiang(1)	宁波Ningbo	121.56	29.89	10		锡林郭勒Xilin Gol League	116.12	43.93	987

区域 Region	非适生区 Unsuitable area	低适生区 Marginally suitable area	中适生区 Moderate suitable area	高适生区 Highly suitable area	最佳适生区 Most suitable area
安徽Anhui	0.01	0.35	2.18	3.33	7.49
北京Beijing	0	0.01	0.09	1.45	0.19
福建Fujian	0.01	0.86	5.29	2.98	1.76
甘肃Gansu	25.63	6.78	7.95	2.92	0.33
广东Guangdong	0.03	0.79	4.89	4.67	5.16
广西Guangxi	0.18	1.89	9.00	5.84	4.01
贵州Guizhou	0.01	0.60	12.34	3.03	0
海南Hainan	0.02	1.16	0.97	0.27	0.46
河北Hebei	0.03	3.14	5.19	7.36	3.90
河南Henan	0	0	0.15	6.12	9.87
黑龙江Heilongjiang	16.15	16.84	16.92	4.41	0.07
湖北Hubei	0.01	1.18	3.16	6.24	6.97
湖南Hunan	0.02	1.90	10.58	5.09	1.79
吉林Jilin	0.29	10.71	5.55	4.60	0.14
江苏Jiangsu	0	0.80	2.31	2.31	4.30
江西Jiangxi	0.02	0.78	7.53	4.54	2.41
辽宁Liaoning	0	2.15	5.18	7.36	0.87
内蒙古Inner Mongolia	52.47	52.73	16.55	7.19	0.01
宁夏Ningxia	0	1.27	3.58	0.43	0
青海Qinghai	67.59	1.48	0.27	0.05	0
山东Shandong	0	0.06	0.62	4.14	10.54
山西Shanxi	0.68	6.42	5.66	3.06	0.16
陕西Shaanxi	0.02	3.12	6.38	9.61	1.23
上海Shanghai	0	0.01	0.10	0.15	0.34
四川Sichuan	21.57	5.33	7.36	8.80	2.68
天津Tianjin	0	0.05	0.59	0.55	0.01
西藏Xizang	107.89	4.35	1.81	0.05	0.08
新疆Xinjiang	142.44	30.35	2.07	0.14	0
云南Yunnan	2.51	10.74	16.07	4.65	0.32
浙江Zhejiang	0.02	1.58	3.39	3.39	1.03
重庆Chongqing	0.02	1.26	4.79	1.63	0
港澳台与其他Hong Kong, Macao, Taiwan and other	2.58	0.78	0.36	0.17	0.25
合计Total	440.21	169.44	168.87	116.53	66.35

基于MaxEnt模型的土耳其扁谷盗在中国的潜在地理分布预测

Potential geographical distribution of Cryptolestes turcicus (Grouville) in China based on MaxEnt model

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变量 Variable	贡献率 Percent contribution/%	随机分布重要性 Permutation importance
Tmin8	34.6	30.5
Altitude	22.6	11.1
Prec8	10.5	9.9
Prec11	10.4	7.9
Prec5	6.3	13.0
Tmax9	6.0	5.6
Bio2	4.4	9.9
Bio4	2.8	7.4
Bio15	1.6	2.4
Bio3	0.8	2.3

气候模型 Climate model	共享社会经济路 Shared socio-economic pathway	时期 Period	AUC	面积Area/10104 km2					占比Ratio/%
气候模型 Climate model	共享社会经济路 Shared socio-economic pathway	时期 Period	AUC	非适生区 Unsuitable area	低适生区 Marginally suitable area	中适生区 Moderate suitable area	高适生区 Highly suitable area	最佳适生区 Most suitable area	非适生区 Unsuitable area	低适生区 Marginally suitable area	中适生区 Moderate suitable area	高适生区 Highly suitable area	最佳适生区 Most suitable area
BCC_CSM2_MR	SSP245	2021－－2040	0.888	439.59(−)	168.60(−)	162.89(−)	127.82	62.50(−)	45.7	17.5	16.9	13.3	6.5
	SSP245	2041－－2060	0.858	373.62(−)	186.93	173.81	143.53	83.51	38.9	19.4	18.1	14.9	8.7
	SSP585	2021－－2040	0.878	410.60(−)	139.43(−)	184.06	145.95	81.35	42.7	14.5	19.1	15.2	8.5
	SSP585	2041－－2060	0.881	414.27(−)	170.79	176.92	135.90	63.52(−)	43.1	17.8	18.4	14.1	6.6
IPSL_CM6A_LR	SSP245	2021－－2040	0.873	408.22(−)	160.54(−)	191.82	125.93	74.88	42.5	16.7	20.0	13.1	7.8
	SSP245	2041－－2060	0.875	409.05(−)	160.92(−)	175.25	139.89	76.28	42.5	16.7	18.2	14.6	7.9
	SSP585	2021－－2040	0.863	424.02(−)	142.43(−)	153.06(−)	132.32	109.58	44.1	14.8	15.9	13.8	11.4
	SSP585	2041－－2060	0.884	414.21(−)	165.44(−)	165.50(−)	140.23	76.02	43.1	17.2	17.2	14.6	7.9
MIROC6	SSP245	2021－－2040	0.851	400.60(−)	146.35(−)	183.63	152.73	78.08	41.7	15.2	19.1	15.9	8.1
	SSP245	2041－－2060	0.87	393.58(−)	185.75	168.39(−)	128.45	85.22	40.9	19.3	17.5	13.4	8.9
	SSP585	2021－－2040	0.879	430.46(−)	146.13(−)	163.13(−)	119.45	102.23	44.8	15.2	17.0	12.4	10.6
	SSP585	2041－－2060	0.878	440.14(−)	174.37	158.86(−)	91.93(−)	96.10	45.8	18.1	16.5	9.6	10.0
MRI_ESM2_0	SSP245	2021－－2040	0.865	403.25(−)	165.43(−)	191.48	124.10	77.14	41.9	17.2	19.9	12.9	8.0
	SSP245	2041－－2060	0.875	406.53(−)	169.61	182.63	121.67	80.96	42.3	17.6	19.0	12.7	8.4
	SSP585	2021－－2040	0.882	408.35(−)	160.42(−)	208.57	127.43	56.62(−)	42.5	16.7	21.7	13.3	5.9
	SSP585	2041－－2060	0.874	406.38(−)	173.13	157.14(−)	132.54	92.21	42.3	18.0	16.3	13.8	9.6

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[2]	黄芳, 张文俊, 张建成, 张红英. 基于BIOCLIM模型的绿圆跳虫在中国的适生区分析[J]. 浙江农业学报, 2021, 33(11): 2098-2103.
[3]	黄芳, 张文俊, 张建成, 吴志毅. 基于BIOCLIM模型的石蒜绵粉蚧在中国的适生区分析[J]. 浙江农业学报, 2019, 31(8): 1331-1336.
[4]	叶宏宝，石晓燕，李冬，华珊，徐志福*. 气候变化对浙江水稻生产影响的集合模拟分析[J]. 浙江农业学报, 2016, 28(7): 1183-.
[5]	谭灵芝1，2. 适应性政策对区域农业产业结构调整与农业发展的影响[J]. 浙江农业学报, 2015, 27(10): 1850-.
[6]	叶宏宝，石晓燕，李冬，徐志福*. 浙江省农业气候资源时空变化特征研究[J]. 浙江农业学报, 2014, 26(4): 1021-.
[7]	何超. 南京市气候变化对农业旅游影响分析[J]. , 2014, 26(2): 0-517521.
[8]	单婵;王咏青;商兆堂. 洪泽湖湿地气候变化对区域农业生产的影响及对策[J]. , 2013, 25(3): 0-673.
[9]	许朗;刘金金. 气候变化与中国农业发展问题的研究[J]. , 2013, 25(1): 0-199.
[10]	李松平;金志凤;冯涛. 浙江杨梅主产地气候资源和气候变化特征[J]. , 2008, 20(3): 0-202.
[11]	金志凤;应义斌. 嘉兴农业气候变化特征分析[J]. , 2004, 16(4): 0-217.