云南广西蒜头果适生区预测及环境影响因子

doi:10.12302/j.issn.1000-2006.202109039

国家林草科技领军期刊
中国精品科技期刊
中国高校百佳科技期刊
江苏省新闻出版政府奖期刊奖
RCCSE林学权威期刊（A+）
CSCD核心期刊
Scopus数据库收录期刊
中文核心期刊
SCD核心期刊

作者加群：102861116

微信公众号：南京林业大学学报

高级检索

南京林业大学学报（自然科学版） ›› 2022, Vol. 46 ›› Issue (2): 44-52.doi: 10.12302/j.issn.1000-2006.202109039

所属专题：第二届中国林草计算机大会论文精选

• 专题报道Ⅱ：第二届中国林草计算机大会论文精选（执行主编李凤日） • 上一篇下一篇

云南广西蒜头果适生区预测及环境影响因子

龚茂佳¹(), 王娟², 付小勇³, 寇卫利³^,^*(), 鲁宁³, 王秋华⁴, 赖虹燕¹

1.西南林业大学林学院,云南昆明 650224
2.西南林业大学绿色发展研究院,云南昆明 650224
3.西南林业大学大数据与智能工程学院,云南昆明 650224
4.西南林业大学土木工程学院,云南昆明 650224

收稿日期:2021-09-25 接受日期:2022-01-05 出版日期:2022-03-30 发布日期:2022-04-08
通讯作者: 寇卫利
基金资助:
国家自然科学基金项目(31760181);国家自然科学基金项目(31400493);云南省农业基础研究联合专项项目(2017FG001-034);云南省农业基础研究联合专项项目(2018FG001-059);云南省重大科技专项生物资源数字化开发应用项目(202002AA10007)

Suitable regions forecasting and environmental influencing factors of Malania oleifera in Yunnan and Guangxi

GONG Maojia¹(), WANG Juan², FU Xiaoyong³, KOU Weili³^,^*(), LU Ning³, WANG Qiuhua⁴, LAI Hongyan¹

1. College of Forestry, Southwest Forestry University, Kunming 650224, China
2. Eco-Development Academy, Southwest Forestry University, Kunming 650224, China
3. College of Big Data and Intelligence Engineering, Southwest Forestry University, Kunming 650224, China
4. College of Civil Engineering, Southwest Forestry University, Kunming 650224, China

Received:2021-09-25 Accepted:2022-01-05 Online:2022-03-30 Published:2022-04-08
Contact: KOU Weili

摘要/Abstract

摘要：

【目的】蒜头果(Malania oleifera)是一种兼有很高经济和生态价值的濒危植物。本研究旨在揭示云南、广西蒜头果的潜在适生区的空间分布格局,并且明确其主要环境影响因子,为蒜头果的保护与开发利用提供理论基础。【方法】通过野外调查、数字标本植物馆、全球生物多样性信息网络等方式获取了蒜头果样本136个。选取常见的20个主要环境因子作为参数,基于最大熵模型(MaxEnt)和ArcGIS地理信息系统平台构建了蒜头果潜在适生区预测模型,模拟蒜头果在云南和广西壮族自治区的潜在适生区。【结果】笔者构建的MaxEnt模型预测得到蒜头果的潜在适生区分布范围为104°~107°E及22°~26°N,预测适生区验证结果的受试者工作特征曲线下方的面积(AUC)均超过0.9。MaxEnt预测的蒜头果潜在适生区前4个环境影响因子及贡献率依次为:气温季节性变动系数因子(39.6%)、等温性因子(16.7%)、平均气温因子(13.7%)、气温年较差因子(11.5%)。【结论】云南文山州东南部以及广西西部是蒜头果的集中分布区,温度是影响蒜头果分布的主要因素,MaxEnt模型在蒜头果适生区预测中表现出极高的精度和可靠性。该研究将为蒜头果资源保护利用和人工繁育选址提供重要依据。

关键词: 蒜头果, MaxEnt模型, 适生区, 环境因子, 空间分布

Abstract:

【Objective】 Malania oleifera is an endangered plant with high economic and ecological value. This study focuses on discovering the spatial distribution pattern of potential suitable areas of M. oleifera, and finding its main environmental affecting factors, laying a solid theory foundation for its conservation and utilization.【Method】 This study got 136 sampling points of M. oleifera by field investigations, specimens of the digital library, and the global biodiversity information network. Based on the ArcGIS geographic information system platform and the maximum entropy model (MaxEnt) with parameters of 20 common main environmental factors, the prediction model of M. oleifera potential suitable areas was built to simulate the M. oleifera distribution in Yunnan Province and Guangxi Zhuang Nationality Autonomous Prefecture (Guangxi).【Result】 The results showed that of M. oleifera is mainly distributed in the longitude of 104°-107°E and the latitude of 22°-26°N. The area under the curve (AUC) of potential suitable areas predicted by the MaxEnt model in this study were all over than 0.9. The top 4 environmental affecting factors and their contribution rates predicted by the MaxEnt model of potential suitable areas of M. oleifera were orderly listed: seasonal variation factors of temperature (contribution rate 39.6%), isothermal factors (contribution rate 16.7%), average daily temperature factors (contribution rate 13.7%), and annual temperature difference factors (contribution rate 11.5%).【Conclusion】 The southeast of Wenshan Zhuang and Miao Nationality Autonomous Prefecture of Yunnan and the west of Guangxi are the concentrated potential distribution suitable areas of M. oleifera, and temperature is the dominated affecting factor. Additionally, the MaxEnt model performs well for predicting potential suitable areas of M. oleifera both in accuracy and reliability. This study will provide an important basis for the conservation and utilization of M. oleifera resources, and an artificial breeding site selection.

Key words: Malania oleifera, MaxEnt, suitable region, environmental factors, spatial distribution

中图分类号:

龚茂佳,王娟,付小勇,等. 云南广西蒜头果适生区预测及环境影响因子[J]. 南京林业大学学报（自然科学版）, 2022, 46(2): 44-52.

GONG Maojia, WANG Juan, FU Xiaoyong, KOU Weili, LU Ning, WANG Qiuhua, LAI Hongyan. Suitable regions forecasting and environmental influencing factors of Malania oleifera in Yunnan and Guangxi[J].Journal of Nanjing Forestry University (Natural Science Edition), 2022, 46(2): 44-52.DOI: 10.12302/j.issn.1000-2006.202109039.

图/表 4

图1

表1

图2

图3

参考文献 49

[1]	国家环境保护局, 中国科学院植物研究所. 中国珍稀濒危保护植物名录[J]. 生物学通报, 1987, 22(7):23-28.
	National environmental protection agency, Institute of botany,the chinese academy of sciences. List of rare and endangered plants in China[J]. Bull Biol, 1987, 22(7):23-28.
[2]	傅立国. 中国植物红皮书(第一册):稀有濒危植物[M]. 北京: 科学出版社, 1991.
	FU L G. Red Book of Chinese plants(Volume 1):rare and endangered plant[M]. Beijing: Science Press, 1991.
[3]	刘伟, 常征, 李付惠. 广南蒜头果群落植物物种组成特征分析[J]. 文山学院学报, 2017, 30(6):17-22.
	LIU W, CHANG Z, LI F H. A study on the floristic composition characteristics of Malania oleifera community in Guangnan County[J]. J Wenshan Univ, 2017, 30(6):17-22. DOI: 10.3969/j.issn.1674-9200.2017.06.005. doi: 10.3969/j.issn.1674-9200.2017.06.005
[4]	卯吉华, 贾代顺, 景跃波, 等. 野生蒜头果丛枝菌根真菌和深色有隔内生真菌调查研究[J]. 中国林副特产, 2016(5):1-4.
	MAO J H, JIA D S, JING Y B, et al. Survey study of wild Malania oleifera AMF and DSE[J]. For Prod Speciality China, 2016(5):1-4. DOI: 10.13268/j.cnki.fbsic.2016.05.001. doi: 10.13268/j.cnki.fbsic.2016.05.001
[5]	卯吉华, 贾代顺, 陈福, 等. 中国特有珍稀植物蒜头果嫁接繁殖技术[J]. 林业科技通讯, 2016(2):35-37.
	MAO J H, JIA D S, CHEN F, et al. Grafting and propagation techniques of Malania oleifera special in China[J]. For Sci Technol, 2016(2):35-37. DOI: 10.13456/j.cnki.lykt.2016.02.013 doi: 10.13456/j.cnki.lykt.2016.02.013
[6]	吕仕洪, 韦春强, 黄甫昭, 等. 珍稀树种蒜头果种实性状及其在石漠化山区的适应性[J]. 生态学杂志, 2016, 35(1):57-62.
	LV S H, WEI C Q, HUANG F Z, et al. Fruit and seed traits and adaptability to rocky desertification mountain of rare tree species Malania oleifera[J]. Chin J Ecol, 201, 35(1)57-62. DOI: 10.13292/j.1000-4890.201601.008. doi: 10.13292/j.1000-4890.201601.008
[7]	郭方斌, 王四海, 王娟, 等. 珍稀植物蒜头果野生植株结实量及果实特征研究[J]. 广西植物, 2018, 38(1):57-64.
	GUO F B, WANG S H, WANG J, et al. Fruit yield and characters of wild Malania oleifera,a rare plant species in southwest China[J]. Guihaia, 2018, 38(1):57-64. DOI: 10.11931/guihaia.gxzw201704040. doi: 10.11931/guihaia.gxzw201704040
[8]	薛冰, 邵志凌. 云南蒜头果油的特征指标及脂肪酸组成研究分析[J]. 粮食储藏, 2015, 44(1):44-45,48.
	XUE B, SHAO Z L. Analysis and research on the characteristic index and fatty acid composition of the Yunnan Malania oleifera Chun oil[J]. Grain Storage, 2015, 44(1):44-45,48. DOI: 10.3969/j.issn.1000-6958.2015.01.010. doi: 10.3969/j.issn.1000-6958.2015.01.010
[9]	胡祥, 刘云, 杨晶晶, 等. 蒜头果油水化脱胶和碱炼脱酸工艺优化[J]. 中国油脂, 2020, 45(8):9-13.
	HU X, LIU Y, YANG J J, et al. Optimization of hydration degumming and alkali deacidification of Malania oleifera oil[J]. China Oils Fats, 2020, 45(8):9-13. DOI: 10.12166/j.zgyz.1003-7969/2020.08.003. doi: 10.12166/j.zgyz.1003-7969/2020.08.003
[10]	刘祥义, 叶志恒, 胡翔飞, 等. 蒜头果挥发油化学成分研究[J]. 云南化工, 2019, 46(8):73-74.
	LIU X Y, YE Z H, HU X F, et al. Studies on the chemical constituents of volatile oil from garlic fruit[J]. Yunnan Chem Technol, 2019, 46(8):73-74.
[11]	李云琴, 陈中华, 原晓龙, 等. 蒜头果中3-酮酯酰-CoA还原酶基因克隆与功能分析[J]. 分子植物育种, 2019, 17(14):4580-4585.
	LI Y Q, CHEN Z H, YUAN X L, et al. Cloning and functional analysis of 3-ketoacyl-CoA reductase gene from Malania oleifera[J]. Mol Plant Breed, 2019, 17(14):4580-4585. DOI: 10.13271/j.mpb.017.004580. doi: 10.13271/j.mpb.017.004580
[12]	王一凡. 蒜头果内生真菌发酵产神经酸油脂的研究[D]. 南宁: 广西大学, 2019.
	WANG Y F. Study on the production of nervonic acid oils by endophytic fungi from Malania oleifera[D]. Nanning: Guangxi University, 2019.
[13]	李洪果, 李武志, 邓硕坤, 等. 蒜头果种质资源及其研究利用现状[J]. 林业科技通讯, 2019(7):32-35.
	LI H G, LI W Z, DENG S K, et al. The status of germplasm resources,research and utilization of Malania oleifera[J]. For Sci Technol, 2019(7):32-35. DOI: 10.13456/j.cnki.lykt.2019.01.08.0007. doi: 10.13456/j.cnki.lykt.2019.01.08.0007
[14]	卯吉华, 贾代顺, 陈福, 等. 珍稀濒危植物蒜头果芽苗砧嫁接技术体系研究[J]. 西部林业科学, 2018, 47(5):39-45,68.
	MAO J H, JIA D S, CHEN F, et al. Hypocotyle grafting techniques of rare and endangered plant Malania oleifera[J]. J West China For Sci, 2018, 47(5):39-45,68. DOI: 10.16473/j.cnki.xblykx1972.2018.05.007. doi: 10.16473/j.cnki.xblykx1972.2018.05.007
[15]	杨玉玲, 王序英, 魏波, 等. 蒜头果种子外观质量分级标准初步研究[J]. 广西林业科学, 2020, 49(4):524-530.
	YANG Y L, WANG X Y, WEI B, et al. Preliminary study on grading stan-dard of appearance quality of Malania oleifera seeds[J]. Guangxi For Sci, 2020, 49(4):524-530. DOI: 10.19692/j.cnki.gfs.2020.04.008. doi: 10.19692/j.cnki.gfs.2020.04.008
[16]	周琴芬. 蒜头果种仁神经酸制备工艺研究[D]. 杭州: 浙江大学, 2017.
	ZHOU Q F. Studies on preparation of nervonic acid from Malania oleifera[D]. Hangzhou: Zhejiang University, 2017.
[17]	付一笑. 蒜头果光合生理以及其种子中神经酸的提取纯化[D]. 哈尔滨: 东北林业大学, 2020.
	FU Y X. Photosynthetic physiology of Malania oleifera and purification of nervonic acid in seeds[D]. Harbin: Northeast Forestry University, 2020.
[18]	张磊, 刘雄民, 唐婷范. 蒜头果果仁中7种金属元素含量的测定[J]. 广西大学学报(自然科学版), 2021, 37(3):521-524.
	ZHANG L, LIU X M, TANG T F. Determination of contents of seven metallic elements in seeds of Malania oleifera[J]. J Guangxi Univ (Nat Sci Ed), 2012, 37(3):521-524. DOI: 10.13624/j.cnki.issn.1001-7445.2012.03.026. doi: 10.13624/j.cnki.issn.1001-7445.2012.03.026
[19]	PHILLIPS S J, ANDERSON R P, SCHAPIRE R E. Maximum entropy modeling of species geographic distributions[J]. Ecol Model, 2006, 190(3/4):231-259. DOI: 10.1016/j.ecolmodel.2005.03.026. doi: 10.1016/j.ecolmodel.2005.03.026
[20]	李安, 李良涛, 高萌萌, 等. 基于MaxEnt模型和气候变化情景入侵种黄顶菊在中国的分布区预测[J]. 农学学报, 2020, 10(1):60-67,76.
	LIAN, LI L T, GAO M M, et al. Distribution prediction of invasive species Flaveria bidentis in China:based on MaxEnt model and climate change scenario[J]. J Agric, 2020, 10(1):60-67,76.
[21]	PUROHIT S, RAWAT N. MaxEnt modeling to predict the current and future distribution of Clerodendrum infortunatum L.under climate change scenarios in Dehradun district,India[J]. Modeling Earth Syst Environ, 2021:1-13. DOI: 10.1007/s40808-021-01205-5. doi: 10.1007/s40808-021-01205-5
[22]	段义忠, 王驰, 王海涛, 等. 不同气候条件下沙冬青属植物在我国的潜在分布:基于生态位模型预测[J]. 生态学报, 2020, 40(21):7668-7680.
	DUAN Y Z, WANG C, WANG H T, et al. Predicting the potential distribution of Ammopiptanthus species in China under different climates using ecological niche models[J]. Chin J Plant Ecol, 2020, 40(21):7668-7680. DOI: 10.5846/stxb201902210306. doi: 10.5846/stxb201902210306
[23]	柳嘉佳. 基于maxEnt模型的米槁潜在适生区分布及其格局模拟[D]. 贵阳: 贵州大学, 2018.
	LIU J J. Prediction and simulation of distribution of Cinnnmamum migao based on maxEnt model and GIS[D]. Guiyang: Guizhou University, 2018.
[24]	陈禹锦, 罗喻才, 于芬, 等. 气候变化情景下毛竹潜在分布及动态预测[J]. 世界竹藤通讯, 2021, 19(3):5-14.
	CHEN Y J, LUO Y C, YU F, et al. Potential distribution and dynamic prediction of Phyllostachys edulis under the scenarios of climate change[J]. World Bamboo Rattan, 2021, 19(3):5-14. DOI: 10.12168/sjzttx.2021.03.002. doi: 10.12168/sjzttx.2021.03.002
[25]	施雯, 朱恩骄, 王宇宸, 等. 基于MaxEnt模型预测戴褐臂金龟在中国的潜在适生区[J]. 生态学杂志, 2021, 40(9):2936-2944.
	SHI W, ZHU E J, WANG Y C, et al. Prediction of potentially suitable distribution area of Propomacrus davidi Deyrolle in China based on MaxEnt model[J]. Chin J Ecol, 2021, 40(9):2936-2944. DOI: 10.13292/j.1000-4890.202109.024. doi: 10.13292/j.1000-4890.202109.024
[26]	兰雪涵, 付聪, 李黎明, 等. 基于MaxEnt模型的濒危植物朝鲜崖柏中国潜在适生区预测[J]. 北华大学学报(自然科学版), 2021, 22(3):292-298.
	LAN X H, FU C, LI L M, et al. Prediction of potential suitable distribution areas in China for Thuja koraiensis Nakai based on MaxEnt model[J]. J Beihua Univ (Nat Sci), 2021, 22(3):292-298. DOI: 10.11713/j.issn.1009-4822.2021.03.003. doi: 10.11713/j.issn.1009-4822.2021.03.003
[27]	段义忠, 王海涛, 王驰, 等. 气候变化下濒危植物半日花在中国的潜在分布[J]. 植物资源与环境学报, 2020, 29(2):55-68.
	DUAN Y Z, WANG H T, WANG C, et al. Potential distribution of endangered plant Helianthemum songaricum in China under climate change[J]. J Plant Resour Environ, 2020, 29(2):55-68. DOI: 10.3969/j.issn.1674-7895.2020.02.07. doi: 10.3969/j.issn.1674-7895.2020.02.07
[28]	张文秀, 寇一翾, 张丽, 等. 采用生态位模拟预测濒危植物白豆杉5个时期的适宜分布区[J]. 生态学杂志, 2020, 39(2):600-613.
	ZHANG W X, KOU Y X, ZHANG L, et al. Suitable distribution of endangered species Pseudotaxus chienii(Cheng) Cheng(Taxaceae) in five periods using niche modeling[J]. Chin J Ecol, 2020, 39(2):600-613. DOI: 10.13292/j.1000-4890.202002.028. doi: 10.13292/j.1000-4890.202002.028
[29]	SHITARA T, FUKUI S, MATSUI T, et al. Climate change impacts on migration of Pinus koraiensis during the Quaternary using species distribution models[J]. Plant Ecol, 2021, 222(7):843-859. DOI: 10.1007/s11258-021-01147-z. doi: 10.1007/s11258-021-01147-z
[30]	柳生吉, 杨健. 基于广义线性模型和最大熵模型的黑龙江省林火空间分布模拟[J]. 生态学杂志, 2013, 32(06):1620-1628.
	LIU S J, YANG J. Modeling spatterns of forest fire in Heilongjiang province using generalized linear model and Maximum entropy model[J]. Chin J Ecol, 2013, 32(06):1620-168. DOI: 10.13292/j.1000-4890.2013.0258. doi: 10.13292/j.1000-4890.2013.0258
[31]	RAJABI A, SHABANLOU S, YOSEFVAND F, et al. Exploring the sample size and replications scenarios effect on spatial prediction of flood, using MARS and MaxEnt methods case study: saliantape catchment, Golestan, Iran[J]. Natural Hazards, 2021(prepublish). DOI: 10.1007/S11069-021-04860-0. doi: 10.1007/S11069-021-04860-0
[32]	谭谋, 汪洋. 栎方翅网蝽Corythucha arcuata(say)在中国的潜在适生区预测[J]. 生物安全学报, 2021, 30(2):137-142.
	TAN M, WANG Y. Potential geographical distribution areas of Corythucha arcuata (Say) in China[J]. J Biosaf, 2021, 30(2):137-142. DOI: 10.3969/j.issn.2095-1787.2021.02.009. doi: 10.3969/j.issn.2095-1787.2021.02.009
[33]	任永建, 肖莺, 周兵. BCC-CSM2-MR模式对北极海冰和气候的模拟及预估[J]. 气候变化研究进展, 2021, 17(1):58-69.
	REN Y J, XIAO Y, ZHOU B. Simulation and projection of Arctic Sea ice and climate by BCC-CSM2-MR[J]. Clim Change Res, 2021, 17(1):58-69. DOI: 10.12006/j.issn.1673-1719.2019.168. doi: 10.12006/j.issn.1673-1719.2019.168
[34]	SU H Y, BISTA M, LI M S. Mapping habitat suitability for Asiatic black bear and red Panda in Makalu Barun National Park of Nepal from Maxent and GARP models[J]. Sci Rep, 2021, 11(1):14135. DOI: 10.1038/s41598-021-93540-x. doi: 10.1038/s41598-021-93540-x
[35]	WEI Q, SUPING J, XUEKE Z, et al. Predicting the potential distribution of Rubia cordifolia L.in China[J]. Medicinal Plant, 2021(3):39-43, 49. DOI: 10.19600/j.cnki.issn2152-3924.2021.03.011. doi: 10.19600/j.cnki.issn2152-3924.2021.03.011
[36]	付小勇, 泽桑梓, 周晓, 等. 基于GIS的云南省薇甘菊潜在适生区研究[J]. 西部林业科学, 2015, 44(1):98-102.
	FU X Y, ZE S Z, ZHOU X, et al. Study on potential sui-table area of Mikania micrantha in Yunnan Province based on GIS[J]. J West China For Sci, 2015, 44(1):98-102. DOI: 10.16473/j.cnki.xblykx1972.2015.01.010. doi: 10.16473/j.cnki.xblykx1972.2015.01.010
[37]	付小勇, 泽桑梓, 周晓, 等. 基于MaxEnt的云南省薇甘菊分布预测及评价[J]. 广东农业科学, 2015, 42(12):159-162,2.
	FU X Y, ZE S Z, ZHOU X, et al. Distribution prediction and assessment of Mikania micrantha in Yunnan Province based on MaxEnt model[J]. Guangdong Agric Sci, 2015, 42(12):159-162,2. DOI: 10.16768/j.issn.1004-874x.2015.12.015. doi: 10.16768/j.issn.1004-874x.2015.12.015
[38]	秦晓威, 郝朝运, 李付鹏, 等. 基于MaxEnt模型的可可潜在适宜分布研究[J]. 热带作物学报, 2016, 37(5):1022-1029.
	QIN X W, HAO C Y, LI F P, et al. Potential distribution of Theobroma cacao L. using maximum entropy[J]. Chin J Trop Crops, 2016, 37(5):1022-1029. DOI: 10.3969/j.issn.1000-2561.2016.05.027. doi: 10.3969/j.issn.1000-2561.2016.05.027
[39]	张立荣, 徐大庆, 刘大群. SSR和ISSR分子标记及其在植物遗传育种研究中的应用[J]. 河北农业大学学报, 2002, 25(1):90-94.
	ZHANG L R, XU D Q, LIU D Q. SSR marker,ISSR marker and their application to plant genetics and breeding[J]. J Agric Univ Hebei, 2002, 25(1):90-94. DOI: 10.3969/j.issn.1000-1573.2002.01.024. doi: 10.3969/j.issn.1000-1573.2002.01.024
[40]	叶学敏, 陈伏生, 孙荣喜, 等. 基于MaxEnt模型的南酸枣潜在适生区预测[J]. 江西农业大学学报, 2019, 41(3):440-446.
	YE X M, CHEN F S, SUN R X, et al. Prediction of potential suitable distribution areas for Choerospondias axillaris besed on MaxEnt model[J]. Acta Agric Univ Jiangxiensis, 2019, 41(3):440-446. DOI: 10.13836/j.jjau.2019052. doi: 10.13836/j.jjau.2019052
[41]	王运生, 谢丙炎, 万方浩, 等. ROC曲线分析在评价入侵物种分布模型中的应用[J]. 生物多样性, 2007, 15(4):365-372.
	WANG Y S, XIE B Y, WAN F H, et al. Application of ROC curve analysis in evaluating the performance of alien species’potential distribution models[J]. Biodivers Sci, 2007, 15(4):365-372. DOI: 10.3321/j.issn:1005-0094.2007.04.005. doi: 10.3321/j.issn:1005-0094.2007.04.005
[42]	ABOLMAALI S M R, TARKESH M, BASHARI H. MaxEnt mode-ling for predicting suitable habitats and identifying the effects of climate change on a threatened species,Daphne mucronata,in central Iran[J]. Ecol Inform, 2018, 43:116-123. DOI: 10.1016/j.ecoinf.2017.10.002. doi: 10.1016/j.ecoinf.2017.10.002
[43]	孙洪兵, 孙辉, 蒋舜媛, 等. 基于3S技术的羌活区划研究Ⅰ.基于MaxEnt和ArcGIS的羌活生长适宜性分析及评价[J]. 中国中药杂志, 2015, 40(5):853-862.
	SUN H B, SUN H, JIANG S Y, et al. Cultural regionalization for Notopterygium incisum based on 3S technology platform I.Evaluation for growth suitability for N.incisum based on ecological factors analysis by Maxent and ArcGIS model[J]. China J Chin Mater Med, 2015, 40(5):853-862. DOI: 10.4268/cjcmm20150514. doi: 10.4268/cjcmm20150514
[44]	TRUE M C, PERRY R W, FORD W M. Forecasting the distribution of a range-expanding bat reveals future response to climate change and habitat[J]. 2021, 23(1):139-152. DOI: 10.3161/15081109ACC2021.23.1.011. doi: 10.3161/15081109ACC2021.23.1.011
[45]	SWETS J A. Measuring the accuracy of diagnostic systems[J]. Science, 1988, 240(4857):1285-1293. DOI: 10.1126/science.3287615. doi: 10.1126/science.3287615
[46]	华桦, 孙洪兵, 刘俐, 等. 变叶海棠及其同域近缘种潜在地理分布及生境特征研究[J]. 时珍国医国药, 2020, 31(1):203-206.
	HUA H, SUN H B, LIU L, et al. Potential geographical distribution and habitat characteristics of Malus toringoides (Rehd.) Hughes and its sympatric relative species[J]. Lishizhen Med Mater Med Res, 2020, 31(1):203-206.
[47]	谢伟东, 陈建华, 赖家业, 等. 蒜头果地理分布与水热关系分析[J]. 热带亚热带植物学报, 2009, 17(4):388-394.
	XIE W D, CHEN J H, LAI J Y, et al. Analysis on relationship between geographic distribution of Malania oleifera and hydro-thermal factors[J]. J Trop Subtrop Bot, 2009, 17(4):388-394. DOI: 10.3969/j.issn.1005-3395.2009.04.013. doi: 10.3969/j.issn.1005-3395.2009.04.013
[48]	陈剑, 王四海, 王娟, 等. 蒜头果在云南德宏州的适应性研究[J]. 绿色科技, 2018(21):85-88.
	CHEN J, WANG S H, WANG J, et al. A study on the adaptability of Malania oleifera in Dehong prefecture,Yunnan Province[J]. J Green Sci Technol, 2018(21):85-88. DOI: 10.16663/j.cnki.lskj.2018.21.035. doi: 10.16663/j.cnki.lskj.2018.21.035
[49]	赖家业, 文祥凤, 潘春柳, 等. 3个分布区蒜头果叶的解剖特征及其生态适应性[J]. 广西科学院学报, 2005, 21(3):144-147.
	LAI J Y, WEN X F, PAN C L, et al. Anatomical characters of leaves of Malania oleifera from three distributions and their ecological adaptabilities[J]. J Guangxi Acad Sci, 2005, 21(3):144-147. DOI: 10.3969/j.issn.1002-7378.2005.03.004. doi: 10.3969/j.issn.1002-7378.2005.03.004

序号 number	主导环境因子 dominant environmental factor	贡献率/% contribution rate
1	气温季节性变动系数temperature seasonality	39.6
2	等温性isothermally	16.7
3	平均气温日较差diurnal range of mean temperature	13.7
4	气温年较差annual temperature range	11.5
5	海拔altitude	9.2
6	最湿季平均气温mean temperature of wettest quarter	5.2
7	年降水量annual precipitation	4.2
8	最热月的最高气温maximum temperature of warmest month	0

云南广西蒜头果适生区预测及环境影响因子

Suitable regions forecasting and environmental influencing factors of Malania oleifera in Yunnan and Guangxi

RichHTML

PDF

可视化

摘要/Abstract

引用本文

使用本文

图/表 4

参考文献 49

相关文章 15

编辑推荐

Metrics

本文评价

作者

读者

审稿

[1]	吴妍, 李若楠, 赵志强. 黑龙江省国家级森林公园空间分布特征研究[J]. 南京林业大学学报（自然科学版）, 2023, 47(5): 189-196.
[2]	杨宏, 董京京, 吴桐, 周华近, 陈洁, 李蒙, 王贤荣, 伊贤贵. 基于MaxEnt模型的迎春樱桃潜在适生区预测[J]. 南京林业大学学报（自然科学版）, 2023, 47(4): 131-138.
[3]	曹林青, 钟秋平, 邹玉玲, 田丰, 贺义昌. 油桐种质资源叶片结构变异及与环境因子的关系[J]. 南京林业大学学报（自然科学版）, 2023, 47(4): 95-102.
[4]	韩淑敏, 闫伟, 杨雪栋, 胡博, 于凤强, 高润红. 白榆在我国的潜在分布格局及未来变化[J]. 南京林业大学学报（自然科学版）, 2023, 47(3): 103-110.
[5]	李俊冬, 查禄慧, 马国强, 肖剑平, 李育武, 李旭. 云南楚雄小百草岭鸟类多样性空间格局[J]. 南京林业大学学报（自然科学版）, 2023, 47(2): 225-233.
[6]	竹磊, 徐军亮, 章异平, 罗鹏飞, 师志强, 候佳玉, 翟乐鑫. 河南洛阳马尾松树干液流昼夜变化特征及其影响因子分析[J]. 南京林业大学学报（自然科学版）, 2023, 47(1): 92-100.
[7]	许浩, 黄婷, 刘伟, 王浩. 长三角风景名胜区空间分布与可达性研究[J]. 南京林业大学学报（自然科学版）, 2022, 46(6): 294-303.
[8]	阎雄飞, 程鑫辉, 郝哲, 王庭昊, 秦富林, 李刚. 枣瘿蚊幼虫在设施枣树上的空间分布型及抽样技术[J]. 南京林业大学学报（自然科学版）, 2022, 46(4): 201-208.
[9]	董京京, 陈洁, 杨宏, 李蒙, 王贤荣, 伊贤贵. 华中樱桃适生区模拟和生态特征分析[J]. 南京林业大学学报（自然科学版）, 2022, 46(3): 213-221.
[10]	张建国, 葛扬. 国家森林城市与国家园林城市空间分布及其影响因素比较研究[J]. 南京林业大学学报（自然科学版）, 2022, 46(3): 41-49.
[11]	崔皓, 韩建刚, 郭俨辉, 季淮, 朱咏莉, 李萍萍. 洪泽湖河湖交汇区典型杨树人工林碳通量月尺度变化特征[J]. 南京林业大学学报（自然科学版）, 2022, 46(2): 19-26.
[12]	吴帆, 朱沛煌, 季孔庶. 马尾松分布格局对未来气候变化的响应[J]. 南京林业大学学报（自然科学版）, 2022, 46(2): 196-204.
[13]	缪菁, 王勇, 王璐, 许晓岗. 基于MaxEnt模型的苦槠潜在地理分布格局变迁预测[J]. 南京林业大学学报（自然科学版）, 2021, 45(3): 193-198.
[14]	董灵波, 刘兆刚. 森林健康评价及其多尺度转换方法[J]. 南京林业大学学报（自然科学版）, 2021, 45(3): 206-216.
[15]	王磊, 刘强, 杨俊杰, 雷宇, 祁天法. 基于卫星跟踪的钳嘴鹳家域研究[J]. 南京林业大学学报（自然科学版）, 2020, 44(6): 33-38.