Prediction and analysis of the global suitability of <i>Lymantria dispar</i> based on MaxEnt

Wang Yanjun; Gao Tai; Shi Juan

doi:10.12171/j.1000-1522.20200416

Journal of Beijing Forestry University > 2021 > 43(9): 59-69. > DOI: 10.12171/j.1000-1522.20200416

Wang Yanjun, Gao Tai, Shi Juan. Prediction and analysis of the global suitability of Lymantria dispar based on MaxEnt[J]. Journal of Beijing Forestry University, 2021, 43(9): 59-69. DOI: 10.12171/j.1000-1522.20200416

Citation:

PDF (53692 KB)

Prediction and analysis of the global suitability of Lymantria dispar based on MaxEnt

Key Laboratory of Beijing for the Control of Forest Pests, Beijing Forestry University, Beijing 100083, China

More Information

Received Date: December 23, 2020
Revised Date: January 27, 2021
Available Online: April 27, 2021
Published Date: October 14, 2021

Graphical Abstract

Abstract

Abstract

Objective Lymantria dispar (Linnaeus) is a leaf-eating international quarantine pest, which has caused serious economic losses to many countries and regions in the world. In this study, environmental variables limiting the distribution of Lymantria dispar were screened out, and MaxEnt software was used to predict the global adaptation range of Lymantria dispar under current and future climatic conditions, so as to clarify the adaptation range of Lymantria dispar under different climatic conditions.
Method We used ArcGIS software to set up buffer zones to screen the global distribution data of Lymantria dispar. We used MaxEnt, SPSS and ArcGIS software to screen four kinds of environmental variables, i.e. biological climate variable, monthly total precipitation, monthly mean maximum temperature and monthly mean minimum temperature, according to the contribution rate of environmental variables, knife cutting method and variable correlation analysis. We used R software to calculate the regularization multiplier and feature classes and other factors to adjust the parameters of MaxEnt model. We used MaxEnt model to predict the global distribution range of Lymantria dispar under current and future climatic conditions.
Result The distribution data of 734 Lymantria dispar were obtained by buffer screening. In the MaxEnt model results, the test omission rate was in high agreement with the theoretical omission rate, and the AUC value of the model was 0.940. MaxEnt model predicted that Lymantria dispar was mainly distributed in most parts of Europe, central and eastern North America, western and eastern Asia, and less in Africa, Oceania and South America under current conditions. In addition, Lymantria dispar boundaries in the northern hemisphere will shift to the north under future climatic conditions, and the areas of high and moderate adaptability in North America and Eurasia will expand significantly.
Conclusion The distribution of Lymantria dispar is influenced by a variety of environmental variables and temperature and precipitation are consistent with the specific development stage of Lymantria dispar. The predicted results of MaxEnt model can reflect the distribution characteristics of Lymantria dispar in the world. This study can provide theoretical basis for prevention and control of Lymantria dispar and establishment of relevant quarantine measures.
- Lymantria dispar,
- MaxEnt,
- adaptive area prediction,
- CMIP6

FullText(HTML)

References (35)

References

[1]	张丽茹. 舞毒蛾的生物学特性及综合防治技术[J]. 现代农业科技, 2020(6):116−117. doi: 10.3969/j.issn.1007-5739.2020.06.072 Zhang L R. Biological characteristics and comprehensive control techniques of Lymantira dispar[J]. Modern Agricultural Science and Technology, 2020(6): 116−117. doi: 10.3969/j.issn.1007-5739.2020.06.072
[2]	Keena M A, Côté M, Grinberg P S, et al. World distribution of female flight and genetic variation in Lymantria dispar (Lepidoptera: Lymantriidae)[J]. Environmental Entomology, 2008, 37(3): 636−649. doi: 10.1603/0046-225X(2008)37[636:WDOFFA]2.0.CO;2
[3]	钱路, 安榆林, 徐梅, 等. 舞毒蛾不同地理种群基于AFLP分子标记的遗传分析[J]. 林业科学, 2011, 47(10):104−110. doi: 10.11707/j.1001-7488.20111016 Qian L, An Y L, Xu M, et al. AFLP analysis of different geographic populations of the gypsy moth, Lymantria dispar (Lepidoptera: Lymantriidae)[J]. Scientia Silvae Sinicae, 2011, 47(10): 104−110. doi: 10.11707/j.1001-7488.20111016
[4]	Luque G M, Bellard C, Bertelsmeier C, et al. The 100th of the world’s worst invasive alien species[J]. Biological Invasions, 2014, 16(5): 981−985. doi: 10.1007/s10530-013-0561-5
[5]	谵运清, 安输林, 刘翔, 等. 由亚洲型舞毒蛾检疫问题引发的几点思考[J]. 植物检疫, 2012, 26(4):76−78. Zhan Y Q, An Y L, Liu X, et al. Some thoughts on quarantine of Gypsy moth in Asia[J]. Plant quarantine, 2012, 26(4): 76−78.
[6]	Srivastava V, Griess V C, Keena M A. Assessing the potential distribution of Asian gypsy moth in Canada: a comparison of two methodological approaches[J]. Sentific Reports, 2020, 10(22): 1−10.
[7]	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.
[8]	Merow C, Smith M J, Silander J A. A practical guide to MaxEnt for modeling species’ distributions: what it does, and why inputs and settings matter[J]. Ecography, 2013, 36(10): 1058−1069. doi: 10.1111/j.1600-0587.2013.07872.x
[9]	Phillips S J, Dudík M. Modeling of species distributions with MaxEnt: new extensions and a comprehensive evaluation[J]. Ecography, 2008, 31(2): 161−175. doi: 10.1111/j.0906-7590.2008.5203.x
[10]	Elith J, Phillips S J, Hastie T, et al. A statistical explanation of MaxEnt for ecologists[J]. Diversity & Distributions, 2011, 17(1): 43−57.
[11]	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(4):365−372. doi: 10.3321/j.issn:1005-0094.2007.04.005 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]. Biodiversity Science, 2007(4): 365−372. doi: 10.3321/j.issn:1005-0094.2007.04.005
[12]	李一琳, 丁长青. 基于GIS和MaxEnt技术对濒危物种褐马鸡的保护空缺分析[J]. 北京林业大学学报, 2016, 38(11):34−41. Li Y L, Ding C Q. Reserve gap analysis of endangered brown eared pheasant (Crossoptilon mantchuricum) through GIS and MaxEnt technology[J]. Journal of Beijing Forest University, 2016, 38(11): 34−41.
[13]	赵佳强, 石娟. 基于新型最大熵模型预测刺槐叶瘿蚊(双翅目: 瘿蚊科)在中国的适生区[J]. 林业科学, 2019, 55(2):118−127. Zhao J Q, Shi J. Prediction of the potential geographical distribution of Obolodiplosis robiniae (Diptera: Cecidomyiidae) in China based on a novel Maximum Entropy model[J]. Scientia Silvae Sinicae, 2019, 55(2): 118−127.
[14]	黄梦伊, 赵佳强, 石娟. 基于MaxEnt对桉树枝瘿姬小蜂在中国发生趋势的预测[J]. 北京林业大学学报, 2020, 42(11):64−71. Huang M Y, Zhao J Q, Shi J. Predicting occurrence tendency of Leptocybe invasa in China based on MaxEnt[J]. Journal of Beijing Forestry University, 2020, 42(11): 64−71.
[15]	Fang W, Duo W, Ge G, et al. Potential distributions of the invasive barnacle scale Ceroplastes cirripediformis (Hemiptera: Coccidae) under climate change and implications for its management[J]. Journal of Economic Entomology, 2021, 114(1): 82−89. doi: 10.1093/jee/toaa245
[16]	张春华, 和菊, 孙永玉, 等. 基于MaxEnt模型的紫椿适生区预测[J]. 北京林业大学学报, 2017, 39(8):33−41. Zhang C H, He J, Sun Y Y, et al. Distributional change in suitable areas for Toona sureni based on MaxEnt model[J]. Journal of Beijing Forest University, 2017, 39(8): 33−41.
[17]	吕汝丹, 何健, 刘慧杰, 等. 羽叶铁线莲的分布区与生态位模型分析[J]. 北京林业大学学报, 2019, 41(2):70−79. Lü R D, He J, Liu H J, et al. Distribution and niche modeling analysis of Clematis pinnata[J]. Journal of Beijing Forestry University, 2019, 41(2): 70−79.
[18]	Inoue M N, Suzuki-Ohno Y, Haga Y, et al. Population dynamics and geographical distribution of the gypsy moth, Lymantria dispar, in Japan[J]. Forest Ecology and Management, 2019, 434: 154−164. doi: 10.1016/j.foreco.2018.12.022
[19]	姜彤, 吕嫣冉, 黄金龙, 等. CMIP6模式新情景(SSP-RCP)概述及其在淮河流域的应用[J]. 气象科技进展, 2020, 10(5):102−109. doi: 10.3969/j.issn.2095-1973.2020.05.016 Jiang T, Lü Y R, Huang J L, et al. New scenarios of CMIP6 model (SSP-RCP) and its application in the Huaihe river basin[J]. Advances in Meteorological Science and Technology, 2020, 10(5): 102−109. doi: 10.3969/j.issn.2095-1973.2020.05.016
[20]	张丽霞, 陈晓龙, 辛晓歌. CMIP6情景模式比较计划(ScenarioMIP)概况与评述[J]. 气候变化研究进展, 2019, 15(5):519−525. doi: 10.12006/j.issn.1673-1719.2019.082 Zhang L X, Chen X L, Xin X G. Short commentary on CMIP6 scenario model intercomparison project (ScenarioMIP)[J]. Climate Change Research, 2019, 15(5): 519−525. doi: 10.12006/j.issn.1673-1719.2019.082
[21]	Sillero N. What does ecological modelling model? A proposed classification of ecological niche models based on their underlying methods[J]. Ecological Modelling, 2011, 222(8): 1343−1346. doi: 10.1016/j.ecolmodel.2011.01.018
[22]	朱耿平, 刘强, 高玉葆. 提高生态位模型转移能力来模拟入侵物种的潜在分布[J]. 生物多样性, 2014, 22(2):223−230. doi: 10.3724/SP.J.1003.2014.08178 Zhu G P, Liu Q, Gao Y B. Improving ecological niche model transferability to predict the potential distribution of invasive exotic species[J]. Biodiversity Science, 2014, 22(2): 223−230. doi: 10.3724/SP.J.1003.2014.08178
[23]	Phillips S J, Anderson R P, Dudík M, et al. Opening the black box: an open-source release of Maxent[J]. Ecography, 2017, 40(7): 887−893. doi: 10.1111/ecog.03049
[24]	刘洋, 石娟. 气候变化背景下埃及吹绵蚧在中国的适生区预测[J]. 植物保护, 2020, 46(1):108−117. Liu Y, Shi J. Prediction of potential geographical distribution of Icerya aegyptiaca in China under climate change[J]. Plant Protection, 2020, 46(1): 108−117.
[25]	Muscarella R, Galante P J, Soley-Guardia M, et al. ENMeval: an R package for conducting spatially independent evaluations and estimating optimal model complexity for Maxent ecological niche models[J]. Methods in Ecology & Evolution, 2015, 5(11): 1198−1205.
[26]	Shcheglovitova M, Anderson R P. Estimating optimal complexity for ecological niche models: a jackknife approach for species with small sample sizes[J]. Ecological Modelling, 2013, 269: 9−17.
[27]	Zhong Q, Zhang J E, Ditommaso A, et al. Predicting invasions of Wedelia trilobata (L.) Hitchc. with MaxEnt and GARP models[J]. Journal of Plant Research, 2015, 128: 763−775. doi: 10.1007/s10265-015-0738-3
[28]	Pearson R G, Raxworthy C J, Nakamura M, et al. Original article: predicting species distributions from small numbers of occurrence records: a test case using cryptic geckos in Madagascar[J]. Journal of Biogeography, 2007, 34(1): 102−117.
[29]	Liu Y, Shi J. Predicting the potential global geographical distribution of two icerya species under climate change[J]. Forests, 2020, 11(6): 1−19.
[30]	赵晓冏, 巩娟霄, 赵莎莎, 等. 样本量及其空间分布对物种分布模型的影响[J]. 兰州大学学报: 自然科学版, 2018, 54(2):70−77. Zhao X J, Gong J X, Zhao S S, et al. Impact of sample size and spatial distribution on species distribution model[J]. Journal of Lanzhou University: Natural Sciences, 2018, 54(2): 70−77.
[31]	Morey A C, Venette R C. Minimizing risk and maximizing spatial transferability: challenges in constructing a useful model of potential suitability for an invasive insect[J]. Annals of the Entomological Society of America, 2020, 113(2): 100−113. doi: 10.1093/aesa/saz049
[32]	闻连仁, 赵春芝, 张洪彦, 等. 舞毒蛾生物学特性观察[J]. 吉林林业科技, 1989(2):34−37. Wen L R, Zhao C Z, Zhang H Y, et al. Observation on biological characteristics of Lymantria dispar[J]. Journal of Jilin Forestry Science and Technology, 1989(2): 34−37.
[33]	卢小雨, 陈洪俊, 陈乃中, 等. 亚洲型舞毒蛾在北美的适生性[J]. 昆虫知识, 2009, 46(3):398−402, 495. Lu X Y, Chen H J, Chen N Z, et al. Potential geographic distribution of Asian gypsy moth, Lymantria dispar, in north America[J]. Chinese Bulletin of Entomology, 2009, 46(3): 398−402, 495.
[34]	Heikkinen R K, Luoto M, Araújo M B, et al. Methods and uncertainties in bioclimatic envelope modelling under climate change[J]. Progress in Physical Geography, 2006, 30(6): 751−777. doi: 10.1177/0309133306071957
[35]	Sinclair S J, White M D, Newell G R. How useful are species distribution models for managing biodiversity under future climates?[J]. Ecology & Society, 2010, 15(1): 175−183.

[1]	Jiang Yibing, Song Xiaoshuang, Wang Zhanbin, Wang Liang, Deng Xun, Yu Wenjing. Diversity and community structure of endophytic fungi in Pinus sibirica needles with different lesion grades[J]. Journal of Beijing Forestry University, 2024, 46(11): 24-33. DOI: 10.12171/j.1000-1522.20230299
[2]	Yan Jiahui, Zhou Chengcheng, Niu Shihui, Li Wei. Identification of SAUR gene family in Pinus tabuliformis and analysis on its expression patterns under drought stress[J]. Journal of Beijing Forestry University, 2024, 46(8): 57-67. DOI: 10.12171/j.1000-1522.20230333
[3]	Sun Fan, Ma Yanguang, Liu Zhanmin, Yang Boning, Wang Huili, Li Wei. Parental selection strategies of high generation seed orchard of Pinus tabuliformis[J]. Journal of Beijing Forestry University, 2024, 46(4): 28-39. DOI: 10.12171/j.1000-1522.20230138
[4]	Liu Fengchen, Tian Na, Cheng Xiaoqin. Releasing variation and bacteriostatic effects of botanic volatile organic compounds from Pinus tabuliformis[J]. Journal of Beijing Forestry University, 2022, 44(9): 72-82. DOI: 10.12171/j.1000-1522.20210125
[5]	Liu Siwen, Ai Yebo, Liu Yanhong. Variations in leaf functional traits along the altitude gradient of Pinus tabuliformis and its environmental explanations in Beijing Songshan Mountain[J]. Journal of Beijing Forestry University, 2021, 43(4): 47-55. DOI: 10.12171/j.1000-1522.20200292
[6]	Jiang Ning, Mu Changcheng, Han Lidong, Shen Zhongqi. Impact of harvesting on carbon source/sink of Alnus sibirica var. hirsuta swamps in Daxing’anling Mountains discontinuous permafrost region of northeastern China[J]. Journal of Beijing Forestry University, 2020, 42(3): 1-13. DOI: 10.12171/j.1000-1522.20190074
[7]	Li Yixuan, Zhao Jian, Fu Shuangbin, Dong Mingliang, Yang Shuo, Li Shanshan, Kong Lisheng, Zhang Jinfeng. Enhancement of embryogenic callus proliferation in Chinese pine (Pinus tabuliformis) by airlift bioreactor[J]. Journal of Beijing Forestry University, 2019, 41(11): 37-43. DOI: 10.13332/j.1000-1522.20190221
[8]	GAO Qiong, WANG Wei-you, LIANG Dong, LI Yue.. Comparison of growth traits and photosynthetic physiology in Pinus tabuliformis from eight provenances of China.[J]. Journal of Beijing Forestry University, 2014, 36(2): 87-93.
[9]	LI Wei, ZHU Song-lin, LI Yue. Comparative study on plant traits between sexual and asexual reproduction for Pinus tabuliformis.[J]. Journal of Beijing Forestry University, 2012, 34(1): 46-50.
[10]	ZHANG Zhi-du, XU Cheng-yang, CAI Bao-jun, LI Cui-cui, YUAN SHi-bao, YANG Cheng, PENG Qiang, JIN Gui-xiang. Influence of stock density on crown structure of Prunus davidiana.[J]. Journal of Beijing Forestry University, 2009, 31(6): 187-192.

Cited By

Cited by

Periodical cited type(7)

1.	张鑫，张丹，代鹏飞，张广森，宋玫. 气候变化下反枝苋潜在中国适生区及生态位研究. 草地学报. 2024(10): 3280-3288 .
2.	陈禹衡，陆家祎，吴鹏飞，毛岭峰. 基于气候与物种扩散的破坏草入侵区域对未来气候变化的响应. 北京林业大学学报. 2022(01): 69-76 . 本站查看
3.	刘佳琪，魏广阔，史常青，赵廷宁，钱云楷. 基于MaxEnt模型的北方抗旱造林树种适宜区分布. 北京林业大学学报. 2022(07): 63-77 . 本站查看
4.	王艳君，高泰，石娟. 基于MaxEnt模型对舞毒蛾全球适生区的预测及分析. 北京林业大学学报. 2021(09): 59-69 . 本站查看
5.	张明珠，叶兴状，李佳慧，刘益鹏，陈世品，刘宝. 气候变化情景下长序榆在中国的潜在适生区预测. 生态学杂志. 2021(12): 3822-3835 .
6.	吕汝丹，何健，刘慧杰，姚敏，程瑾，谢磊. 羽叶铁线莲的分布区与生态位模型分析. 北京林业大学学报. 2019(02): 70-79 . 本站查看
7.	塞依丁·海米提，努尔巴依·阿布都沙力克，迈迪娜·吐尔逊，阿尔曼·解思斯，阿腾古丽. 外来入侵植物意大利苍耳在新疆的潜在分布及扩散趋势. 江苏农业科学. 2019(13): 126-130 .

Other cited types(3)

Get Citation

PDF

XML

Article views (1818) PDF downloads (209) Cited by(10)

Turn off MathJax

Article Contents

Abstract

References

Prediction and analysis of the global suitability of Lymantria dispar based on MaxEnt