Prediction of the potential geographical distribution of <i>Hylurgus ligniperda</i> at the global scale and in China using the Maxent model

Zhou Yuting; Ge Xuezhen; Zou Ya; Guo Siwei; Wang Tao; Tao Jing; Zong Shixiang

doi:10.12171/j.1000-1522.20210345

Journal of Beijing Forestry University > 2022 > 44(11): 90-99. > DOI: 10.12171/j.1000-1522.20210345

Zhou Yuting, Ge Xuezhen, Zou Ya, Guo Siwei, Wang Tao, Tao Jing, Zong Shixiang. Prediction of the potential geographical distribution of Hylurgus ligniperda at the global scale and in China using the Maxent model[J]. Journal of Beijing Forestry University, 2022, 44(11): 90-99. DOI: 10.12171/j.1000-1522.20210345

Citation:

PDF (1542 KB)

Prediction of the potential geographical distribution of Hylurgus ligniperda at the global scale and in China using the Maxent model

1.
Key Laboratory for Silviculture and Conservation of Ministry of Education, Beijing Forestry University, Beijing 100083, China
2.
Department of Integrative Biology, University of Guelph, Guelph N1G 2W1, Ontario, Canada
3.
Mentougou Forestry Station, Beijing 102300, China

More Information

Received Date: September 04, 2021
Revised Date: November 04, 2021
Accepted Date: September 06, 2022
Available Online: November 04, 2022
Published Date: November 24, 2022

Graphical Abstract

Abstract

Abstract

Objective Hylurgus ligniperda is an important quarantine pest of pine species, which has caused severe damages on pines in Chile, New Zealand and many other countries. The pest was recently confirmed to have invaded and established populations in Shandong Province of eastern China through imported wood materials, posing threats to the forestry security of China. Thus, accurate prediction of the climate-suitable regions of H. ligniperda is urgent for relevant institutions to take efficient quarantine and control measures, thus reducing the potential ecological and economic losses.
Method Our research used Maxent model to predict the climate-suitable regions of H. ligniperda both at the global scale and in China, following the steps of bioclimatic variable selecting and model parameter optimizing. ArcGIS software was used to visualize the predictive results and make area statistics. The model results were used to analyze the contributing bioclimatic variables affecting the colonization of the pest.
Result Compared with the Maxent model with the default parameters, the accuracy of the model with optimized parameters was significantly improved, with the average AUC value of 0.964 6. Meanwhile, the predictive results showed that the climate-suitable regions were mainly distributed in Europe, the east and west coasts of North America, the east of Asia, the southeast of South America, the southernmost tip of Africa and the southeast coast of Oceania, with the very suitable regions, suitable regions and marginal regions accounting for 1.21%, 1.92% and 3.95%, respectively. Within China, the climate-suitable regions of the pest were mainly located in the middle and south zones, which was bounded to Taiwan Province in the South and Dalian of Liaoning Province in the north. The areas of very suitable regions, suitable regions and marginal regions accounted for 0.28%, 5.00% and 13.43%, respectively. The bioclimatic variables having the most significant contribution are the precipitation of the driest quarter (37.9%), the minimum temperature of the coldest month (25.4%), the temperature seasonality (12.7%), the maximum temperature of the warmest month (7%) and the annual precipitation (6.6%).
Conclusion Except for the areas where H. ligniperda already existed, the pest has not occurred in the climate-suitable regions in the southeast and northwest of the United States, the east of Argentina, the southern Brazil and most of China, indicating the high risk of H. ligniperda invasion in these regions. The climate-suitable areas of H. ligniperda in China are mainly represented by suitable regions and marginal regions, including almost all provinces in central and southern China, among them the coastal areas and surrounding regions of Shandong Province and Jiangsu Province, Western China, central and southern China have relatively higher suitability. Therefore, it is suggested that the survey and prevention measures should be implemented to control the spread of H. ligniperda, while the quarantine and control on wooden material should be strengthened at entry ports.
- Hylurgus ligniperda,
- Maxent,
- climate-suitable region,
- climate suitability

FullText(HTML)

References (34)

References

[1]	王仿, 陈寿铃, 周卫川. 长林小蠹的识别鉴定[J]. 植物检疫, 2006(1): 30−31. doi: 10.3969/j.issn.1005-2755.2006.01.012 Wang F, Chen S L, Zhou W C. Identification of Hylurgus ligniperda[J]. Plant Quarantine, 2006(1): 30−31. doi: 10.3969/j.issn.1005-2755.2006.01.012
[2]	Khoury Y E, Binazzi F, Nemer N, et al. Bark beetles Coleoptera Curculionidae Scolytinae associated with Pinus pinea in Lebanon new records with remarks on their ecology, distribution and potential threat for forest stands[J]. Redia-Giornale Di Zoologia, 2019(102): 121−128.
[3]	Huanquilef C, Espinoza J, Mutis A, et al. Antifeedant activities of organic fractions from Cestrum parqui leaves on the red-haired bark beetle Hylurgus ligniperda[J]. Journal of Soil Science and Plant Nutrition, 2020, 21(1): 13−21.
[4]	Pawson S M, Kerr J L, Somchit C, et al. Flight activity of wood-and bark-boring insects at New Zealand ports[J/OL]. New Zealand Journal of Forestry Science, 2020, 14(50) [2021−08−14]. https://doi.org/10.33494/nzjfs502020x132x.
[5]	Tribe G D. Colonisation sites on Pinus radiata logs of the bark beetles, Orthotomicus erosus, Hylastes angustatus and Hylurgus ligniperda (Coleoptera: Scolytidae)[J]. Journal of the Entomological Society of Southern Africa, 1992, 55(1): 77−84.
[6]	Romo C M, Massar X, Early J W. ‘Unestablished’ biological control agent found in Canterbury 40 years later[J]. New Zealand Entomologist, 2017, 40(1): 1−6. doi: 10.1080/00779962.2017.1306901
[7]	Liu D, Bohne M J, Lee J C, et al. New introduction in California: the redhaired pine bark beetle, Hylurgus ligniperda Fabricius[Z/OL]. Pest Alert R5-PR-07. Berkeley: US Department of Agriculture, Forest Service, Pacific Southwest Research Station. 3 p., 2007[2021−09−16]. https://www.srs.fs.usda.gov/pubs/48536.
[8]	Eckehard G B, John B, Mark K, et al. Interception frequency of exotic bark and ambrosia beetles (Coleoptera: Scolytinae) and relationship with establishment in New Zealand and worldwide[J]. Canadian Journal of Forest Research, 2006, 36(2): 289−298.
[9]	綦虎山, 张俊华, 陈乃中, 等. 中国口岸截获的小蠹科昆虫概况[J]. 植物检疫, 2014, 28(6): 1−6. doi: 10.3969/j.issn.1005-2755.2014.06.002 Qi H S, Zhang J H, Chen N Z, et al. Survey on intercepted scolitids of China ports[J]. Plant Quarantine, 2014, 28(6): 1−6. doi: 10.3969/j.issn.1005-2755.2014.06.002
[10]	中国国门时报. 四川口岸截获长林小蠹[N/OL]. (2014−03−10) [2021−05−01]. http://roll.sohu.com/20140320/n396941777.shtml. Zhongguo Guomen Shibao. Sichuan port intercepts Hylurgus ligniperda[N/OL]. (2014−03−10) [2021−05−01]. http://roll.sohu.com/20140320/n396941777.shtml.
[11]	央广网. 无锡江阴口岸首次截获检疫性有害生物长林小蠹[Z/OL]. (2017−09−11) [2021−05−01]. http://js.cnr.cn/2011jsfw/whly/20170911/t20170911_523944283.shtml. CNR News. The quarantine pest Hylurgus ligniperda was intercepted for the first time at Jiangyin port of Wuxi[Z/OL]. (2017−09−11) [2021−05−01]. http://js.cnr.cn/2011jsfw/whly/20170911/t20170911_523944283.shtml.
[12]	中华人民共和国石家庄海关. 石家庄海关在进境去皮原木中截获有害生物长林小蠹[Z/OL]. (2019−04−28) [2021−05−01]. http://xiamen.customs.gov.cn/shijiazhuang_customs/456970/456972/2406167/index.html. Shijiangzhuang Customs District, P. R. China. Shijiazhuang customs intercepted Hylurgus ligniperda (Coleoptera: Scolytidae) from peeled logs[Z/OL]. (2019−04−28) [2021−05−01]. http://xiamen.customs.gov.cn/shijiazhuang_customs/456970/456972/2406167/index.html.
[13]	南方报业传媒集团. 江门海关首次截获“林木杀手”长林小蠹[Z/OL]. (2019−11−22) [2021−05−01]. http://static.nfapp.southcn.com/content/201911/22/c2829370.html. Southern Daily Group. Jiangmen Customs intercepts “forest killer” Hylurgus ligniperda for the first time[Z/OL]. (2019−11−22) [2021−05−01]. http://static.nfapp.southcn.com/content/201911/22/c2829370.html.
[14]	任利利, 陶静, 武海卫, 等. 重大害虫长林小蠹入侵我国的首次发现与侵染特征[J]. 林业科学, 2021, 57(5): 140−150. doi: 10.11707/j.1001-7488.20210513 Ren L L, Tao J, Wu H W, et al. The first discovery and infective characteristics of a major invasive pest Hylurgus ligniperda (Coleoptera: Scolytidae) in China[J]. Scientia Silvae Sinicae, 2021, 57(5): 140−150. doi: 10.11707/j.1001-7488.20210513
[15]	国家林业和草原局国家公园管理局. 警惕林业入侵害虫: 长林小蠹危害的警示通报[EB/OL]. (2021−02−20) [2021−05−01]. http://www.forestry.gov.cn/main/394/20210220/152345808244287.html. National Forestry and Grassland Administration National Park Administration. Be alert to forest invasive pests-warning notice of Hylurgus ligniperda[EB/OL]. (2021−02−20) [2021−05−01]. http://www.forestry.gov.cn/main/394/20210220/152345808244287.html.
[16]	Shabani F, Kumar L, Ahmadi M. A comparison of absolute performance of different correlative and mechanistic species distribution models in an independent area[J]. Ecology and Evolution, 2016, 6(16): 5983−5986.
[17]	Srivastava V, Lafond V, Griess V C. Species distribution models (SDM): applications, benefits and challenges in invasive species management[J]. CAB Reviews Perspectives in Agriculture Veterinary Science Nutrition and Natural Resources, 2019, 14(20): 1−13.
[18]	Elith J, Kearney M, Phillips S. The art of modelling range-shifting species[J]. Methods in Ecology & Evolution, 2010, 1(4): 330−342.
[19]	Steven J P, Robert P A, Robert E S. Maximum entropy modeling of species geographic distributions[J]. Ecological Modelling, 2006, 190(3): 231−259.
[20]	王璐, 吴秀萍, 李垚, 等. 北美银钟花在中国的适宜栽培区研究[J]. 南京林业大学学报(自然科学版), 2018, 42(5): 10−16. Wang L, Wu X P, Li Y, et al. Prediction of suitable cultivation area for Halesia carolina L. in China[J]. Journal of Nanjing Forestry University (Natural Sciences Edition), 2018, 42(5): 10−16.
[21]	王梦琳, 范靖宇, 李敏, 等. 入侵害虫蔗扁蛾在我国的潜在分布区[J]. 生物安全学报, 2017, 26(2): 129−133. doi: 10.3969/j.issn.2095-1787.2017.02.004 Wang M L, Fan J Y, Li M, et al. Potential geographical distribution of the introduced banana moth, Opogona sacchari (Lepidoptera: Tineidae) in China[J]. Journal of Biosafety, 2017, 26(2): 129−133. doi: 10.3969/j.issn.2095-1787.2017.02.004
[22]	刘蒙蒙, 邢咏梅, 郭顺星. 基于Maxent生态位模型预测药用真菌猪苓在中国潜在适生区[J]. 中国中药杂志, 2015, 40(14): 2792−2795. Liu M M, Xing Y M, Guo S X. Habitat suitability assessment of medicinal Polyporus umbellatusin China based on Maxent modeling[J]. China Journal of Chinese Material Medicine, 2015, 40(14): 2792−2795.
[23]	Yu Y, Chi Z, Zhang J, et al. Assessing the invasive risk of bark beetles (Curculionidae: Scolytinae and Platypodinae)[J]. Annals of the Entomological Society of America, 2019, 112(5): 451−457. doi: 10.1093/aesa/saz030
[24]	宋光远, 张俊华, 杨定, 等. 长林小蠹在中国的适生区预测[J]. 植物检疫, 2018, 32(5): 66−70. doi: 10.19662/j.cnki.issn1005-2755.2018.05.015 Song G Y, Zhang J H, Yang D, et al. Potential geographical distributions of Hylurgus ligniperda (Coleoptera: Scolytinae) in China[J]. Plant Quarantine, 2018, 32(5): 66−70. doi: 10.19662/j.cnki.issn1005-2755.2018.05.015
[25]	Aiello-Lammens M E, Boria R A, Radosavljevic A, et al. spThin: an R package for spatial thinning of species occurrence records for use in ecological niche models[J]. Ecography, 2015, 38(5): 541−545. doi: 10.1111/ecog.01132
[26]	尚忠慧. 基于Maxent的物种空间分布预测不确定性分析: 以当归为例[D]. 西安: 陕西师范大学, 2016. Shang Z H. Analysis on the uncertainty of species’ distribution prediction based on Maxent-case study of Angelica sinensis [D]. Xi’an: Shaanxi Normal University, 2016.
[27]	Merow C, Smith M J, John A, et al. A practical guide to Maxent for modeling species’ distributions: what it does, and why inputs and settings matter[J]. Ecography, 2013, 10(36): 1058−1069.
[28]	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.
[29]	Swets J. Measuring the accuracy of diagnostic systems[J]. Science, 1988, 240: 1285−1293. doi: 10.1126/science.3287615
[30]	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. 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, 15(4): 365−372. doi: 10.3321/j.issn:1005-0094.2007.04.005
[31]	朱耿平, 范靖宇, 王梦琳, 等. ROC曲线形状在生态位模型评价中的重要性: 以美国白蛾为例[J]. 生物安全学报, 2017, 26(3): 184−190. doi: 10.3969/j.issn.2095-1787.2017.03.002 Zhu G P, Fan J Y, Wang M L, et al. The importance of the shape of receiver operating characteristic (ROC) curve in ecological niche model evaluation-case study of Hlyphantria cunea[J]. Journal of Biosafety, 2017, 26(3): 184−190. doi: 10.3969/j.issn.2095-1787.2017.03.002
[32]	Lin W, Park S, Jiang Z R, et al. Native or invasive? the red-haired pine bark beetle Hylurgus ligniperda (Fabricius) (Curculionidae: Scolytinae) in east Asia[J/OL]. Forests, 2021, 12(7) [2021−12−29]. https://doi.org/10.3390/f12070950.
[33]	Tribe G D. Phenology of Pinus radiata log colonization by the red-haired pine bark beetle Hylurgus ligniperda (Fabricius) (Coleoptera: Scolytidae) in the south-western Cape Province[J]. Journal of the Entomological Society of Southern Africa, 1991, 54(1): 1−7.
[34]	Fabre J P, Carle P. On the biology of Hylurgus ligniperda in south-eastern France[J]. Annales Des Sciences Forestieres, 1975, 1(32): 55−71.

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 (744) PDF downloads (74) Cited by(10)

Turn off MathJax

Article Contents

Abstract

References

Prediction of the potential geographical distribution of Hylurgus ligniperda at the global scale and in China using the Maxent model

Abstract

References

Cited by

Periodical cited type(7)

Other cited types(3)

Catalog

Export File

Citation

Format

Content