Simulation and prediction of potential distribution area of <i>Euproctis pseudoconspersa</i> under climate change in China

Yang Liu; Liu Jun’ang; Zhou Guoying; He Yuanhao; Duan Xiang; Zhou Jiechen

doi:10.12171/j.1000-1522.20230034

Journal of Beijing Forestry University > 2024 > 46(6): 93-105. > DOI: 10.12171/j.1000-1522.20230034

Yang Liu, Liu Jun’ang, Zhou Guoying, He Yuanhao, Duan Xiang, Zhou Jiechen. Simulation and prediction of potential distribution area of Euproctis pseudoconspersa under climate change in China[J]. Journal of Beijing Forestry University, 2024, 46(6): 93-105. DOI: 10.12171/j.1000-1522.20230034

Citation:

PDF (11205 KB)

Simulation and prediction of potential distribution area of Euproctis pseudoconspersa under climate change in China

Yang Liu^{1, 2,},
Liu Jun’ang¹,
Zhou Guoying^1, ,,
He Yuanhao¹,
Duan Xiang^{1, 2},
Zhou Jiechen^{1, 2}

1.
College of Forestry, Central South University of Forestry and Technology, Changsha 410004, Hunan, China
2.
Xiangtan Research Institute of Forestry, Xiangtan 411206, Hunan, China

More Information

Received Date: February 13, 2023
Revised Date: March 06, 2023
Accepted Date: August 31, 2023
Available Online: September 05, 2023

Graphical Abstract

Abstract

Abstract

Objective
Camellia oleifera is a woody oleiferous plant with high economic and ecological value. Euproctis pseudoconspersa is one of the main pests of Camellia oleifera, which seriously restricts the yield and quality of C. oleifera. In order to provide scientific basis for early warning and specific prevention and control of the Euproctis pseudoconspersa, the potential distribution of E. pseudoconspersa was simulated and predicted in this study.
Method
Based on the distribution data and biological climate data of E. pseudoconspersa in China, MaxEnt model and ArcGIS software were used to predict the suitable area for E. pseudoconspersa in China under the current climate, and the distribution range and potential suitable area of E. pseudoconspersa in China in the 2050 and 2070 under SSP1-2.6, SSP2-4.5 and SSP5-8.5 climate change scenarios, and then the dominant environmental variables affecting the distribution of its potential suitable area were analyzed.
Result
(1) The dominant environmental variables affecting the distribution of suitable habitat of E. pseudoconspersa were precipitation of the driest month, annual precipitation, min. temperature of the coldest month, and the mean diurnal range (mean of monthly (max. temp – min. temp)). The optimum conditions were precipitation of the driest month of 28−148 mm, annual precipitation of 1 290−2 080 mm, the min. temperature of the coldest month of 1.0−10.1 ℃, and mean diurnal range of 7.2−8.5 ℃. (2) Under the current climate conditions, the total suitable area of E. pseudoconspersa accounted for 20.0% of China’s land area, mainly distributed in the middle and lower reaches of the Yangtze River and South China, with a high suitable area of 642 000 km², a medium suitable area of 618 000 km², and a low suitable area of 660 000 km². (3) In the next two periods and three climate scenarios, the suitable area of Camellia oleifera moth will be enlarged to different degrees, with the newly increased area of 93 000−330 000 km² and the migration distance of geographical distribution center between 24.4−125.1 km. The more obvious the climate warming was, the more the potential distribution area increased, and the farther the geographical distribution center moved.
Conclusion
E. pseudoconspersa has a wide range of habitats in China, including almost all the provinces in southern China. In the future climate change scenario, the suitable habitat of E. pseudoconspersa will expand northward, westward and other high latitude inland areas. Therefore, it is suggested that relevant departments should make plans and policies in advance to strengthen the observation and control of E. pseudoconspersa, so as to reduce its losses to Camellia oleifera industry.
- Camellia oleifera,
- Euproctis pseudoconspersa,
- MaxEnt model,
- environmental factor,
- suitable area

FullText(HTML)

References (31)

References

[1]	Bedia J, Busque J, Gutierrez J M. Predicting plant species distribution across an alpine rangeland in northern Spain: a comparison of probabilistic methods[J]. Applied Vegetation Science, 2011, 14(3): 415−432. doi: 10.1111/j.1654-109X.2011.01128.x
[2]	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
[3]	Wang Y M, Ge F, Liu X H, et al. Evaluation of mass-trapping for control of tea tussock moth Euproctis pseudoconspersa (Strand) (Lepidoptera: Lymantriidae) with synthetic sex pheromone in south China[J]. International Journal of Pest Management, 2005, 51(4): 289−295. doi: 10.1080/09670870500337858
[4]	刘万才, 刘振东, 朱晓明, 等. 我国昆虫性信息素技术的研发与应用进展[J]. 中国生物防治学报, 2022, 38(4): 803−811. Liu W C, Liu Z D, Zhu X M, et al. Development and application of insect sex pheromone technology in China[J]. Chinese Journal of Biological Control, 2022, 38(4): 803−811.
[5]	李霞, 盛如, 王振营, 等. 二点委夜蛾两种新型性诱芯田间诱蛾性能比较[J]. 植物保护, 2013, 39(3): 141−143, 166. doi: 10.3969/j.issn.0529-1542.2013.03.028 Li X, Sheng R, Wang Z Y, et al. Comparison of trapping efficacy of two kinds of sex pheromone lure of Athetis lepigone in field[J]. Plant Protection, 2013, 39(3): 141−143, 166. doi: 10.3969/j.issn.0529-1542.2013.03.028
[6]	Dong W W, Dong S Y, Jiang G F, et al. Characterization of the complete mitochondrial genome of tea tussock moth, Euproctis pseudoconspersa (Lepidoptera: Lymantriidae) and its phylogenetic implications[J]. Gene, 2016, 577(1): 37−46. doi: 10.1016/j.gene.2015.11.020
[7]	Lehmann P, Ammunet T, Barton M, et al. Complex responses of global insect pests to climate warming[J]. Frontiers in Ecology and the Environment, 2020, 18(3): 141−149. doi: 10.1002/fee.2160
[8]	许仲林, 彭焕华, 彭守璋. 物种分布模型的发展及评价方法[J]. 生态学报, 2015, 35(2): 557−567. Xu Z L, Peng H H, Peng S Z. The development and evaluation of species distribution models[J]. Acta Ecologica Sinica, 2015, 35(2): 557−567.
[9]	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.
[10]	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
[11]	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. doi: 10.11707/j.1001-7488.20190212 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. doi: 10.11707/j.1001-7488.20190212
[12]	杜志宏, 刘伟, 曹学仁, 等. 气候变化情景下基于MaxEnt的麦瘟病在全球及中国的适生性分析[J]. 植物保护, 2022, 48(5): 158−166. Du Z H, Liu W, Cao X R, et al. Suitability analysis of wheat blast in the world and China under climate change scenarios based on MaxEnt[J]. Plant Protection, 2022, 48(5): 158−166.
[13]	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[J]. 北京林业大学学报, 2021, 43(9): 59−69. doi: 10.12171/j.1000-1522.20200416 Wang Y J, Gao T, Shi J. 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
[14]	孙红云, 徐亮胜, 冯浩, 等. 基于MaxEnt模型预测苹果树腐烂病在中国的潜在地理分布[J]. 西北农业学报, 2020, 29(3): 461−466. doi: 10.7606/j.issn.1004-1389.2020.03.017 Sun H Y, Xu L S, Feng H, et al. Prediction for potential geographic distribution of valsamali in China based on MaxEnt model[J]. Acta Agriculturae Boreali-Occidentalis Sinica, 2020, 29(3): 461−466. doi: 10.7606/j.issn.1004-1389.2020.03.017
[15]	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
[16]	Sillero N, Barbosa A M. Common mistakes in ecological niche models[J]. International Journal of Geographical Information Science, 2021, 35(2): 213−226. doi: 10.1080/13658816.2020.1798968
[17]	谭洁, 黄安宁, 史学丽, 等. BCC-CSM2-MR模式对中国陆面过程模拟能力评估[J]. 高原气象, 2022, 41(5): 1335−1347. Tan J, Huang A N, Shi X L, et al. Evaluating the performance of BCC-CSM2-MR model in simulating the land surface processes in China[J]. Plateau Meteorology, 2022, 41(5): 1335−1347.
[18]	Guisan A, Zimmermann N E. Predictive habitat distribution models in ecology[J]. Ecological Modelling, 2000, 135(2–3): 147−186.
[19]	Kumar R S, Kala R H, Kumar G S, et al. Predicting the impact of climate change on the distribution of two threatened Himalayan medicinal plants of Liliaceae in Nepal[J]. Journal of Mountain Science, 2017, 14(3): 558−570. doi: 10.1007/s11629-015-3822-1
[20]	Elith J, Phillips S J, Hastie T, et al. A statistical explanation of MaxEnt for ecologists: statistical explanation of MaxEnt[J]. Diversity and Distributions, 2011, 17(1): 43−57. doi: 10.1111/j.1472-4642.2010.00725.x
[21]	Kalboussi M, Achour H. Modelling the spatial distribution of snake species in northwestern Tunisia using maximum entropy (Maxent) and geographic information system (GIS)[J]. Journal of Forestry Research, 2018, 29(1): 13. doi: 10.1007/s11676-017-0517-1
[22]	Fielding A. A review of methods for the assessment of prediction errors in conservation presence/absence models[J]. Environmental Conservation, 1997, 24(1): 38−49. doi: 10.1017/S0376892997000088
[23]	Kaky E, Nolan V, Alatawi A, et al. A comparison between Ensemble and MaxEnt species distribution modelling approaches for conservation: a case study with Egyptian medicinal plants[J]. Ecological Informatics, 2020, 60: 101150. doi: 10.1016/j.ecoinf.2020.101150
[24]	刘欣. 基于GARP和MAXENT的空心莲子草在中国的入侵风险预测[D]. 济南: 山东师范大学, 2012. Liu X. Prediction of the invasion risk for Alternanthera philoxeroides in China based on the GARP and MAXENT Model[D]. Jinan: Shandong Normal University, 2012.
[25]	崔相艳, 王文娟, 杨小强, 等. 基于生态位模型预测野生油茶的潜在分布[J]. 生物多样性, 2016, 24(10): 1117−1128. doi: 10.17520/biods.2016164 Cui X Y, Wang W J, Yang X Q, et al. Potential distribution of wild Camellia oleifera based on ecological niche modeling[J]. Biodiversity Science, 2016, 24(10): 1117−1128. doi: 10.17520/biods.2016164
[26]	Punyasena S W, Eshel G, Mcelwain J C. The influence of climate on the spatial patterning of Neotropical plant families[J]. Journal of Biogeography, 2010, 35(1): 117−130.
[27]	杨丽娜. 舞毒蛾对环境因子及Pb胁迫的响应机制[D]. 哈尔滨: 东北林业大学, 2018. Yang L N. Response mechanism of gypsy moth to environmental factors and Pb stress[D]. Harbin: Northeast Forestry University, 2018.
[28]	郭明明, 李晓峰, 邓盼, 等. 豆天蛾越冬幼虫滞育解除后生物学特性的研究[J]. 应用昆虫学报, 2021, 58(4): 966−972. doi: 10.7679/j.issn.2095-1353.2021.095 Guo M M, Li X F, Deng P, et al. Diapause termination and post-diapause of overwintering Clanis bilineata tsingtauica larvae[J]. Chinese Journal of Applied Entomology, 2021, 58(4): 966−972. doi: 10.7679/j.issn.2095-1353.2021.095
[29]	孙淑霞. 气候变化下中国栎属物种及其丰富度潜在分布格局模拟预测研究[D]. 济南: 山东大学, 2021. Sun S X. The effect of climate change on potential distribution pattern of oaks (Quercus L.) and its richness in China[D]. Jinan: Shandong University, 2021.
[30]	赵光华. 气候变化背景下我国酸枣空间分布格局演变及影响因素分析[D]. 临汾: 山西师范大学, 2020. Zhao G H. Analysis on spatial distribution pattern evolution and influencing factors of Ziziphus jujuba var. spinosa in China under climate change[D]. Linfen: Shanxi Normal University, 2020.
[31]	Musolin D L. Insects in a warmer world: ecological, physiological and life-history responses of true bugs (Heteroptera) to climate change[J]. Global Change Biology, 2007, 13(8): 1565−1585. doi: 10.1111/j.1365-2486.2007.01395.x

[1]	Zhang Minghui, Yin Yunzhou, Wang Ke, Wang Shuli. Effects of spatial structure characteristics of Fraxinus mandshurica plantation on soil nutrient content[J]. Journal of Beijing Forestry University, 2023, 45(9): 73-82. DOI: 10.12171/j.1000-1522.20220476
[2]	Hui Gangying, Zhao Zhonghua, Hu Yanbo, Zhang Ganggang, Zhang Gongqiao, Cheng Shiping, Lu Yanlei. Research on the measurement method of forest spatial structure diversity based on 4 neighborhood tree relationship[J]. Journal of Beijing Forestry University, 2023, 45(7): 18-26. DOI: 10.12171/j.1000-1522.20220282
[3]	Wang Lina, Wu Junwen, Dong Qiong, Shi Zhuogong, Hu Haocheng, Wu Danzi, Li Luping. Effects of tending and thinning on non-structural carbon and stoichiometric characteristics of Pinus yunnanensis[J]. Journal of Beijing Forestry University, 2021, 43(8): 70-82. DOI: 10.12171/j.1000-1522.20210115
[4]	Hu Xuefan, Zhang Huiru, Zhou Chaofan, Zhang Xiaohong. Effects of different thinning patterns on the spatial structure of Quercus mongolica secondary forests[J]. Journal of Beijing Forestry University, 2019, 41(5): 137-147. DOI: 10.13332/j.1000-1522.20190037
[5]	LI Jian, PENG Peng, HE Huai-jiang, TAN Ling-zhao, ZHANG Xin-na, WU Xiang-ju, LIU Zhao-gang. Effects of thinning intensity on spatial structure of multi-species temperate forest at Jiaohe in Jilin Province, northeastern China[J]. Journal of Beijing Forestry University, 2017, 39(9): 48-57. DOI: 10.13332/j.1000-1522.20170220
[6]	LI Ji-ping, FENG Yao, ZHAO Chun-yan, ZHANG Cai-cai. Quantitative analysis of stand spatial structure of Cunninghamia lanceolata non-commercial forest based on Voronoi diagram.[J]. Journal of Beijing Forestry University, 2014, 36(4): 1-7. DOI: 10.13332/j.cnki.jbfu.2014.04.005
[7]	WANG Ping, JIA Li-ming, WEI Song-po, WANG Qi-feng. Analysis of stand spatial structure of Platycladus orientalis recreational forest based on Voronoi diagram method[J]. Journal of Beijing Forestry University, 2013, 35(2): 39-44.
[8]	DONG Ling-bo, LIU Zhao-gang, MA Yan, NI Bao-long, LI Yuan. A new composite index of stand spatial structure for natural forest.[J]. Journal of Beijing Forestry University, 2013, 35(1): 16-22.
[9]	DUAN Chang-sheng, WANG Jun-hui, MA Jian-wei, YUAN Shi-yun, DU Yan-chang. Evaluation of Quercus aliena var. acuteserrata forest at the western segment of Qinling Mountain，northwestern China[J]. Journal of Beijing Forestry University, 2009, 31(5): 61-66.
[10]	LIU Yan, YU Xin-xiao, YUE Yong-jie, GAN Jing, WANG Xiao-ping, LI Jin-hai. Spatial structure of Robinia pseudoacacia plantation in Miyun Reservoir Watershed of Beijing.[J]. Journal of Beijing Forestry University, 2009, 31(5): 25-28.

Cited By

Cited by

Periodical cited type(8)

1.	张慧，燕怡帆，朱雅，陈玉婷，王菁华，崔志鹏，杨迪，任学敏. 林分密度对伏牛山南麓山茱萸人工林林下草本植物多样性和土壤性质的影响. 西南林业大学学报(自然科学). 2025(01): 96-105 .
2.	赵金同，马俊. 刺槐扦插育苗技术与精细抚育要点. 现代园艺. 2024(08): 49-51 .
3.	何欢，康必均，尹婧，李菲，彭栋，李桂静，查同刚. 不同营林措施对川东华蓥山杉木林土壤团聚体稳定性及细根分布的影响. 土壤通报. 2024(02): 351-359 .
4.	史小鹏，苟贺然，何淑勤，刘柏廷，冉兰芳，杨琪琳，扎西拉姆，陈雨馨，骆紫藤. 成都市温江区两种绿地土壤抗蚀抗冲性及其影响因素. 水土保持通报. 2024(04): 117-125 .
5.	刘忆南，申振宏，都都，张知然，林勇明. 蒋家沟泥石流堆积扇不同植被类型区土壤抗蚀性评价. 应用与环境生物学报. 2024(05): 886-893 .
6.	王依瑞，王彦辉，段文标，李平平，于澎涛，甄理，李志鑫，尚会军. 黄土高原刺槐人工林郁闭度对林下植物多样性特征的影响. 应用生态学报. 2023(02): 305-314 .
7.	赵云鹤，钟鹏，高晗，付玉. 土地利用类型对典型黑土团聚体稳定性和抗蚀性的影响. 东北林业大学学报. 2023(09): 112-119 .
8.	胡亚伟，施政乐，刘畅，徐勤涛，张建军. 晋西黄土区刺槐林密度对林下植物多样性及土壤理化性质的影响. 生态学杂志. 2023(09): 2072-2080 .

Other cited types(9)

Get Citation

PDF

XML

Article views PDF downloads Cited by(17)

Turn off MathJax

Article Contents

Abstract

References

Simulation and prediction of potential distribution area of Euproctis pseudoconspersa under climate change in China