Simulation and prediction of potential distribution area of Euproctis pseudoconspersa under climate change scenarios in China
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摘要:
目的 油茶是国家重点发展的木本油料作物，具有较高的经济价值和生态价值。油茶毒蛾是油茶的主要病虫害之一，严重制约了油茶的产量和质量。本研究针对油茶毒蛾的潜在分布开展模拟预测，以期为油茶毒蛾的预警和具体防控行动提供科学依据。 方法 基于油茶毒蛾在中国的有效地理分布数据和生物气候数据，利用MaxEnt生态位模型和ArcGIS软件，模拟预测油茶毒蛾在当前气候条件的潜在分布，以及SSP1-2.6、SSP2-4.5和SSP5-8.5 3种气候情景下2050年和2070年油茶毒蛾在中国潜在适生区分布范围，并分析了制约其适生区分布的主导环境因子。 结果 （1）影响油茶毒蛾适生区分布的主导环境因子为最干月降水量、年降水量、最冷月最低温度和温度日较差月均值。当最干月降水量28 ~ 148 mm、年降水量1 290 ~ 2 080 mm、最冷月最低温度1.0 ~ 10.1 ℃、温度日较差月均值7.2 ~ 8.5 ℃时最适宜油茶毒蛾的生存。（2）当前气候条件下，油茶毒蛾总适生区面积占我国国土面积的20.0%，主要分布在我国南方的长江中下游地区和华南地区，高度适生区面积为64.2 × 104 km2，中度适生区面积为61.8 × 104 km2，低度适生区面积为66.0 × 104 km2。（3）在未来两个时期和三种气候情景下，油茶毒蛾适生区面积出现不同程度的扩大，新增适生区面积在9.4 × 104 ~ 33.1 × 104 km2之间，地理分布中心迁移距离在24.4 ~ 125.1 km之间。气候变暖越明显，油茶毒蛾的潜在分布区面积增加越多，地理分布中心点的迁移距离越远。 结论 油茶毒蛾在中国的适生区范围较广，几乎囊括了中国南部所有的省份。在未来气候情景下，油茶毒蛾的适生区将向北、向西等高纬度的内陆地区扩张。为此，建议相关部门应提前制定预案和政策，加强对油茶毒蛾的观测和防控，减少其对油茶产业造成的损失。 Abstract:Objective Camellia oleifera is a woody oleiferous plant with high economic and ecological value. Euproctis pseudoconspersa is one of the main pests of C. 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 E. 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 areas for E. pseudoconspersa in China under the current climate, and the distribution range and potential suitable areas 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 areas were analyzed. Result (1) The dominant environmental variables affecting the distribution of the suitable habitat of E. pseudoconspersa were the precipitation of driest month, the annual precipitation, the min temperature of coldest month, the mean diurnal range (mean of monthly (max temp - min temp)) The optimum conditions were 28-148 mm the precipitation of driest month, 1 290−2 080 mm the annual precipitation, 1.0−10.1 ℃ the min temperature of coldest month and 7.2−8.5 ℃ the mean diurnal range (mean of monthly (max temp - min temp)). (2) Under the current climate conditions, the total suitable area of E. pseudoconspersa accounts 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 94 000-331 000 km2 and the migration distance of the geographical distribution center between 24.4-125.1 km. The more obvious the climate warming is, the more the potential distribution area increases, and the farther the geographical distribution center moves. 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. -
Key words:
- Camellia oleifera /
- Euproctis pseudoconspersa /
- MaxEnt model /
- environmental variables /
- suitable area
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图 1 油茶毒蛾危害点19个环境因子贡献率及相关性分析
Figure 1. Percent contribution and correlation analysis of 19 environmental variables of E. pseudoconspersa hazard points
图 3 建模环境因子刀切图分析
Figure 3. Jacknife analysis of environmental variables of E. pseudoconspersa in MaxEnt model
图 4 适生分布对主要环境因子的响应曲线
Figure 4. Response curves of E. pseudoconspersa to major environmental variables
图 5 当前油茶毒蛾适生区分布情况
基于自然资源部标准地图服务网站GS（2020）4619号标准地图制作，底图边界无修改。下同。Based on the standard map production of the Ministry of Natural Resources’ GS(2020)4619 standard map service website, the basemap boundaries have not been modified. The same below.
Figure 5. Suitable area of E. pseudoconspersa anthracnose
图 6 油茶毒蛾不同时期在中国的潜在适生区面积
Figure 6. Suitable area of E. pseudoconspersa in China under different climate change
图 7 油茶毒蛾不同气候情景下在中国的适生分布区
Figure 7. Suitable distribution areas of E. pseudoconspersa in China under different climatic conditions
图 8 未来气候变化情景下油茶毒蛾空间分布格局变化
Figure 8. Spatial changes of E. pseudoconspersa in China under different climate change scenarios
表 1 WorldClim2 数据中的气候变量
Table 1. Bioclimatic variables of WorldClim2
名称 Name 描述 Description 单位 Unit Bio1 年平均温度 Annual mean temperature ℃ Bio2 温度日较差月均值 Mean diurnal range (mean of monthly (max temp - min temp)) ℃ Bio3 等温性 Isothermality (Bio2/Bio7) (× 100) ℃ Bio4 温度季节性变化标准差 Temperature seasonality (standard deviation × 100) ℃ Bio5 最热月最高温度 Max temperature of warmest month ℃ Bio6 最冷月最低温度 Min temperature of coldest month ℃ Bio7 温度年际变化 Temperature annual range (Bio5 - Bio6) ℃ Bio8 最湿季平均温度 Mean temperature of wettest quarter ℃ Bio9 最干季平均温度 Mean temperature of driest quarter ℃ Bio10 最热季平均温度 Mean temperature of warmest quarter ℃ Bio11 最冷季平均温度 Mean temperature of coldest quarter ℃ Bio12 年降水量 Annual precipitation mm Bio13 最湿月降水量 Precipitation of wettest month mm Bio14 最干月降水量 Precipitation of driest month mm Bio15 降水量变异系数 Precipitation seasonality (coefficient of variation) mm Bio16 最湿季降水量 Precipitation of wettest quarter mm Bio17 最干季降水量 Precipitation of driest quarter mm Bio18 最热季降水量 Precipitation of warmest quarter mm Bio19 最冷季降水量 Precipitation of coldest quarter mm 
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表 2 物种潜在分布变化矩阵
Table 2. Matrix of potential distribution changes for species
未来气候情景 Future climate scenarios 当前气候情景 Current climate scenarios 适生 Suitable 非适生 Unsuitable 适生 Suitable 保留适生区 Reserve suitable area 新增适生区 New suitable area 非适生 Unsuitable 缩减适生区 Lost suitable area 非适生区 Unsuitable area
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表 3 环境因子贡献率和置换重要值
Table 3. Percent contribution and permutation importance of environmental variables
环境变量
Environmental variables贡献率
Percent contribution/%置换重要值
Permutation importance/%Bio14 72.1 0.7 Bio12 16.2 2.0 Bio6 3.0 52.9 Bio5 1.9 14.2 Bio4 1.8 4.8 Bio2 1.7 4.2 Bio15 1.4 9.5 Bio3 1.4 4.8 Bio8 0.4 4.7 Bio18 0.1 2.2
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表 4 不同时期几何中心（质心）迁移距离及适生区变化
Table 4. The migration distance of the geometric center (centroid) and the change of the suitable area in different periods
气候情景
Climate scenario质心迁移距离
Migration distance of
the centroid/km非适生区
Unsuitable area/
(104 km2)新增适生区
New suitable area/
(104 km2)保留适生区
Reserve suitable area/
(104 km2)缩减适生区
Lost suitable area/
(104 km2)当前 − − − − − 2050SSP1-2.6 24.4 758.7 9.4 191.7 0.2 2050SSP2-4.5 61.3 741.1 27.0 191.9 0.1 2050SSP5-8.5 71.4 739.3 28.8 191.9 0.0 2070SSP1-2.6 50.1 746.1 22.0 191.9 0.0 2070SSP2-4.5 38.9 751.0 17.1 191.9 0.0 2070SSP5-8.5 125.1 735.0 33.1 191.9 0.1
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[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(4): 365–372. doi: 10.3321/j.issn:1005-0094.2007.04.005Wang Y S, Xie B Y, Wang 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 [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.0529-1542.2013.03.028Li 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.1001-7488.20190212Zhao 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.1000-1522.20200416Wang 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.1004-1389.2020.03.017Sun 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]. Environ Conser, 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.2016164Cui X X, 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.2095-1353.2021.095Guo 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]. Linfeng: 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 -
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