林业科学 ›› 2024, Vol. 60 ›› Issue (11): 139-148.doi: 10.11707/j.1001-7488.LYKX20230073
刘磊1,2,赵立娟1,2,刘佳奇1,2,张辉盛1,2,张志伟1,2,黄瑞芬3,高瑞贺1,2,*
收稿日期:
2023-02-25
出版日期:
2024-11-25
发布日期:
2024-11-30
通讯作者:
高瑞贺
基金资助:
Lei Liu1,2,Lijuan Zhao1,2,Jiaqi Liu1,2,Huisheng Zhang1,2,Zhiwei Zhang1,2,Ruifen Huang3,Ruihe Gao1,2,*
Received:
2023-02-25
Online:
2024-11-25
Published:
2024-11-30
Contact:
Ruihe Gao
摘要:
目的: 预测松褐天牛在我国最新的潜在适生区及其对气候变化的响应,为松褐天牛的传播风险分析及其精准防控提供理论依据。方法: 利用松褐天牛650个分布点数据和20个环境变量,基于优化的MaxEnt模型和ArcGIS 10.7软件,预测当前和未来气候情景下松褐天牛在我国的潜在适生区分布及其空间格局变化。结果: 模型优化结果显示,当特征组合选取线型、二次型、铰链型、乘积型,调控倍频设置为1.5时,模型为最优模型,准确度最高;Jackknife检验表明,温差月均值、年降水量、最干月降水量、最暖季度降水量和海拔是影响松褐天牛分布的主导环境变量;在当前气候情景下,松褐天牛在我国的适生区主要分布于黄河以南地区;在未来气候情景下,松褐天牛的适生区面积呈显著增加趋势,新增适生区主要集中于陕西省、河南省、甘肃省、山东省和辽宁省。结论: 优化后的MaxEnt模型能够准确预测松褐天牛在我国的潜在适生区分布;温度和降水是影响松褐天牛分布的主导环境变量;气候变化会引起松褐天牛适生区迁移。
中图分类号:
刘磊,赵立娟,刘佳奇,张辉盛,张志伟,黄瑞芬,高瑞贺. 基于优化的MaxEnt模型预测气候变化下松褐天牛在我国的潜在适生区[J]. 林业科学, 2024, 60(11): 139-148.
Lei Liu,Lijuan Zhao,Jiaqi Liu,Huisheng Zhang,Zhiwei Zhang,Ruifen Huang,Ruihe Gao. Potentially Suitable Distribution Areas of Monochamus alternatus in China under Current and Future Climatic Scenarios Based on Optimized MaxEnt Model[J]. Scientia Silvae Sinicae, 2024, 60(11): 139-148.
表1
影响松褐天牛分布的环境变量贡献率"
环境变量 Environment variables | 贡献率 Contribution rate(%) | 适宜范围 Suitable range |
最干月降水量Precipitation of driest month (Bio14)/mm | 59 | 21~75 |
年降水量Annual precipitation (Bio12)/mm | 18.9 | 1 080~1 912 |
海拔 Elevation (Elev)/m | 9.9 | 6~269 |
温度季节性标准差Temperature seasonality (Bio4)/℃ | 3.7 | 1 285~1 345 |
等温性Isothermality (Bio3)(%) | 3.3 | 24~28 |
降水量季节性Precipitation seasonality (Bio15)(%) | 1.9 | 44~70 |
昼夜温差月均值Mean diurnal range (Bio2)/℃ | 1.4 | 6~9 |
最湿季度平均温度Mean temperature of the wettest quarter (Bio8)/℃ | 1.4 | 19~37 |
最暖季度降水量Precipitation of warmest quarter (Bio18)/mm | 0.5 | 444~692 |
崔骁芃, 石 娟, 王海香, 等. 基于MaxEnt模型的红脂大小蠹在中国适生区的预测. 植物保护学报, 2019, 46 (4): 925- 926. | |
Cui X P, Shi J, Wang H X, et al. Predicting potential geographical distribution of red turpentine beetle Dendroctonus valens in China based on MaxEnt model. Journal of Plant Protection, 2019, 46 (4): 925- 926. | |
何贵友, 付觉民, 高富先, 等. 松墨天牛的生物学特性及防治控制技术. 河南林业科技, 2005, 25 (2): 53- 54.
doi: 10.3969/j.issn.1003-2630.2005.02.026 |
|
He G Y, Fu J M, Gao F X, et al. Biological characteristics and control techniques of Monochamus alternatus. Journal of Henan Forestry Science and Technology, 2005, 25 (2): 53- 54.
doi: 10.3969/j.issn.1003-2630.2005.02.026 |
|
李 慧. 2021. 热激蛋白在松墨天牛响应高温胁迫中的功能研究. 南京: 南京林业大学. | |
Li H. 2021. Study on the function of heat shock protein in response to high temperature stress of Monochamus alternatus. Nanjing: Nanjing Forestry University. [in Chinese] | |
李 璇, 李 垚, 方炎明. 基于优化的MaxEnt模型预测白栎在中国的潜在分布区. 林业科学, 2018, 54 (8): 153- 164.
doi: 10.11707/j.1001-7488.20180817 |
|
Li X, Li Y, Fang Y M. Prediction of potential suitable distribution areas of Quercus fabri in China based on an optimized MaxEnt model. Scientia Silvae Sinicae, 2018, 54 (8): 153- 164.
doi: 10.11707/j.1001-7488.20180817 |
|
李世成, 易自力, 廖剑锋, 等. 基于MaxEnt模型对尼泊尔芒适生区时空分布的预测. 湖南农业大学学报(自然科学版), 2020, 46 (2): 176- 183. | |
Li S C, Yi Z L, Liao J F, et al. Prediction of temporal and spatial distribution of suitable areas of Miscanthus nepalensis based on MaxEnt. Journal of Hunan Agricultural University (Natural Sciences), 2020, 46 (2): 176- 183. | |
李霜雯, 吕晓亮, 田宇明, 等. 大连市松材线虫病典型发生区松墨天牛种群动态. 辽宁林业科技, 2019, 6, 20- 22.
doi: 10.3969/j.issn.1001-1714.2019.04.006 |
|
Li S W, Lü X L, Tian Y M, et al. Population dynamics of Monochamus alternatus in the typical area of pine wilt disease in Dalian City. Liaoning Forestry Science and Technology, 2019, 6, 20- 22.
doi: 10.3969/j.issn.1001-1714.2019.04.006 |
|
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. 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. Plant Protection, 2020, 46 (1): 108- 117. | |
沈梦伟, 陈圣宾, 毕孟杰, 等. 中国蚂蚁丰富度地理分布格局及其与环境因子的关系. 生态学报, 2016, 36 (23): 7732- 7739. | |
Shen M W, Chen S B, Bi M J, et al. Relationships between geographic patterns of ant species richness and environmental factors in China. Acta Ecologica Sinica, 2016, 36 (23): 7732- 7739. | |
时 鹏, 王 壮, 曾 辉, 等. 低温条件下松墨天牛在我国适生区分布预测. 西北林学院学报, 2019, 34 (4): 156- 161.
doi: 10.3969/j.issn.1001-7461.2019.04.23 |
|
Shi P, Wang Z, Zeng H, et al. Tolerance to temperature stresses on Monochamus alternatus and its potential range in China. Journal of Northwest Forestry University, 2019, 34 (4): 156- 161.
doi: 10.3969/j.issn.1001-7461.2019.04.23 |
|
宋红敏, 徐汝梅. 松墨天牛的全球潜在分布区分析. 昆虫知识, 2006, 43 (4): 535- 539. | |
Song H M, Xu R M. Global potential geographical distribution of Monochamus alternatus. Chinese Journal of Applied Entomology, 2006, 43 (4): 535- 539. | |
王玲萍. 松墨天牛生物学特性的研究. 福建林业科技, 2004, 31 (3): 23- 26. | |
Wang L P. Study on the biological characteristic of Monochamus alternatus Hope. Journal of Fujian Forestry Science and Technology, 2004, 31 (3): 23- 26. | |
王曦茁, 曹业凡, 汪来发, 等. 松材线虫病发生及防控现状. 环境昆虫学报, 2018, 40 (2): 256- 267. | |
Wang X Z, Cao Y F, Wang L F, et al. Current status of pine wilt disease and its control status. Journal of Environmental Entomology, 2018, 40 (2): 256- 267. | |
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, 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. Journal of Beijing Forestry University, 2021, 43 (9): 59- 69.
doi: 10.12171/j.1000-1522.20200416 |
|
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. 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. Biodiversity Science, 2007, 15 (4): 365- 372.
doi: 10.3321/j.issn:1005-0094.2007.04.005 |
|
徐瑞钧, 周汝良, 刘乾飞, 等. 气候变暖趋势下松墨天牛适生区分布模拟与预测. 林业资源管理, 2020, 4, 109- 116. | |
Xu R J, Zhou R L, Liu Q F, et al. Prediction and simulation of the suitable habitat of Monochamus alternatus under climate warming. Forest Resources Management, 2020, 4, 109- 116. | |
杨贵军, 王 敏, 杨益春, 等. 贺兰山甲虫物种丰富度分布格局及其环境解释. 生物多样性, 2019, 27 (12): 1309- 1319.
doi: 10.17520/biods.2019184 |
|
Yang G J, Wang M, Yang Y C, et al. Distribution patterns and environmental interpretation of beetle species richness in Helan Mountain of northern China. Biodiversity Science, 2019, 27 (12): 1309- 1319.
doi: 10.17520/biods.2019184 |
|
曾 权, 朱雪珍, 周利娟. 基于优化MaxEnt模型的南方三棘果在中国的潜在适生区预测. 华南农业大学学报, 2023, 44 (2): 254- 262.
doi: 10.7671/j.issn.1001-411X.202203041 |
|
Zeng Q, Zhu X Z, Zhou L J. Prediction of potential suitable region for Emex australis in China based on the optimized MaxEnt model. Journal of South China Agricultural University, 2023, 44 (2): 254- 262.
doi: 10.7671/j.issn.1001-411X.202203041 |
|
张彦龙, 王小艺, 杨忠岐, 等. 松材线虫病媒介昆虫的天敌及其应用研究进展. 中国森林病虫, 2022, 41 (3): 21- 29. | |
Zhang Y L, Wang X Y, Yang Z Q, et al. Research progress on natural enemies and their application of the vector insects of Bursaphelenchus xylophilus. Forest Pest and Disease, 2022, 41 (3): 21- 29. | |
周玉婷, 葛雪贞, 邹 娅, 等. 基于MaxEnt模型的长林小蠹的全球及中国适生区预测. 北京林业大学学报, 2022, 44 (11): 90- 99.
doi: 10.12171/j.1000-1522.20210345 |
|
Zhou Y T, Ge X Z, Zou Y, et al. Prediction of the potential geographical distribution of Hylurgus ligniperda at the global scale and in China using the MaxEnt model. Journal of Beijing Forestry University, 2022, 44 (11): 90- 99.
doi: 10.12171/j.1000-1522.20210345 |
|
朱耿平, 刘国卿, 卜文俊, 等. 生态位模型的基本原理及其在生物多样性保护中的应用. 生物多样性, 2013, 21 (1): 90- 98.
doi: 10.3724/SP.J.1003.2013.09106 |
|
Zhu G P, Liu G Q, Bu W J, et al. Ecological niche modeling and its applications in biodiversity conservation. Biodiversity Science, 2013, 21 (1): 90- 98.
doi: 10.3724/SP.J.1003.2013.09106 |
|
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. 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. Biodiversity Science, 2014, 22 (2): 223- 230.
doi: 10.3724/SP.J.1003.2014.08178 |
|
朱耿平, 乔慧捷. MaxEnt模型复杂度对物种潜在分布区预测的影响. 生物多样性, 2016, 24 (10): 1189- 1196.
doi: 10.17520/biods.2016265 |
|
Zhu G P, Qiao H J. Effect of MaxEnt model complexity on the prediction of species potential distribution area. Biodiversity Science, 2016, 24 (10): 1189- 1196.
doi: 10.17520/biods.2016265 |
|
Anderson R P, Gonzalez I. Species-specific tuning increases robustness to sampling bias in models of species distributions: an implementation with MaxEnt. Ecological Modelling, 2011, 222 (15): 2796- 2811. | |
Bradie J, Leung B. A quantitative synthesis of the importance of variables used in MaxEnt species distribution models. Journal of Biogeography, 2017, 44 (6): 1344- 1361. | |
Carpenter G, Gillison A N, Winter J. DOMAIN: a flexible modelling procedure for mapping potential distributions of plants and animals. Biodiversity & Conservation, 1993, 2 (6): 667- 680. | |
Chejara V K, Kriticos D J, Kristiansen P, et al. The current and future potential geographical distribution of Hyparrhenia hirta. Weed Research, 2010, 50 (2): 174- 184. | |
Choi W I, Song H J, Kim D S, et al. Dispersal patterns of pine wilt disease in the early stage of its invasion in south Korea. Forests, 2017, 8 (11): 411. | |
Cruz G M, Faria A P J, Juen L. Patterns and metacommunity structure of aquatic insects (Trichoptera) in Amazonian streams depend on the environmental conditions. Hydrobiologia, 2022, 849 (12): 2831- 2843. | |
Fielding A H, Bell J F. A review of methods for the assessment of prediction errors in conservation presence/absence models. Environmental Conservation, 1997, 24 (1): 38- 49. | |
Fotheringham A S, Oshan T M. Geographically weighted regression and multicollinearity: dispelling the myth. Journal of Geographical Systems, 2016, 18 (4): 303- 329. | |
Gao R H, Liu L, Zhao L J, et al. Potentially suitable geographical area for Monochamus alternatus under current and future climatic scenarios based on optimized MaxEnt model. Insects, 2023, 14 (2): 182. | |
Heikkinen R K, Luoto M, Araújo M B, et al. Methods and uncertainties in bioclimatic envelope modelling under climate change. Progress in Physical Geography: Earth and Environment, 2006, 30 (6): 751- 777. | |
Jin Z N, Yu W T, Zhao H X, et al. Potential global distribution of invasive alien species, Anthonomus grandis boheman, under current and future climate using optimal MaxEnt model. Agriculture, 2022, 12 (11): 1759. | |
Kim J, Jung H, Park Y H. Predicting Potential Distribution of Monochamus alternatus Hope responding to climate change in Korea. Korean Journal of Applied Entomology, 2016, 55 (4): 501- 511. | |
Lee C M, Lee D S, Kwon T S, et al. Predicting the global distribution of Solenopsis geminata (Hymenoptera: Formicidae) under climate change using the MaxEnt model. Insects, 2021, 12 (3): 229. | |
Li X Y, Xu D P, Jin Y W, et al. Predicting the current and future distributions of Brontispa longissima (Coleoptera: Chrysomelidae) under climate change in China. Global Ecology and Conservation, 2021, 25, e01444. | |
Ma R Y, Hao S G, Kong W N, et al. Cold hardiness as a factor for assessing the potential distribution of the Japanese pine sawyer Monochamus alternatus(Coleoptera: Cerambycidae) in China. Annals of Forest Science, 2006, 63 (5): 449- 456. | |
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. Ecography, 2013, 36 (10): 1058- 1069.
doi: 10.1111/j.1600-0587.2013.07872.x |
|
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. Methods in Ecology and Evolution, 2014, 5 (11): 1198- 1205. | |
Phillips S J, Anderson R P, Dudík M, et al. Opening the black box: an open-source release of MaxEnt. Ecography, 2017, 40 (7): 887- 893. | |
Phillips S J, Anderson R P, Schapire R E. Maximum entropy modeling of species geographic distributions. Ecological Modelling, 2006, 190 (3/4): 231- 259. | |
Porfirio L L, Harris R M B, Lefroy E C, et al. Improving the use of species distribution models in conservation planning and management under climate change. PLOS ONE, 2014, 9 (11): e113749. | |
Qin Z, Zhang J E, DiTommaso A, et al. Predicting invasions of Wedelia trilobata (L. ) Hitchc. with MaxEnt and GARP models. Journal of Plant Research, 2015, 128 (5): 763- 775. | |
Rutherford T A, Mamiya Y, Webster J M. Nematode-induced pine wilt disease: factors influencing its occurrence and distribution. Forest Science, 1990, 36 (1): 145- 155. | |
Sillero N. What does ecological modelling model? A proposed classification of ecological niche models based on their underlying methods. Ecological Modelling, 2011, 222 (8): 1343- 1346. | |
Sultana S, Baumgartner J B, Dominiak B C, et al. 2020. Impacts of climate change on high priority fruit fly species in Australia. PLOS ONE, 15(2): e0213820. | |
Takahashi D, Park Y S. 2020. Spatial heterogeneities of human-mediated dispersal vectors accelerate the range expansion of invaders with source–destination-mediated dispersal. Scientific Reports, 10: 21410. | |
Wan J, Wang R, Ren Y L, et al. Potential distribution and the risks of Bactericera cockerelli and its associated plant pathogen candidatus Liberibacter solanacearum for global potato production. Insects, 2020, 11 (5): 298. | |
Wang C, Hawthorne D, Qin Y J, et al. Impact of climate and host availability on future distribution of Colorado potato beetle. Scientific Reports, 2017, 7, 4489. | |
Warren D L, Seifert S N. Ecological niche modeling in MaxEnt: the importance of model complexity and the performance of model selection criteria. Ecological Applications, 2011, 21 (2): 335- 342. | |
Xu D P, Zhuo Z H, Wang R L, et al. Modeling the distribution of Zanthoxylum armatum in China with MaxEnt modeling. Global Ecology and Conservation, 2019, 19, e00691. | |
Yan H Y, He J, Xu X C, et al. Prediction of potentially suitable distributions of Codonopsis pilosula in China based on an optimized MaxEnt model. Frontiers in Ecology and Evolution, 2021, 9, 773396. |
[1] | 张雨田,石军南,张怀清,吴炳伦. 洞庭湖湿地植被时空动态及其驱动力分析[J]. 林业科学, 2024, 60(8): 1-13. |
[2] | 朱教君,王高峰,张怀清,高添. 关于“气候智慧林业”研究的思考[J]. 林业科学, 2024, 60(7): 1-7. |
[3] | 赵杼祺, 胡振宏, 何鲜, 黄志群. 森林木质残体微生物群落构建机制研究进展[J]. 林业科学, 2024, 60(2): 106-117. |
[4] | 申佳艳,范泽鑫,张慧,彭新华,李金花,余潇,杨文雄,李云芳,李新宇,刘悦宁,苏建荣. 云南3种松树径向生长的气候因子响应异质性[J]. 林业科学, 2024, 60(11): 48-62. |
[5] | 葛婉婷,刘莹,赵智佳,张珅,李洁,杨桂娟,曲冠证,王军辉,麻文俊. 不同气候情景下黄心梓木在我国的潜在适生区预测[J]. 林业科学, 2024, 60(11): 63-74. |
[6] | 薛盼盼,缪宁,岳喜明,陶琼,张远东,冯秋红,毛康珊. 青藏高原东缘岷江冷杉径向生长对升温响应分异的坡向和海拔差异[J]. 林业科学, 2023, 59(7): 65-77. |
[7] | 韦雪蕾,张国钢,贾茹,姬云瑞,徐红英,杨泽玉,刘化金,刘宇霖,杨培宇. 黑龙江兴凯湖水鸟多样性变化及其影响因素[J]. 林业科学, 2023, 59(6): 118-129. |
[8] | 王亚,王军辉,王福德,刘逸夫,谭灿灿,袁艳超,聂稳,刘建锋,常二梅,贾子瑞. 末次间冰期以来及未来气候情景下红皮云杉适生分布区模拟[J]. 林业科学, 2023, 59(12): 1-12. |
[9] | 刘怡彤,郭慧,裴顺祥,吴莎,吴迪,辛学兵. 基于MaxEnt模型的天然元宝枫在我国的适生区区划及合理性分析[J]. 林业科学, 2023, 59(12): 13-24. |
[10] | 张淑宁,陈俊兴,敖敦,红梅,张雅茜,包福海,王淋,乌云塔娜,白玉娥,包文泉. 气候变化背景下我国长柄扁桃潜在适生区预测[J]. 林业科学, 2023, 59(12): 25-36. |
[11] | 司莉青,王明玉,陈锋,舒立福,赵凤君,李伟克. 雷电分布特征与雷击森林火预警[J]. 林业科学, 2023, 59(10): 1-8. |
[12] | 郑光楠,杨秀好,韦曼丽,郑霞林. 广西松褐天牛成虫种群动态规律及其与林分和气象因子相关性[J]. 林业科学, 2023, 59(1): 128-142. |
[13] | 李俊楠,陈润恺,付煜,蔡梦玲,黄炳荣,徐云,吴松青,张飞萍. 松天牛小首螨的研究Ⅲ.携播特性[J]. 林业科学, 2022, 58(9): 128-137. |
[14] | 李俊楠,陈润恺,付煜,蔡梦玲,黄炳荣,徐云,吴松青,张飞萍. 松天牛小首螨的研究Ⅱ.雌成螨的移动和趋性[J]. 林业科学, 2022, 58(8): 149-156. |
[15] | 王东升,赵伟,程蓓蓓,张吉军. 基于MaxEnt模型的中国山楂潜在适生区[J]. 林业科学, 2022, 58(7): 43-50. |
阅读次数 | ||||||
全文 |
|
|||||
摘要 |
|
|||||