An ensemble learning approach to map the genetic connectivity of the parasitoid Stethynium empoasca (Hymenoptera: Mymaridae) and identify the key influencing environmental and landscape factors
文献类型： 外文期刊
作者： Sun, Linyang 1 ; Li, Jinyu 1 ; Chen, Jie 1 ; Chen, Wei 1 ; Yue, Zhen 6 ; Shi, Jingya 6 ; Huang, Huoshui 7 ; You, Minsheng 1 ; You, Shijun 1 ;
作者机构： 1.Fujian Agr & Forestry Univ, Inst Appl Ecol, State Key Lab Ecol Pest Control Fujian & Taiwan Cr, Fuzhou, Peoples R China
2.Fujian Agr & Forestry Univ, Int Joint Res Lab Ecol Pest Control, Minist Educ, Fuzhou, Peoples R China
3.Fujian Agr & Forestry Univ, Ministerial & Prov Joint Innovat Ctr Safety Prod C, Fuzhou, Peoples R China
4.Fujian Acad Agr Sci, Tea Res Inst, Fuzhou, Peoples R China
5.Minist Agr & Rural Affairs, Key Lab Integrated Pest Management Fujian Taiwan C, Fuzhou, Peoples R China
6.BGI Genom Shenzhen Technol Co Ltd, BGI Sanya, Sanya, Peoples R China
7.Comprehens Technol Serv Ctr Quanzhou Customs, Quanzhou, Peoples R China
关键词： landscape genetics; machine learning; parasitoid; climate change; biology conservation
期刊名称：FRONTIERS IN ECOLOGY AND EVOLUTION （ 影响因子：4.493； 五年影响因子：4.648 ）
ISSN： 2296-701X
年卷期： 2022 年 10 卷
页码：
收录情况： SCI
摘要： The effect of landscape patterns and environmental factors on the population structure and genetic diversity of organisms is well-documented. However, this effect is still unclear in the case of Mymaridae parasitoids. Despite recent advances in machine learning methods for landscape genetics, ensemble learning still needs further investigation. Here, we evaluated the performance of different boosting algorithms and analyzed the effects of landscape and environmental factors on the genetic variations in the tea green leafhopper parasitoid Stethynium empoasca (Hymenoptera: Mymaridae). The S. empoasca populations showed a distinct pattern of isolation by distance. The minimum temperature of the coldest month, annual precipitation, the coverage of evergreen/deciduous needleleaf trees per 1 km(2), and the minimum precipitation of the warmest quarter were identified as the dominant factors affecting the genetic divergence of S. empoasca populations. Notably, compared to previous machine learning studies, our model showed an unprecedented accuracy (r = 0.87) for the prediction of genetic differentiation. These findings not only demonstrated how the landscape shaped S. empoasca genetics but also provided an essential basis for developing conservation strategies for this biocontrol agent. In a broader sense, this study demonstrated the importance and efficiency of ensemble learning in landscape genetics.
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