气候变化领域集成服务门户(CCPortal): A machine-learning approach to map landscape connectivity in Aedes aegypti with genetic and environmental data

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DOI	10.1073/pnas.2003201118
	A machine-learning approach to map landscape connectivity in Aedes aegypti with genetic and environmental data
	Pless E.; Saarman N.P.; Powell J.R.; Caccone A.; Amatulli G.
发表日期	2021
ISSN	00278424
卷号	118 期号:9
英文摘要	Mapping landscape connectivity is important for controlling invasive species and disease vectors. Current landscape genetics methods are often constrained by the subjectivity of creating resistance surfaces and the difficulty of working with interacting and correlated environmental variables. To overcome these constraints, we combine the advantages of a machine-learning framework and an iterative optimization process to develop a method for integrating genetic and environmental (e.g., climate, land cover, human infrastructure) data. We validate and demonstrate this method for the Aedes aegypti mosquito, an invasive species and the primary vector of dengue, yellow fever, chikungunya, and Zika. We test two contrasting metrics to approximate genetic distance and find Cavalli-Sforza-Edwards distance (CSE) performs better than linearized FST. The correlation (R) between the model's predicted genetic distance and actual distance is 0.83. We produce a map of genetic connectivity for Ae. aegypti's range in North America and discuss which environmental and anthropogenic variables are most important for predicting gene flow, especially in the context of vector control. © This open access article is distributed under Creative Commons Attribution-NonCommercial-NoDerivatives License 4.0 (CC BY-NC-ND).
英文关键词	Gene flow; Invasive species; Landscape genetics; Random forest; Vector control
语种	英语
来源期刊	Proceedings of the National Academy of Sciences of the United States of America
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/180496
作者单位	Department of Ecology and Evolutionary Biology, Yale University, New Haven, CT 06511, United States; Department of Anthropology, University of California, Davis, CA 95616, United States; Department of Biology, Utah State University, Logan, UT 84321, United States; School of the Environment, Yale University, New Haven, CT 06511, United States; Center for Research Computing, Yale University, New Haven, CT 06511, United States
推荐引用方式 GB/T 7714	Pless E.,Saarman N.P.,Powell J.R.,et al. A machine-learning approach to map landscape connectivity in Aedes aegypti with genetic and environmental data[J],2021,118(9).
APA	Pless E.,Saarman N.P.,Powell J.R.,Caccone A.,&Amatulli G..(2021).A machine-learning approach to map landscape connectivity in Aedes aegypti with genetic and environmental data.Proceedings of the National Academy of Sciences of the United States of America,118(9).
MLA	Pless E.,et al."A machine-learning approach to map landscape connectivity in Aedes aegypti with genetic and environmental data".Proceedings of the National Academy of Sciences of the United States of America 118.9(2021).