气候变化领域集成服务门户(CCPortal): Predictive mapping of mosquito distribution based on environmental and anthropogenic factors in Taita Hills, Kenya

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DOI	10.1016/j.jag.2018.11.004
	Predictive mapping of mosquito distribution based on environmental and anthropogenic factors in Taita Hills, Kenya
	Uusitalo R.; Siljander M.; Culverwell C.L.; Mutai N.C.; Forbes K.M.; Vapalahti O.; Pellikka P.K.E.
发表日期	2019
ISSN	15698432
起始页码	84
结束页码	92
卷号	76
英文摘要	Mosquitoes are vectors for numerous pathogens, which are collectively responsible for millions of human deaths each year. As such, it is vital to be able to accurately predict their distributions, particularly in areas where species composition is unknown. Species distribution modeling was used to determine the relationship between environmental, anthropogenic and distance factors on the occurrence of two mosquito genera, Culex Linnaeus and Stegomyia Theobald (syn. Aedes), in the Taita Hills, southeastern Kenya. This study aims to test whether any of the statistical prediction models produced by the Biomod2 package in R can reliably estimate the distributions of mosquitoes in these genera in the Taita Hills; and to examine which factors best explain their presence. Mosquito collections were acquired from 122 locations between January–March 2016 along transects throughout the Taita Hills. Environmental-, anthropogenic- and distance-based geospatial data were acquired from the Taita Hills geo-database, satellite- and aerial imagery and processed in GIS software. The Biomod2 package in R, intended for ensemble forecasting of species distributions, was used to generate predictive models. Slope, human population density, normalized difference vegetation index, distance to roads and elevation best estimated Culex distributions by a generalized additive model with an area under the curve (AUC) value of 0.791. Mean radiation, human population density, normalized difference vegetation index, distance to roads and mean temperature resulted in the highest AUC (0.708) value in a random forest model for Stegomyia distributions. We conclude that in the process towards more detailed species-level maps, with our study results, general assumptions can be made about the distribution areas of Culex and Stegomyia mosquitoes in the Taita Hills and the factors which influence their distribution. © 2018 Elsevier B.V.
英文关键词	biomod2; GIS; Mosquito; Predictive mapping; Species distribution modeling; Vector-borne disease
语种	英语
scopus关键词	disease vector; geographical distribution; GIS; mapping; mosquito; pathogen; population density; prediction; spatial data; Kenya; Taita Hills; Taita-Taveta; Stegomyia
来源期刊	International Journal of Applied Earth Observation and Geoinformation
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/156526
作者单位	Department of Geosciences and Geography, University of Helsinki, P.O. Box 64FI-00014, Finland; Department of Virology, University of Helsinki, Haartmaninkatu 3, P.O. Box 21FI-00014, Finland; Department of Veterinary Biosciences, University of Helsinki, Agnes Sjöberginkatu 2, P.O. Box 66FI-00014, Finland; Department of Life Sciences, Natural History Museum, Cromwell Road, London, SW5 7BD, United Kingdom; Department of Mathematics and Informatics, Taita Taveta University, P. O. Box 635-80300, Voi, Kenya; Virology and Immunology, HUSLAB, Helsinki University Hospital, Finland; Helsinki Institute of Sustainability Science, University of Helsinki, Finland; Institute for Atmospheric and Earth System Research, University of Helsinki, Finland
推荐引用方式 GB/T 7714	Uusitalo R.,Siljander M.,Culverwell C.L.,et al. Predictive mapping of mosquito distribution based on environmental and anthropogenic factors in Taita Hills, Kenya[J],2019,76.
APA	Uusitalo R..,Siljander M..,Culverwell C.L..,Mutai N.C..,Forbes K.M..,...&Pellikka P.K.E..(2019).Predictive mapping of mosquito distribution based on environmental and anthropogenic factors in Taita Hills, Kenya.International Journal of Applied Earth Observation and Geoinformation,76.
MLA	Uusitalo R.,et al."Predictive mapping of mosquito distribution based on environmental and anthropogenic factors in Taita Hills, Kenya".International Journal of Applied Earth Observation and Geoinformation 76(2019).