Predictive mapping of mosquito distribution based on environmental and anthropogenic factors in Taita Hills, Kenya

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dc.contributor.author Uusitalo, Ruut Jaael
dc.contributor.author Siljander, Mika
dc.contributor.author Culverwell, Christine Lorna
dc.contributor.author Mutai, Noah
dc.contributor.author Forbes, Kristian Michael
dc.contributor.author Vapalahti, Olli
dc.contributor.author Pellikka, Petri Kauko Emil
dc.date.accessioned 2020-11-24T02:27:13Z
dc.date.available 2021-12-17T03:45:38Z
dc.date.issued 2019-04
dc.identifier.citation Uusitalo , R J , Siljander , M , Culverwell , C L , Mutai , N , Forbes , K M , Vapalahti , O & 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 , vol. 76 , pp. 84-92 . https://doi.org/10.1016/j.jag.2018.11.004
dc.identifier.other PURE: 120596422
dc.identifier.other PURE UUID: 4e0ab8c1-1d61-4a1d-95fe-61183c37a6b9
dc.identifier.other ORCID: /0000-0003-2270-6824/work/52694505
dc.identifier.other WOS: 000457660800008
dc.identifier.other Scopus: 85062818358
dc.identifier.other ORCID: /0000-0003-0755-5079/work/85520054
dc.identifier.uri http://hdl.handle.net/10138/321847
dc.description.abstract 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. en
dc.format.extent 9
dc.language.iso eng
dc.relation.ispartof International Journal of Applied Earth Observation and Geoinformation
dc.rights cc_by_nc_nd
dc.rights.uri info:eu-repo/semantics/openAccess
dc.subject 1171 Geosciences
dc.subject 413 Veterinary science
dc.subject Species distribution modeling
dc.subject Vector-borne disease
dc.subject GIS
dc.subject Predictive mapping
dc.subject Mosquito
dc.subject biomod2
dc.subject UNMANNED AERIAL VEHICLES
dc.subject WEST NILE VIRUS
dc.subject DISTRIBUTION MODELS
dc.subject INFECTIOUS-DISEASE
dc.subject AEDES-AEGYPTI
dc.subject DIPTERA
dc.subject CULICIDAE
dc.subject VECTORS
dc.subject CLASSIFICATION
dc.subject ABUNDANCE
dc.title Predictive mapping of mosquito distribution based on environmental and anthropogenic factors in Taita Hills, Kenya en
dc.type Article
dc.contributor.organization Department of Geosciences and Geography
dc.contributor.organization Earth Change Observation Laboratory (ECHOLAB)
dc.contributor.organization Viral Zoonosis Research Unit
dc.contributor.organization Department of Virology
dc.contributor.organization HUSLAB
dc.contributor.organization Veterinary Microbiology and Epidemiology
dc.contributor.organization Veterinary Biosciences
dc.contributor.organization Olli Pekka Vapalahti / Principal Investigator
dc.contributor.organization Medicum
dc.contributor.organization University of Helsinki
dc.contributor.organization Clinicum
dc.contributor.organization Helsinki Institute of Sustainability Science (HELSUS)
dc.contributor.organization Helsinki One Health (HOH)
dc.description.reviewstatus Peer reviewed
dc.relation.doi https://doi.org/10.1016/j.jag.2018.11.004
dc.relation.issn 1569-8432
dc.rights.accesslevel openAccess
dc.type.version acceptedVersion

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