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

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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

Title: Predictive mapping of mosquito distribution based on environmental and anthropogenic factors in Taita Hills, Kenya
Author: Uusitalo, Ruut Jaael; Siljander, Mika; Culverwell, Christine Lorna; Mutai, Noah; Forbes, Kristian Michael; Vapalahti, Olli; Pellikka, Petri Kauko Emil
Other contributor: University of Helsinki, Department of Geosciences and Geography
University of Helsinki, Earth Change Observation Laboratory (ECHOLAB)
University of Helsinki, Viral Zoonosis Research Unit
University of Helsinki, HUSLAB
University of Helsinki, Helsinki Institute of Sustainability Science (HELSUS)












Date: 2019-04
Language: eng
Number of pages: 9
Belongs to series: International Journal of Applied Earth Observation and Geoinformation
ISSN: 1569-8432
DOI: https://doi.org/10.1016/j.jag.2018.11.004
URI: http://hdl.handle.net/10138/321847
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.
Subject: 1171 Geosciences
413 Veterinary science
Species distribution modeling
Vector-borne disease
GIS
Predictive mapping
Mosquito
biomod2
UNMANNED AERIAL VEHICLES
WEST NILE VIRUS
DISTRIBUTION MODELS
INFECTIOUS-DISEASE
AEDES-AEGYPTI
DIPTERA
CULICIDAE
VECTORS
CLASSIFICATION
ABUNDANCE
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