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

Ruut Jaael Uusitalo, Mika Siljander, Christine Lorna Culverwell, Noah Mutai, Kristian Michael Forbes, Olli Vapalahti, Petri Kauko Emil Pellikka

Tutkimustuotos: ArtikkelijulkaisuArtikkeliTieteellinenvertaisarvioitu

Kuvaus

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.
Alkuperäiskielienglanti
LehtiInternational Journal of Applied Earth Observation and Geoinformation
Vuosikerta76
Sivut84-92
Sivumäärä9
ISSN1569-8432
DOI - pysyväislinkit
TilaJulkaistu - huhtikuuta 2019
OKM-julkaisutyyppiA1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä, vertaisarvioitu

Tieteenalat

  • 1171 Geotieteet
  • 413 Eläinlääketiede

Lainaa tätä

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title = "Predictive mapping of mosquito distribution based on environmental and anthropogenic factors in Taita Hills, Kenya",
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 wherespecies 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 Linnaeusand 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 distributionsof 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 of0.791. 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.",
keywords = "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",
author = "Uusitalo, {Ruut Jaael} and Mika Siljander and Culverwell, {Christine Lorna} and Noah Mutai and Forbes, {Kristian Michael} and Olli Vapalahti and Pellikka, {Petri Kauko Emil}",
year = "2019",
month = "4",
doi = "10.1016/j.jag.2018.11.004",
language = "English",
volume = "76",
pages = "84--92",
journal = "International Journal of Applied Earth Observation and Geoinformation",
issn = "1569-8432",
publisher = "Elsevier Scientific Publ. Co",

}

Predictive mapping of mosquito distribution based on environmental and anthropogenic factors in Taita Hills, Kenya. / Uusitalo, Ruut Jaael; Siljander, Mika; Culverwell, Christine Lorna; Mutai, Noah; Forbes, Kristian Michael; Vapalahti, Olli ; Pellikka, Petri Kauko Emil.

julkaisussa: International Journal of Applied Earth Observation and Geoinformation, Vuosikerta 76, 04.2019, s. 84-92.

Tutkimustuotos: ArtikkelijulkaisuArtikkeliTieteellinenvertaisarvioitu

TY - JOUR

T1 - Predictive mapping of mosquito distribution based on environmental and anthropogenic factors in Taita Hills, Kenya

AU - Uusitalo, Ruut Jaael

AU - Siljander, Mika

AU - Culverwell, Christine Lorna

AU - Mutai, Noah

AU - Forbes, Kristian Michael

AU - Vapalahti, Olli

AU - Pellikka, Petri Kauko Emil

PY - 2019/4

Y1 - 2019/4

N2 - 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 wherespecies 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 Linnaeusand 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 distributionsof 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 of0.791. 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.

AB - 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 wherespecies 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 Linnaeusand 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 distributionsof 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 of0.791. 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.

KW - 1171 Geosciences

KW - 413 Veterinary science

KW - Species distribution modeling

KW - Vector-borne disease

KW - GIS

KW - Predictive mapping

KW - Mosquito

KW - biomod2

KW - UNMANNED AERIAL VEHICLES

KW - WEST NILE VIRUS

KW - DISTRIBUTION MODELS

KW - INFECTIOUS-DISEASE

KW - AEDES-AEGYPTI

KW - DIPTERA

KW - CULICIDAE

KW - VECTORS

KW - CLASSIFICATION

KW - ABUNDANCE

U2 - 10.1016/j.jag.2018.11.004

DO - 10.1016/j.jag.2018.11.004

M3 - Article

VL - 76

SP - 84

EP - 92

JO - International Journal of Applied Earth Observation and Geoinformation

JF - International Journal of Applied Earth Observation and Geoinformation

SN - 1569-8432

ER -