Table 1.
LGAs and ecological zones of sites where Aedes mosquitoes were collected.
Fig 1.
Map of Lagos State showing the sampling sites from eight selected LGAs.
This figure was created by the authors in R programming software (R version 4.1.2, Vienna, Austria). Available at https://www.R-project.org/. The Nigerian shapefile was obtained from World Bank Data Catalog (https://data.humdata.org/dataset/geoboundaries-admin-boundaries-for-nigeria), an Open license standardized resource of boundaries (i.e., state, county) for every country in the world.
Fig 2.
Forty unique occurrence points of Aedes mosquitoes in Lagos State were used for the model.
This figure was created by the authors in R programming software (R version 4.1.2, Vienna, Austria). Available at https://www.R-project.org/. The Nigerian shapefile was obtained from World Bank Data Catalog (https://data.humdata.org/dataset/geoboundaries-admin-boundaries-for-nigeria).
Table 2.
Environmental variables used for modeling the potential distribution of Aedes spp.
Table 3.
Total number of Aedes larval habitat encountered during the study.
Fig 3.
Types of larval habitat and their positivity for Aedes spp.
Fig 4.
Proportion of different types of larval habitats encountered in the study.
This figure was created by the authors in R programming software (R version 4.1.2, Vienna, Austria). Available at https://www.R-project.org/. The Nigerian shapefile was obtained from World BankDataCatalog (https://data.humdata.org/dataset/geoboundaries-admin-boundaries-for-nigeria).
Table 4.
Container index for Aedes mosquitoes’ larvae across the study locations.
Fig 5.
Aedes larval density for used tires and discarded containers.
This figure was created by the authors in R programming software (R version 4.1.2, Vienna, Austria). Available at https://www.R-project.org/. The Nigerian shapefile was obtained from World BankDataCatalog (https://data.humdata.org/dataset/geoboundaries-admin-boundaries-for-nigeria) an Open license standardized resource of boundaries (i.e., state, county) for every country in the world.
Fig 6.
Predominant species of Aedes mosquitoes found in Lagos State.
This figure was created by the authors in R programming software (R version 4.1.2, Vienna, Austria). Available at https://www.R-project.org/. The Nigerian shapefile was obtained from World BankDataCatalog (https://data.humdata.org/dataset/geoboundaries-admin-boundaries-for-nigeria) an Open license standardized resource of boundaries (i.e., state, county) for every country in the world.
Fig 7.
Predictive map of (a) Aedes mosquitoes distribution in Lagos and Ogun States (b) Overlay of population density with species distribution.
This figure was created by the authors in R programming software (R version 4.1.2, Vienna, Austria). Available at https://www.R-project.org/. The Nigerian shapefile was obtained from World BankDataCatalog (https://data.humdata.org/dataset/geoboundaries-admin-boundaries-for-nigeria) an Open license standardized resource of boundaries (i.e., state, county) for every country in the world.
Table 5.
Average percent contribution and permutation importance of the variables used in the modeling of Aedes species distribution in Lagos State.
Fig 8.
Estimation of model performance for Aedes spp.
(a)Area under the curve (AUC) for Aedes species distribution. Red line indicates the mean value for 40 MaxEnt replicate runs. (b) Jackknife analysis for regularized training gain.
Fig 9.
Estimates of the top contributing variables determining the geographical distribution of Aedes mosquitoes (a) The environmental variables anticipated to provide significant contributions to the geographical distribution of Aedes mosquitoes in Lagos State.
Variable contributions (precipitation of wettest month, annual mean temperature, mean temperature of coldest quarter and precipitation of coldest quarter), (b) Response curves of four environmental predictors used in MaxEnt model for Aedes spp.