Fig 1.
Map of Nigeria showing the sentinel sites from the 20 selected states for Anopheles surveillance.
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 (an Open license standardized resource of boundaries (i.e., state, county) for every country in the world).
Table 1.
Environmental variables used for modeling the potential distribution of Anopheline species (non-gambiae) In the present study.
Fig 2.
Spatial distribution of An. non-gambiae species collected.
(a) Distribution of all species in Nigeria. (b) Distribution of An. coustani from the collection sites. 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 (an Open license standardized resource of boundaries (i.e., state, county) for every country in the world.
Fig 3.
Spatial distribution of An. non-gambiae species collected.
(a) Distribution of An. funestus in Nigeria. (b) Distribution of An. maculipalpis in Nigeria. 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 (an Open license standardized resource of boundaries (i.e., state, county) for every country in the world.
Fig 4.
Spatial distribution of An. non-gambiae species collected.
(a) Distribution of An. rufipes in Nigeria. (b) Distribution of other An. non-gambiae spp in Nigeria. 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 (an Open license standardized resource of boundaries (i.e., state, county) for every country in the world.
Fig 5.
Predicted distribution of Anopheles (non-gambiae) species in Nigeria.
(a) All non-gambiae species (b) An. coustani (c) An. funestus (d) An. maculipalpis (e) An. rufipes (f) Other non gambiae species. 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 (an Open license standardized resource of boundaries (i.e., state, county) for every country in the world.
Fig 6.
Predicted abundance of Anopheles non-gambiae species in Nigeria.
(a) All non-gambiae species (b) An. coustani (c) An. funestus. 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 (an Open license standardized resource of boundaries (i.e., state, county) for every country in the world.
Fig 7.
Predicted abundance of Anopheles (non-gambiae) species in Nigeria.
(a) An. maculipalpis (b) An. rufipes (c) Other non gambiae species. 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 (an Open license standardized resource of boundaries (i.e., state, county) for every country in the world.
Table 2.
Model performance characteristics.
Fig 8.
Level of importance of the variables used in the modeling of the Anopheles (non-gambiae) species distribution.
Light blue line connotes %IncMSE while blue circle or dots connotes IncNodePurity. The longer the blue line and the bigger the circle the more important the variable contribution to the model.
Fig 9.
Area under the curve (AUC) for all the Anopheles (non-gambiae) species.
Blue line indicates the mean value for 10 random forest replicate runs.
Table 3.
Variable importance ranking using the %IncMSE values for all the predicted Anopheles species.
Fig 10.
Estimates of the highest contributing variables that determines the geographical distribution of An. non gambiae species.
(a) The highest environmental variables that estimate to control the geographical distribution in Nigeria. Variable contributions (mean diurnal range, isothermality and mean annual temperature), (b) Response curves of three environmental predictors used in Random Forest model for An. non-gambiae species. 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 (an Open license standardized resource of boundaries (i.e., state, county) for every country in the world.
Fig 11.
Estimates of the highest contributing variables that determines the geographical distribution of An. coustani.
(a) The highest environmental variables that estimate to control the geographical distribution of An. coustani in Nigeria. Variable contributions (temperature of driest quarter, mean annual temperature and isothermality), (b) response curves of three environmental predictors used in Random Forest model for An. coustani. 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 (an Open license standardized resource of boundaries (i.e., state, county) for every country in the world.
Fig 12.
Estimates of the highest contributing variables that determines the geographical distribution of An. funestus.
(a) The highest environmental variables that estimate to control the geographical distribution of An. funestus in Nigeria. Variable contributions (mean annual temperature, temperature of driest quarter, and precipitation of wettest month), (b) response curves of three environmental predictors used in Random Forest model for An. funestus. 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 (an Open license standardized resource of boundaries (i.e., state, county) for every country in the world.
Fig 13.
Estimates of the highest contributing variables that determines the geographical distribution of An. maculipalpis.
(a) The highest environmental variables that estimate to control the geographical distribution of An. maculipalpis in Nigeria. Variable contributions (temperature of driest quarter, isothermality and mean diurnal range), (b) Response curves of three environmental predictors used in Random Forest model for An. maculipalpis. 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 (an Open license standardized resource of boundaries (i.e., state, county) for every country in the world.
Fig 14.
Estimates of the highest contributing variables that determines the geographical distribution of An. rufipes.
(a) The highest environmental variables that estimate to control the geographical distribution of An. rufipes in Nigeria. Variable contributions (temperature of driest quarter, mean diurnal range and mean annual temperature), (b) response curves of three environmental predictors used in Random Forest model for An. rufipes. 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 (an Open license standardized resource of boundaries (i.e., state, county) for every country in the world.
Fig 15.
Estimates of the highest contributing variables that determines the geographical distribution of other Anopheles non-gambiae species.
(a) The highest environmental variables that estimate to control the geographical distribution of other Anopheles (non-gambiae) species in Nigeria. Variable contributions (mean annual temperature, slope and isothermality), (b) response curves of three environmental predictors used in Random Forest model for other Anopheles (non-gambiae) species. 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 (an Open license standardized resource of boundaries (i.e., state, county) for every country in the world.