Modelling species distribution of Anopheles gambiae s.l. in Osun state using random forest modeling approach

Abstract

Malaria continues to be a public health problem in Nigeria. Effective vector control has been acknowledged as one of the major strategies to combat malaria in Nigeria. In order to inform context-specific policies, there is need to understand the species niche/colonization and geographical distribution of local Anopheles mosquitoes in different geographical areas in Nigeria. Therefore, this study was designed to model the distribution of Anopheles gambiae s.l. in Osun State, Nigeria. Immature stages of Anopheles mosquitoes were collected from 17 Local Government Areas (LGAs) of Osun State between 2022 and 2023, resulting in the identification of over 20,000 Anopheline mosquitoes. Of these, 146 breeding sites were identified. Two sibling species of An. gambiae s.l were later confirmed using molecular techniques (An. gambiae s.s. and An. coluzzii) with 99% of the Anopheles being An. coluzzii in all the 17 LGAs. The results were modelled using Random Forest to predict their potentially suitable habitats across the entire state. A total of 23 environmental variables (19 bioclimatic and four topographic) were used in the model, resulting in maps of the potential geographical distribution of dominant vector species under current climatic conditions. The model suggests that members of the Anopheles gambiae s.l. encountered would be found in the three senatorial districts (from high to moderate), with more suitable areas in the central part of the state. Temperature-related climatic conditions seems to be ranked higher in order of variable importance that contributed to the distribution of these species. Mean diurnal range (monthly temperature fluctuation) and temperature of the coldest month seem to be strongly associated with the presence of these species. Our model suggested areas with higher monthly temperature fluctuations in the state (between 9 and 10 °C) to support the breeding of Anopheles species, and they are likely to be abundant in areas with lower temperature during the coldest month (wet season) of the year (between 18 and 19 °C). The results of this study provide a baseline data allowing decision-makers to monitor the distribution of these species and establish a management plan for future mosquito surveillance and control programs in Osun State.

Keywords: Anopheles; Larval distribution; Modelling; Osun state.

Fig. 1

Study area across the three senatorial districts of Osun State. This figure was created by the authors in Arc GIS software (version 10.7). Available at https://desktop.arcgis.com/en/arcmap/. 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. 2

Larval Site Positivity (a) and Number of larvae collected (b) from each site, in Osun State. This figure was created by the authors in Arc GIS software (version 10.7). Available at https://desktop.arcgis.com/en/arcmap/. 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

Predicted Anopheles larva habitat suitability for Osun Sate (a) habitat suitability unclassified (b) habitat suitability classified. 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

Model performance and influencing factors. (a) Mean Decrease Gini of the 23 variables used in modeling the distribution of An. gambiae (b) The ROC curve, obtained as an average of the 10 replication runs.

Fig. 5

Estimates of the highest contributing variables that determines the geographical distribution of An. gambiae (a) The highest environmental variables that estimate to control the geographical distribution in Nigeria. (b) Response curves of six temperature and two precipitation predictors used in Random Forest model for An. gambiae. 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.