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. 2023 Aug 22;13(1):13679.
doi: 10.1038/s41598-023-40929-5.

Spatial distribution and ecological niche modeling of geographical spread of Anopheles gambiae complex in Nigeria using real time data

Adedapo Adeogun  1   2 , Ayodele Samuel Babalola  3   4 , Okefu Oyale Okoko  5 , Tolulope Oyeniyi  6 , Ahmed Omotayo  6 , Romoke Tawakalitu Izekor  6 , Oluwakemi Adetunji  6 , Abiodun Olakiigbe  6 , Olalekan Olagundoye  6 , Monsuru Adeleke  7 , Cynthia Ojianwuna  8 , Dagona Adamu  9 , Abdullahi Daskum  9 , Jibrin Musa  9 , Obadiah Sambo  10 , Oduola Adedayo  11 , Petrus Uchenna Inyama  11 , Lazarus Samdi  11 , Abiodun Obembe  12 , Musa Dogara  13 , Poloma Kennedy  14 , Suleiman Mohammed  15 , Rebecca Samuel  16 , Chioma Amajoh  17 , Musa Adesola  6 , Mohammed Bala  18 , Mary Esema  18 , Mamudu Omo-Eboh  18 , Marianne Sinka  19 , Olufunmilayo Ajoke Idowu  20 , Adeolu Ande  21 , Israel Olayemi  22 , Abdulsalami Yayo  23 , Perpetua Uhomoibhi  24 , Samson Awolola  25 , Babatunde Salako  26
Affiliations

Spatial distribution and ecological niche modeling of geographical spread of Anopheles gambiae complex in Nigeria using real time data

Adedapo Adeogun et al. Sci Rep. .

Abstract

The need for evidence-based data, to inform policy decisions on malaria vector control interventions in Nigeria, necessitated the establishment of mosquito surveillance sites in a few States in Nigeria. In order to make evidence-based-decisions, predictive studies using available data becomes imperative. We therefore predict the distribution of the major members of the Anopheles gambiae s.l. in Nigeria. Immature stages of Anopheles were collected from 72 study locations which span throughout the year 2020 resulted in the identification of over 60,000 Anopheline mosquitoes. Of these, 716 breeding sites were identified with the presence of one or more vector species from the An. gambiae complex and were subsequently used for modelling the potential geographical distribution of these important malaria vectors. Maximum Entropy (MaxEnt) distribution modeling was used to predict their potentially suitable vector habitats across Nigeria. 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 three dominant vector species under current climatic conditions. Members of the An. gambiae complex dominated the collections (98%) with Anopheles stephensi, Anopheles coustani, Anopheles funestus, Anopheles moucheti, Anopheles nilli also present. An almost equal distribution of the two efficient vectors of malaria, An. gambiae and Anopheles coluzzii, were observed across the 12 states included in the survey. Anopheles gambiae and Anopheles coluzzii had almost equal, well distributed habitat suitability patterns with the latter having a slight range expansion. However, the central part of Nigeria (Abuja) and some highly elevated areas (Jos) in the savannah appear not suitable for the proliferation of these species. The most suitable habitat for Anopheles arabiensis was mainly in the South-west and North-east. The results of this study provide a baseline allowing decision makers to monitor the distribution of these species and establish a management plan for future national mosquito surveillance and control programs in Nigeria.

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Conflict of interest statement

The authors declare no competing interests.

Figures

Figure 1
Figure 1
Map of Nigeria showing the sentinel sites from 12 selected states for the surveillance project. 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 () an Open license standardized resource of boundaries (i.e., state, county) for every country in the world.
Figure 2
Figure 2
Map showing the positive occurrence records for each of the members of An. gambiae complex. (a) An. gambiae (b) An. arabiensis (c) An. coluzzii. 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 () an Open license standardized resource of boundaries (i.e., state, county) for every country in the world.
Figure 3
Figure 3
Spatial distribution of An. species collected from the 12 states. (a) Distribution of Anopheline mosquitoes in Nigeria. (b) Distribution of the members of the An. gambiae s.l. complex 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 BankDataCatalog () an Open license standardized resource of boundaries (i.e., state, county) for every country in the world.
Figure 4
Figure 4
Predictive maps of geographical spread of the members of An. gambiae s.l. 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 () an Open license standardized resource of boundaries (i.e., state, county) for every country in the world.
Figure 5
Figure 5
Estimation of model performance for An. coluzzii (a) Area under the curve (AUC) for An. coluzzii distribution. Red line indicates the mean value for 10 MaxEnt replicate runs. (b) Jackknife analysis for regularized training gain.
Figure 6
Figure 6
Estimates of the highest contributing variables that determines the geographical distribution of An. coluzzii (a) The highest environmental variables that estimate to control the geographical distribution of An. coluzzii in Nigeria. Variable contributions (precipitation of coldest quarter, annual mean temperature and mean temperature of driest quarter), (b) Response curves of three environmental predictors used in MaxEnt model for An. coluzzii. 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 () an Open license standardized resource of boundaries (i.e., state, county) for every country in the world.
Figure 7
Figure 7
Estimation of model performance for An. gambiae (a) Area under the curve (AUC) for An. gambiae distribution. Red line indicates the mean value for 10 MaxEnt replicate runs. (b) Jacknife analysis for regularized training gain.
Figure 8
Figure 8
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 of An. gambiae in Nigeria. Variable contributions (annual mean temperature and mean diurnal range and isothermality), (b) Response curves of three environmental predictors used in MaxEnt 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 BankDataCatalog () an Open license standardized resource of boundaries (i.e., state, county) for every country in the world.
Figure 9
Figure 9
Estimation of model performance for An. arabiensis (a) Area under the curve (AUC) for An. arabiensis distribution. Red line indicates the mean value for 10 MaxEnt replicate runs. (b) Jackknife analysis for regularized training gain. The dark blue, light blue and red bars represent results of the model with each individual variable, all the remaining variables and all variables respectively.
Figure 10
Figure 10
Estimates of the highest contributing variables that determines the geographical distribution of An. arabiensis (a) The highest environmental variables that estimate to control the geographical distribution of An. arabiensis in Nigeria. Variable contributions (mean temperature of driest quarter, precipitation of coldest quarter and annual mean temperature), (b) Response curves of three environmental predictors used in MaxEnt model for An. arabiensis. 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 () an Open license standardized resource of boundaries (i.e., state, county) for every country in the world.
Figure 11
Figure 11
Relationship between the strongest environmental predictors of the distribution of the three Anopheles species.
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