IMPACT OF CLIMATE CHANGE ON MALARIA VECTORS DISTRIBUTION IN NIGERIA

Abstract

Malaria plagues in Nigeria more than the rest of the world, maybe due to increased malaria vectors exacerbated by anthropogenic climate change and socio-ecological factors. Information on spatial distribution of malaria vector species is needed to improve malaria control efforts. This study aimed to develop a GIS-based model to map current and future suitable environments and distribution of malaria vector species with respect to climate change, having carried out a historical assessment of malaria cases and mortality in Nigeria for a period of sixty one years, with result showing that malaria prevalence increased with warmer climates. Maximum Entropy Model (MaxEnt) was used to estimate suitable environments and potential distribution of eight Anopheles species found in Nigeria: An. gambiae sensu lato, An. funestus complex, An. gambiae sensu stricto, An. arabiensis, An. funestus sensu stricto, An. leesoni, An. nili and An. moucheti. Species occurrence data between 1900 and 2010 obtained from Nigeria Anopheles database was used alongside landuse and terrain variables, and 19 bioclimatic variables from WorldClim - Global Climate Data for current and future climate scenarios of 2050s and 2070s, based on Representative Concentration Pathways (RCPs) 2.6 and 8.5. Results show that dominant species: An. gambiae sensu lato, An. funestus complex, An. gambiae sensu stricto, and An. arabiensis are currently widespread across all ecological zones of Nigeria, and will experience large shift in potential range and population under RCP2.6, and larger under RCP8.5. Also widespread are An. funestus sensu stricto, An. nili and the rare species, An. leesoni, which are projected to reduce in potential range under future warmer climates. An. nili shows strong occurrence capacity within highlands and Mid Altitude zone just as An. leesoni along Sudano-Sahelian belt. Temperature fluctuation from mean diurnal temperature range, extreme temperature and precipitation conditions, high humidity in dry season from precipitation during warm months, and land use and land cover dynamics have the greatest influence on the current seasonal distribution and the future potential range of these malaria vector species. MaxEnt performed statistically significantly better than random with area under the receiver operating characteristic curve (AUC) approximately 0.7 for estimation of the Anopheles species environmental suitability, distribution and variable importance. Hence this study provides information on suitable environments and spatial distribution of malaria vector species in all spatial locations across topographic relief, ecological and regional zones in Nigeria; stating the contribution of climate and ecological variables used to indicate exact positions of the suitable environments. This model result can contribute to surveillance efforts and control strategies for malaria eradication.