Ecological Niche Modeling Of Malaria Vector Distribution For Climate Change Adaptation In Kenya
This study employed Ecological Niche Modeling (ENM), a technique that encompasses a suite of tools that relate known occurrences of species or phenomena to raster geographic information system layers that summarize variation in several environmental dimensions. The spatialtemporal distributions of the main malaria vectors in Kenya were quantified using BIOCLIM and DOMAIN models to determine the relationship between vector distribution and climate change. The biological data used was from published sources (Okara et al., 2010 and MARA/ARMA, 1998), comprising of point samples for geo-referenced malaria vector occurrences. The climate data used was maximum temperature, minimum temperature and precipitation for current climate (1950-2000) and climate projection for HADCM3, CCCMA and SCIRO models of IPCC projected future climate under the A2a scenario by the years 2020, 2050 and 2080. The climate data was acquired in grid format from WorldClim global climate data which was further processed to generate 19 bioclimatic variables for Kenya. The predictions showed that by the year 2020, the suitability areas for malaria vectors in Kenya will start to change from the current ecological suitability. Most areas where the malaria vectors are thriving currently will still remain suitable ecologies. New suitability zones will emerge in most counties ranging from low to very high suitability as shown by the predictions. By the year 2050, areas of suitability will expand at an alarming extend. The year 2080 has been predicted to show that the suitable ecologies will start to revert to the original areas of suitability as in the current climate. Therefore, climate change in Kenya will adversely affect the environment at an alarming rate by 2050, but beyond that there will be a level of stabilization, where further change will trigger reversal to the past climate. For instance, BIOCLIM True or False prediction from HADCM3 by the year 2050 showed wide spread of malaria in counties like Narok, Kajiado, Kitui, Makueni, Machakos, Meru, Marsabit, Isiolo, Samburu, Baringo, West Pokot. Turkana county and Mandera among a few others will have some emerging isolated malaria hot spots. ENM prediction with HADCM3 future climate showed that Laikipia County will become unsuitable malaria ecology by the year 2050 and the case remains the same by the year 2080. Validation results for prediction model performance showed that all the models used had errors in prediction as none of them had kappa =1 or AUC=1. The highest kappa (k = 0.909) and Area under ROC Curve (AUC=0.954) values were achieved from DOMAIN model with CCCMA projection by the year 2020. The lowest model performance values of k = 0.427 and AUC = 0.714 were obtained from BIOCLIM True or False model with HADCM3 projections by the year 2020. The following conclusions were drawn from the Ecological Niche Modeling done using BIOCLIM, BIOCLIM True or False and DOMAIM prediction models: There is correlation between climate change as an explanatory variable and the distribution of main malaria vectors in Kenya. The spatial-temporal distribution of the main malaria vectors in Kenya varies under different IPCC future climate projections which are HADCM3, CCCMA and CSIRO. The future ecological niches for malaria vector occurrence in Kenya will extend from the current niches in most endemic areas, new hotspots will emerge and some suitable ecology will become unsuitable, resulting in varying areas from current climate predictions to projections by the year 2020, 2050 and 2080 under IPCC A2a scenario. Intervention strategies such as indoor or outdoor residual spraying, distribution of insecticide-treated mosquito nets (ITNs) and long-lasting insecticide-treated nets (LLINs) should be diversified in new emerging areas for disaster risk reduction and increase adaptive capacity and resilience among local communities.