Optimal prediction model of mosquito larval abundance in benthic macroinvertebrate communities of natural and artificial habitats, Korean Peninsula (2016-2017).
Mosquitos are the most prolific invasive insect species contributing to spread of endemic or exotic diseases, which exert a large burden on human health. Humans continue to develop and use physical, chemical, and biological methods for control of mosquito-borne infections. However, mosquito control methods are usually more effective when they target larvae, density of which is closely associated with biological factors, such as the community index and the predator population. Predictive models of mosquito larval abundance were developed based on species richness and on diversity and richness index ratio of individual Odonata, Coleoptera and Hemiptera group (OCH index) to evaluate suitability of the models using r2 value and Akaike information criterion (AIC) score. The most suitable model had an r2 value of 0.532 and an AIC score of 2.659 and was employed to estimate mosquito larval abundance. The prediction model developed should help explain correlation between benthic macroinvertebrate communities and mosquito larval abundance.