Foley DH, Klein TA, Kim HC, Sames WJ, Wilkerson RC, Rueda LM. J Med Entomol. 2009, 46: 680-692. DOI: 10.1603/033.046.0336
Larval and adult mosquito collection data were used to develop ecological niche models for the potential geographic distribution for eight anopheline species in the Republic of Korea with the intention to compare species distribution of the mosquitos to areas of suspected malaria transmission in order to understand current and potential malaria risk and determine whether there is a particular species that has been responsible for malaria resurgence. Ecological requirements for each species were studied using occurrence only data. Mosquito occurrences that had between 9 and 106 points were used, however, to avoid spatial autocorrelation and ensure data independence, localities that were at least 5km apart were used. Environmental data was downloaded from the WorldClim dataset, which included monthly temperature, precipitation, and the 19 “bioclimatic layers”. Topographic, historic (?) land use, and NDVI layers were also used. Both GARP and MaxEnt were used to develop the ENM because they have both been used in previous mosquito distribution models and overall have been well received. Garp utilizes the iterative process of rule selection, evaluation, testing, and incorporation or rejection with the intent to “evolve” to maximize predictive accuracy, while MaxEnt also uses testing and training data with a decision threshold to determine presence or absence of a species given the environmental data layers. Prediction success of the distribution models were better than random except for Garp models for 2 mosquito species and MaxEnt for one mosquito species. Reasons for poor distribution prediction may have stemmed from spatial resolution that is too coarse and does not match up with some mosquito species smaller scale environmental needs. Fitting species distribution with records of malaria outbreaks suggested that all species occurred where malaria outbreaks occurred, but some species occurred less frequently. Furthermore, the models were able to determine which environmental variables or landscape characteristics are considered more influential on the distribution of a species. Although these models proved useful in determining geographic distribution across large scales, it could be improved by using absence data as well (instead of presence only), more spatially separated data (instead of data ‘clumped’ around certain locations, higher resolution environmental data that is more up-to-date and land use data (to find whether certain types of disturbance may be associated with a greater abundance of a certain type of mosquito?). Also, another way to improve these models would be to incorporate some kind of interaction variable between species, especially during larval stages.