Fig 1.
Ecuador, situated on the northwestern coast of South America (inset), has historically high prevalence of mosquito-borne diseases.
The Ecuadorian Ministerio de Salud Pública (MSP) conducted household entomological surveys of Aedes aegypti throughout the country from 2000–2012. Spatially unique larval index (LI) occurrence records (n = 478) collected in the survey were aggregated to cities and towns and used to model the ecological distribution of Ae. aegypti in Ecuador. This figure was produced in ArcMap 10.4 (ESRI, Redlands, CA) using shapefiles from the GADM database of Global Administrative Areas, ver. 2.8 (gadm.org), elevation data freely available from NASA’s Shuttle Radar Topography Mission (jpl.nasa.gov/srtm), and georeferenced mosquito surveillance data provided by the MSP and edited by CAL.
Table 1.
Environmental variables used in building GARP models for Aedes aegypti in Ecuador.
Table 2.
Accuracy metrics for best model subsets built using the full set of environmental coverage variables.
Each experiment was performed with a randomly chosen subset (75%) of LI presence points. The subset of LI presence points used in variable selection is shown in bold.
Fig 2.
Agreement of best model subsets built with best-ranked suite of environmental variables for larval Aedes aegypti presence in Ecuador under current climate conditions.
Models had high levels of agreement in the western coastal lowlands, and lower levels of agreement in the eastern Amazon basin. This figure was produced in ArcMap 10.4 (ESRI, Redlands, CA) using rasters of model output produced with DesktopGARP (ver. 1.1.3), and elevation data freely available from NASA’s Shuttle Radar Topography Mission (jpl.nasa.gov/srtm).
Table 3.
Accuracy metrics for best model subsets built using the best-ranked training dataset and selected subsets of environmental coverages.
The variable subset used in building the final models is shown in bold.
Fig 3.
Agreement of best model subsets built with best ranked suite of environmental variables for larval Aedes aegypti presence in Ecuador under A) current climate conditions and future climate conditions projected to the year 2050 under Representative Concentration Pathway (RCP) 2.6 (B1,C1,D1) and 8.5 (B2,C2,D2) for the B) BCC-CSM-1, C) CCSM4, and D) HADGEM2-ES General Circulation Models (GCM) climate models. This figure was produced in ArcMap 10.4 (ESRI, Redlands, CA) using rasters of model output produced with DesktopGARP (ver. 1.1.3), and elevation data freely available from NASA’s Shuttle Radar Topography Mission (jpl.nasa.gov/srtm).
Fig 4.
Best model subsets for current and future climate (GCMs projected to the year 2050) were combined by RCP emissions scenarios to illustrate the estimated contraction and expansion of larval Aedes aegypti geographic range in Ecuador.
This figure was produced in ArcMap 10.4 (ESRI, Redlands, CA) using rasters of model output produced with DesktopGARP (ver. 1.1.3), and elevation data freely available from NASA’s Shuttle Radar Topography Mission (jpl.nasa.gov/srtm).
Table 4.
Estimated human population inhabiting areas of transitional elevation in Ecuador, which may experience increased exposure to moquito-borne disease transmission under climate change.