Table 1.
Explanatory variables used in Ae. aegypti, Ae. albopictus and dengue virus models in South America. Climate variables which do not have a pairwise correlation value above 0.80 according to Spearmanʼs test are shown in bolditalic
| Abbreviation | Variable | Abbreviation | Variable |
|---|---|---|---|
| SP | Spatial lineal combination (ysp)a | ||
| Topography | |||
| A | Mean altitude (m)b | S | Slope (◦) (calculated from altitude) |
| DA | Difference altitude (m) (calculated from altitude) | ON/S | Orientation N/S (calculated from slope) |
| Climatic variables | |||
| BIO1 | Mean annual temperature (°C) c | BIO11 | Mean annual temperatures of the coldest quarter (°C)c |
| BIO2 | Mean diurnal range temperatures (°C)c | BIO 12 | Annual precipitation (mm) c |
| BIO3 | Isotermality (BIO2/BIO17)(*100) (°C)c | BIO13 | Precipitation of the wettest month (mm)c |
| BIO4 | Seasonal temperatures (°C)c | BIO14 | Precipitation of the driest month (mm)c |
| BIO5 | Maximum temperatures of the warmest month (°C)c | BIO 15 | Seasonal precipitation (coeficiente de variación) (mm) c |
| BIO6 | Minimum temperatures of the coldest month (°C)c | BIO16 | Precipitation of wettest quarter (mm)c |
| BIO7 | Annual temperatures range (BIO5–BIO6)c | BIO17 | Precipitation of dry quarterc |
| BIO8 | Mean annual temperatures of the wetter quarterc | BIO 18 | Precipitation of warmest quarter c |
| BIO9 | Mean annual temperatures of the dry quarterc | BIO 19 | Precipitation of coldest quarter c |
| BIO10 | Mean annual temperatures of the warmest quarterc | ||
| Hydrology | |||
| DistRiver | Minimum distance to rivers (km)d | SumRiver | Sum of km of rivers per grid (km)d |
| Land use | |||
| Forests | Forests (%)e | Crops | Crops (%)e |
| NatField | Natural field (%)e | BareSoil | Bare soil (%)e |
| FlooVeg | Flooding vegetation (%)e | ||
| Human activities | |||
| PopDen | Population densityf | DistRoad | Minimum distance to paved roads (km)h |
| DistUrban | Minimum distance to urban centers (km)g | ||
aSpatial variables, latitude and longitude, were generated using QGIS (www.qgis.org) according to the vector geometry tools: (i) with “centroids of polygons” the centroid of each grid was calculated, and (ii) with “Export/Add columns of geometry” values of length and latitude expressed in the 1984 World Geodetic System were assigned to each centroid (WGS84). The spatial variable used in the multivariate modelling procedure is the linear polynomial combination (ysp) resulting from a spatial logistic regression
bUnited States Geological Survey. GTOPO30. Land Processes Distributed Active Archive Center. EROS Data Center, https://www.usgs.gov/centers/eros/science/usgs-eros-archive-digital-elevation-global-30-arc-second-elevation-gtopo30?qt-science_center_objects=0#qt-science_center_objects. 1996 (Accessed April 2016)
cWorldClim—Global Climate Data available. Described in: Fick, S. E. and R. J. Hijmans. Worldclim 2: New 1-km spatial resolution climate surfaces for global land areas. International Journal of Climatology. 2017. In: http://www.worldclim.org/ (Accessed May 2016)
dUnited States Geological Survey. HydroShed. Hydrological data and maps based on SHuttle Elevation Derivatives at multiple Scales. Available in: http://hydrosheds.cr.usgs.gov/index.php/ (Accessed May 2016)
eGlobCover 2009. Global land cover map. 2006. Avalaible at: http://due.esrin.esa.int/page_globcover.php (Accessed April 2016)
fGridded Population of the World (GPW), v4. Socioeconomic Data and Applications Center (SEDAC). A Data Center in NASA’s Earth Observing System Data and Information System (EOSDIS). Hosted by CIESIN at the Columbia University. 2010. (Accessed June 2016)
gNatural Earth Data. North American Cartographic Information Society (NACIS). Available at: http://www.naturalearthdata.com/ (Accessed April 2016)
hDiva-Gis 1.4, Plant Genetic Resources Newsletter. Available in: http://www.diva-gis.org/ (Accesed April 2016)