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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

From: Applying fuzzy logic to assess the biogeographical risk of dengue in South America

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

  
  1. 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
  2. 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)
  3. 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)
  4. 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)
  5. eGlobCover 2009. Global land cover map. 2006. Avalaible at: http://due.esrin.esa.int/page_globcover.php (Accessed April 2016)
  6. 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)
  7. gNatural Earth Data. North American Cartographic Information Society (NACIS). Available at: http://www.naturalearthdata.com/ (Accessed April 2016)
  8. hDiva-Gis 1.4, Plant Genetic Resources Newsletter. Available in: http://www.diva-gis.org/ (Accesed April 2016)
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Parasites & Vectors

ISSN: 1756-3305

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