Fig 1.
Study area in the Northern Bolivian Altiplano human fascioliasis hyperendemic area.
The map shows the meteorological stations included in the study (blue circles, detailed in Table 1). Former endemic area defined throughout the 1990’s [17], in red; and current endemic area, in orange (for further details see [22,27]). Base layer image by Stamen Design, under CC BY 4.0 license (https://maps.stamen.com/), roads shapefile from Natural Earth are in the public domain (https://www.naturalearthdata.com/about/terms-of-use/), and countries border shapes from GADM are freely available for academic use and other non-commercial use (https://gadm.org/license.html).
Table 1.
Meteorological stations and respective time periods analysed in the region of the Northern Bolivian Altiplano where human fascioliasis is hyperendemic.
Table 2.
Detail of the physiographical variables included in the models analysing the influence of geographical features and El Niño–Southern Oscillation (ENSO) on climatic factors and climatic forecast indices.
Fig 2.
Shortest distances from the meteorological stations included in the study to physiographical features of interest: A) distance to Lake Titicaca; B) distance to the Oriental Andean Chain; C) distance to the nearer border of inter-hill corridors; D) distance to nearest hill.
References of meteorological stations (see details in Table 1): a) Ayo Ayo; b) Chirapaca, c) Collana; d) El Alto; e) El Belén; f) Hichucota; g) Huarina; h) Huaycorondo; i) Laykacota; j) Santiago de Huata; k) Tiwanaku; l) Viacha. Polygon of the Lake Titicaca extracted from HydroLAKES under CC BY 4.0 license (https://www.hydrosheds.org/products/hydrolakes), and contour lines derived from a georeferenced 3 arc second (~90 m resolution) SRTM DEM from CGIAR-CSI under CC BY 4.0 license (https://csidotinfo.wordpress.com/data/srtm-90m-digital-elevation-database-v4-1/).
Table 3.
Mean monthly values ± standard deviation, and (ranges) for climatic factors recorded at a number of meteorological stations in the Northern Bolivian Altiplano human fascioliasis hyperendemic area.
Table 4.
Second-order Akaike Information Criterion (AICc) and weights for the selection of simplified models from the first set of multivariate linear mixed models for the analysis of the influence of physiographical features and El Niño–Southern Oscillation (ENSO).
Fig 3.
Violin plots summarizing yearly data for climatic factors recorded at a number of meteorological stations in the region of the Northern Bolivian Altiplano human fascioliasis hyperendemic area.
Violin plots with a common letter are not significantly different according to the Tukey-test.
Table 5.
Second-order Akaike Information Criterion (AICc) and weights for the selection of simplified models from the second set of multivariate linear mixed models for the analysis of the long/term variation of the influence of physiographical features and El Niño–Southern Oscillation (ENSO).
Fig 4.
Model coefficients plots showing the influence of physiographical features and El Niño–Southern Oscillation (ENSO) on climatic factors and climatic forecast indices.
Terms (y-axis) correspond to those in the best-approximating models. The x-axis displays model coefficients. Dots signify means and error bars 95% confidence intervals; filled dots depict significant coefficients (<0.05) and hollow dots depict non-significant coefficients. A coefficient overlapping with 0 signifies a neutral effect. Coefficients <0 and >0 signify negative and positive effects, respectively.
Fig 5.
Model coefficients plots showing the long-term influence of physiographical features and El Niño–Southern Oscillation (ENSO) on climatic factors and climatic forecast indices.
Terms (y-axis) correspond to those in the best-approximating models. The x-axis displays model coefficients. Dots signify means and error bars 95% confidence intervals; filled dots depict significant coefficients (<0.05) and hollow dots depict non-significant coefficients. A coefficient overlapping with 0 signifies a neutral effect. Coefficients <0 and >0 signify negative and positive effects, respectively.