Table 1.
Settings and variables used for the construction of the ecological niche modeling for Hyalomma marginatum.
Fig 1.
Hyalomma marginatum occurrence records used in model calibration and final model evaluation across Europe, North Africa, Western and South-Central Asia.
The blue dotted circles represent the occurrence records of H. marginatum collected from GBIF used for model calibration across the “M” area. The red dotted circles represent the retrieved occurrence records of H. marginatum from the literature used for the final model evaluation. Dark grey polygons represent the accessible areas (“M”) where the H. marginatum model was calibrated. The base layer of all the maps (from Fig 1 to Fig 5) is extracted from ArcGIS Hub developed by ESRI. https://arc-gis-hub-home-arcgishub.hub.arcgis.com/datasets/esri::world-continents/explore?location=0.736373%2C36.668919%2C2.65.
Table 2.
The best candidate model for the construction of the ecological niche modeling for Hyalomma marginatum.
Model performance under optimal parameters using sets of environmental predictors (SEP), statistically significant models (SSM), best candidate models (BCM), regularization multiplier (RM), features classes (FC), mean Area Under the Curve ratio (AUC.r), partial Receiver Operating Characteristic (p.ROC), omission rate 5% (O.rate 5%), Akaike information criterion corrected (AICc), delta Akaike information criterion corrected (ΔAICc), Akaike information criterion corrected weight (AICc.W), number of parameters (#; summarizes the combination of environmental variables, multiple regularizations, and features other than 0 that provide information for the construction of the model based on lambdas—lambda refers to counting all parameters with a nonzero weight in a Maxent-generated text file), and environmental variables of Set4 tested during calibration of Hyalomma marginatum model.
Fig 2.
Predicted potential geographic distribution of Crimean-Congo haemorrhagic fever vector Hyalomma marginatum on a global scale.
Red colors indicate highest habitat suitability and blue lowest suitability. The base layer of all the maps (from Fig 1 to Fig 5) is extracted from ArcGIS Hub developed by ESRI. https://arc-gis-hub-home-arcgishub.hub.arcgis.com/datasets/esri::world-continents/explore?location=0.736373%2C36.668919%2C2.65.
Fig 3.
Predicted potential distribution of Crimean-Congo haemorrhagic fever vector Hyalomma marginatum on a global scale (left top), and close-ups of Europe (A) and Central Europe (B), to provide additional detail to predictions in the region.
Red areas indicate modeled highest suitable conditions, and blue areas are lowest suitable conditions. The base layer of all the maps (from Fig 1 to Fig 5) is extracted from ArcGIS Hub developed by ESRI. https://arc-gis-hub-home-arcgishub.hub.arcgis.com/datasets/esri::world-continents/explore?location=0.736373%2C36.668919%2C2.65.
Fig 4.
Relationship of Hyalomma marginatum ecological niche modeling prediction to the distribution of the independent set of Hyalomma marginatum occurrence records.
Blue shading shows areas predicted suitable for Hyalomma marginatum occurrences. Black and yellow dots represent the independent records of Hyalomma marginatum used for the final model evaluation; black dots are records with successful prediction and yellow dots are records where the prediction is not captured by the model. The base layer of all the maps (from Fig 1 to Fig 5) is extracted from ArcGIS Hub developed by ESRI. https://arc-gis-hub-home-arcgishub.hub.arcgis.com/datasets/esri::world-continents/explore?location=0.736373%2C36.668919%2C2.65.
Fig 5.
Mobility-oriented parity (MOP) 10% extrapolation risk analysis for the ecological niche model of Hyalomma marginatum from the calibration area (“M”) to a projection area (“G”) (top), and close-ups of East Africa (A) and Eastern Asia (B), to provide additional detail to strict extrapolations occurred in the areas.
The MOP analysis indicated that areas with the most dissimilar variables conditions (i.e., where one or more covariate variables are outside the range present in the training data) were found beyond the potential distributional areas predicted by the model in the “G” area. Areas with the most dissimilar variables conditions display strict extrapolative areas and are represented by zero value. Other values represent levels of similarity between the calibration area and the “G” transfer area. The MOP raster output was reclassified into five categories; the first category represented a strict extrapolation (i.e., zero value), and the fifth category represented the highest environmental similarities between calibration and projection areas. The base layer of all the maps (from Fig 1 to Fig 5) is extracted from ArcGIS Hub developed by ESRI. https://arc-gis-hub-home-arcgishub.hub.arcgis.com/datasets/esri::world-continents/explore?location=0.736373%2C36.668919%2C2.65.