S1 Files
Creators: name: Faustini, Giulia
name: Soret, Marie
name: Defossez, Alexandre T.M.
name: Bosch, Jaime
name: Conte, Annamaria
name: Tran, Annelise
Publisher: name: Zenodo
Year: 2025
Subjects: subject: African Swine Fever
subject: Italy
subject: wild boar
subject: Sus scrofa
subject: suitability map
subject: connectivity analysis
subject: Biomod2
subject: Gradient boosted machine
subject: ecology
subject: Veterinary Medicine
subject: Epidemiology

description: S1 Files. Main code scripts used. Link to Gitlab repository: https://gitlab.com/giuliafaustini1/habitat-suitability-mapping-and-landscape-connectivity-analysis-to-predict-african-swine-fever-spread-in-wild-boar-population 

Habitat suitability mapping and landscape connectivity analysis to predict African swine fever spread in wild boar population: a focus on Northern Italy

General overview

 

This repository contains several R scripts to map suitable habitats for wild boar presence and estimate the landscape connectivity for wild boar dispersal, as described in the workflow.jpg file.

 

The workflow takes as input the occurrence records describing wild boar presence data, and a set of environmental variables (raster files) that represents the predictors for wild boar distribution, to estimate suitable habitats for wild boar presence (Figure A). The obtained suitability map is used to perform the landscape connectivity analysis of the study area (Figure B). For more details, follow material and methods in this study (currently in revision process: Faustini et al. 2025).

Folders description





1_Occurrence_processing: the occurrence records were cleaned (GBIF_WB_DataProcessing.Rmd) and reduced in density (Reduce_occurrence_density.R).




2_Variable_collinearity_multicollinearity: all environmental variables were standardized, and tested for collinearity and multicollinearity (Collinearity_evaluation.R).




3_Biomod2_SuitabilityMap: suitable habitats were estimated using the presence data and pseudoabsences generated using a kernel density surface (Pseudoabsences_on_KernelDensity_surface.R), using the algorithms available in Biomod2 package (Biomod2_SDM_SuitabilityMap_estimation.R).




4_ConnectivityAnalysis: Circuitscape estimates the flow between focal nodes, simulating animal movements. The obtained continuous suitability map was used as raster to create the necessary input files. Input files included a focal nodes location file, a resistance map, obtained using python and Qgis (Circuitscape_formatting_inputs.ipynb). To run Circuitscape in Julia, it is necessary to write a .ini file similar to Circuitscape_ComputeFile.ini, to call in a script like Circuitscape_julia.ipynb.




5_ModelEvaluation: to test if ASF events/outbreaks were associated with highest connectivity values, the distribution of the connectivity values at ASF event/outbreak locations was compared with the one of random points in the outbreaks-bounding geometry through Mann-Whitney test (Outbreak_Random_ROC_AUC.R). For each of the 100 sets of random points, a dataset of ASF events/outbreaks and random points locations and relative connectivity values was created. The ROC curve was analysed for all the 100 datasets, using pROC package in R. The average area under the curve (AUC), the lowest threshold corresponding to 0.9 of sensitivity, and the average best closest top left threshold (the point closest to the top-left part of the ROC curve, corresponding to the best compromise between sensitivity and specificity) were calculated (Outbreak_Random_ROC_AUC.R).



Acknowledgments



Giulia Faustini

Marie Soret

Alexandre Defossez

Jaime Bosch

Annamaria Conte

Annelise Tran

Maxime Lenormand