Wildlife Disease Surveillance Using GIS Tools (International Journal of Animal Biodiversity, Conservation and Systematics ( IJABC), 2023)
Geographic Information Systems (GIS) integrated with remote sensing and epidemiological modelling have transformedwildlife disease surveillance by enabling spatially explicit mapping of disease outbreak risk, pathogen distribution, andhost-vector-reservoir interfaces at landscape scales. This study developed and validated a GIS-based wildlife diseasesurveillance framework integrating satellite land cover data, GPS animal tracking, environmental DNA (eDNA) sampling,and machine learning-based outbreak risk models across 48 surveillance sites in Central Europe (Germany, France,Estonia) covering four priority wildlife diseases: African swine fever (ASF), chronic wasting disease (CWD), avianinfluenza (HPAI H5N1), and bat lyssavirus (BLV). The GIS risk model achieved outbreak prediction AUC = 0.88 +- 0.04(cross-validated; 2019-2023 outbreak records), outperforming conventional buffer-zone surveillance. eDNAmetabarcoding at 284 water sampling points detected pathogen signatures 14-21 days before conventionalcarcass-based confirmation in 72% of ASF outbreak events, demonstrating the early-warning value of environmentalsurveillance. GPS-tracked animal movement corridors explained 84% of inter-farm ASF spread events, confirming wildboar movement as the dominant spatial driver of disease spread. These results provide a validated GIS-integratedsurveillance framework directly applicable to EU wildlife disease early-warning systems.