Identification methods and deterministic factors of owl roadkill hotspot locations in Mediterranean landscapes

Abstract

Road fatalities are among the major causes of mortality for Strigiformes species and may affect the population’s survival. The use of mitigation strategies must be considered to overcome this problem. However, because mitigation along the total length of all roads is not financially feasible, the locations where Strigiformes roadkills are more frequent (i.e., road fatality hotspots) must be identified. In addition to hotspot identification, factors that influence the occurrence of such fatalities should be recognized to allow mitigation measures to be delineated. We used road fatality data collected from 311 km of southern Portugal roads over a 2-year period to compare the performance of five hotspot identification methods: binary logistic regression (BLR), ecological niche factor analysis (ENFA), Kernel density estimation, nearest neighbor hierarchical clustering (NNHC), and Malo’s method. BLR and ENFA modelling were also used for recognizing roadkill deterministic factors. Our results suggest that Malo’s method should be preferred for hotspot identification. The main factors driving owl roadkillings are those associated with good habitat conditions for species occurrence and specific conditions that promote hunting behavior near roads. Based on these factors, several mitigation measures are recommended.