Towards building a speciesspecific risk model for mammal-aircraft strikes

Wildlife strikes are a significant issue in the aviation industry, especially strikes with medium- to large-sized mammals, which pose a high risk of damage to aircraft and human safety. Despite the identified threat that mammals can pose to aircraft, few works have been published on ways to rank and predict the risk of mammalian species to aircraft. This study used remote camera trap data collected from an array of wildlife camera traps at the Prince George International Airport (YXS), Prince George, British Columbia, Canada, to calculate strike risk for various species of mammals involved in runway incidents between January 2012 and December 2018. Carnivores such as red foxes and coyotes were found to be the highest risk mammal species at YXS, but foxes were found airside infrequently compared to coyotes. Binary logistic regression modelling was used in an attempt to predict variables leading to runway incidents with coyotes at YXS. The highest supported logistic regression model predicting coyote incidents included the variables ‘weekday’, ‘month’ and ‘season’. Although data from camera traps did not help to predict incidents, trend data collected from camera traps mirrored coyote incident data, suggesting that camera traps are useful for capturing times of the day and seasons of the year in which coyotes are active at the airport. Suggestions are provided as to how cameras might be used to track the movement of animals more accurately and what other data could be useful in helping to build risk assessments and models to predict aircraft incidents with mammals of interest.