Table 1.
Definitions of United States Navy (USN) and United States Air Force (USAF) damage classes in U.S. dollars.
Damage class ‘H’ was divided for the USN dataset based on internal discussions.
Table 2.
Relative hazard scores (RHS) for 108 species groups from most to least hazardous for military aircraft within the United States.
Fig 1.
Comparison of relative hazard scores for 6 avian species groups for 4 military airframe groups and all civil airframes for 6 species.
Relative hazard scores are calculated from bird strikes within the U.S. See S2 Table for airframe group compositions. Civil relative hazard scores come from [11]. Species are ordered from left to right by ascending averaged body mass.
Fig 2.
Relationship between avian body mass and relative hazard score for avian groups with military cargo (a), fighter (b), rotorcraft (c) and all military airframes (d). Only strikes identified to species and occurred within the U.S. were included. Strike data are from separate databases maintained by the U.S. Navy (1990–2017) and U.S. Air Force (1994–2017). Equations and coefficient of determination (r2 values) and 95% confidence intervals are displayed.
Fig 3.
Interaction plot of airframes, avian log body mass, and predicted probability of damage (a) and substantial damage (b). Only strikes identified to species and occurred within the U.S. were included. Strike data are from separate databases maintained by the United States Navy (1990–2017) and Air Force (1994–2017).
Table 3.
Binary logistic regression models predicting any level of damage to Navy (USN) and Air Force (USAF) within the United States.
This model represents the best as evaluated by the lowest Akaike’s Information Criterion (AIC) value (see S8 Table for other candidate models). McFadden’s r2 value is 0.12. Predictor variables include migration flyway (Central, Mississippi, Pacific, Atlantic), airframe (cargo, rotorcraft, stealth, fighter), avian log body mass, military branch (USN or USAF), and the airframe × avian log body mass interaction.
Table 4.
Binary logistic regression models predicting substantial damage to Navy (USN) and Air Force (USAF) within the United States.
This model represents the best as evaluated by the lowest Akaike’s Information Criterion (AIC) value (see S8 Table for other candidate models). McFadden’s r2 value is 0.23. Predictor variables include flyway (Central, Mississippi, Pacific, Atlantic), airframe (cargo, rotorcraft, stealth, fighter), avian log body mass, military branch, and the airframe × avian log body mass interaction.