DOI: 10.25338/b8m939
title: Impacts of leg, wing, and body collisions on flight performance in carpenter bees

description: Flying insects often forage among cluttered vegetation that forms a series
 of obstacles in their flight path. Recent studies have focused on
 behaviors needed to navigate clutter while avoiding all physical contact,
 and as a result, we know little about flight behaviors that do involve
 encounters with obstacles. Here, we challenged carpenter bees (Xylocopa
 varipuncta) to fly through narrow gaps in an obstacle course to determine
 the kinds of obstacle encounters they experience, as well as the
 consequences for flight performance. We observed three kinds of
 encounters: leg, body, and wing collisions. Wing collisions occurred most
 frequently (in about 40% of flights, up to 25 times per flight) but these
 had little effect on flight speed or body orientation. In contrast, body
 and leg collisions, which each occurred in about 20% of flights (1-2 times
 per flight), resulted in decreased flight speeds and increased rates of
 body rotation (yaw). Wing and body collisions, but not leg collisions,
 were more likely to occur in wind versus still air. Thus, physical
 encounters with obstacles may be a frequent occurrence for insects flying
 in some environments, and the immediate effects of these encounters on
 flight performance depend on the body part involved.
description: Freely flying female carpenter bees (Xylocopa varipuncta, n = 15)
 were collected from the University of California, Davis campus and
 subjected to flight challenges in a laboratory flight tunnel. Individual
 bees were placed in the flight tunnel (20 x 19 x 115 cm; width x height x
 length), which included a series of vertical columns (hereafter
 ‘obstacles’) that spanned the middle of the tunnel (obstacle diameter = 7
 mm, space between obstacles = 34.44 ± 2.80 mm; mean ± SD). The bees’ wing
 spans (45.19 ± 2.11 mm, from tip to tip) were larger than the size of the
 gaps between obstacles; thus, bees could not fly straight through gaps,
 but instead needed to rotate their body (e.g., yaw) in order to pass
 through. Obstacles were attached to a mechanical arm that oscillated
 laterally (amplitude = 21 mm, frequency = 2 Hz) or remained stationary.
 Fans at each end of the tunnel could be turned on to produce a gentle
 breeze (mean velocity = 0.54 m/s) or off for still air. Wind direction was
 constant: bees flying in one direction experienced headwinds and in the
 other direction tailwinds. Up to 12 flights through the obstacles were
 elicited from each bee, using full spectrum lights that were alternately
 turned on and off at each end of the tunnel. Obstacle motion (stationary
 versus oscillating) was fixed for a given bee, but all bees experienced
 both wind and still air, with the order of the wind condition switched
 after approximately six flights. Thus, four different flight conditions
 were tested on the group of bees: still air with stationary obstacles (n =
 40 flights), still air with oscillating obstacles (n = 34), wind with
 stationary obstacles (n = 42), and wind with oscillating obstacles (n =
 29). Flights were filmed with two synchronized Phantom
 v611 cameras (Vision Research, Inc., Wayne, NJ, USA) sampling at 1500
 frames/s and positioned 30º from the vertical on opposite sides of the
 obstacles. Cameras were calibrated using a standard checkerboard
 calibration method and built-in MATLAB functions. In
 each video, the positions of the bee’s head (midpoint between antennae),
 thorax (approximating the body centroid), and wing tips were tracked with
 the machine-learning software DeepLabCut. Tracked points were checked and
 manually corrected, and obstacle positions were labeled using DLTdv6 in
 MATLAB. Labeled positions were converted from two-dimensional coordinates
 in each camera view into three-dimensional space using built-in MATLAB
 functions. We classified and counted each physical
 encounter that occurred between the bees and obstacles. The most common
 encounters were body collisions (the head, thorax, or abdomen contacted
 the obstacles), leg collisions (one or more forelegs contacted the
 obstacles), and wing collisions (the distal half of one or more forewings
 contacted the obstacles).   We next
 assessed how encounters with obstacles affected flight performance. In
 each video, we identified the first occurrence of each body, leg, and wing
 collision, and defined a 20-ms period before and after each encounter.
 This temporal window allows us to quantify performance immediately before
 and after obstacle encounters, as in, to determine how flight performance
 changes during encounters. Some of these pre- and post-encounter periods
 contained additional collisions with obstacles, but they were included in
 the analysis to reflect the true nature of flight in clutter (i.e.,
 flights with just one obstacle encounter were rare). From this analysis,
 videos yielded either a single encounter type (<em>n </em>= 42
 flights), two encounter types (<em>n = 26</em>), or all three
 encounter types (<em>n </em>= 9). For each
 encounter, we measured the change in horizontal ground speed and yaw
 between the pre- and post-collision periods, as well as the post-collision
 yaw rate, where yaw was the body angle about the vertical axis. To
 calculate kinematics, we smoothed the three-dimensional position data with
 cubic smoothing spline curves via the ‘smooth.spline’ function in the R
 package stats. Horizontal ground speed was calculated as the change in x-y
 position (lateral and longitudinal motions, omitting vertical motion) per
 time. Yaw was calculated by converting the Cartesian coordinates of the
 head and thorax to spherical coordinates via the ‘cart2sph’ function in
 the R package pracma and finding the horizontal angle between the two body
 points and the long axis of the tunnel. The yaw rate was calculated over
 the 20 ms post-collision by taking the derivative of yaw with respect to
 time.