10.6075/J0RR1WM2
Clifton, Glenna
0000-0002-5806-7254
University of California, San Diego
Holway, David
,
Gravish, Nick
,
Uneven substrates constrain walking speed in ants through modulation of
stride frequency more than stride length
Dryad
dataset
2019
Walking
Terrain
roughness
Linepithema humile
United States Army Research Office
https://ror.org/05epdh915
W911NF-17-1-0145
Chancellor’s Research Excellence Scholarship
2020-03-04T00:00:00Z
2020-03-04T00:00:00Z
en
2051179895 bytes
3
CC0 1.0 Universal (CC0 1.0) Public Domain Dedication
Natural terrain is rarely flat. Substrate irregularities challenge walking
animals to maintain stability, yet we lack quantitative assessments of
walking performance and limb kinematics on naturally uneven ground. We
measured how continually uneven 3D-printed substrates influence walking
performance of Argentine ants by measuring walking speeds of workers from
lab colonies and by testing colony-wide substrate preference in field
experiments. Tracking limb motion in over 8,000 videos, we used
statistical models that associate walking speed with limb kinematic
parameters to compare movement over flat versus uneven ground of
controlled dimensions. We found that uneven substrates reduced preferred
and peak walking speeds by up to 42% and that ants actively avoided uneven
terrain in the field. Observed speed reductions were modulated primarily
by shifts in stride frequency instead of stride length (flat R2: 0.91 vs.
0.50), a pattern consistent across flat and uneven substrates.
Mixed-effect modeling revealed that walking speeds on uneven substrates
were accurately predicted based on flat walking data for over 89% of
strides. Those strides that were not well modeled primarily involved limb
perturbations, including missteps, active foot repositioning, and
slipping. Together these findings relate kinematic mechanisms underlying
walking performance on uneven terrain to ecologically-relevant measures
under field conditions.
All files are saved as .pickle and can be read into python using the
pandas package and function pandas.read_pickle All distance variables are
in pixels. 31.992 pixels per mm Data was tracked from videos filmed at 240
fps GENERAL INFO FOR EACH TRACKWAY 'ID' - identifier for the
ant (number of trackway found in given video) 'colony' - date of
the colony collected 'date' - date of recording
'datetime' - date and time of recording 'substrate' -
what substrate 0 = flat, 1/3/5 = checkerboards of mm dimension
'time' - time of recording 'video' - full path of
video associated with trackway FULL BODY TRACKING AND ASSOCIATING
OUTPUT 'x_kal' - raw x-position output of body centroid from
OpenCV contour fit, associated across frames using kalman filter
'y_kal' - raw y-position output of body centroid from OpenCV
contour fit, associated across frames using kalman filter
'angle' - raw angle of body from OpenCV contour fit, associated
across frames using kalman filter, +y axis = 0 radians 'frames'
- frames associated with x_kal, y_kal, x_raw, y_raw, angle, and all LEAP
output 'area' - area of the tracked contour from OpenCV contour
fit 'xy_cov_matrix' - output from kalman filter associating
across frames 'error' - norm of covariance output from kalman
filter associating across frames 'measurements' - xhat, P, and K
measurements from kalman filter associating across frames 'LA' -
previous version of tracking left antennal tip, should be empty
'RA' - previous version of tracking right antennal tip, should
be empty POST-PROCESSING OUTPUT 'x_raw' - lowpass
butterworth filtered x_kal 'y_raw' - lowpass butterworth
filtered y_kal 'vfilt' - instantaneous speed of ant using x_raw,
y_raw 'vxfilt' - instantaneous speed of ant in x-axis using
x_raw 'vyfilt' - instantaneous speed of ant in y-axis using
y_raw 'movave_v' - moving average of vfilt across 24 frames =
0.1 s 'movave_fr' - frames associated with movave_v
'dist_90fr' - moving calculation of distance traveled over 90
frames = 375 ms 'x' - removed x_raw data where ant is close to
frame edge and when ant is stopped/moving slowly 'y' - removed
y_raw data where ant is close to frame edge and when ant is stopped/moving
slowly 'v' - instantaneous speed of ant using x, y
'vx' - instantaneous speed of ant in x-axis using x
'vy' - instantaneous speed of ant in y-axis using y
'median_v' - median velocity of 'v'
'median_vx' - median velocity of 'vx'
'median_vy' - median velocity of 'vy'
'sinuosity' - sinuosity calculation of trackway using x, y
'angle_improved' - raw OpenCV contour angle output does not
factor in facing. this uses antennae and comparisons across a tracked
trailway to correct the angle to be the ant facing. it is then
interpolated and smoothed 'frames_final' - frame
number associated with x, y LEAP TRACKING OUTPUT
'thorax_conf' - body center confidence 'thorax_x' -
body center x position 'thorax_y' - body center y position
'neck_conf' - neck confidence 'neck_x' - neck x
position 'neck_y' - neck y position 'antenna0_conf'
- left antenna confidence 'antenna0_x' - left antenna tip
x-position 'antenna0_y' - left antenna tip y-position
'antenna1_conf' - right antenna confidence
'antenna1_x' - right antenna tip x-position
'antenna1_y' - right antenna tip y-position
'joint0_conf' - left hindlimb 'joint0_x',
'joint0_y', 'joint1_conf' - left midlimb
'joint1_x', 'joint1_y', 'joint2_conf' -
left forelimb 'joint2_x', 'joint2_y',
'joint3_conf' - right hindlimb 'joint3_x',
'joint3_y', 'joint4_conf' - right midlimb
'joint4_x', 'joint4_y', 'joint5_conf' -
right forelimb 'joint5_x', 'joint5_y',