DOI: 10.6078/d11q5q
title: Data from: Acrobatic squirrels learn to leap and land on tree branches without falling

description: Arboreal animals often leap through complex canopies to travel and avoid
 predators. Their success at making split-second potentially
 life-threatening decisions of biomechanical capability depends on skillful
 use of acrobatic maneuvers and learning from past efforts. Here, we found
 that free-ranging fox squirrels (Sciurus niger) leaping across novel,
 simulated branches decided where to launch by balancing a trade-off
 between gap distance and branch-bending compliance. Squirrels quickly
 learned to modify impulse generation upon repeated leaps from novel,
 compliant beams. A repertoire of agile landing maneuvers enabled targeted
 leaping without falling. Unanticipated adaptive landing and leaping
 “parkour” behavior revealed an innovative solution for, particularly
 challenging leaps. Squirrels deciding and learning how to launch and land
 demonstrate the synergistic roles of biomechanics and cognition in robust
 gap crossing strategies.
description: Experiment 1 - Launch-point decision. All trials were recorded
 with two Phantom V10 high speed cameras (Vision Research Inc) at 200
 frames per second using 50mm lenses and Phantom Camera Control 1.3
 software. One camera was placed such that it had a lateral view of
 squirrels to record their movements in the sagittal plane, and bisected
 the distance jumped by the squirrel during tests. It was also level with
 the jumping platform and framed such that only the overhanging beam and
 the landing perch were in frame. The second camera was placed to record
 the squirrels’ movements in the coronal plane. Twelve squirrels were
 recorded in 96 leaping trials. The number of trials per individual was 23,
 27, 3, 19, 4, 4, 2, 1, 2, 4, 2, and 5. The data sheet
 contains metadata and analysis for each trial.<br>
 DataSheetExperiment1.csv<br> DataSheetExperiment1.xlsx
 Experiment1/AnalysisScripts<br> The primary analysis script
 is launch_point_decisions.m. This MATLAB script uses the data in
 launch_points.xlsx to analyze the launch point decisions from each trial.
 The script force_displacement.m uses the data from
 testing_branch_compliance.xlsx to determine the local compliance along
 each simulated branch. The other two scripts, linspecer.m and plot2axes,
 are called by the launch_point_decisions.m script and used for
 plotting. <br> Experiment 2 - Learning to leap. A
 single Phantom V10 camera recorded sagittal plane movements of the
 squirrel at 200 frames per second. In total, we recorded 47 trials from 5
 squirrels. Four squirrels completed 10 leaps, 5 from the highly flexibly
 beam and 5 from the rigid control beam. A fifth squirrel completed 8
 trials including all 5 leaps from the flexibly beam, but left the area
 after 3 leaping trials from the rigid control beam. Times between leaping
 trials from the flexible beam varied from 33 to 197 seconds with an
 average of 109 seconds. The data sheet contains
 metadata and analysis for each trial.<br>
 DataSheetExperiment2.csv<br> DataSheetExperiment2.xlsx
 Experiment2/AnalysisScripts<br> The primary analysis script
 is squirrel_datasheet_generator.m. This MATLAB script uses the tracked
 aerial phases to calculate the landing errors and body curvatures. The
 script squirrel_tracker is used to manually track body positions on the
 squirrels from the video data and output a tracked data file. The script
 squirrel_COM is called by squirrel_datasheet_generator.m, and uses the
 tracked data to estimate the center of mass position and landing
 error.
 Experiment2/AnalysisScripts/tracked_aerial_phase.zip<br>
 The manually tracked kinematics are stored in separate .mat files (MATLAB)
 for each leaping trial. Experiment 3 - Parkour leaping.
 One camera was placed to record squirrel movements in the sagittal plane
 Another camera was mounted above the wall apparatus so that it had a
 downward view of the squirrels. Lens distortions were corrected prior to
 kinematic analysis using custom MATLAB code and the native functions
 estimateCameraParameters, and undistortImage. Squirrels were presented
 with a single landing perch at one of the eight locations (Fig. 3B) in a
 randomized order. In total, 324 parkour leaping trials were recorded from
 ten individual squirrels. The number of leaping trials per individual was
 31, 47, 31, 8, 9, 32, 51, 54, 41, and 20. The center of mass was
 tracked. Experiment 3/AnalysisScripts<br> The
 primary analysis script is
 calculate_velocity_before_and_after_wall_jump.m. This MATLAB script uses
 the tracked video data to calculate velocities during the aerial phases
 before and after the wall contact phase of each leaping trial. The script
 struct2vec.m is used to reformat the data structure from the tracked video
 data. This script is called by
 calculate_velocity_before_and_after_wall_jump.m. The script
 calculate_COM.m is used to estimate the center of mass from the tracked
 video data. This script is called by
 calculate_velocity_before_and_after_wall_jump.m.
 Experiment3/AnalysisScripts/tracked_video_data.zip<br> The
 manually tracked kinematics are stored in separate .mat files (MATLAB) for
 each leaping trial.