DOI: 10.5061/dryad.qbzkh18vg
title: Data from: Exploring the effects of giraffe skin disease limb lesions on locomotion

description: Emerging skin diseases have severely impacted wildlife in recent decades
 with consequences ranging from increased morbidity and mortality to local
 extinction and widespread biodiversity loss. Individuals that persist with
 various skin diseases can have sublethal consequences including altered
 behavior and impaired locomotor function. Giraffe skin disease (GSD) is a
 condition that results in skin lesions of varying severity among different
 giraffe (Giraffa spp.) populations throughout Africa. Prior reports have
 suggested that individuals with limb lesions from GSD do not have
 increased mortality but rather suffer from lameness. We examined whether
 GSD severity and unilateral vs. bilateral forelimb lesions differentially
 impact spatiotemporal gait kinematics and carpus joint angle kinematics of
 Masai giraffe (G. tippelskirchi tippelskirchi) in Ruaha National Park,
 Tanzania. We found that GSD lesions altered normal walking gait kinematics
 (i.e., decreased walking speed and increased stride duration) largely
 irrespective of lesion severity or number of limbs affected. Impaired
 movement due to GSD could negatively impact foraging efficiency,
 dispersal, and predator susceptibility. Given that wildlife skin diseases
 are predicted to become more prevalent with climate change, examinations
 of their sublethal effects, in addition to their effects on mortality, are
 required to better understand long-term ramifications.
description: # Data from: Exploring the effects of giraffe skin disease limb lesions on
 locomotion Dataset DOI: [10.5061/dryad.qbzkh18vg](10.5061/dryad.qbzkh18vg)
 ## Description of the data and file structure This file contains data on
 spatiotemporal limb kinematics and carpus joint angle kinematics. ###
 Files and variables #### File: Giraffe_Gait_Data.csv **Description:** 
 ##### Variables * Animal_ID: Individual identification number of animal *
 Sex: assigned female or male * Video_Number: identifying feature of video
 * Frame_Number: video frame number designating the start of the stride *
 Lesion_Score: scored as absent, mild, or moderate/severe * Legs_Affected:
 indicates whether the right forelimb (RF) and/or left forelimb (LF) had
 skin lesions; NA indicates that neither leg was affected * Area_Affected:
 includes area where lesions were present (i.e., radioulna, carpus joint,
 and/or metacarpal). Individuals lacking discernible lesions were scored as
 "healthy" * Relative_Stride_Length: Relative stride length was
 calculated by dividing stride length in pixels by shoulder height in
 pixels * Mean_Stride_Duration: Stride duration was recorded as the
 duration (in seconds) between the initial and subsequent touchdown of a
 reference limb. Mean stride duration was generated based on the values for
 each of the four limbs. * Relative_Speed: Relative speed was calculated by
 dividing relative stride length by mean stride duration, resulting in
 values with units of % of shoulder height per second. * Mean_NSL: We
 quantified the portion of stride duration in which individuals were
 supported by zero, one, two, three or four limbs, to generate mean number
 of supporting limbs (i.e., mean NSL) throughout the stride. *
 Peak_Flexion: We digitized carpus angle in each video frame using ImageJ
 and identified peak flexion. NA indicates that carpus angle was not
 digitized for this video due to parallax concerns. * Peak_Extension: We
 digitized carpus angle in each video frame using ImageJ and identified
 peak extension. NA indicates that carpus angle was not digitized for this
 video due to parallax concerns. * ROM: We then calculated carpus angle
 range of motion (ROM) as the difference between peak flexion and peak
 extension. NA indicates that carpus angle was not digitized for this video
 due to parallax concerns. ## Code/software We used GaitKeeper, an
 open-source MATLAB package, to digitize limb liftoff and touchdown events,
 shoulder height, and stride length (Dunham et al. 2018)
 ([http://www.younglaboratory.org/GaitKeeper](http://www.younglaboratory.org/GaitKeeper)). We recorded neck angle measurements for individual videoframes using the angle tool in ImageJ (Rueden et al. 2017).